From 4643db3b53fbbecedcf6f0357e0e3447efd7afd2 Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 13:02:48 +0200
Subject: [PATCH 001/158] Create new branch


From 7ec03d53779adffa461073d7633fe5041a076b78 Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 13:11:46 +0200
Subject: [PATCH 002/158] Create new branch


From a8a6cc20cec462a7d9ee8344f418b2761bb9466a Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 14:01:52 +0200
Subject: [PATCH 003/158] Add kinase-similarity-focused template talktorial

---
 .../T0xx_kinase_similarity_xxx/README.md      |  78 +++++
 .../T0xx_kinase_similarity_xxx/data/README.md |   6 +
 .../images/README.md                          |   5 +
 .../talktorial.ipynb                          | 318 ++++++++++++++++++
 4 files changed, 407 insertions(+)
 create mode 100644 teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
 create mode 100644 teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
 create mode 100644 teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
 create mode 100644 teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb

diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
new file mode 100644
index 00000000..a38d14a4
--- /dev/null
+++ b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
@@ -0,0 +1,78 @@
+<div class="alert alert-block alert-info">
+
+<b>Thank you for contributing to TeachOpenCADD!</b>
+
+</div>
+
+
+<div class="alert alert-block alert-info">
+
+<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
+
+</div>
+
+
+# T000 · Talktorial topic title
+
+Authors:
+
+- First and last name, year(s) of contribution, lab, institution
+- First and last name, year(s) of contribution, lab, institution
+
+
+*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
+
+
+<div class="alert alert-block alert-info">
+
+<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
+
+</div>
+
+
+## Aim of this talktorial
+
+Add a short summary of this talktorial's content.
+
+
+### Contents in *Theory*
+
+_Add Table of Contents (TOC) for Theory section._
+
+* ChEMBL database
+* Compound activity measures
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+
+</div>
+
+
+### Contents in *Practical*
+
+_Add Table of Contents (TOC) for Practical section._
+
+* Connect to ChEMBL database
+* Load and draw molecules
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
+
+</div>
+
+
+### References
+
+* Paper 
+* Tutorial links
+* Other useful resources
+
+*We suggest the following citation style:*
+* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
+
+*Example:*
+* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
new file mode 100644
index 00000000..cc6d1e4c
--- /dev/null
+++ b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
@@ -0,0 +1,6 @@
+# Data
+
+This folder stores input and output data for the Jupyter notebook.
+
+- `xxx.csv`: Describe data.
+- `xxx.sdf`: Describe data.
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
new file mode 100644
index 00000000..d4ebaa47
--- /dev/null
+++ b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
@@ -0,0 +1,5 @@
+# Talktorial title
+
+## Images
+
+This folder stores images used in the Jupyter notebook.
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
new file mode 100644
index 00000000..0fa255f7
--- /dev/null
+++ b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T000 · Kinase similarity: XXX\n",
+    "\n",
+    "Authors:\n",
+    "\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aim of this talktorial\n",
+    "\n",
+    "Add a short summary of this talktorial's content."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Theory*\n",
+    "\n",
+    "* Kinase dataset\n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Practical*\n",
+    "\n",
+    "* Retrieve and preprocess data\n",
+    "* Show kinase coverage\n",
+    "* Compare kinases\n",
+    "* Visualize similarity as kinase matrix\n",
+    "* Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+   ]
+  },
+  {
+   "attachments": {
+    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: XXX\n",
+    "\n",
+    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "\n",
+    "- XXX = KLIFS pocket sequence\n",
+    "- XXX = KiSSim fingerprint\n",
+    "- XXX = KLIFS IFP\n",
+    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compound activity measures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "    \n",
+    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you place links, please link descriptive words.\n",
+    "\n",
+    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "\n",
+    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HERE = Path(_dh[-1])\n",
+    "DATA = HERE / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quiz\n",
+    "\n",
+    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
+    "\n",
+    "1. Question\n",
+    "2. Question\n",
+    "3. Question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 033282d022070b55140b3073f3d6e5d667965561 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 10 Aug 2021 14:05:05 +0200
Subject: [PATCH 004/158] :tada: start branch


From 33cdaa22b0212a4ebe6eafdea59cd1f0c1bd4273 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 10 Aug 2021 14:09:33 +0200
Subject: [PATCH 005/158] :tada: start branch


From 794a1b2c02e254a527f18cec0194f37c86174d4d Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 14:13:29 +0200
Subject: [PATCH 006/158] :tada: start branch


From f114306f077d0ad077adc569b068878b93b0020d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 14:14:12 +0200
Subject: [PATCH 007/158] Add folder

---
 .../T023_what_is_a_kinase/README.md           |  78 +++++
 .../T023_what_is_a_kinase/data/README.md      |   6 +
 .../T023_what_is_a_kinase/images/README.md    |   5 +
 .../T023_what_is_a_kinase/talktorial.ipynb    | 318 ++++++++++++++++++
 4 files changed, 407 insertions(+)
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/README.md
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/data/README.md
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/images/README.md
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/README.md b/teachopencadd/talktorials/T023_what_is_a_kinase/README.md
new file mode 100644
index 00000000..a38d14a4
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/README.md
@@ -0,0 +1,78 @@
+<div class="alert alert-block alert-info">
+
+<b>Thank you for contributing to TeachOpenCADD!</b>
+
+</div>
+
+
+<div class="alert alert-block alert-info">
+
+<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
+
+</div>
+
+
+# T000 · Talktorial topic title
+
+Authors:
+
+- First and last name, year(s) of contribution, lab, institution
+- First and last name, year(s) of contribution, lab, institution
+
+
+*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
+
+
+<div class="alert alert-block alert-info">
+
+<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
+
+</div>
+
+
+## Aim of this talktorial
+
+Add a short summary of this talktorial's content.
+
+
+### Contents in *Theory*
+
+_Add Table of Contents (TOC) for Theory section._
+
+* ChEMBL database
+* Compound activity measures
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+
+</div>
+
+
+### Contents in *Practical*
+
+_Add Table of Contents (TOC) for Practical section._
+
+* Connect to ChEMBL database
+* Load and draw molecules
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
+
+</div>
+
+
+### References
+
+* Paper 
+* Tutorial links
+* Other useful resources
+
+*We suggest the following citation style:*
+* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
+
+*Example:*
+* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/README.md b/teachopencadd/talktorials/T023_what_is_a_kinase/data/README.md
new file mode 100644
index 00000000..cc6d1e4c
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/README.md
@@ -0,0 +1,6 @@
+# Data
+
+This folder stores input and output data for the Jupyter notebook.
+
+- `xxx.csv`: Describe data.
+- `xxx.sdf`: Describe data.
diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/images/README.md b/teachopencadd/talktorials/T023_what_is_a_kinase/images/README.md
new file mode 100644
index 00000000..d4ebaa47
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/images/README.md
@@ -0,0 +1,5 @@
+# Talktorial title
+
+## Images
+
+This folder stores images used in the Jupyter notebook.
diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
new file mode 100644
index 00000000..29d5b255
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T023 · What is a kinase?\n",
+    "\n",
+    "Authors:\n",
+    "\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aim of this talktorial\n",
+    "\n",
+    "Add a short summary of this talktorial's content."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Theory*\n",
+    "\n",
+    "* Kinase dataset\n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Practical*\n",
+    "\n",
+    "* Retrieve and preprocess data\n",
+    "* Show kinase coverage\n",
+    "* Compare kinases\n",
+    "* Visualize similarity as kinase matrix\n",
+    "* Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+   ]
+  },
+  {
+   "attachments": {
+    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: XXX\n",
+    "\n",
+    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "\n",
+    "- XXX = KLIFS pocket sequence\n",
+    "- XXX = KiSSim fingerprint\n",
+    "- XXX = KLIFS IFP\n",
+    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compound activity measures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "    \n",
+    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you place links, please link descriptive words.\n",
+    "\n",
+    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "\n",
+    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HERE = Path(_dh[-1])\n",
+    "DATA = HERE / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quiz\n",
+    "\n",
+    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
+    "\n",
+    "1. Question\n",
+    "2. Question\n",
+    "3. Question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "toc-autonumbering": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 0bb739446f40ed0a0fc5c3342c3d4dcd33cffcab Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 14:15:17 +0200
Subject: [PATCH 008/158] :tada: start branch


From 9b7c99519e792228474617acec1bb6e8272a25f4 Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@posteo.de>
Date: Tue, 10 Aug 2021 14:20:58 +0200
Subject: [PATCH 009/158] :tada: start branch


From 6ff4e77b44669a6a1de17df2000bd4d7d69e5dff Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 10 Aug 2021 14:24:09 +0200
Subject: [PATCH 010/158] :tada: start new branch


From 2c1f1a7d350c242d53732ff04ae515a17c9bece5 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 09:39:54 +0200
Subject: [PATCH 011/158] Add talktorial folder + theory content

---
 .../README.md                                 |  0
 .../data/README.md                            |  0
 .../images/README.md                          |  0
 .../talktorial.ipynb                          | 77 +++++++------------
 4 files changed, 27 insertions(+), 50 deletions(-)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T026_kinase_similarity_ifp}/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T026_kinase_similarity_ifp}/data/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T026_kinase_similarity_ifp}/images/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T026_kinase_similarity_ifp}/talktorial.ipynb (93%)

diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md b/teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
rename to teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
rename to teachopencadd/talktorials/T026_kinase_similarity_ifp/data/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md b/teachopencadd/talktorials/T026_kinase_similarity_ifp/images/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
rename to teachopencadd/talktorials/T026_kinase_similarity_ifp/images/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
similarity index 93%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
rename to teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 0fa255f7..2ffad351 100644
--- a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# T000 · Kinase similarity: XXX\n",
+    "# T026 · Kinase similarity: Interaction fingerprints\n",
     "\n",
     "Authors:\n",
     "\n",
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "Add a short summary of this talktorial's content."
+    "We will assess the similarity between a set of kinases based on the KLIFS interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex.  "
    ]
   },
   {
@@ -29,7 +29,7 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor: Interaction fingerprint"
    ]
   },
   {
@@ -52,7 +52,14 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
+    "* Protein Data Bank\n",
+    "  - PDB URL: http://www.rcsb.org/\n",
+    "  - PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)\n",
+    "* KLIFS\n",
+    "  - KLIFS URL: https://klifs.net/\n",
+    "  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "  - KLIFS binding site definition and interaction fingerprint calculation: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
+    "* Interaction fingerprint (IFP): [<i>J. Chem. Inf. Model.</i> (2007), <b>71(1)</b>, 195-207](https://doi.org/10.1021/ci600342e)"
    ]
   },
   {
@@ -73,10 +80,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined **EGFR** and **ErbB2** as targets (indicated by a ‘+’) and **BRAF** (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2** (cyclic-dependent kinase 2), **LCK** (lymphocyte-specific protein tyrosine kinase), **MET** (mesenchymal-epithelial transition factor) and **p38α** (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
    ]
   },
   {
@@ -98,56 +105,34 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: XXX\n",
+    "### Kinase similarity descriptor: Interaction fingerprints\n",
     "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "Interaction fingerprints encode the binding mode of a ligand in a binding site that is seen in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand intentially targets multiple proteins at once.\n",
     "\n",
-    "- XXX = KLIFS pocket sequence\n",
-    "- XXX = KiSSim fingerprint\n",
-    "- XXX = KLIFS IFP\n",
-    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. We will make use of pre-calculated interaction fingerprints provided by the kinase-focused resource KLIFS."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compound activity measures"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    \n",
-    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
-    "</div>"
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
+    "\n",
+    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
+    "*Figure 1:* \n",
+    "Kinase binding site residues as defined by KLIFS.\n",
+    "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you place links, please link descriptive words.\n",
+    "The KLIFS authors calculate for each of these structures &mdash; that are co-crystallized with a ligand &mdash; an interaction fingerprint (IFP) as proposed by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e)). This is how the IFP is described in the respective KLIFS pulication [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w):\n",
     "\n",
-    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "> For each amino acid in the catalytic cleft, seven types of protein−ligand interactions are determined. The presence of a certain type of interaction results in the type-setting ofa “1” in the bit-string; otherwise a “0” is used to indicate the absence of the interaction. The following seven types of interactions are summarized in one bit-string; bit 1 = hydrophobic contact (HYD); bit 2 = face to face aromatic interactions (F−F); bit 3 = face to edge aromatic interactions (F−E); bit 4 = protein H-bond donor (DON); bit 5 = protein H- bond acceptor (ACC); bit 6 = protein cationic interactions (ION+), and bit 7 = protein anionic interactions (ION−).\n",
     "\n",
-    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+    "This results in a $85 \\times 7 = 595$ bit vector, which describes the interactions seen in a ligand-kinase complex. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do now in the practical part of this tutorial."
    ]
   },
   {
@@ -161,15 +146,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",

From 60eb636340e5da6bb53058a3e99bd9fdcf2de942 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 10:21:29 +0200
Subject: [PATCH 012/158] Add opencadd to theory; finish dataset preparation

---
 .../talktorial.ipynb                          | 584 +++++++++++++++++-
 1 file changed, 575 insertions(+), 9 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 2ffad351..1508d7ad 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -29,7 +29,8 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: Interaction fingerprint"
+    "* Kinase similarity descriptor: KLIFS interaction fingerprint\n",
+    "* Fetching KLIFS data with `opencadd.databases.klifs`"
    ]
   },
   {
@@ -59,7 +60,10 @@
     "  - KLIFS URL: https://klifs.net/\n",
     "  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
     "  - KLIFS binding site definition and interaction fingerprint calculation: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
-    "* Interaction fingerprint (IFP): [<i>J. Chem. Inf. Model.</i> (2007), <b>71(1)</b>, 195-207](https://doi.org/10.1021/ci600342e)"
+    "* Interaction fingerprint (IFP): [<i>J. Chem. Inf. Model.</i> (2007), <b>71(1)</b>, 195-207](https://doi.org/10.1021/ci600342e)\n",
+    "* `opencadd`, a Python library for structural cheminformatics\n",
+    "  - GitHub repository: https://github.com/volkamerlab/opencadd\n",
+    "  - Documentation: https://opencadd.readthedocs.io"
    ]
   },
   {
@@ -105,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: Interaction fingerprints\n",
+    "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
     "Interaction fingerprints encode the binding mode of a ligand in a binding site that is seen in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand intentially targets multiple proteins at once.\n",
     "\n",
@@ -135,6 +139,21 @@
     "This results in a $85 \\times 7 = 595$ bit vector, which describes the interactions seen in a ligand-kinase complex. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do now in the practical part of this tutorial."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fetching KLIFS data with `opencadd.databases.klifs`\n",
+    "\n",
+    "`opencadd` is a Python library for structural cheminformatics developed by the Volkamer lab at the Charité in Berlin. \n",
+    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS IFP as `pandas` DataFrame.\n",
+    "\n",
+    "- GitHub repository: https://github.com/volkamerlab/opencadd\n",
+    "- Documentation: https://opencadd.readthedocs.io\n",
+    "\n",
+    "For more information about this library and the KLIFS OpenAPI, please refer to __Talktorial T012__."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -150,7 +169,9 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import pandas as pd"
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from opencadd.databases.klifs import setup_remote"
    ]
   },
   {
@@ -170,26 +191,571 @@
     "### Retrieve and preprocess data"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define our kinases of interest and all their synoyms as described in Table 1 in the global variable `query_kinases`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "query_kinases = [\n",
+    "    'EGFR', \n",
+    "    'ErbB2', 'Her2',\n",
+    "    'PI3K', 'PI3KCA', 'p110a', \n",
+    "    'BRAF', \n",
+    "    'CDK2',\n",
+    "    'LCK', \n",
+    "    'MET', \n",
+    "    'p38a', 'MAPK14', \n",
+    "    'VEGFR2', 'KDR'\n",
+    "]"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Show kinase coverage"
+    "Now, we query the KLIFS database using the `opencadd.databases.klifs` module to generate our IFP dataset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from opencadd.databases.klifs import setup_remote"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set up a remote KLIFS session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "klifs_session = setup_remote()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fetch all structures describing these kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 2466\n",
+      "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
+     ]
+    }
+   ],
+   "source": [
+    "structures = klifs_session.structures.by_kinase_name(kinase_names=query_kinases)\n",
+    "print(f\"Number of structures: {len(structures)}\")\n",
+    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filter structures\n",
+    "\n",
+    "We filter the structures by different criteria:\n",
+    "\n",
+    "- Species: human\n",
+    "- Conformation: DFG-in\n",
+    "- Resolution: $\\le 3$ Angström\n",
+    "- Quality score*: $\\ge 6$\n",
+    "\n",
+    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms (the higher the better the structure quality)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n",
+      "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
+     ]
+    }
+   ],
+   "source": [
+    "structures = structures[\n",
+    "    (structures[\"species.klifs\"] == \"Human\")\n",
+    "    & (structures[\"structure.dfg\"] == \"in\") \n",
+    "    & (structures[\"structure.resolution\"] <= 3) \n",
+    "    & (structures[\"structure.qualityscore\"] >= 6)\n",
+    "]\n",
+    "print(f\"Number of structures: {len(structures)}\")\n",
+    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the structure KLIFS IDs for the next step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fetch the structures' IFP (if available)\n",
+    "\n",
+    "We fetch the IFPs for the set of structures. Not all structures will have an IFP because not all structures have a co-crystallized ligand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures with IFPs: 1466\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>interaction.fingerprint</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>775</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>777</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>778</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>779</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>782</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   structure.klifs_id                            interaction.fingerprint\n",
+       "0                 775  0000000000000010000000000000000000000000000000...\n",
+       "1                 777  0000000000000010000001000000000000000000000000...\n",
+       "2                 778  0000000000000010000000000000000000000000000000...\n",
+       "3                 779  0000000000000010000001000000000000000000000000...\n",
+       "4                 782  0000000000000010000001000000000000000000000000..."
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ifps = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
+    "print(f\"Number of structures with IFPs: {len(ifps)}\")\n",
+    "ifps.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Merge structural and IFP data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures with IFPs: 1466\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>interaction.fingerprint</th>\n",
+       "      <th>structure.pdb_id</th>\n",
+       "      <th>structure.alternate_model</th>\n",
+       "      <th>structure.chain</th>\n",
+       "      <th>species.klifs</th>\n",
+       "      <th>kinase.klifs_id</th>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>kinase.family</th>\n",
+       "      <th>kinase.group</th>\n",
+       "      <th>...</th>\n",
+       "      <th>structure.bp_ii_b_in</th>\n",
+       "      <th>structure.bp_ii_out</th>\n",
+       "      <th>structure.bp_ii_b</th>\n",
+       "      <th>structure.bp_iii</th>\n",
+       "      <th>structure.bp_iv</th>\n",
+       "      <th>structure.bp_v</th>\n",
+       "      <th>structure.grich_distance</th>\n",
+       "      <th>structure.grich_angle</th>\n",
+       "      <th>structure.grich_rotation</th>\n",
+       "      <th>structure.filepath</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>775</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "      <td>4r5s</td>\n",
+       "      <td>-</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>406</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>21.684799</td>\n",
+       "      <td>71.330002</td>\n",
+       "      <td>42.087002</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>777</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "      <td>3vjo</td>\n",
+       "      <td>-</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>406</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>778</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "      <td>3w2s</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>406</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>16.591600</td>\n",
+       "      <td>53.096100</td>\n",
+       "      <td>37.676601</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>779</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "      <td>2itu</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>406</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>20.174601</td>\n",
+       "      <td>65.518700</td>\n",
+       "      <td>21.431101</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>782</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "      <td>3w33</td>\n",
+       "      <td>A</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>406</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>14.820200</td>\n",
+       "      <td>48.159698</td>\n",
+       "      <td>43.046001</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   structure.klifs_id                            interaction.fingerprint  \\\n",
+       "0                 775  0000000000000010000000000000000000000000000000...   \n",
+       "1                 777  0000000000000010000001000000000000000000000000...   \n",
+       "2                 778  0000000000000010000000000000000000000000000000...   \n",
+       "3                 779  0000000000000010000001000000000000000000000000...   \n",
+       "4                 782  0000000000000010000001000000000000000000000000...   \n",
+       "\n",
+       "  structure.pdb_id structure.alternate_model structure.chain species.klifs  \\\n",
+       "0             4r5s                         -               A         Human   \n",
+       "1             3vjo                         -               A         Human   \n",
+       "2             3w2s                         A               A         Human   \n",
+       "3             2itu                         A               A         Human   \n",
+       "4             3w33                         A               A         Human   \n",
+       "\n",
+       "   kinase.klifs_id kinase.klifs_name kinase.family kinase.group  ...  \\\n",
+       "0              406              EGFR          <NA>         <NA>  ...   \n",
+       "1              406              EGFR          <NA>         <NA>  ...   \n",
+       "2              406              EGFR          <NA>         <NA>  ...   \n",
+       "3              406              EGFR          <NA>         <NA>  ...   \n",
+       "4              406              EGFR          <NA>         <NA>  ...   \n",
+       "\n",
+       "  structure.bp_ii_b_in structure.bp_ii_out structure.bp_ii_b structure.bp_iii  \\\n",
+       "0                False               False             False            False   \n",
+       "1                False               False             False            False   \n",
+       "2                False               False             False            False   \n",
+       "3                False               False             False            False   \n",
+       "4                False               False             False            False   \n",
+       "\n",
+       "  structure.bp_iv structure.bp_v structure.grich_distance  \\\n",
+       "0           False          False                21.684799   \n",
+       "1           False          False                 0.000000   \n",
+       "2           False          False                16.591600   \n",
+       "3           False          False                20.174601   \n",
+       "4           False          False                14.820200   \n",
+       "\n",
+       "   structure.grich_angle  structure.grich_rotation  structure.filepath  \n",
+       "0              71.330002                 42.087002                <NA>  \n",
+       "1               0.000000                  0.000000                <NA>  \n",
+       "2              53.096100                 37.676601                <NA>  \n",
+       "3              65.518700                 21.431101                <NA>  \n",
+       "4              48.159698                 43.046001                <NA>  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "structures = ifps.merge(structures, on=\"structure.klifs_id\", how=\"inner\")\n",
+    "print(f\"Number of structures with IFPs: {len(structures)}\")\n",
+    "structures.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage\n",
+    "\n",
+    "Let's get the number of structures that describe our kinases (kinase coverage)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kinase.klifs_name\n",
+       "ErbB2      4\n",
+       "KDR        6\n",
+       "LCK       30\n",
+       "p110a     45\n",
+       "BRAF      57\n",
+       "MET       95\n",
+       "p38a     127\n",
+       "EGFR     339\n",
+       "CDK2     763\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
+    "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
+    "n_structures_per_kinase"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot our results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "n_structures_per_kinase.plot(kind=\"barh\", ax=ax)\n",
+    "ax.set_xlabel(\"Number of structures\")\n",
+    "ax.set_ylabel(\"Kinase name\")\n",
+    "for i, value in enumerate(n_structures_per_kinase):\n",
+    "    ax.text(value, i, str(value), va=\"center\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not: See e.g. CDK2 and EGFR vs. KDR and ErbB2. We will have to keep this in mind when interpreting our results later."
+   ]
   },
   {
    "cell_type": "markdown",

From 7d266aea75c92ee673d30b7883bc9c796c12c842 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 23 Aug 2021 10:32:12 +0200
Subject: [PATCH 013/158] create table for kinases of interest.

---
 .../README.md                                 |   78 +
 .../data/README.md                            |    6 +
 .../images/README.md                          |    5 +
 .../talktorial.ipynb                          | 1284 +++++++++++++++++
 4 files changed, 1373 insertions(+)
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/README.md
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/README.md
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
new file mode 100644
index 00000000..a38d14a4
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
@@ -0,0 +1,78 @@
+<div class="alert alert-block alert-info">
+
+<b>Thank you for contributing to TeachOpenCADD!</b>
+
+</div>
+
+
+<div class="alert alert-block alert-info">
+
+<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
+
+</div>
+
+
+# T000 · Talktorial topic title
+
+Authors:
+
+- First and last name, year(s) of contribution, lab, institution
+- First and last name, year(s) of contribution, lab, institution
+
+
+*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
+
+
+<div class="alert alert-block alert-info">
+
+<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
+
+</div>
+
+
+## Aim of this talktorial
+
+Add a short summary of this talktorial's content.
+
+
+### Contents in *Theory*
+
+_Add Table of Contents (TOC) for Theory section._
+
+* ChEMBL database
+* Compound activity measures
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+
+</div>
+
+
+### Contents in *Practical*
+
+_Add Table of Contents (TOC) for Practical section._
+
+* Connect to ChEMBL database
+* Load and draw molecules
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
+
+</div>
+
+
+### References
+
+* Paper 
+* Tutorial links
+* Other useful resources
+
+*We suggest the following citation style:*
+* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
+
+*Example:*
+* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/README.md b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/README.md
new file mode 100644
index 00000000..cc6d1e4c
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/README.md
@@ -0,0 +1,6 @@
+# Data
+
+This folder stores input and output data for the Jupyter notebook.
+
+- `xxx.csv`: Describe data.
+- `xxx.sdf`: Describe data.
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/README.md b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/README.md
new file mode 100644
index 00000000..d4ebaa47
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/README.md
@@ -0,0 +1,5 @@
+# Talktorial title
+
+## Images
+
+This folder stores images used in the Jupyter notebook.
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
new file mode 100644
index 00000000..a8031442
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -0,0 +1,1284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T027 · Kinase similarity: ligand-profile\n",
+    "\n",
+    "Authors:\n",
+    "\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aim of this talktorial\n",
+    "\n",
+    "Add a short summary of this talktorial's content."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Theory*\n",
+    "\n",
+    "* Kinase dataset\n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Practical*\n",
+    "\n",
+    "* Retrieve and preprocess data\n",
+    "* Show kinase coverage\n",
+    "* Compare kinases\n",
+    "* Visualize similarity as kinase matrix\n",
+    "* Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+   ]
+  },
+  {
+   "attachments": {
+    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: ligand-profile\n",
+    "\n",
+    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "\n",
+    "- XXX = KLIFS pocket sequence\n",
+    "- XXX = KiSSim fingerprint\n",
+    "- XXX = KLIFS IFP\n",
+    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compound activity measures"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "    \n",
+    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you place links, please link descriptive words.\n",
+    "\n",
+    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "\n",
+    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HERE = Path(_dh[-1])\n",
+    "DATA = HERE / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We retrieve a pre-curated version of ChEMBL v.28 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Take v.29 instead of v.28."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (186972, 17)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>activities.activity_id</th>\n",
+       "      <th>assays.chembl_id</th>\n",
+       "      <th>target_dictionary.chembl_id</th>\n",
+       "      <th>molecule_dictionary.chembl_id</th>\n",
+       "      <th>molecule_dictionary.max_phase</th>\n",
+       "      <th>activities.standard_type</th>\n",
+       "      <th>activities.standard_value</th>\n",
+       "      <th>activities.standard_units</th>\n",
+       "      <th>compound_structures.canonical_smiles</th>\n",
+       "      <th>compound_structures.standard_inchi</th>\n",
+       "      <th>component_sequences.sequence</th>\n",
+       "      <th>assays.confidence_score</th>\n",
+       "      <th>docs.chembl_id</th>\n",
+       "      <th>docs.year</th>\n",
+       "      <th>docs.authors</th>\n",
+       "      <th>UniprotID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>96251</td>\n",
+       "      <td>16291323</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666724</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.096910</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>97672</td>\n",
+       "      <td>16306943</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL1968705</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>93879</td>\n",
+       "      <td>16264754</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666728</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>100714</td>\n",
+       "      <td>16340050</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL1997433</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>13.958607</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>95877</td>\n",
+       "      <td>16287186</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666721</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>13.920819</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  activities.activity_id assays.chembl_id  \\\n",
+       "0       96251                16291323    CHEMBL3705523   \n",
+       "1       97672                16306943    CHEMBL3705523   \n",
+       "2       93879                16264754    CHEMBL3705523   \n",
+       "3      100714                16340050    CHEMBL3705523   \n",
+       "4       95877                16287186    CHEMBL3705523   \n",
+       "\n",
+       "  target_dictionary.chembl_id molecule_dictionary.chembl_id  \\\n",
+       "0                  CHEMBL2973                 CHEMBL3666724   \n",
+       "1                  CHEMBL2973                 CHEMBL1968705   \n",
+       "2                  CHEMBL2973                 CHEMBL3666728   \n",
+       "3                  CHEMBL2973                 CHEMBL1997433   \n",
+       "4                  CHEMBL2973                 CHEMBL3666721   \n",
+       "\n",
+       "   molecule_dictionary.max_phase activities.standard_type  \\\n",
+       "0                              0                    pIC50   \n",
+       "1                              0                    pIC50   \n",
+       "2                              0                    pIC50   \n",
+       "3                              0                    pIC50   \n",
+       "4                              0                    pIC50   \n",
+       "\n",
+       "   activities.standard_value activities.standard_units  \\\n",
+       "0                  14.096910                        nM   \n",
+       "1                  14.000000                        nM   \n",
+       "2                  14.000000                        nM   \n",
+       "3                  13.958607                        nM   \n",
+       "4                  13.920819                        nM   \n",
+       "\n",
+       "                compound_structures.canonical_smiles  \\\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "\n",
+       "                  compound_structures.standard_inchi  \\\n",
+       "0  InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...   \n",
+       "1  InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...   \n",
+       "2  InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...   \n",
+       "3  InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...   \n",
+       "4  InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...   \n",
+       "\n",
+       "                        component_sequences.sequence  assays.confidence_score  \\\n",
+       "0  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "1  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "2  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "3  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "4  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "\n",
+       "  docs.chembl_id  docs.year docs.authors UniprotID  \n",
+       "0  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "1  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "2  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "3  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "4  CHEMBL3639077     2014.0          NaN    O75116  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path = \"https://github.com/openkinome/kinodata/releases/download/\\\n",
+    "v0.2/activities-chembl28_v0.2.zip\"\n",
+    "data = pd.read_csv(path, index_col=None)\n",
+    "print(f\"Current shape of data: {data.shape}\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We look at the type of activity and the associated units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activities: {'pIC50', 'pKi', 'pKd'}\n",
+      "Units: {'nM'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
+    "      f\"Units: {set(data['activities.standard_units'])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's keep the entries which have pIC50 values only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data[data[\"activities.standard_type\"] == \"pIC50\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the uniprot ID of the kinase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data[[\"compound_structures.canonical_smiles\", \"activities.standard_value\", \"UniprotID\"]] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (159978, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>compound_structures.canonical_smiles</th>\n",
+       "      <th>activities.standard_value</th>\n",
+       "      <th>UniprotID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.096910</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.958607</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.920819</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                compound_structures.canonical_smiles  \\\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "\n",
+       "   activities.standard_value UniprotID  \n",
+       "0                  14.096910    O75116  \n",
+       "1                  14.000000    O75116  \n",
+       "2                  14.000000    O75116  \n",
+       "3                  13.958607    O75116  \n",
+       "4                  13.920819    O75116  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(f\"Current shape of data: {data.shape}\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also drop NA values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (159823, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Current shape of data: {data.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Hit or non-hit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff = 6.3\n",
+    "# TODO: Check if this cut-off is the same as in the paper "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binarize_pic50(pic50_value, threshold):\n",
+    "    \"\"\"\n",
+    "    Binarizes a scalar value given a threshold.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    pic50_value : float\n",
+    "        The measurement pIC50 value of a kinase-ligand pair.\n",
+    "    threshold : float\n",
+    "        The cutoff to determine activity.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    1 if the pIC50 value is above the threshold, which indicates activity.\n",
+    "    0 otherwise.\n",
+    "    \"\"\"\n",
+    "    if pic50_value >= threshold:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"activity\"] = data[\"activities.standard_value\"].apply(binarize_pic50, args=(cutoff,))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>compound_structures.canonical_smiles</th>\n",
+       "      <th>activities.standard_value</th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>activity</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.096910</td>\n",
+       "      <td>O75116</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.958607</td>\n",
+       "      <td>O75116</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.920819</td>\n",
+       "      <td>O75116</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                compound_structures.canonical_smiles  \\\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "\n",
+       "   activities.standard_value UniprotID  activity  \n",
+       "0                  14.096910    O75116         1  \n",
+       "1                  14.000000    O75116         1  \n",
+       "2                  14.000000    O75116         1  \n",
+       "3                  13.958607    O75116         1  \n",
+       "4                  13.920819    O75116         1  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Kinases of interest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We focus on the kinases of interest and map them to their uniprot IDs in accordance with the Table in the Theory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name_to_uniprot = {'EGFR': 'P00533',\n",
+    " 'ErbB2': 'P04626',\n",
+    " 'BRAF': 'P15056',\n",
+    " 'CDK2': 'P24941',\n",
+    " 'LCK': 'P06239',\n",
+    " 'MET': 'P08581',\n",
+    " 'p38a': 'Q16539',\n",
+    " 'KDR': 'P35968',\n",
+    " 'p110a': 'P42336'}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now look at the kinase coverage.\n",
+    "\n",
+    "For each of them, we compute three metrics:\n",
+    "\n",
+    "1. the total number of measured compounds against the given kinase,\n",
+    "2. the number of active compounds, and\n",
+    "3. the fraction of active compouds, i.e. the ratio of active compounds and total number of measured compounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Use percentage instead of fraction?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
+    "    \"\"\"\n",
+    "    Retrieve the three metrics for a given kinase.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_name : str\n",
+    "        The name of the kinase of interest, e.g. \"EGFR\".\n",
+    "    \n",
+    "    activity_dataframe :  pd.DataFrame\n",
+    "        The dataframe with activity values for kinases.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    tuple : (int, int, float)\n",
+    "        The three metrics:\n",
+    "        1. The total number of measured compounds against the kinase\n",
+    "        2. The number of active compounds against the kinase.\n",
+    "        3. The fraction of active compounds against the kinase.\n",
+    "    \"\"\"\n",
+    "    uniprot_id = name_to_uniprot[kinase_name]\n",
+    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"] == uniprot_id]\n",
+    "    total_measured_compounds = len(kinase_data)\n",
+    "    active_compounds = len(kinase_data[kinase_data[\"activity\"] == 1])\n",
+    "    if total_measured_compounds > 0:\n",
+    "        fraction = active_compounds/total_measured_compounds\n",
+    "    else:\n",
+    "        print(\"No compounds were measured for this kinase.\")\n",
+    "        fraction = np.nan\n",
+    "    return (total_measured_compounds, active_compounds, fraction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what information we get for EGFR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6026, 3706, 0.6150016594756057)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EGFR_metrics = kinase_to_activity_numbers(\"EGFR\", data)\n",
+    "EGFR_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a table from this information for all kinases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def from_numbers_to_table(activity_dataframe):\n",
+    "    \"\"\"\n",
+    "    Create a metric table for all kinases.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------    \n",
+    "    activity_dataframe :  pd.DataFrame\n",
+    "        The dataframe with activity values for kinases.\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    table : pd.DataFrame\n",
+    "        A dataframe with the kinases as rows and metrics as columns.\n",
+    "    \"\"\"\n",
+    "    table = pd.DataFrame(index=name_to_uniprot.keys(), columns=[\"total\", \"actives\", \"fraction\"])\n",
+    "    for name in name_to_uniprot:\n",
+    "        metrics = kinase_to_activity_numbers(name, activity_dataframe)\n",
+    "        table.loc[name] = metrics\n",
+    "    return table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total</th>\n",
+       "      <th>actives</th>\n",
+       "      <th>fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>6026</td>\n",
+       "      <td>3706</td>\n",
+       "      <td>0.615002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>1723</td>\n",
+       "      <td>1049</td>\n",
+       "      <td>0.608822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>3708</td>\n",
+       "      <td>3013</td>\n",
+       "      <td>0.812567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>1513</td>\n",
+       "      <td>825</td>\n",
+       "      <td>0.545274</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>1550</td>\n",
+       "      <td>935</td>\n",
+       "      <td>0.603226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>2867</td>\n",
+       "      <td>2279</td>\n",
+       "      <td>0.794908</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>3654</td>\n",
+       "      <td>2796</td>\n",
+       "      <td>0.765189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>7734</td>\n",
+       "      <td>5401</td>\n",
+       "      <td>0.698345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>4394</td>\n",
+       "      <td>2815</td>\n",
+       "      <td>0.640646</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      total actives  fraction\n",
+       "EGFR   6026    3706  0.615002\n",
+       "ErbB2  1723    1049  0.608822\n",
+       "BRAF   3708    3013  0.812567\n",
+       "CDK2   1513     825  0.545274\n",
+       "LCK    1550     935  0.603226\n",
+       "MET    2867    2279  0.794908\n",
+       "p38a   3654    2796  0.765189\n",
+       "KDR    7734    5401  0.698345\n",
+       "p110a  4394    2815  0.640646"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_table = from_numbers_to_table(data)\n",
+    "kinase_table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's beautify the table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_19ff7_row0_col2 {\n",
+       "  background-color: #d8d8ea;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_19ff7_row1_col2 {\n",
+       "  background-color: #dcdcec;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_19ff7_row2_col2 {\n",
+       "  background-color: #3f007d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_19ff7_row3_col2 {\n",
+       "  background-color: #fcfbfd;\n",
+       "  color: #000000;\n",
+       "  background-color: yellow;\n",
+       "}\n",
+       "#T_19ff7_row4_col2 {\n",
+       "  background-color: #e0dfee;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_19ff7_row5_col2 {\n",
+       "  background-color: #4a1486;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_19ff7_row6_col2 {\n",
+       "  background-color: #5d3897;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_19ff7_row7_col2 {\n",
+       "  background-color: #8d89c0;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_19ff7_row8_col2 {\n",
+       "  background-color: #c0c1de;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_19ff7_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >total</th>\n",
+       "      <th class=\"col_heading level0 col1\" >actives</th>\n",
+       "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_19ff7_row0_col0\" class=\"data row0 col0\" >6026</td>\n",
+       "      <td id=\"T_19ff7_row0_col1\" class=\"data row0 col1\" >3706</td>\n",
+       "      <td id=\"T_19ff7_row0_col2\" class=\"data row0 col2\" >0.615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_19ff7_row1_col0\" class=\"data row1 col0\" >1723</td>\n",
+       "      <td id=\"T_19ff7_row1_col1\" class=\"data row1 col1\" >1049</td>\n",
+       "      <td id=\"T_19ff7_row1_col2\" class=\"data row1 col2\" >0.609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_19ff7_row2_col0\" class=\"data row2 col0\" >3708</td>\n",
+       "      <td id=\"T_19ff7_row2_col1\" class=\"data row2 col1\" >3013</td>\n",
+       "      <td id=\"T_19ff7_row2_col2\" class=\"data row2 col2\" >0.813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_19ff7_row3_col0\" class=\"data row3 col0\" >1513</td>\n",
+       "      <td id=\"T_19ff7_row3_col1\" class=\"data row3 col1\" >825</td>\n",
+       "      <td id=\"T_19ff7_row3_col2\" class=\"data row3 col2\" >0.545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_19ff7_row4_col0\" class=\"data row4 col0\" >1550</td>\n",
+       "      <td id=\"T_19ff7_row4_col1\" class=\"data row4 col1\" >935</td>\n",
+       "      <td id=\"T_19ff7_row4_col2\" class=\"data row4 col2\" >0.603</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_19ff7_row5_col0\" class=\"data row5 col0\" >2867</td>\n",
+       "      <td id=\"T_19ff7_row5_col1\" class=\"data row5 col1\" >2279</td>\n",
+       "      <td id=\"T_19ff7_row5_col2\" class=\"data row5 col2\" >0.795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_19ff7_row6_col0\" class=\"data row6 col0\" >3654</td>\n",
+       "      <td id=\"T_19ff7_row6_col1\" class=\"data row6 col1\" >2796</td>\n",
+       "      <td id=\"T_19ff7_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_19ff7_row7_col0\" class=\"data row7 col0\" >7734</td>\n",
+       "      <td id=\"T_19ff7_row7_col1\" class=\"data row7 col1\" >5401</td>\n",
+       "      <td id=\"T_19ff7_row7_col2\" class=\"data row7 col2\" >0.698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_19ff7_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_19ff7_row8_col0\" class=\"data row8 col0\" >4394</td>\n",
+       "      <td id=\"T_19ff7_row8_col1\" class=\"data row8 col1\" >2815</td>\n",
+       "      <td id=\"T_19ff7_row8_col2\" class=\"data row8 col2\" >0.641</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fef26cdbfa0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_table_styler = kinase_table.style.\\\n",
+    "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
+    "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
+    "    highlight_min(color=\"yellow\", axis=None)\n",
+    "kinase_table_styler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quiz\n",
+    "\n",
+    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
+    "\n",
+    "1. Question\n",
+    "2. Question\n",
+    "3. Question"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "toc-autonumbering": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 54633a120dfe56640bb2dd970274035831dae2b5 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 23 Aug 2021 10:40:28 +0200
Subject: [PATCH 014/158] flake8 fix.

---
 .../talktorial.ipynb                          | 206 ++++++++++--------
 1 file changed, 110 insertions(+), 96 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index a8031442..d9d58fee 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -157,9 +157,18 @@
     "## Practical"
    ]
   },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "# !pip install flake8 pycodestyle_magic\n",
+    "%load_ext pycodestyle_magic\n",
+    "%pycodestyle_on"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -415,7 +424,7 @@
        "4  CHEMBL3639077     2014.0          NaN    O75116  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -437,14 +446,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pIC50', 'pKi', 'pKd'}\n",
+      "Activities: {'pKd', 'pIC50', 'pKi'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -463,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,16 +488,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = data[[\"compound_structures.canonical_smiles\", \"activities.standard_value\", \"UniprotID\"]] "
+    "data = data[[\"compound_structures.canonical_smiles\",\n",
+    "             \"activities.standard_value\",\n",
+    "             \"UniprotID\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -575,7 +586,7 @@
        "4                  13.920819    O75116  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -603,7 +614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -634,31 +645,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
     "cutoff = 6.3\n",
-    "# TODO: Check if this cut-off is the same as in the paper "
+    "# TODO: Check if this cut-off is the same as in the paper"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "def binarize_pic50(pic50_value, threshold):\n",
     "    \"\"\"\n",
     "    Binarizes a scalar value given a threshold.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    pic50_value : float\n",
     "        The measurement pIC50 value of a kinase-ligand pair.\n",
     "    threshold : float\n",
     "        The cutoff to determine activity.\n",
-    "    \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    1 if the pIC50 value is above the threshold, which indicates activity.\n",
@@ -672,16 +683,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data[\"activity\"] = data[\"activities.standard_value\"].apply(binarize_pic50, args=(cutoff,))"
+    "data[\"activity\"] = data[\"activities.standard_value\"].apply(binarize_pic50,\n",
+    "                                                           args=(cutoff,))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -767,7 +779,7 @@
        "4                  13.920819    O75116         1  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,19 +804,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "name_to_uniprot = {'EGFR': 'P00533',\n",
-    " 'ErbB2': 'P04626',\n",
-    " 'BRAF': 'P15056',\n",
-    " 'CDK2': 'P24941',\n",
-    " 'LCK': 'P06239',\n",
-    " 'MET': 'P08581',\n",
-    " 'p38a': 'Q16539',\n",
-    " 'KDR': 'P35968',\n",
-    " 'p110a': 'P42336'}"
+    "                   'ErbB2': 'P04626',\n",
+    "                   'BRAF': 'P15056',\n",
+    "                   'CDK2': 'P24941',\n",
+    "                   'LCK': 'P06239',\n",
+    "                   'MET': 'P08581',\n",
+    "                   'p38a': 'Q16539',\n",
+    "                   'KDR': 'P35968',\n",
+    "                   'p110a': 'P42336'}"
    ]
   },
   {
@@ -829,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,22 +850,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
     "    \"\"\"\n",
     "    Retrieve the three metrics for a given kinase.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    kinase_name : str\n",
     "        The name of the kinase of interest, e.g. \"EGFR\".\n",
-    "    \n",
+    "\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
-    "    \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    tuple : (int, int, float)\n",
@@ -863,7 +875,8 @@
     "        3. The fraction of active compounds against the kinase.\n",
     "    \"\"\"\n",
     "    uniprot_id = name_to_uniprot[kinase_name]\n",
-    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"] == uniprot_id]\n",
+    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"]\n",
+    "                                     == uniprot_id]\n",
     "    total_measured_compounds = len(kinase_data)\n",
     "    active_compounds = len(kinase_data[kinase_data[\"activity\"] == 1])\n",
     "    if total_measured_compounds > 0:\n",
@@ -883,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -892,7 +905,7 @@
        "(6026, 3706, 0.6150016594756057)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,25 +924,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "def from_numbers_to_table(activity_dataframe):\n",
     "    \"\"\"\n",
     "    Create a metric table for all kinases.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
-    "    ----------    \n",
+    "    ----------\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
-    "    \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    table : pd.DataFrame\n",
     "        A dataframe with the kinases as rows and metrics as columns.\n",
     "    \"\"\"\n",
-    "    table = pd.DataFrame(index=name_to_uniprot.keys(), columns=[\"total\", \"actives\", \"fraction\"])\n",
+    "    table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "                         columns=[\"total\", \"actives\", \"fraction\"])\n",
     "    for name in name_to_uniprot:\n",
     "        metrics = kinase_to_activity_numbers(name, activity_dataframe)\n",
     "        table.loc[name] = metrics\n",
@@ -938,7 +952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1039,7 +1053,7 @@
        "p110a  4394    2815  0.640646"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1058,52 +1072,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_19ff7_row0_col2 {\n",
+       "#T_3e895_row0_col2 {\n",
        "  background-color: #d8d8ea;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_19ff7_row1_col2 {\n",
+       "#T_3e895_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_19ff7_row2_col2 {\n",
+       "#T_3e895_row2_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_19ff7_row3_col2 {\n",
+       "#T_3e895_row3_col2 {\n",
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        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_19ff7_row4_col2 {\n",
+       "#T_3e895_row4_col2 {\n",
        "  background-color: #e0dfee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_19ff7_row5_col2 {\n",
+       "#T_3e895_row5_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_19ff7_row6_col2 {\n",
+       "#T_3e895_row6_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_19ff7_row7_col2 {\n",
+       "#T_3e895_row7_col2 {\n",
        "  background-color: #8d89c0;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_19ff7_row8_col2 {\n",
+       "#T_3e895_row8_col2 {\n",
        "  background-color: #c0c1de;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_19ff7_\">\n",
+       "<table id=\"T_3e895_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1114,67 +1128,67 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_19ff7_row0_col0\" class=\"data row0 col0\" >6026</td>\n",
-       "      <td id=\"T_19ff7_row0_col1\" class=\"data row0 col1\" >3706</td>\n",
-       "      <td id=\"T_19ff7_row0_col2\" class=\"data row0 col2\" >0.615</td>\n",
+       "      <th id=\"T_3e895_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_3e895_row0_col0\" class=\"data row0 col0\" >6026</td>\n",
+       "      <td id=\"T_3e895_row0_col1\" class=\"data row0 col1\" >3706</td>\n",
+       "      <td id=\"T_3e895_row0_col2\" class=\"data row0 col2\" >0.615</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_19ff7_row1_col0\" class=\"data row1 col0\" >1723</td>\n",
-       "      <td id=\"T_19ff7_row1_col1\" class=\"data row1 col1\" >1049</td>\n",
-       "      <td id=\"T_19ff7_row1_col2\" class=\"data row1 col2\" >0.609</td>\n",
+       "      <th id=\"T_3e895_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_3e895_row1_col0\" class=\"data row1 col0\" >1723</td>\n",
+       "      <td id=\"T_3e895_row1_col1\" class=\"data row1 col1\" >1049</td>\n",
+       "      <td id=\"T_3e895_row1_col2\" class=\"data row1 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_19ff7_row2_col0\" class=\"data row2 col0\" >3708</td>\n",
-       "      <td id=\"T_19ff7_row2_col1\" class=\"data row2 col1\" >3013</td>\n",
-       "      <td id=\"T_19ff7_row2_col2\" class=\"data row2 col2\" >0.813</td>\n",
+       "      <th id=\"T_3e895_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_3e895_row2_col0\" class=\"data row2 col0\" >3708</td>\n",
+       "      <td id=\"T_3e895_row2_col1\" class=\"data row2 col1\" >3013</td>\n",
+       "      <td id=\"T_3e895_row2_col2\" class=\"data row2 col2\" >0.813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_19ff7_row3_col0\" class=\"data row3 col0\" >1513</td>\n",
-       "      <td id=\"T_19ff7_row3_col1\" class=\"data row3 col1\" >825</td>\n",
-       "      <td id=\"T_19ff7_row3_col2\" class=\"data row3 col2\" >0.545</td>\n",
+       "      <th id=\"T_3e895_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_3e895_row3_col0\" class=\"data row3 col0\" >1513</td>\n",
+       "      <td id=\"T_3e895_row3_col1\" class=\"data row3 col1\" >825</td>\n",
+       "      <td id=\"T_3e895_row3_col2\" class=\"data row3 col2\" >0.545</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_19ff7_row4_col0\" class=\"data row4 col0\" >1550</td>\n",
-       "      <td id=\"T_19ff7_row4_col1\" class=\"data row4 col1\" >935</td>\n",
-       "      <td id=\"T_19ff7_row4_col2\" class=\"data row4 col2\" >0.603</td>\n",
+       "      <th id=\"T_3e895_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_3e895_row4_col0\" class=\"data row4 col0\" >1550</td>\n",
+       "      <td id=\"T_3e895_row4_col1\" class=\"data row4 col1\" >935</td>\n",
+       "      <td id=\"T_3e895_row4_col2\" class=\"data row4 col2\" >0.603</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_19ff7_row5_col0\" class=\"data row5 col0\" >2867</td>\n",
-       "      <td id=\"T_19ff7_row5_col1\" class=\"data row5 col1\" >2279</td>\n",
-       "      <td id=\"T_19ff7_row5_col2\" class=\"data row5 col2\" >0.795</td>\n",
+       "      <th id=\"T_3e895_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_3e895_row5_col0\" class=\"data row5 col0\" >2867</td>\n",
+       "      <td id=\"T_3e895_row5_col1\" class=\"data row5 col1\" >2279</td>\n",
+       "      <td id=\"T_3e895_row5_col2\" class=\"data row5 col2\" >0.795</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_19ff7_row6_col0\" class=\"data row6 col0\" >3654</td>\n",
-       "      <td id=\"T_19ff7_row6_col1\" class=\"data row6 col1\" >2796</td>\n",
-       "      <td id=\"T_19ff7_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th id=\"T_3e895_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_3e895_row6_col0\" class=\"data row6 col0\" >3654</td>\n",
+       "      <td id=\"T_3e895_row6_col1\" class=\"data row6 col1\" >2796</td>\n",
+       "      <td id=\"T_3e895_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_19ff7_row7_col0\" class=\"data row7 col0\" >7734</td>\n",
-       "      <td id=\"T_19ff7_row7_col1\" class=\"data row7 col1\" >5401</td>\n",
-       "      <td id=\"T_19ff7_row7_col2\" class=\"data row7 col2\" >0.698</td>\n",
+       "      <th id=\"T_3e895_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_3e895_row7_col0\" class=\"data row7 col0\" >7734</td>\n",
+       "      <td id=\"T_3e895_row7_col1\" class=\"data row7 col1\" >5401</td>\n",
+       "      <td id=\"T_3e895_row7_col2\" class=\"data row7 col2\" >0.698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_19ff7_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_19ff7_row8_col0\" class=\"data row8 col0\" >4394</td>\n",
-       "      <td id=\"T_19ff7_row8_col1\" class=\"data row8 col1\" >2815</td>\n",
-       "      <td id=\"T_19ff7_row8_col2\" class=\"data row8 col2\" >0.641</td>\n",
+       "      <th id=\"T_3e895_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_3e895_row8_col0\" class=\"data row8 col0\" >4394</td>\n",
+       "      <td id=\"T_3e895_row8_col1\" class=\"data row8 col1\" >2815</td>\n",
+       "      <td id=\"T_3e895_row8_col2\" class=\"data row8 col2\" >0.641</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef26cdbfa0>"
+       "<pandas.io.formats.style.Styler at 0x7fc2b52a9460>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }

From b14de3b2e3c9156502d4b63bc14139cd530dbb80 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 11:03:47 +0200
Subject: [PATCH 015/158] Add structure/kinase matrix calculation + heatmap

---
 .../talktorial.ipynb                          | 645 +++++++++++++++++-
 1 file changed, 638 insertions(+), 7 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 1508d7ad..8216c32d 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -170,7 +170,9 @@
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
+    "from sklearn.metrics import pairwise_distances\n",
     "from opencadd.databases.klifs import setup_remote"
    ]
   },
@@ -761,15 +763,624 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "### Compare structures\n",
+    "\n",
+    "We will compare the structures' IFP pairwise using the Tanimoto/Jaccard distance as implemented in `sklearn.metrics.pairwise_distances`, which uses under the hood the method `scipy.spatial.distance`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 1. Prepare IFPs as `numpy` array\n",
+    "\n",
+    "KLIFS deposits the IFP as a string of 0 and 1. We have to prepare our IFPs as an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "structure.klifs_id  kinase.klifs_name\n",
+       "775                 EGFR                 0000000000000010000000000000000000000000000000...\n",
+       "777                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "778                 EGFR                 0000000000000010000000000000000000000000000000...\n",
+       "779                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "782                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "Name: interaction.fingerprint, dtype: string"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\n",
+    "ifps = structures.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
+    "ifps.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "structure.klifs_id  kinase.klifs_name\n",
+       "775                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
+       "777                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
+       "778                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
+       "779                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
+       "782                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
+       "Name: interaction.fingerprint, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cast string to list of \"0\" and \"1\"\n",
+    "ifps = ifps.apply(list)\n",
+    "ifps.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "structure.klifs_id  kinase.klifs_name\n",
+       "775                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "777                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "778                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "779                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "782                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "Name: interaction.fingerprint, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cast \"0\" and \"1\" to boolean False and True\n",
+    "ifps = ifps.apply(lambda x: [True if i == \"1\" else False for i in x])\n",
+    "ifps.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to numpy array\n",
+    "ifps_array = np.array(ifps.to_list())\n",
+    "ifps_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Calculate pairwise Jaccard distances"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 0.72      , 0.54285714, ..., 0.40740741, 0.73333333,\n",
+       "        0.73333333],\n",
+       "       [0.72      , 0.        , 0.74193548, ..., 0.70833333, 0.76190476,\n",
+       "        0.76190476],\n",
+       "       [0.54285714, 0.74193548, 0.        , ..., 0.61111111, 0.78378378,\n",
+       "        0.78378378],\n",
+       "       ...,\n",
+       "       [0.40740741, 0.70833333, 0.61111111, ..., 0.        , 0.57692308,\n",
+       "        0.57692308],\n",
+       "       [0.73333333, 0.76190476, 0.78378378, ..., 0.57692308, 0.        ,\n",
+       "        0.        ],\n",
+       "       [0.73333333, 0.76190476, 0.78378378, ..., 0.57692308, 0.        ,\n",
+       "        0.        ]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "structure_distance_matrix = pairwise_distances(ifps_array, metric=\"jaccard\")\n",
+    "structure_distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure distance matrix size: (1466, 1466)\n",
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>775</th>\n",
+       "      <th>777</th>\n",
+       "      <th>778</th>\n",
+       "      <th>779</th>\n",
+       "      <th>782</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.483871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.703704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.411765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.655172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>0.483871</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.655172</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "structure.klifs_id       775       777       778       779       782\n",
+       "structure.klifs_id                                                  \n",
+       "775                 0.000000  0.720000  0.542857  0.500000  0.483871\n",
+       "777                 0.720000  0.000000  0.741935  0.500000  0.703704\n",
+       "778                 0.542857  0.741935  0.000000  0.696970  0.411765\n",
+       "779                 0.500000  0.500000  0.696970  0.000000  0.655172\n",
+       "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create DataFrame with structure KLIFS IDs as index/columns\n",
+    "structure_klifs_ids = ifps.index.get_level_values(0)\n",
+    "structure_distance_matrix = pd.DataFrame(\n",
+    "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
+    ")\n",
+    "print(f\"Structure distance matrix size: {structure_distance_matrix.shape}\")\n",
+    "print(\"Show matrix subset:\")\n",
+    "structure_distance_matrix.iloc[:5, :5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Map structure to kinase distance matrix\n",
+    "\n",
+    "One kinase pair can be represented by multiple structure pairs. \n",
+    "\n",
+    "TBA\n",
+    "\n",
+    "We use minimum!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.483871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.703704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.411765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.655172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.483871</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.655172</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR      EGFR\n",
+       "kinase.klifs_name                                                  \n",
+       "EGFR               0.000000  0.720000  0.542857  0.500000  0.483871\n",
+       "EGFR               0.720000  0.000000  0.741935  0.500000  0.703704\n",
+       "EGFR               0.542857  0.741935  0.000000  0.696970  0.411765\n",
+       "EGFR               0.500000  0.500000  0.696970  0.000000  0.655172\n",
+       "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Copy distance matrix to kinase matrix\n",
+    "kinase_distance_matrix = structure_distance_matrix\n",
+    "# Replace structure KLIFS IDs with the structures' kinase names\n",
+    "kinase_names = ifps.index.get_level_values(1)\n",
+    "kinase_distance_matrix.index = kinase_names\n",
+    "kinase_distance_matrix.columns = kinase_names\n",
+    "print(\"Show matrix subset:\")\n",
+    "kinase_distance_matrix.iloc[:5, :5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.277778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.406250</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.382353</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.258065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.406250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.043478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.190476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.382353</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.352941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
+       "kinase.klifs_name                                                               \n",
+       "BRAF               0.000000  0.300000  0.277778  0.393939  0.363636  0.320000   \n",
+       "CDK2               0.300000  0.000000  0.111111  0.451613  0.238095  0.190476   \n",
+       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.166667   \n",
+       "ErbB2              0.393939  0.451613  0.258065  0.000000  0.406250  0.419355   \n",
+       "KDR                0.363636  0.238095  0.238095  0.406250  0.000000  0.058824   \n",
+       "LCK                0.320000  0.190476  0.166667  0.419355  0.058824  0.000000   \n",
+       "MET                0.368421  0.133333  0.227273  0.382353  0.333333  0.250000   \n",
+       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
+       "p38a               0.333333  0.277778  0.173913  0.258065  0.043478  0.190476   \n",
+       "\n",
+       "kinase.klifs_name       MET     p110a      p38a  \n",
+       "kinase.klifs_name                                \n",
+       "BRAF               0.368421  0.500000  0.333333  \n",
+       "CDK2               0.133333  0.347826  0.277778  \n",
+       "EGFR               0.227273  0.380952  0.173913  \n",
+       "ErbB2              0.382353  0.571429  0.258065  \n",
+       "KDR                0.333333  0.533333  0.043478  \n",
+       "LCK                0.250000  0.375000  0.190476  \n",
+       "MET                0.000000  0.428571  0.352941  \n",
+       "p110a              0.428571  0.000000  0.473684  \n",
+       "p38a               0.352941  0.473684  0.000000  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: Select the minimum structure pair distance for each kinase pair\n",
+    "kinase_distance_matrix = (\n",
+    "    kinase_distance_matrix.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
+    "kinase_distance_matrix"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -780,10 +1391,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(kinase_distance_matrix)\n",
+    "plt.colorbar(label=\"Kinase distance\")\n",
+    "plt.title(\"Kinase distance matrix\")\n",
+    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
+    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
+   ]
   },
   {
    "cell_type": "markdown",

From 5ad3cdf8f025e2c4f89765a00b478d578a52a617 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 12:10:43 +0200
Subject: [PATCH 016/158] More structure>kinase mapping; first draft for phylo
 tree

---
 .../talktorial.ipynb                          | 1020 ++++++++++++++++-
 1 file changed, 1000 insertions(+), 20 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 8216c32d..952a01ad 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -1064,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1165,7 +1165,7 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1181,6 +1181,677 @@
     "kinase_distance_matrix.iloc[:5, :5]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>...</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.483871</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.655172</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.629630</td>\n",
+       "      <td>0.684211</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.681818</td>\n",
+       "      <td>0.620690</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.565217</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.695652</td>\n",
+       "      <td>0.709677</td>\n",
+       "      <td>0.592593</td>\n",
+       "      <td>0.743590</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.407407</td>\n",
+       "      <td>0.708333</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.541667</td>\n",
+       "      <td>0.562500</td>\n",
+       "      <td>0.517241</td>\n",
+       "      <td>0.440000</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.413793</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>0.589744</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.407407</td>\n",
+       "      <td>0.708333</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.541667</td>\n",
+       "      <td>0.562500</td>\n",
+       "      <td>0.517241</td>\n",
+       "      <td>0.440000</td>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.714286</td>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.413793</td>\n",
+       "      <td>0.240000</td>\n",
+       "      <td>0.589744</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.761905</td>\n",
+       "      <td>0.783784</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.677419</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.766667</td>\n",
+       "      <td>0.760000</td>\n",
+       "      <td>0.783784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.760000</td>\n",
+       "      <td>0.535714</td>\n",
+       "      <td>0.607143</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.807692</td>\n",
+       "      <td>0.807692</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.761905</td>\n",
+       "      <td>0.783784</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.677419</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.785714</td>\n",
+       "      <td>0.766667</td>\n",
+       "      <td>0.760000</td>\n",
+       "      <td>0.783784</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.760000</td>\n",
+       "      <td>0.535714</td>\n",
+       "      <td>0.607143</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.807692</td>\n",
+       "      <td>0.807692</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1466 rows × 1466 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR      EGFR      EGFR  \\\n",
+       "kinase.klifs_name                                                               \n",
+       "EGFR               0.000000       NaN       NaN       NaN       NaN       NaN   \n",
+       "EGFR               0.720000  0.000000       NaN       NaN       NaN       NaN   \n",
+       "EGFR               0.542857  0.741935  0.000000       NaN       NaN       NaN   \n",
+       "EGFR               0.500000  0.500000  0.696970  0.000000       NaN       NaN   \n",
+       "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000       NaN   \n",
+       "...                     ...       ...       ...       ...       ...       ...   \n",
+       "p110a              0.629630  0.684211  0.705882  0.681818  0.620690  0.576923   \n",
+       "CDK2               0.407407  0.708333  0.611111  0.541667  0.562500  0.517241   \n",
+       "CDK2               0.407407  0.708333  0.611111  0.541667  0.562500  0.517241   \n",
+       "CDK2               0.733333  0.761905  0.783784  0.750000  0.677419  0.642857   \n",
+       "CDK2               0.733333  0.761905  0.783784  0.750000  0.677419  0.642857   \n",
+       "\n",
+       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR  ...      EGFR  \\\n",
+       "kinase.klifs_name                                          ...             \n",
+       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
+       "...                     ...       ...       ...       ...  ...       ...   \n",
+       "p110a              0.565217  0.615385  0.750000  0.705882  ...  0.695652   \n",
+       "CDK2               0.440000  0.444444  0.714286  0.611111  ...  0.560000   \n",
+       "CDK2               0.440000  0.444444  0.714286  0.611111  ...  0.560000   \n",
+       "CDK2               0.785714  0.766667  0.760000  0.783784  ...  0.760000   \n",
+       "CDK2               0.785714  0.766667  0.760000  0.783784  ...  0.760000   \n",
+       "\n",
+       "kinase.klifs_name      CDK2      CDK2      EGFR     p110a     p110a      CDK2  \\\n",
+       "kinase.klifs_name                                                               \n",
+       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "...                     ...       ...       ...       ...       ...       ...   \n",
+       "p110a              0.709677  0.592593  0.743590  0.000000  0.000000       NaN   \n",
+       "CDK2               0.413793  0.240000  0.589744  0.615385  0.615385  0.000000   \n",
+       "CDK2               0.413793  0.240000  0.589744  0.615385  0.615385  0.000000   \n",
+       "CDK2               0.535714  0.607143  0.750000  0.807692  0.807692  0.576923   \n",
+       "CDK2               0.535714  0.607143  0.750000  0.807692  0.807692  0.576923   \n",
+       "\n",
+       "kinase.klifs_name      CDK2  CDK2  CDK2  \n",
+       "kinase.klifs_name                        \n",
+       "EGFR                    NaN   NaN   NaN  \n",
+       "EGFR                    NaN   NaN   NaN  \n",
+       "EGFR                    NaN   NaN   NaN  \n",
+       "EGFR                    NaN   NaN   NaN  \n",
+       "EGFR                    NaN   NaN   NaN  \n",
+       "...                     ...   ...   ...  \n",
+       "p110a                   NaN   NaN   NaN  \n",
+       "CDK2                    NaN   NaN   NaN  \n",
+       "CDK2               0.000000   NaN   NaN  \n",
+       "CDK2               0.576923   0.0   NaN  \n",
+       "CDK2               0.576923   0.0   0.0  \n",
+       "\n",
+       "[1466 rows x 1466 columns]"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = kinase_distance_matrix\n",
+    "df = df.where(np.tril(np.ones(df.shape)).astype(bool))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kinase.klifs_name  kinase.klifs_name\n",
+       "EGFR               EGFR                 0.000000\n",
+       "                   EGFR                 0.720000\n",
+       "                   EGFR                 0.000000\n",
+       "                   EGFR                 0.542857\n",
+       "                   EGFR                 0.741935\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.stack().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kinase.klifs_name  kinase.klifs_name\n",
+       "BRAF               BRAF                 0.000000\n",
+       "p110a              p110a                0.000000\n",
+       "MET                MET                  0.000000\n",
+       "LCK                LCK                  0.000000\n",
+       "KDR                KDR                  0.000000\n",
+       "ErbB2              ErbB2                0.000000\n",
+       "EGFR               EGFR                 0.000000\n",
+       "CDK2               CDK2                 0.000000\n",
+       "p38a               p38a                 0.000000\n",
+       "KDR                p38a                 0.043478\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.stack().groupby(level=[0, 1]).min().sort_values().head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.387097</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.423077</td>\n",
+       "      <td>0.277778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.423077</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.382353</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.406250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.343750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.043478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.235294</td>\n",
+       "      <td>0.433333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.368421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.304348</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.192308</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
+       "kinase.klifs_name                                                               \n",
+       "BRAF               0.000000  0.300000  0.277778  0.416667  0.428571  0.320000   \n",
+       "CDK2               0.300000  0.000000  0.166667  0.451613  0.363636  0.190476   \n",
+       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.190476   \n",
+       "ErbB2              0.393939  0.454545  0.272727  0.000000       NaN  0.419355   \n",
+       "KDR                0.363636  0.238095  0.250000  0.406250  0.000000  0.058824   \n",
+       "LCK                     NaN       NaN  0.166667       NaN       NaN  0.000000   \n",
+       "MET                0.368421  0.250000  0.235294  0.433333  0.333333  0.250000   \n",
+       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
+       "p38a               0.333333  0.304348  0.238095  0.258065  0.192308  0.190476   \n",
+       "\n",
+       "kinase.klifs_name       MET     p110a      p38a  \n",
+       "kinase.klifs_name                                \n",
+       "BRAF               0.380952  0.583333  0.387097  \n",
+       "CDK2               0.133333  0.423077  0.277778  \n",
+       "EGFR               0.227273  0.423077  0.173913  \n",
+       "ErbB2              0.382353       NaN       NaN  \n",
+       "KDR                0.343750       NaN  0.043478  \n",
+       "LCK                     NaN       NaN       NaN  \n",
+       "MET                0.000000  0.500000  0.368421  \n",
+       "p110a              0.428571  0.000000  0.473684  \n",
+       "p38a               0.352941  0.576923  0.000000  "
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = (\n",
+    "    df.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
+    "df"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -1369,16 +2040,261 @@
        "p38a               0.352941  0.473684  0.000000  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TODO: Select the minimum structure pair distance for each kinase pair\n",
+    "kinase_distance_matrix = (\n",
+    "    kinase_distance_matrix.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
+    "kinase_distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CNxVwO/PJzXCa2Wzgt8AFkvrB/Jvok0HG04Rm/zfz0stxnMbSgVIdVdgMmGZmz5nZx8BkYGyFel8GrgJez/cuFqRWw9k/NsNLxxkx/zvA/4D/SnoYuAO4DHglg+yLgG0lrVyjbo7jNAuWW1N9eWB6Ij2DskFkScsD+xBsSKHUNDhkVjnep5nNBU6JR6XyQRXybgNuS6Tn4KPqjtMjMGBeR2r/bJikBxLpiWY2MX6uZFnLQwScA3zTzNqlYgekfOWQ4ziFkTFY20wz26STshnA8ER6BRZuyW4CTI5Gcxiwh6R5ZnZNeo3T4YbTcZxCsXymI90PjIxdeC8DBwIHLXgdm9+9J2kSYYbONXlcvBw3nI7jFEoem3yY2TxJEwij5W3AJWY2VdKxsbzwfs0kbjgdxykMs/wmwJvZFGBKWV5Fg2lm43O5aCf0vLVQjuM0FWZKdTQCSStJ2jl+7l+ah14N9zgdxykQ0Z5+VL1bkXQUYQn3ksCqhAGni4Cdqp3bsoazz4C5DB+VZXpoNjbe+cXCZANcc92WhcoHGPHdewqV3/u2ZQuVP6DXx4XKn/7+0ELlA9yzwVWFyl/jweMKk93et34ZTb5W/UuEifX/BjCzZyR9Os2JLWs4HcdpASz0czYpH5nZx6U5n3E/4VTauuF0HKdQmjh0xr8kfZuwEnIX4Hjgb2lObM7OB8dxegRGUw8OnQK8QYhKcQxhxP7UNCe6x+k4ToGI9o6m9Tj7E+aDXgzzNyXqD8yudqJ7nI7jFEoTe5y3EAxlif7AP9Kc6B6n4ziFYZbbkssi6Gdms0oJM5sVQwBVxT1Ox3EKJa8d4AvgA0kblRKSNgbmpDnRPU7HcQqliacjfRX4s6TShPBlgQPSnJjacHYWS71CvduAk83sgbL88cDPCDub9CYEdzvUzGZLOhE4EphHGOU6wsyKnYHuOE630KxNdTO7X9KawBqE/T6fjHsKVyWLx9lZLPX5xFGprrjSzCbEun8kWPdLgYeBTaIRPQ44k5SW33Gc5iXjfpyNYFNgBMEWbigJM/tdtZPqbqpLegG4BNgVuCBmf0HSecBggvd4X9k5vYCBwNsAZnZrovhe4Av16uU4ThPQxINDkn5PWKP+CNAesw3I1XAuFEs9ERb4QzMbHZU5FhhoZltJ2pZgVNeN9Q6QNJrQl/A0lWfpfxG4IYNejuM0M83bx7kJsLZZ9l7YvJrq5XHVrwAws9slDZY0tFTPzCYoLA69EPg6ML+fVNIXCDezXaWLSDqasJsJfT+davcnx3EaTLN6nMB/gWUIASYzkdd0pA/K0uUWfIF0tPB/A7Yt5cU98b4DfMbMPqp0ETObaGabmNkmvYemmm7lOE6DMUt3NIBhwOOSbpJ0belIc2JR05EOAG6NzfJ3zezdClHnRgPPAkjaEPg1MMbMCo2H7DhO91Faq96knFbrifX0cd5oZhXDAANvS7qbODiUyC/1cS5GiFo3Pub/DBhEmFMF8JKZfSaDbo7jNCMG1qRr1c3sX7Wem9pwdhFLfURZevtO6k0CJnVStnNaPRzHaTGadHBI0hbA+cBaQB9CELgPzGxwtXN95ZDjOAXSuHhCKbiAEGb4z4RB6UOBkWlOdMPpOE6xNKnHCWBm0yS1mVk7cGnsYqyKG07HcYqjiSfAA7Ml9QEekXQmYVrSwDQn+u5IjuMUiynd0f0cQrCBEwhTKocDn0tzohtOx3GKxVIe3c9nzexDM3vPzH5gZicCe6U50Q2n4zjF0ryG87AKeePTnNiyfZzz2hdj5qxU3RE1cd2tWxUmG7rniy867vnc7TOvVMvErNVWLlT+69+vOuukbqZ+nGpf3JpZ7aLidl98/Y0c4tobjWqGd4qkccBBwMplK4UGA2+mkdGyhtNxnNagCTcyvpswEDQMODuR/z7waBoBbjgdxymWJjOccZP0F+P+GHPMrEPS6sCaLLhZe6d4H6fjOIWiDqU6qsqRxkh6StI0SQst95Y0VtKjkh6R9EBc3t0VtwP9JC1PiHh5OJ2sbizHDafjOMWRdmCoilcao0tcCOwOrA2Mk7R2WbVbgA3i9pdHAL+pop3MbDZhCtL5ZrZPlF0VN5yO4xRIyjmc1QeQNgOmmdlzZvYxMBkYm6xgZrMSmxIPpHongSRtCRwMXB/zUnVfuuF0HKdY8pmOtDwwPZGeEfMWQNI+kp4kGMIjysvL+CrwLeAvZjZV0irArV2fEvDBIcdxiiX94NAwScnouBPNbGL8XMklXUiymf0F+EsM2/MjoNOd1+K2cv9KpJ8DTkijqBtOx3GKw4D0+3HONLNNOimbQVgSWWIF4JVO6pbC9qwqaZiZzUyWSTrHzL4q6W9UNr5V9wLO1XBKmmVmg+LnPYBzgZ0ILvNRhJjpAwlD/qea2eOx7m2EAG4fAh8DR5nZI3nq5jhOY1A+05HuB0ZKWhl4mbAd3EELXEdaDXjWzEzSRoQ9NitNaP99/HtWrcoU4nFK2omwQeiuZvZS3NX9F2Z2Viw/APinpPXM7I142sFm9oCkwwk7wu9ShG6O43QzORhOM5snaQJwE2HD4Utiv+SxsfwiYF/gUElzgTnAAZUiWJrZg/Fv8TvAp0XSNsDFwB5m9mylOmZ2paQ9CW+Mc8uK7yFEv3Qcx5mPmU0BppTlXZT4/FPgp9XkSHqMLsy5ma1fTUbehrMv8FdgezN7skrdhwgz9csZA1yTs16O4zSInJrqeVLaAelL8W+p6X4wMDuNgLwN51zCOtAvAl+pUre8x/hySQMJbvhGFU9IxFXv/akh9WnqOE730GSbfMQll0ja2sy2ThSdIuku4IfVZOQ9j7MD2B/YVNK3q9TdEHgikT4YWBn4I2GFwEIk46q3Dfa46o7T9BjBKqQ5up+ByWWZkrYi5Q7wufdxmtlsSXsBd0h6zcx+W15H0r7ArsBJZefOlXQq8KyktczsifJzHcdpLZqwqV7ii8AlkoYQTPy7VJ80DxQ0qm5mb0kaA9wuqTSH6muSvkCw6P8FdkyMqCfPnSPpbOBkwo05jtPKNKnhjKPrG0gaTFi3/m7ac3M1nKU5nPHzdELTG8KA0WldnLd9WfrsTqo6jtNqNKnhLGFm72U9x1cOOY5TGLKmbqrXjBtOx3GKpclG1fPAd0dyHKdQ1JHu6Ha9pAGSvivp4pgeGQe2q+KG03GcYmneKJeXAh8BW8b0DOD0NCe64XQcpzjsk37OakcDWNXMziQs3MHM5lB5+7qF8D5Ox3GKpXkHhz6W1J+ooaRVCR5oVVrWcHbMW4xZbxW3emjJmcX+2r0/KP5pGtArh7jYXVB03PP2ac8XKr/Ps1sVKh/g0KGHFXuBPZcqTPTcq/rkI6h5Def3gRuB4ZIuB7YGxqc5sWUNp+M4rUGzTkcys5slPQRsQWiif6V80+PO8D5Ox3GKpUkHhyRtDXxoZtcDQ4FvS1opzbluOB3HKY7mHhz6FTBb0gaEPYBfBH6X5kQ3nI7jFEuTepzAvLhD/FjgPDM7F1g8zYnex+k4TrE0aR8n8L6kbwFfALaV1Ab0TnOie5yO4xSGaOqm+gGE6UdfNLNXCXHaf5bmRPc4HccpDmvMcso0RGP580T6JbyP03GcpqBJ+zglbSHpfkmzJH0sqV1Sqj05CzGckmZ1kn+opP9KmirpcUknx/xJkj4fPy8p6eEYJthxnFanSQ0ncAEwDngG6A8cSSdhe8rpNo9T0u7AVwmx1tchBGR7t6zOEELc5Ilmdml36eY4TnE0cR8nZjYNaDOz9mhztk9zXnf2cX4LONnMXgEwsw8J8ddLDAJuAP5oZr/qRr0cxymS5h1Vny2pD/CIpDOB/5EyWFt39nGuCzzYRfnPgTvN7BfdpI/jOEWTtpneGON6CMEGTgA+AIYD+6Y5sZlG1f8JjJV0lpm9XqlCMq5621JDu1E1x3FqpYlH1V+MHz8EfpDl3O70OKcCG3dRPpmwBGqKpIqz9xeIqz4olUftOE6DadY+TklbS7pZ0tOSnisdac7tTsP5E+BMScsASOor6YRkBTM7B7gF+Evse3Acp9Vp3qb6bwldhKOBTRNHVYpqqg+QNCOR/rmZ/VzS0sA/JInwVV1SfqKZfVPSpcDvJY0zsyZ19B3HqUrjjGIa3jWzG2o5sRDDaWYVPdk43L/QNCMzG1+W9jmcjtMDECljUTSGWyX9DLiaxM7vZvZQtRObaXDIcZweSF6DQ5LGAOcCbcBvzOyMsvKDgW/G5CzgODP7TxciN49/N0nkGbBjNV3ccDqOUyw5NNXjzkUXArsQolHeL+laM3s8Ue15YDszezsuuJnIJ8ZxYbXMdqhVHzecjuMUSz59nJsB08zsOQBJkwn7aM43nGZ2d6L+vcAKlQRJ+oKZ/UHSiRXVNft5pfwkbjgdxymObFONhkl6IJGeaGYT4+flgemJshl04U0CXySsRKxEaS5jpWmPqbR1w+k4TrGkN5wzzWyTTsoqjTFVlCxpB4LhHN2JrOsBzGyhSe+S9k6hp28r5zhOseQ0AX4GYUlkiRWAVxa6lrQ+8BtgrJm92YmsWySNqHDu4cA51e+ohT3Ott4dDB1Wcfe6XFj85VShR2rmzXVS7dBfF9PfH1qo/Ne/P7hQ+UXHPV/xtLurV6qT5e4t9jm6d5e+hcnuuDmf4fCcRtXvB0ZKWhl4GTgQOGiB60grEqYWHWJmT3ch62vAzZL2MLNn4rnfivK2S6NMyxpOx3FagJwmwJvZPEkTCNtOtgGXmNlUScfG8ouA7wFLAb8Ma2yYV6npb2ZTJH0E3CDps4R9ODcFtjWzt9Po44bTcZxiyWnlkJlNAaaU5V2U+HwkwQimkXWLpPHAbcDdwE5xq8tUuOF0HKcwSsHamglJ7xPMuYC+wE7A66Wl4GZWtQ/KDafjOMXSZIbTzOrueHbD6ThOociazHLmgBtOx3GKo4nDA9eDG07HcYql5zmcbjgdxymWZhscyoNcVw5JMkm/T6R7SXpD0nUxPT6mH0kcGyQ+vyXp+fj5H3nq5jhOg2jeHeBrJm+P8wNgXUn9zWwOYQuol8vqXGlmE8ryRgFImgRcZ2b/L2e9HMdpBA2MmV4kRaxVvwHYM34eB1xRwDUcx2kBRBgcSnO0EkUYzsnAgZL6AesD/y4rP6Csqd6/AB0cx2kWzNIdLUTug0Nm9mjceWQcZcujIpWa6qlIxlXv/aliN5hwHCcfvKmenmuBs8i5mZ6Mq95riMdVd5ymJ+3AUIsZ16KmI11CCL35mKTtC7qG4zgtQKv1X6ahEI/TzGaY2bmdFJf3cRa76aLjOI3FPc6uMbNBFfJuI2zdhJlNAiZ1cf74PPVxHKfBGKijxaxiCnzlkOM4hdITB4fccDqOUyxuOB3HcdLTjBsZ54EbTsdxiqMFJ7enwQ2n4ziF0hOnI7nhdBynULyp7jiOkwUDfDpS87Bcv3f4/trXFSb/G4d8rjDZAEsOThW+uS7u2eCqQuVP/XhOofIPHXpYofKXu7fumF1VeWWL9wuVf8zUOwqT/Yv+Oene8+xm6xpOx3FaA2+qO47jZMVH1R3HcTLgUS4dx3GyESbAu8fpOI6TDfc4HcdxstETPc6idoB3HMfJdQd4SWMkPSVpmqRTKpSvKekeSR9JOjm/m1iYmg2npAnxBkzSsER+p8pXu3HHcXoaKQO1VfFKJbUBFwK7A2sD4yStXVbtLeAEQtieQqnH47wL2Bl4sSy/ovIpb9xxnB6GOizVUYXNgGlm9pyZfUyIpjs2WcHMXjez+4G5xdzJJ1Q1nJJGSHpS0mWSHpX0/yQNMLOHzeyF8vpdKN/pjUs6StL9kv4j6SpJA+q/NcdxGo5liqs+TNIDiePohKTlgemJ9IyY1xDSepxrABPNbH3gPeD4Gq7V1Y1fbWabmtkGwBPAF2uQ7zhOM5K+qT6zFMU2HhMTUlRJcvfcwMKkNZzTzeyu+PkPwOgartXVja8r6Q5JjwEHA+tUFCAdXXobvfvWvBpUcByn28lncGgGMDyRXgF4JWdNU5PWcJbfVi2WvqsbnwRMMLP1gB8A/SoqkYirPmRJn0nlOK2AzFIdVbgfGClpZUl9gAOBawtXvhPSWp8VJW1pZvcA44A7a7jW/BsHXibc+EGxbHHgf5J6EzzOl2uQ7zhOs2FAe/0tajObJ2kCcBPQBlxiZlMlHRvLL5K0DPAAMBjokPRVYG0ze69uBcpIazifAA6T9GvgGeBXkk4AvgEsAzwqaYqZHdmV8pVuPMr/LvBvwgj9YwRD6jhOiyNSeZOpMLMpwJSyvIsSn18ltGQLJ63h7DCzY8vyzovHAnSlfKUbj/m/An6VUhfHcVqJHrhyyDsKHccplkXRcMa5musWr4rjOD0Owzf5cBzHyUpP3OTDDafjOAVi0NHzXE43nI7jFIexaPZxOo7j1EXPczjdcDqOUyzex9lETP9gCU66d7/C5OvViqs+c8MeG1iofIA1HjyuUPmrXVS+o2DO7LlUoeLv3aVvofKh2LjnADesM7Qw2e9aTubBDafjOE4GDKi+12bL4YbTcZwC8VF1x3Gc7HhT3XEcJwPeVHccx8mKgXlT3XEcJxveVHccx8mAAe3ucTqO42SjB3qc9cRVr4ik38Ywv6VQwoNi/hBJf4tlUyUdnve1HcdpNlJGuGwx45q74QS+ZmYbxFDCLwETYv6XgMdjCODtgbNj0CXHcXoqRpjHmeZoIWo2nJJGSHpS0mUJ73JAKTCSJAH9+SQipgGLx/xBwFvAvFj3GkkPRk/06AqXcxynVXGPcyHWACZG7/I94HgASZcCrwJrAufHuhcAaxFCAj8GfMVs/jyFI8xsY2AT4ARJFRcpJ+Oqt7/3QZ2qO47TLbjhXIjpZnZX/PwHYDSAmR0OLEeIjnlALN8NeCTmjwIukDQ4lp0g6T/AvYTY6yMrXSwZV71tcPGbZDiOUydmWHt7qqOVqNdwlr8m5qfNrB24Etg3Zh0OXG2BacDzwJqStgd2BraM/Z8PA8VuTeQ4TvfRYemOFqJew7mipC3j53HAnZJWg/l9nHsDT8byl4CdYtnShGb+c8AQ4G0zmy1pTWCLOnVyHKeZ6IFN9XrncT4BHCbp18AzhNjoN8cmuID/AKVNIX8ETJL0WCz7ppnNlHQjcKykR4GnCM11x3F6Aua7I1Wiw8yOLcvbulJFM3sF2LVC/kfA7nXq4ThOs9Ji3mQafOWQ4ziFYu5xfoKZvQCsm58qjuP0OMx8rbrjOE5meuC2ckUsuXQcxwFiWPUOS3VUQ9IYSU9JmibplArlknReLH9U0kZF3BO44XQcp0gsbmSc5ugCSW3AhYSB5LWBcZLWLqu2O2HxzEjgaMIsn0Jww+k4TqHk5HFuBkwzs+fM7GNgMjC2rM5Y4Hdxkc29wFBJy+Z/RyBr0akCkt4AsgT2HgbMLEgdl994+d1xjUVN/kpm9ql6LhjnaQ9LWb0f8GEiPdHMJkY5nwfGmNmRMX0IsLmZlXZfQ9J1wBlmdmdM30KYL/5APfdQiZYdHMr6g0p6wMw2KUofl99Y+d1xDZefHTMbk5MoVRJfQ51c8Ka64zitwAzCBkAlViDstJa1Ti644XQcpxW4HxgpaeW4AfqBwLVlda4FDo2j61sA75rZ/4pQpmWb6jUw0eX3aPndcQ2X3yDMbJ6kCcBNQBtwiZlNlXRsLL8ImALsAUwDZhN2ZCuElh0cchzHaRTeVHccx8mIG07HcZyMuOFsUeJG0U4DkLReo3VwGssiazglbSSpW+e05UzhoZUlrSppaUl1TYIuk9lPUu+85FWQv7WkXeLn3F8uknYDJsdoBc4iyiJpOCWNIQSX+6gg+ZtJOlLSFnkanYT8XYGLJZ0uaYO85cdrjAGuBn4C/EnS6jnKvFjS+HrllckuGclvAZ8GsJxHPqPRPB/4kpk9Kanw/5+iWhYluZKGSPIYXxlZ5AxnNDpnAMeb2WOShuYsf0/gMmBT4JvAZ2J+Lv8AknYnGLO/E/ZDPTxvD07SVsB5hHDPXwP+ARwSy2q6j2h0/g/4I3Az8BVJ2+aicKBv/PshBbwQo/6XEaa6vCapTyK8dSFIkpmZpNGSDop/625pJOR+lnBPV0ra2Q1oehYpwylpEHAqcLuZ3SZpOHC7pM1ykr868DNgvJkdA1wBHC1pUB7ej6RVCJFDf2tmfyDMU9uAGJY5Ua9W46boRa0C/MDM7jKzdwmxpdaE2rw4SSsDvwTOM7M/mNnlBI+/rRY9K8hfD/h8TL5MXGYnqW/JK6zHO5Q0Cvg1cBBwK/AlYMu4Y09hROO2N+EltgrwdeBkSQNykLsL8B3gGGAWYSehfST17fJkB1jEDKeZzQK+DawV9/P7E3Cxmd1Xr2yF6J4zgB8CD8br/Ql4DVgpB/lDgLcI3vI2kjY0s7eBt4HjJV0o6QBJw+ow0iUvajLwz0T+fcDiCV0GZRFqZs8D1wM7SlojZu8E/EzStZJO0ifRUmthNWC/aGSGE71PM/so4RXW+jLZkmCI9zKzfxKa6u8SDPXWRRpPSSsBRwJ7AU8DqwIrA9+Q1D+jrCHRUSgxEjiBEFV2BHAuIaDiIfFZc7rCzHr8AQwsS28N3AZcnshTHfJXI3gig4HeMa8t/v0HsH38vB7wqRrk700wPAOBpYGvAlcRvLbbgd0IXslVwJ1A3xqusSvwZ+BkYFRZ2crAk/Hz4cDvSvdZRWYvYFAifTrhZfVb4BZgB2AfQrfDxcDSGXUeHfXpBewLXAq8QDAyf4nfzRWE7oGzgMVq+E6mA5vE9GLxb594L+dHHXrl9JyuBOwPrAMMiPe1JmFLtYfjc3YE8F/CCzTV/RD2r/w38C/gikT+EvF7Wiemr4q/RabfYVE8Gq5A4TcIO0ejc0xM94p/t4rG7hhg8TqvsXx8KAcm5PeJf68gxJAfS1gulslwRqN4N7BnIq8fof/0VcLWWsl/6swPPTCG4CUfG43xhLLyJYD/F/9p/136R0uh92WEcM+/Az4b879OaPqvn6g7CBiQUefdo4E8Btgw5u0Z//HPJniEuwP7AV8GVskof0/C+uitY3oZYGiivA/wA4Kx3iqH53RNQjfDxQSD/xNg21i2F2G7NAjG/FfAuinlLhd/g/1j+j5g1UT5b4AfE14AV5eeJz+qfK+NVqDwGwxv8DcInsj5BG9tyVi2BWGg4sSs/7jx/DUIzZyh8WFfyPOID+XfgXuA9TLK3wDoALaM6dWAMwmeyDDCwM2VwOjEOZk8Z2Aj4B1g75jej/ASOBTYL1HvRYKns3YKmbsBUwne5FbAd6MR/XosPx34fbx2Ji8wofMzJcNSVnYAcEk0nP1qfGaWBR4BzonpZYCngANjOul5ngosk8Nzegzw7cT9TYhGdDShWd0ef/sXgB0yyB0JXE70/IHHCDupXwysSOgyuTT+tnvWex+LytFwBQq5qdCfVVqHPzj+444hNOd+EI3A/sBSwFqEXVWGZrzGEMIo8S8JTf8rSTRfE9f/NTAXWK3Ge7mT4LV+itC98LVE2acI02/+SA3N8yhjO2AScBShifgQ4QVzKqEZ96X4fU4E1kghb2vCVl7bJPJ6AZ8jeJ67xbyJ0cD1qUHnzwDnln3PbYnyscB1hJdALzK8TIAh8e8RBM/1RMJL8Ziyepn1rnLdLwO3JNIrReN5dkxvEo3rdhnlLkt4aV1MaC1cRmghTSZsFFyqt0Ke99PTj4Yr0C03GTyca+LntYGP44M0NRrQqv11ncjdDPh+fGM/DZwCjIvGY0w0SsPJ3kxck0STidDE7wCOjumkkRhG9KAzXqNkcETozriQMJBV8goHRWP64/JrdiGzN3Ac4SWyLQlvkjCCfgZwaSLv0zV+77sQ+koX4xPvb7F4L7tEY7knsFxGubsRuiy2ielDYnpyWb2xwPZZDHJXv0EifQnwnUR6vfjbb1WpfhdyVwYOi89f3yhne+AaYESi3n3AWvXcw6J69LhRdUk7SDpf0mFxGglmdirwgaSLCN7lPmZ2FDAeeNTM5maQv6yk1eOI5wMEb28u0J8wiLMlYf7jt4DvEZrvz2WQP4bgmR2c0H83wpZZn4np9tLcTTObaWZvpZUfryGL/zkW+Afhn/amWD7IwgyEJYHV4tzBLucsStqB4IFPJnRLfI7QVKekM6Fb5NOSesW819PqW5Y1k+CRrWNmHZIWM7OOeE9bAJ8zs+vNLOsmtqsTvO7vSRpjZr8HzgHeVgjVgKT9CE3ml0vfYS1IWhUYL2mpmG4j9C8vJ+lbAGb2GKHLYKOYrnq9uKLpz4Qm/nHAIWb2mJndRnhe15LUS9JahBfdrFrvYVGmR+3HGScpnwH8jbAv3yqSngfeI0yv+QGhn+pOSb3N7P6M8vckDMp0EIxlO8E4/AB4nTAS+hMzeyNh2LIY5T2AnxI8y3uSZWa2l6R/SPq7me2aRW7ZNZYmTGuaq7CX4XIEb/BUQvN5P8Lc01nx3o6wEByrM3klI7xkUNPelnQpoak7WhJmdlWsvhZhlLo9o9ptwLz4m801s4clXQvcGBcEPAPMicZtHOEFVgtXEOZLTgeOidf7fbTbm8XnazXCi/eZGq9Rmtf6MKHpPEDSn+Izc2+81wMk3UBoVo8ndDGlkbss4Tc8N+r9FcKLajBhYcB/gZMI39GGwHfNbHqt97FI02iXN68DWJ9g0EojkZsRBmVGxPSywJOE+XiQfRCl1IzbgTB6vgzBQ3ia4G2OJHhcE4EVM8oWoWl8JSEgVbLsF8AvEun7id0ONVxjacLAzz6E5uZ9wGcJU6auJozWb07wWGaQbvS8NP3qSOA3ifzBhMGrXxCa7QcDj6aRWSZ/GGFApDSg1zdR9m1CH/ANhClOT9Qgf33iCD+huf9TwkjzrsQXcOL+7iDlaHaVa+4YdR9LmD/5ZeJsC4Iz0w84LdbZK4Pcocnnh2CY7yT0s0+Kz+36wDbAprX8H/gRv9tGK5DbjQTDczULzs28kdD/+GVgFOFNO5GMo62EPsekUU72Mf4hXleEZvqp1DgPLj7cmyXSR8R/1jtYsCN/pRpkl/o09ye8UCYRPKdS+dXAn+LnLYHhKWSWjNoQgpd3ZVn5EvG7/xvwHClG5Du5zt6El94SMZ00nqsQjP32ZH9hLRV/15cIo/CbRsN1IeFFWQrP8NlYf3BOz2p/oH/8vG/CeFYcnU9j3Cib0UHot/5h6XmJz/1BeejvRw/o45Q0TNKSFvrkDgRM0p8lnUn4x32K0Hd1JaGP8Htm9mHnEheSvyGhaXktYV14m4U+xtLStNMIRnuYheb1WWb2Wgb5G0vaKvb7LUUwACWeMLNtzGwbYHVJpb6uLGGRiX2Apf6xpwiGcy1g88QqkYOBNoU12PdYiiacmc0krD65k7Cy6CFJK0gaKmkpCyubphBGuHc2s8ez6J24zt8I3usDkpYws48S3/9KwPtmdpuZvZRR7psEA7MCwRMr9S/PJniAkwkzC8ZJGmhm79WiPyy42srM5hDD4FroxriD0GLZUdL+kk4r07Nq36aF0BJbSzpc0mjgLjP7Xix7kTDlbEhXMpwMNNpy13MQ+jHvI4ywlkZ/BxEGbN4nMWWE8NBkmnJBGJl9nNDvtzLBS7smUa4o9+/A8jXovzuh+T8qpjckDAaML6v3OUJzelid39e2wM2Je7uVMHK8KmH+43+owasiNGs7gDmEOYMPE+YL3kp4YQ2qR++y7+tZPvE8jydsupHJ06wgdyfCFLWlCFN+/hXvow/hhVCXp0nYjOVN4LCy/OSsg9GEl/N7xPmiKWWXWhJbELpCJhGM/x9Kz0t8rh4lMUXMjzqfxUYrULPiwTu4k9BPNIowobrU/OkTH5w/UvtUo+3iP2VyWtCg+FBek3hgxxNGi4fUoP9dwK4xPYwwmX7vaKy/E+scEw1a5mYusDGwRfx8QDRkByfK9yV4O38nzN2suf+O0G/2XryPNoJHuzSwcs6/++7RCJxI6NMclZPcPaKxL00Uz0VvQl/vZMLKq8eT338sLz1HWwLziJPQydD3SOjPv4VPVpGNIEzB+xGhC+I5MvSV+pHiO2+0AjUpHUZwO4h9dPHB+R9hMvrEmNeH8Ab/fY3XOBH4SvycnNg+kDDaeSnwBYLHm3VFUEn/z8b0atGAjY7p9aPRvyheJ7NB4xNvtjTwMSIanEvK6u1DaE5nXkNf4Zp7EEZuC13rTPCWO4ANcpa7RzTGSyby6p2r2S9hDHcCni8ZTxb0OHcExpaumdFw7kLoTiqtPOoV7+V8wt6kdS8J9aPsO2+0AjUrHv55HiYsS7yZMCVoOGEkcXKsM5Dsk6BLHsD5wOnJvESdNYC/EvrCah3w2JOwSmf9qP/JMX+xMj1qWVlT7s1+mjBVagXCpP9Ty+rn0pSOssbG3yXzUsqM18m8RDaD/g8RJ9TXKWuhRQOEbo3nCPMrS8/SsER5JqNZpvc0YFxMbxtfnEOL/B0W1aPhCtSlfDAQHcApibxBhGbLUnXK3pHQr7hxTCdXqRxH8ODq8qzK9eeTHZV2JwymLGS0U8gs92ZXJXRp7BTTKxP6Uc8o8HfJzRA36LnK80WyNWEHpx34ZErVGEKz/UxCK2DDnK61N6G75EpCS8Wb50U9I41WoO4bCM2Up0pv1viQ3k39Ox4NJIyYn1kynjH/gOhR5bK2N+r/ZEL/8YTmf819bCzszZ4U80uGeQSha6CuwSY/Ov3+Kw3YTCIMOC0Ty76dfMHleO3PxN/+xJIuWV++fqT4nhutQC43ETy0/xJGWW8nh0nKUe7yhGWT/yLs53h6NHK5yC/T/7Hoyd5BxkncncjszJvdi7Bev9Cm9KJ+0PWAzQjC8tZ9Y1muho3QHTCDsPS04d9FTzxKb8aWR9JehEncG5rZ1Bzl9ieMTu9MGIC61cyezkt+4jq56x/DI5xPGFl/RyFA2vGE6S6p18872Ynf/Y2EZY3/F+fp7kpYqXUCYYOTl0rr8C3nf8R4/Wf9dy6GHmM4ASQNMLPZjdajVorQP67lPpMw4+Ag4Ng8XyxO50gaS9ia7rtmdoVCcLqzCcsi32ysdk499CjD6VSmKG/cqU6Mg3Q5YT39bOAqM7uusVo59eKGcxGh1b3xVkbSZwgDjX8ws58X1Tx3uo8eta2c0zluNBuHmV0r6UPgEkkvmNnVjdbJqQ/3OB2nm/ABm56DG07HcZyMtPy2co7jON2NG07HcZyMuOF0HMfJiBtOx3GcjLjhdBzHyYgbTsdxnIz8f5sDxqWrurBfAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(kinase_distance_matrix)\n",
+    "plt.colorbar(label=\"Kinase distance\")\n",
+    "plt.title(\"Kinase distance matrix\")\n",
+    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
+    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bio.Phylo.TreeConstruction with DistanceTreeConstructor and DistanceMatrix. Wrangle your distances into the correct format for DistanceMatrix, convert it into a tree and draw the tree with upgma/nj. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.277778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.173913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.406250</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.382353</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.258065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.363636</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.238095</td>\n",
+       "      <td>0.406250</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.043478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.058824</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.190476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.382353</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.352941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.347826</td>\n",
+       "      <td>0.380952</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.533333</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.473684</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.173913</td>\n",
+       "      <td>0.258065</td>\n",
+       "      <td>0.043478</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.352941</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
+       "kinase.klifs_name                                                               \n",
+       "BRAF               0.000000  0.300000  0.277778  0.393939  0.363636  0.320000   \n",
+       "CDK2               0.300000  0.000000  0.111111  0.451613  0.238095  0.190476   \n",
+       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.166667   \n",
+       "ErbB2              0.393939  0.451613  0.258065  0.000000  0.406250  0.419355   \n",
+       "KDR                0.363636  0.238095  0.238095  0.406250  0.000000  0.058824   \n",
+       "LCK                0.320000  0.190476  0.166667  0.419355  0.058824  0.000000   \n",
+       "MET                0.368421  0.133333  0.227273  0.382353  0.333333  0.250000   \n",
+       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
+       "p38a               0.333333  0.277778  0.173913  0.258065  0.043478  0.190476   \n",
+       "\n",
+       "kinase.klifs_name       MET     p110a      p38a  \n",
+       "kinase.klifs_name                                \n",
+       "BRAF               0.368421  0.500000  0.333333  \n",
+       "CDK2               0.133333  0.347826  0.277778  \n",
+       "EGFR               0.227273  0.380952  0.173913  \n",
+       "ErbB2              0.382353  0.571429  0.258065  \n",
+       "KDR                0.333333  0.533333  0.043478  \n",
+       "LCK                0.250000  0.375000  0.190476  \n",
+       "MET                0.000000  0.428571  0.352941  \n",
+       "p110a              0.428571  0.000000  0.473684  \n",
+       "p38a               0.352941  0.473684  0.000000  "
+      ]
+     },
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# TODO: Select the minimum structure pair distance for each kinase pair\n",
-    "kinase_distance_matrix = (\n",
-    "    kinase_distance_matrix.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
-    ")\n",
     "kinase_distance_matrix"
    ]
   },
@@ -1386,19 +2302,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as kinase matrix"
+    "#### Example 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Bio import Phylo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -1408,27 +2333,82 @@
     }
    ],
    "source": [
-    "plt.imshow(kinase_distance_matrix)\n",
-    "plt.colorbar(label=\"Kinase distance\")\n",
-    "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
-    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
+    "#https://biopython.org/wiki/Phylo\n",
+    "\n",
+    "from Bio.Phylo.TreeConstruction import DistanceMatrix, DistanceTreeConstructor\n",
+    "\n",
+    "matrix = [\n",
+    "    [0], \n",
+    "    [0.23076923076923073, 0], \n",
+    "    [0.3846153846153846, 0.23076923076923073, 0], \n",
+    "    [0.5384615384615384, 0.5384615384615384, 0.5384615384615384, 0], \n",
+    "    [0.6153846153846154, 0.3846153846153846, 0.46153846153846156, 0.15384615384615385, 0]\n",
+    "]\n",
+    "names = ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon']\n",
+    "\n",
+    "dm = DistanceMatrix(names, matrix)\n",
+    "\n",
+    "constructor = DistanceTreeConstructor()\n",
+    "tree = constructor.upgma(dm)\n",
+    "tree.ladderize()  # Flip branches so deeper clades are displayed at top\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "Phylo.draw(tree, axes=ax)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as phylogenetic tree"
+    "#### Example 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "matrix = kinase_distance_matrix.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "matrix_tril = [list(vector[:i]) for i, vector in enumerate(matrix, 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dm = DistanceMatrix(kinase_distance_matrix.columns.to_list(), matrix_tril)\n",
+    "\n",
+    "constructor = DistanceTreeConstructor()\n",
+    "tree = constructor.upgma(dm)\n",
+    "tree.ladderize()  # Flip branches so deeper clades are displayed at top\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "Phylo.draw(tree, axes=ax)"
+   ]
   },
   {
    "cell_type": "markdown",

From 5254f5084f0f1811cb70a001b1b3d91a47fda2e8 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 16:32:54 +0200
Subject: [PATCH 017/158] Use scipy to cluster and plot the dendrogram

---
 .../talktorial.ipynb                          | 1124 +++--------------
 1 file changed, 151 insertions(+), 973 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 952a01ad..4e04de7b 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -165,7 +165,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -173,6 +181,8 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from sklearn.metrics import pairwise_distances\n",
+    "from scipy.spatial import distance_matrix, distance\n",
+    "from scipy.cluster import hierarchy\n",
     "from opencadd.databases.klifs import setup_remote"
    ]
   },
@@ -246,7 +256,16 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    }
+   ],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -732,7 +751,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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cObJU8czMdkghhWZ9RPw88ySJu4EfA0OBfYp0zLKzatUqunTpwksvvcTtt9/Oo48+ysqVK+nWrRsAd9xxB/36+WnYZtY8FFJorpZ0KclNAO/XNUbEUxnkuR5YHRGLJA3NYP/Nwoknnsibb77Jbrvtxi9+8Qs+9KEPcfrpp5PL5ZBEVVUVv/71r0sd08ysIIUUmv7A6cCx/GPoLNLlJhURK0jucGvIaElH5S1/LSL+3NQZysHDDz/8gbabbrqpBEnMzHZdIYVmFNAzIjZkFSIiOjTQ9hDwUPr+RuDGRrY/I5NgZma2ywopNAuATsCqbKMUR//uHanxnGJmZkVTSKHpCjwr6Qm2vkbz+cxSmZlZi1FIobk08xRmZtZibbfQRMQfixHEzMxapkJmBjhc0hOS/i5pg6RaSe8WI5yZmTV/hTwmYDLJt/WXAnsAZ6VtZmZm21XINRoi4m+SKiKiFrhBUov8/oqZmTW9QgrN2vQ5NDlJV5A8J2bPbGOZmVlLUcjQ2elpv/NInhuzH3BilqHMzKzlKOSusxfTt+uB72Ubx8zMWprtFhpJnwIuAw7I7x8RPbOLZWZmLUUh12iuAy4AngRqs41jZmYtTSGFZnVE3Jt5kiJZ9PJqqibOyGz/yzyPmpnZVgopNHMkXQncTvbPozEzsxamkEIzJP05KK8tk+fRtBRVVVXstddeVFRUUFlZSU1NDaNHj2bJkiUAvPPOO3Tq1IlcLlfaoGZmRVDIXWefbsoDSqoFFgEiueZzXkT8WVIV8AywBGgL1AATImJjul0l8CpwbURckre/h4BuwLq06QcR8bumzLwz5syZQ+fOnbcs33rrrVveX3jhhXTs2LEUsczMiq6gmQGa2LqIqAaQ9FngcuCYdN1zEVEtqQK4HzgZmJauO46kCJ0s6TsREXn7HBsRNUVJv4sigt/+9rfMnj271FHMzIqikC9sZmlv4O36jelUN/OA7nnNY0ge8/wScHhR0u0kSRx33HEcdthhTJkyZat1Dz/8MF27dqVXr14lSmdmVlylOKPZQ1IO2J1kyOsD13ok7U5ybegb6fIewDDgKyRP+xwDPJq3yTRJdUNnwyLizXr7Owc4B6Bi74804Udp2COPPMK+++7LqlWrGD58OL179+boo48G4JZbbmHMmDGZZzAzKxeFPCagvaT/kHRtutxL0shdOOa6iKiOiN7ACOB/JCldd2BahN4EXoqIhWn7SGBORKwFfg+MSofX6oxN91ldv8gARMSUiBgUEYMq2md/bWTfffcFoEuXLowaNYp58+YBsGnTJm6//XZGjx6deQYzs3JRyNDZDSS3NR+RLq8AftAUB4+IR4HOQN1pxnPp9ZuPA4dLqntc9BjgM5KWkXxxdB+gSW9SaCpr1qzhvffe2/L+D3/4A/369QPggQceoHfv3vTo0aOUEc3MiqqQobMDI2K0pDEAEbEu7wxkl0jqDVSQnMG0r2uPiJWSJgKXpHeVHQXsFxHvp9udSVJ8HmiKHE3ptddeY9SoUUByBnPqqacyYsQIAKZPn+5hMzNrdQopNBvSayQBIOlA8r64uRPqrtFAcovzuIiobaB23Ukyx9o3gNl1RSZ1F3CFpHa7kCMTPXv2ZMGCBQ2uu/HGG4sbxsysDBRSaC4FZgH7SZoGfAo4Y2cPGBEV22hfBvTLWw5g4Db6vsU/htuG7mwWMzPLnrb+Oso2Okn7kNxSLOCxiHgj62BZGTRoUNTUNIuv3JiZlQ1JT0bEoO33/KBC7jr7FLA+ImaQ3Fr8HUkH7MzBzMys9SnkrrNfkjzOeSBwMfAi8D+ZpjIzsxajkEKzKb1e8gXg5xFxNbBXtrHMzKylKORmgPckXQKcBhydflFyt2xjmZlZS1HIGc1oktuZJ0TEqyTzj12ZaSozM2sxCnlMwKvAT/OWX8LXaMzMrECF3HV2uKQnJP1d0gZJtZJWFyOcmZk1f4UMnU0mme5lKbAHcBbwiyxDmZlZy1HQYwIi4m+SKtLnxNwg6c8Z5zIzsxaikEKzVlJbICfpCmAlsGe2sczMrKUoZOjs9LTfecAaYD/gxCxDmZlZy1HQXGctSbtuvaLbuKuaZF/LJn2uSfZjZlbudmWus+0OnaVznV0GHJDfPyJ67swBzcysdSlk6Ow6ku/RHAV8Mu9lqdraWg455BBGjkyecH3ZZZfRvXt3qqurqa6uZubMmSVOaGZWOoUUmtURcW9ErIqIN+teO3tASedJ+pukkNQ5r723pEclvS/ponrbjJC0JN1u4s4eOytXX301ffr02artggsuIJfLkcvlOOGEE0qUzMys9AopNHMkXSnpCEmH1r124ZiPAJ8hmQU631vA14Ef5zemc6v9AjgeOBgYI+ngXTh+k1qxYgUzZszgrLPOKnUUM7OyVEihGQIMAv4L+En6+nGjWwCSqiQ9K2mqpIWSfiepfUTMT5+muZX0jOkJYGO9VYOBv0XE8xGxAZhOMpM0ks5OZy1YIOn3ktoX8Hma1Pnnn88VV1xBmzZb/yonT57MgAEDGD9+PG+//XaxY5mZlY3tFpqI+HQDr2ML3P9BwJSIGAC8C3xtJzJ2B5bnLa9I2wBuj4hPRsRA4BlgQkM7kHSOpBpJNbVrm272nHvuuYcuXbpw2GGHbdV+7rnn8txzz5HL5ejWrRsXXnhhkx3TzKy52eZdZ5JOi4ibJX2zofUR8dOG2utZHhGPpO9vpoGhsQKoocOnP/tJ+gHJkz87APc1tIOImAJMgeT25h08/jY98sgj3H333cycOZP169fz7rvvctppp3HzzTdv6XP22WdvuUnAzKw1auyMpu7b/3s18OpQ4P7r/099Z/4nv4LkS6J1egCvpO9vBM6LiP7A94Ddd2L/O+3yyy9nxYoVLFu2jOnTp3Psscdy8803s3Llyi197rjjDvr161fMWGZmZaWx79HMAIiI79VfIemfC9z//pKOiIhHSSbm/NOOR+QJoJekjwEvA6cAp6br9gJWStoNGJuuL7lvfetb5HI5JFFVVcWvf/3rUkcyMyuZbc4MIGkJ8Nn6F+4lnQn8e0Qc2OiOpSpgJjAXOJJk9ufTSWZ//hbwUWAVMDMizpL0UaAG2BvYDPwdODgi3pV0AnAVUAFcHxE/TI9xbrqvF4FFwF4RcUZjuTwzgJnZjstqZoALgPslnRARS9MDXUJyNnFMgfvfHBFfrdf28/S1lfQBaz0a2klEzCQpWvXbfwn8ssAsZmZWAtssNBExU9L7wL2SvkhyJvJJ4OiIaLb36/bv3pEan4mYmRVNo7c3R8SDwBnAQ0BPYFihRSYilkWEr4KbmbVyjd3e/B7JXWIC2gHDgFWSBERE7F2ciGZm1pw1NnS2VzGDmJlZy1TIFDRmZmY7zYXGzMwy5UJjZmaZcqExM7NMudCYmVmmXGjMzCxTLjRmZpYpFxozM8tUY5NqtkiLXl5N1cQZBff3DM1mZrvGZzRmZpYpF5oCrF+/nsGDBzNw4ED69u3LpZdeCsBbb73F8OHD6dWrF8OHD+ftt5vtpNZmZpkpq0Ij6e/baP8XSYslPS3pL5IuSttvlHRS+v7DkuanD2ZrUu3atWP27NksWLCAXC7HrFmzeOyxx5g0aRLDhg1j6dKlDBs2jEmTJjX1oc3Mmr2yKjQNkXQ8cD5wXET0BQ4FVtfr0xG4D5gSETdkkIEOHToAsHHjRjZu3Igk7rrrLsaNGwfAuHHjuPPOO5v60GZmzV7ZFxrgEuCiiHgFICLWR8S1ees7APcC/5s+cTMTtbW1VFdX06VLF4YPH86QIUN47bXX6NatGwDdunVj1apVWR3ezKzZag6Fph/wZCPrfwr8KSJ+tq0Oks6RVCOppnbt6m11a1RFRQW5XI4VK1Ywb948Fi9evFP7MTNrbZpDodme2cAXJHXZVoeImBIRgyJiUEX7jrt0sE6dOjF06FBmzZpF165dWblyJQArV66kS5dtRjAza7WaQ6F5GjiskfXTgV8CMyVl8rC2119/nXfeeQeAdevW8cADD9C7d28+//nPM3XqVACmTp3KF77whSwOb2bWrDWHL2xeDlwhaWREvCqpHfCViPh5XYeIuEpSN+AOSSdExIamDLBy5UrGjRtHbW0tmzdv5uSTT2bkyJEcccQRnHzyyVx33XXsv//+3HbbbU15WDOzFqHcCk17SSvyln8aET+V1BV4QJKAAK6vv2FEfFvSDcBNksZExOamCjVgwADmz5//gfZ99tmHBx98sKkOY2bWIpVVoYmIBofy0luWP3DbckScUW+5yb9DY2Zmu6asCk0x9O/ekRrPX2ZmVjTN4WYAMzNrxlxozMwsUy40ZmaWKRcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqExM7NMudCYmVmmXGjMzCxTLjRmZpYpFxozM8tUqys0i15eTdXEGaWOYWbWarS6QlPfO++8w0knnUTv3r3p06cPjz76aKkjmZm1KGVRaCT9Pe/9CZKWStpf0mWSXpaUS9tul3RwXt+HJC2RtEDSE5Kqd/TY3/jGNxgxYgTPPvssCxYsoE+fPk30qczMDMqk0NSRNAy4BhgRES+lzT+LiOqI6AXcCsyW9JG8zcZGxEDgv4Erd+R47777LnPnzmXChAkAtG3blk6dOu3qxzAzszxlU2gk/RNwLfC5iHiuoT4RcSvwB+DUBlY/CnTfkWM+//zzfOQjH+HMM8/kkEMO4ayzzmLNmjU7Gt3MzBpRLoWmHXAX8MWIeHY7fZ8CejfQPgK4c0cOumnTJp566inOPfdc5s+fz5577smkSZN2ZBdmZrYd5VJoNgJ/BiYU0Ff1lqdJWgF8m2TY7YMbSOdIqpFUU7t29Zb2Hj160KNHD4YMGQLASSedxFNPPbUz+c3MbBvKpdBsBk4GPinpO9vpewjwTN7yWOBjwP8Cv2hog4iYEhGDImJQRfuOW9o/+tGPst9++7FkyRIAHnzwQQ4++OCGdmFmZjupstQB6kTEWkkjgYclvRYR19XvI+lE4DjgwnrbbpT078BzkvpExDP1t92Wa665hrFjx7JhwwZ69uzJDTfcsKsfxczM8pRNoQGIiLckjQDmSnojbb5A0mnAnsBi4NiIeL2BbddJ+glwEYUNwQFQXV1NTU1NE6Q3M7OGlEWhiYgOee+XkwyFQXKDwGWNbDe03vJPMohnZma7oFyu0ZiZWQvV6gpN/+4dWTbpc6WOYWbWarS6QmNmZsXlQmNmZplyoTEzs0y50JiZWaZcaMzMLFMuNGZmlikXGjMzy5QLjZmZZcqFxszMMuVCY2ZmmXKhMTOzTLW6QrPo5dXb72RmZk2m1RUaMzMrrlZfaGpraznkkEMYOXJkqaOYmbVImRcaSbWScnmvidvo95CkQQ20nyHp9XTbpyX9TlL7dN03Jf1F0kJJD0o6YEfzXX311fTp02fHP5iZmRWkGGc06yKiOu81qX4HSRXb2cet6bZ9gQ3A6LR9PjAoIgYAvwOu2JFgK1asYMaMGZx11lk7spmZme2Akg2dSVom6T8l/Qn4ctp8mqQ/S1osaXAD21QCewJvA0TEnIhYm65+DOixIxnOP/98rrjiCtq0afUjiGZmmSnG/2H3qDd0Njpv3fqIOCoipqfLe0bEkcDXgOvz+o2WlANeBj4M/F8Dx5kA3NtQAEnnSKqRVFO7Nrnr7J577qFLly4cdthhu/bpzMysUaUYOrs1b92t9freAhARc4G9JXWq6xcR1cBHgUXAxfkbSToNGARc2VCAiJgSEYMiYlBF+44APPLII9x9991UVVVxyimnMHv2bE477bRd+6RmZvYBpR4zWlNvORpbjoggOZs5uq5N0meA7wKfj4j3Cz3w5ZdfzooVK1i2bBnTp0/n2GOP5eabb96x9GZmtl2lLjT1jQaQdBSwOiIa+nblUcBzab9DgF+TFJlVRUtpZmYFqyzCMfZIr6/UmRURDd7iDLwt6c/A3sD4vPbRafFpA6wAzkjbrwQ6ALdJAngpIj6/owGHDh3K0KFDd3QzMzMrgJLRqNajXbde8f7KpaWOYWbWrEh6MiI+8F3HQpTb0Fnm+nfvWOoIZmatSqsrNGZmVlwuNGZmlikXGjMzy5QLjZmZZcqFxszMMtXqbm+W9B6wpNQ5tqMz8EapQxTAOZuWczad5pARmk/O3YEVETFiZzZujYWmZmfvBS+W5pARnLOpOWfTaQ4ZofXk9NCZmZllyoXGzMwy1RoLzZRSByhAc8gIztnUnLPpNIeM0EpytrprNGZmVlyt8YzGzMyKyIXGzMwy1WoKjaQRkpZI+pukbT0Pp1hZrpe0StLivLYPS7pf0tL054fy1l2S5l4i6bNFyrifpDmSnpH0tKRvlGnO3SXNk7Qgzfm9csyZd+wKSfMl3VOuOSUtk7RIUk5STRnn7CTpd5KeTf87PaKccko6KP0d1r3elXR+OWXMO+4F6d+fxZJuSf9eNV3OiGjxL6CC5KmcPYG2wALg4BLmORo4FFic13YFMDF9PxH4Ufr+4DRvO+Bj6eeoKELGbsCh6fu9gL+mWcotp4AO6fvdgMeBw8stZ17ebwL/C9xTjn/u6bGXAZ3rtZVjzqnAWen7tkCncsyZHr8CeBU4oNwyAt2BF4A90uXfkjxcsslyFuWXXOoXcARwX97yJcAlJc5UxdaFZgnQLX3fDVjSUFbgPuCIEuS9CxhezjmB9sBTwJByzAn0AB4EjuUfhaYccy7jg4WmrHKSPIX3BdIbmso1Z97xjgMeKceMJIVmOfBhkqcu35PmbbKcrWXorO4XWWdF2lZOukbESoD0Z5e0veTZJVUBh5CcLZRdznQ4KgesAu6PiLLMCVwFfAvYnNdWjjkD+IOkJyWdU6Y5ewKvAzekQ5G/kbRnGeascwpwS/q+rDJGxMvAj4GXgJXA6oj4Q1PmbC2FRg20NZf7ukuaXVIH4PfA+RHxbmNdG2grSs6IqI2IapIzhsGS+jXSvSQ5JY0EVkXEk4Vu0kBbsf7cPxURhwLHA/8q6ehG+pYqZyXJ8PMvI+IQYA3J8M62lOz3Kakt8Hngtu11baCtGP9tfgj4Askw2L7AnpJOa2yTBtoazdlaCs0KYL+85R7AKyXKsi2vSeoGkP5clbaXLLuk3UiKzLSIuL1cc9aJiHeAh4ARlF/OTwGfl7QMmA4cK+nmMsxJRLyS/lwF3AEMLsOcK0gmeXw8Xf4dSeEpt5yQFOynIuK1dLncMn4GeCEiXo+IjcDtwJFNmbO1FJongF6SPpb+6+IU4O4SZ6rvbmBc+n4cyTWRuvZTJLWT9DGgFzAv6zCSBFwHPBMRPy3jnB+R1Cl9vwfJX5pnyy1nRFwSET0ioorkv7/ZEXFaueWUtKekverek4zVLy63nBHxKrBc0kFp0zDgL+WWMzWGfwyb1WUpp4wvAYdLap/+vR8GPNOkOYt1MazUL+AEkjunngO+W+Ist5CMhW4k+dfBBGAfkgvFS9OfH87r/9009xLg+CJlPIrkdHghkEtfJ5RhzgHA/DTnYuA/0/ayylkv81D+cTNAWeUkufaxIH09Xfd3pdxypsetBmrSP/s7gQ+VW06SG1TeBDrmtZVVxvS43yP5B9pi4CaSO8qaLKenoDEzs0y1lqEzMzMrERcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqGxZktSSPpJ3vJFki5ron3fKOmkptjXdo7z5XTm4TkF9v9OEx67k6SvNdX+zLbFhcaas/eBL0nqXOog+SRV7ED3CcDXIuLTBfZvsNAosaN/nzsBO1xodvDzmbnQWLO2ieRZ5hfUX1H/jETS39OfQyX9UdJvJf1V0iRJY5U802aRpAPzdvMZSQ+n/Uam21dIulLSE5IWSvpK3n7nSPpfYFEDecak+18s6Udp23+SfDH2V5KurNe/m6S5Sp5jsljSP0maBOyRtk2TVJWeDf03yazV+9V9znQfJ0m6MX3fVdIdSp7bs0DSkcAk4MB0f1emn+GevO0nSzojfb9M0n9K+hPwZUnHSXpU0lOSblMyJx7p7/Mv6e/mx4X9MVpLV1nqAGa76BfAQklX7MA2A4E+wFvA88BvImKwkoe7/RtwftqvCjgGOBCYI+njwL+QzG77SUntgEck/SHtPxjoFxEv5B9M0r7Aj4DDgLdJZkb+YkR8X9KxwEURUVMv46kkj7b4YXoG0T4iHpZ0XiQTiNbNqn0QcGZEfC1t29Zn/jnwx4gYle6vA8kklP3y9jd0O7+39RFxVHoGeTvwmYhYI+nbwDclTQZGAb0jIuqmBjJzobFmLSLelfQ/wNeBdQVu9kSk059Leg6oKxSLgPwhrN9GxGZgqaTngd4kc38NyDtb6kgy19MGYF79IpP6JPBQRLyeHnMaycPv7mwsI3C9kolN74yI3Db6vRgRjzX2YVPHkhRJIqIWWK28JyYW6Nb05+EkD796JC1sbYFHgXeB9cBvJM0gea6JmYfOrEW4iuRax555bZtI//tOJwpsm7fu/bz3m/OWN7P1P77qz88UJFOk/1tEVKevj0Xy7A5IpqpvyDZPM7YlIuaSFKOXgZsk/cs2utY/Zn7m3XfwsFt+Z9vYvu5YInnuT93v4OCImBARm0jO6n4PfBGYtYPHtxbKhcaavYh4i+TxsxPympeRDFVB8qyN3XZi11+W1Ca9btOTZALB+4Bz0zMNJH1CySzHjXkcOEZS53TYagzwx8Y2kHQAyfNrriWZRfvQdNXGumNvw2uS+qQ3BozKa38QODfdd4WkvYH3SB7TXedF4GAls/J2JJnFtyGPAZ9KhxJRMuvvJ9LrNB0jYibJ8GN1Y5/RWg8XGmspfgLk3312Lcn/3OeRPNp5W2cbjVlCUhDuBb4aEeuB35BMR/+UpMXAr9nOEHQ6THcJMIdkVuSnIuKuxrYhmeE5J2k+cCJwddo+heSa1LRtbDeRZMhqNskM4XW+AXxa0iLgSaBvRLxJMvy1WNKVEbGcpGAvBKaRzIrd0Od5neSZ8rdIWkhSeHqTFK170rY/0sBNGtY6efZmMzPLlM9ozMwsUy40ZmaWKRcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqExM7NM/X9NBGuncZxNrQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1051,20 +1070,18 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Map structure to kinase distance matrix\n",
     "\n",
-    "One kinase pair can be represented by multiple structure pairs. \n",
-    "\n",
-    "TBA\n",
-    "\n",
-    "We use minimum!"
+    "First &mdash; as an intermediate step &mdash; we will create a structure distance matrix but instead of labeling the data with structure KLIFS IDs we add the corresponding kinase name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1165,7 +1182,7 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,479 +1199,50 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 107,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>...</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>CDK2</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.720000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.542857</td>\n",
-       "      <td>0.741935</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.696970</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.483871</td>\n",
-       "      <td>0.703704</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.655172</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.629630</td>\n",
-       "      <td>0.684211</td>\n",
-       "      <td>0.705882</td>\n",
-       "      <td>0.681818</td>\n",
-       "      <td>0.620690</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.565217</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.705882</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.695652</td>\n",
-       "      <td>0.709677</td>\n",
-       "      <td>0.592593</td>\n",
-       "      <td>0.743590</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.407407</td>\n",
-       "      <td>0.708333</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>0.541667</td>\n",
-       "      <td>0.562500</td>\n",
-       "      <td>0.517241</td>\n",
-       "      <td>0.440000</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.560000</td>\n",
-       "      <td>0.413793</td>\n",
-       "      <td>0.240000</td>\n",
-       "      <td>0.589744</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.407407</td>\n",
-       "      <td>0.708333</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>0.541667</td>\n",
-       "      <td>0.562500</td>\n",
-       "      <td>0.517241</td>\n",
-       "      <td>0.440000</td>\n",
-       "      <td>0.444444</td>\n",
-       "      <td>0.714286</td>\n",
-       "      <td>0.611111</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.560000</td>\n",
-       "      <td>0.413793</td>\n",
-       "      <td>0.240000</td>\n",
-       "      <td>0.589744</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.733333</td>\n",
-       "      <td>0.761905</td>\n",
-       "      <td>0.783784</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.677419</td>\n",
-       "      <td>0.642857</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.766667</td>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>0.783784</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>0.535714</td>\n",
-       "      <td>0.607143</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.807692</td>\n",
-       "      <td>0.807692</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.733333</td>\n",
-       "      <td>0.761905</td>\n",
-       "      <td>0.783784</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.677419</td>\n",
-       "      <td>0.642857</td>\n",
-       "      <td>0.785714</td>\n",
-       "      <td>0.766667</td>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>0.783784</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.760000</td>\n",
-       "      <td>0.535714</td>\n",
-       "      <td>0.607143</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.807692</td>\n",
-       "      <td>0.807692</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1466 rows × 1466 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR      EGFR      EGFR  \\\n",
-       "kinase.klifs_name                                                               \n",
-       "EGFR               0.000000       NaN       NaN       NaN       NaN       NaN   \n",
-       "EGFR               0.720000  0.000000       NaN       NaN       NaN       NaN   \n",
-       "EGFR               0.542857  0.741935  0.000000       NaN       NaN       NaN   \n",
-       "EGFR               0.500000  0.500000  0.696970  0.000000       NaN       NaN   \n",
-       "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000       NaN   \n",
-       "...                     ...       ...       ...       ...       ...       ...   \n",
-       "p110a              0.629630  0.684211  0.705882  0.681818  0.620690  0.576923   \n",
-       "CDK2               0.407407  0.708333  0.611111  0.541667  0.562500  0.517241   \n",
-       "CDK2               0.407407  0.708333  0.611111  0.541667  0.562500  0.517241   \n",
-       "CDK2               0.733333  0.761905  0.783784  0.750000  0.677419  0.642857   \n",
-       "CDK2               0.733333  0.761905  0.783784  0.750000  0.677419  0.642857   \n",
-       "\n",
-       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR  ...      EGFR  \\\n",
-       "kinase.klifs_name                                          ...             \n",
-       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN  ...       NaN   \n",
-       "...                     ...       ...       ...       ...  ...       ...   \n",
-       "p110a              0.565217  0.615385  0.750000  0.705882  ...  0.695652   \n",
-       "CDK2               0.440000  0.444444  0.714286  0.611111  ...  0.560000   \n",
-       "CDK2               0.440000  0.444444  0.714286  0.611111  ...  0.560000   \n",
-       "CDK2               0.785714  0.766667  0.760000  0.783784  ...  0.760000   \n",
-       "CDK2               0.785714  0.766667  0.760000  0.783784  ...  0.760000   \n",
-       "\n",
-       "kinase.klifs_name      CDK2      CDK2      EGFR     p110a     p110a      CDK2  \\\n",
-       "kinase.klifs_name                                                               \n",
-       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
-       "EGFR                    NaN       NaN       NaN       NaN       NaN       NaN   \n",
-       "...                     ...       ...       ...       ...       ...       ...   \n",
-       "p110a              0.709677  0.592593  0.743590  0.000000  0.000000       NaN   \n",
-       "CDK2               0.413793  0.240000  0.589744  0.615385  0.615385  0.000000   \n",
-       "CDK2               0.413793  0.240000  0.589744  0.615385  0.615385  0.000000   \n",
-       "CDK2               0.535714  0.607143  0.750000  0.807692  0.807692  0.576923   \n",
-       "CDK2               0.535714  0.607143  0.750000  0.807692  0.807692  0.576923   \n",
-       "\n",
-       "kinase.klifs_name      CDK2  CDK2  CDK2  \n",
-       "kinase.klifs_name                        \n",
-       "EGFR                    NaN   NaN   NaN  \n",
-       "EGFR                    NaN   NaN   NaN  \n",
-       "EGFR                    NaN   NaN   NaN  \n",
-       "EGFR                    NaN   NaN   NaN  \n",
-       "EGFR                    NaN   NaN   NaN  \n",
-       "...                     ...   ...   ...  \n",
-       "p110a                   NaN   NaN   NaN  \n",
-       "CDK2                    NaN   NaN   NaN  \n",
-       "CDK2               0.000000   NaN   NaN  \n",
-       "CDK2               0.576923   0.0   NaN  \n",
-       "CDK2               0.576923   0.0   0.0  \n",
-       "\n",
-       "[1466 rows x 1466 columns]"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df = kinase_distance_matrix\n",
-    "df = df.where(np.tril(np.ones(df.shape)).astype(bool))\n",
-    "df"
+    "A kinase pair can be representated by many different structure pairs which are associated with different distance values. For example, if we compare all EGFR structure to each other, the range of distance values is already quite high because we can observe quite different binding modes of the co-crystallized ligands."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": "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\n",
       "text/plain": [
-       "kinase.klifs_name  kinase.klifs_name\n",
-       "EGFR               EGFR                 0.000000\n",
-       "                   EGFR                 0.720000\n",
-       "                   EGFR                 0.000000\n",
-       "                   EGFR                 0.542857\n",
-       "                   EGFR                 0.741935\n",
-       "dtype: float64"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df.stack().head()"
+    "# Select EGFR-EGFR strcture pairs only\n",
+    "D = kinase_distance_matrix.loc[\"EGFR\", \"EGFR\"]\n",
+    "# Extract all pairwise distances without identical structure pairs \n",
+    "# = lower triangular matrix without the diagonal\n",
+    "D_condensed = distance.squareform(D)\n",
+    "# Plot pairwise distances\n",
+    "plt.boxplot(D_condensed);"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 109,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "kinase.klifs_name  kinase.klifs_name\n",
-       "BRAF               BRAF                 0.000000\n",
-       "p110a              p110a                0.000000\n",
-       "MET                MET                  0.000000\n",
-       "LCK                LCK                  0.000000\n",
-       "KDR                KDR                  0.000000\n",
-       "ErbB2              ErbB2                0.000000\n",
-       "EGFR               EGFR                 0.000000\n",
-       "CDK2               CDK2                 0.000000\n",
-       "p38a               p38a                 0.000000\n",
-       "KDR                p38a                 0.043478\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "df.stack().groupby(level=[0, 1]).min().sort_values().head(10)"
+    "In this talktorial, we will consider per kinase pair the two structures that show the most similar binding mode for their co-crystallized ligands. Hence, we select the structure pair with the minimum distance as representative for a kinase pair."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1707,23 +1295,23 @@
        "      <td>0.000000</td>\n",
        "      <td>0.300000</td>\n",
        "      <td>0.277778</td>\n",
-       "      <td>0.416667</td>\n",
-       "      <td>0.428571</td>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.363636</td>\n",
        "      <td>0.320000</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.583333</td>\n",
-       "      <td>0.387097</td>\n",
+       "      <td>0.368421</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDK2</th>\n",
        "      <td>0.300000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.166667</td>\n",
+       "      <td>0.111111</td>\n",
        "      <td>0.451613</td>\n",
-       "      <td>0.363636</td>\n",
+       "      <td>0.238095</td>\n",
        "      <td>0.190476</td>\n",
        "      <td>0.133333</td>\n",
-       "      <td>0.423077</td>\n",
+       "      <td>0.347826</td>\n",
        "      <td>0.277778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1733,58 +1321,58 @@
        "      <td>0.000000</td>\n",
        "      <td>0.258065</td>\n",
        "      <td>0.238095</td>\n",
-       "      <td>0.190476</td>\n",
+       "      <td>0.166667</td>\n",
        "      <td>0.227273</td>\n",
-       "      <td>0.423077</td>\n",
+       "      <td>0.380952</td>\n",
        "      <td>0.173913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ErbB2</th>\n",
        "      <td>0.393939</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.272727</td>\n",
+       "      <td>0.451613</td>\n",
+       "      <td>0.258065</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.406250</td>\n",
        "      <td>0.419355</td>\n",
        "      <td>0.382353</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.258065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KDR</th>\n",
        "      <td>0.363636</td>\n",
        "      <td>0.238095</td>\n",
-       "      <td>0.250000</td>\n",
+       "      <td>0.238095</td>\n",
        "      <td>0.406250</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.058824</td>\n",
-       "      <td>0.343750</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.533333</td>\n",
        "      <td>0.043478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LCK</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.320000</td>\n",
+       "      <td>0.190476</td>\n",
        "      <td>0.166667</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.058824</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.375000</td>\n",
+       "      <td>0.190476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MET</th>\n",
        "      <td>0.368421</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.235294</td>\n",
-       "      <td>0.433333</td>\n",
+       "      <td>0.133333</td>\n",
+       "      <td>0.227273</td>\n",
+       "      <td>0.382353</td>\n",
        "      <td>0.333333</td>\n",
        "      <td>0.250000</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.368421</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.352941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p110a</th>\n",
@@ -1801,208 +1389,8 @@
        "    <tr>\n",
        "      <th>p38a</th>\n",
        "      <td>0.333333</td>\n",
-       "      <td>0.304348</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.192308</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.352941</td>\n",
-       "      <td>0.576923</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
-       "kinase.klifs_name                                                               \n",
-       "BRAF               0.000000  0.300000  0.277778  0.416667  0.428571  0.320000   \n",
-       "CDK2               0.300000  0.000000  0.166667  0.451613  0.363636  0.190476   \n",
-       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.190476   \n",
-       "ErbB2              0.393939  0.454545  0.272727  0.000000       NaN  0.419355   \n",
-       "KDR                0.363636  0.238095  0.250000  0.406250  0.000000  0.058824   \n",
-       "LCK                     NaN       NaN  0.166667       NaN       NaN  0.000000   \n",
-       "MET                0.368421  0.250000  0.235294  0.433333  0.333333  0.250000   \n",
-       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
-       "p38a               0.333333  0.304348  0.238095  0.258065  0.192308  0.190476   \n",
-       "\n",
-       "kinase.klifs_name       MET     p110a      p38a  \n",
-       "kinase.klifs_name                                \n",
-       "BRAF               0.380952  0.583333  0.387097  \n",
-       "CDK2               0.133333  0.423077  0.277778  \n",
-       "EGFR               0.227273  0.423077  0.173913  \n",
-       "ErbB2              0.382353       NaN       NaN  \n",
-       "KDR                0.343750       NaN  0.043478  \n",
-       "LCK                     NaN       NaN       NaN  \n",
-       "MET                0.000000  0.500000  0.368421  \n",
-       "p110a              0.428571  0.000000  0.473684  \n",
-       "p38a               0.352941  0.576923  0.000000  "
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = (\n",
-    "    df.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
-    ")\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th>BRAF</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>ErbB2</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>LCK</th>\n",
-       "      <th>MET</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>p38a</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.277778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.173913</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.258065</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.043478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.190476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.352941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.473684</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.173913</td>\n",
+       "      <td>0.277778</td>\n",
+       "      <td>0.173913</td>\n",
        "      <td>0.258065</td>\n",
        "      <td>0.043478</td>\n",
        "      <td>0.190476</td>\n",
@@ -2040,16 +1428,17 @@
        "p38a               0.352941  0.473684  0.000000  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# TODO: Select the minimum structure pair distance for each kinase pair\n",
-    "kinase_distance_matrix = (\n",
-    "    kinase_distance_matrix.stack().groupby(level=[0, 1]).min().unstack(level=1)\n",
-    ")\n",
+    "# We unstack the matrix (each pairwise comparison in a single row)\n",
+    "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
+    "# We take the minimum value in each kinase pair group\n",
+    "# We unstack the remaining data points\n",
+    "kinase_distance_matrix = kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     "kinase_distance_matrix"
    ]
   },
@@ -2057,17 +1446,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as kinase matrix"
+    "### Visualize similarity as kinase matrix\n",
+    "\n",
+    "We visualize the kinase matrix in the form of a heatmap."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'kinase_similarity_matrix' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_12417/207115603.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Kinase distance\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Kinase distance matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mkinase_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinase_similarity_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'kinase_similarity_matrix' is not defined"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -2079,10 +1481,10 @@
     }
    ],
    "source": [
-    "plt.imshow(kinase_distance_matrix)\n",
+    "plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
     "plt.colorbar(label=\"Kinase distance\")\n",
     "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
+    "kinase_names = kinase_similarity_matrix.columns.to_list()\n",
     "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
     "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
    ]
@@ -2091,332 +1493,110 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as phylogenetic tree"
+    "We have calculated distances between fingerprints that range from 0 to 1 (identical to dissimilar). The term similarity is often defined between 0 and 1 (dissimilar to identical), hence for the following heatmap, we will use the transformation $similarity = 1 - distance$."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Bio.Phylo.TreeConstruction with DistanceTreeConstructor and DistanceMatrix. Wrangle your distances into the correct format for DistanceMatrix, convert it into a tree and draw the tree with upgma/nj. "
+    "kinase_similarity_matrix = 1 - kinase_distance_matrix\n",
+    "plt.imshow(kinase_similarity_matrix, vmin=0.0, vmax=1.0)\n",
+    "plt.colorbar(label=\"Kinase similarity\")\n",
+    "plt.title(\"Kinase distance matrix\")\n",
+    "kinase_names = kinase_similarity_matrix.columns.to_list()\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
+    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 25,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th>BRAF</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>ErbB2</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>LCK</th>\n",
-       "      <th>MET</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>p38a</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.277778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.173913</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.258065</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.043478</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.190476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.352941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.473684</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.173913</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.043478</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.352941</td>\n",
-       "      <td>0.473684</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
-       "kinase.klifs_name                                                               \n",
-       "BRAF               0.000000  0.300000  0.277778  0.393939  0.363636  0.320000   \n",
-       "CDK2               0.300000  0.000000  0.111111  0.451613  0.238095  0.190476   \n",
-       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.166667   \n",
-       "ErbB2              0.393939  0.451613  0.258065  0.000000  0.406250  0.419355   \n",
-       "KDR                0.363636  0.238095  0.238095  0.406250  0.000000  0.058824   \n",
-       "LCK                0.320000  0.190476  0.166667  0.419355  0.058824  0.000000   \n",
-       "MET                0.368421  0.133333  0.227273  0.382353  0.333333  0.250000   \n",
-       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
-       "p38a               0.333333  0.277778  0.173913  0.258065  0.043478  0.190476   \n",
-       "\n",
-       "kinase.klifs_name       MET     p110a      p38a  \n",
-       "kinase.klifs_name                                \n",
-       "BRAF               0.368421  0.500000  0.333333  \n",
-       "CDK2               0.133333  0.347826  0.277778  \n",
-       "EGFR               0.227273  0.380952  0.173913  \n",
-       "ErbB2              0.382353  0.571429  0.258065  \n",
-       "KDR                0.333333  0.533333  0.043478  \n",
-       "LCK                0.250000  0.375000  0.190476  \n",
-       "MET                0.000000  0.428571  0.352941  \n",
-       "p110a              0.428571  0.000000  0.473684  \n",
-       "p38a               0.352941  0.473684  0.000000  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "kinase_distance_matrix"
+    "### Visualize similarity as dendrogram"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Example 1"
+    "We use the `scipy` function `squareform` to create a condensed vector of the distance matrix of shape $n*(n-1)/2$, where $n$ is the shape of the quadratic distance matrix. The values in this vector correspond to the values of the lower triangular matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from Bio import Phylo"
+    "D = kinase_distance_matrix.to_numpy()\n",
+    "D_condensed = distance.squareform(D)\n",
+    "D_condensed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can submit this condensed vector to a hierarchical clustering to extract the relationship between the different kinases. We use here `method=\"average\"`, which stands for the linkage method UPGMA (unweighted pair group method with arithmetic mean). This means that the distance between two clusters A and B is defined as the avarge of all distances between pairs of elements in clusters A and B. At each clustering step, the two clusters with the lowest average distance are combined."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "#https://biopython.org/wiki/Phylo\n",
-    "\n",
-    "from Bio.Phylo.TreeConstruction import DistanceMatrix, DistanceTreeConstructor\n",
-    "\n",
-    "matrix = [\n",
-    "    [0], \n",
-    "    [0.23076923076923073, 0], \n",
-    "    [0.3846153846153846, 0.23076923076923073, 0], \n",
-    "    [0.5384615384615384, 0.5384615384615384, 0.5384615384615384, 0], \n",
-    "    [0.6153846153846154, 0.3846153846153846, 0.46153846153846156, 0.15384615384615385, 0]\n",
-    "]\n",
-    "names = ['Alpha', 'Beta', 'Gamma', 'Delta', 'Epsilon']\n",
-    "\n",
-    "dm = DistanceMatrix(names, matrix)\n",
-    "\n",
-    "constructor = DistanceTreeConstructor()\n",
-    "tree = constructor.upgma(dm)\n",
-    "tree.ladderize()  # Flip branches so deeper clades are displayed at top\n",
-    "\n",
-    "fig, ax = plt.subplots()\n",
-    "Phylo.draw(tree, axes=ax)"
+    "hclust = hierarchy.linkage(D_condensed, method=\"average\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Example 2"
+    "We now generate a phylogenetic tree based on our clustering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "matrix = kinase_distance_matrix.to_numpy()"
+    "tree = hierarchy.to_tree(hclust)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 69,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "matrix_tril = [list(vector[:i]) for i, vector in enumerate(matrix, 1)]"
+    "We visualize this tree as dendrogram."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "dm = DistanceMatrix(kinase_distance_matrix.columns.to_list(), matrix_tril)\n",
-    "\n",
-    "constructor = DistanceTreeConstructor()\n",
-    "tree = constructor.upgma(dm)\n",
-    "tree.ladderize()  # Flip branches so deeper clades are displayed at top\n",
-    "\n",
     "fig, ax = plt.subplots()\n",
-    "Phylo.draw(tree, axes=ax)"
+    "labels = kinase_distance_matrix.columns.to_list()\n",
+    "hierarchy.dendrogram(hclust, labels=labels, orientation=\"left\", ax=ax)\n",
+    "ax.set_xlabel(\"Distance\");"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+    "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity. "
    ]
   },
   {
@@ -2425,11 +1605,9 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
+    "1. Why is the structural coverage between kinases so different?\n",
+    "2. At one point, we are mapping structure pairs to kinase pairs. We use here the minimum structure distance. What other mapping strategies can you think of? What advantages and disadvantages could they have?\n",
+    "3. What is the difference between the distances that we show in the heatmap and the dendrogram? "
    ]
   },
   {
@@ -2442,7 +1620,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2456,7 +1634,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.9.6"
   },
   "toc-autonumbering": true,
   "widgets": {

From 507479f9302f82788210de57a4fb655249697f06 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 23 Aug 2021 16:42:09 +0200
Subject: [PATCH 018/158] unique measurement for a compound against a kinase.

---
 .../talktorial.ipynb                          | 1492 ++++++++++++-----
 1 file changed, 1103 insertions(+), 389 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index d9d58fee..ef59ae60 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -158,8 +158,10 @@
    ]
   },
   {
-   "cell_type": "raw",
+   "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
+   "outputs": [],
    "source": [
     "# !pip install flake8 pycodestyle_magic\n",
     "%load_ext pycodestyle_magic\n",
@@ -173,7 +175,8 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "from collections import Counter"
    ]
   },
   {
@@ -453,7 +456,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKd', 'pIC50', 'pKi'}\n",
+      "Activities: {'pKd', 'pKi', 'pIC50'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -483,23 +486,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the uniprot ID of the kinase."
+    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the uniprot ID of the kinase. We rename these columns for readability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
     "data = data[[\"compound_structures.canonical_smiles\",\n",
     "             \"activities.standard_value\",\n",
-    "             \"UniprotID\"]]"
+    "             \"UniprotID\"]]\n",
+    "data = data.rename(columns={\"compound_structures.canonical_smiles\": \"smiles\",\n",
+    "                            \"activities.standard_value\": \"activity_value\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -530,8 +535,8 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>compound_structures.canonical_smiles</th>\n",
-       "      <th>activities.standard_value</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>activity_value</th>\n",
        "      <th>UniprotID</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -571,22 +576,15 @@
        "</div>"
       ],
       "text/plain": [
-       "                compound_structures.canonical_smiles  \\\n",
-       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "\n",
-       "   activities.standard_value UniprotID  \n",
-       "0                  14.096910    O75116  \n",
-       "1                  14.000000    O75116  \n",
-       "2                  14.000000    O75116  \n",
-       "3                  13.958607    O75116  \n",
-       "4                  13.920819    O75116  "
+       "                                              smiles  activity_value UniprotID\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.096910    O75116\n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.000000    O75116\n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.000000    O75116\n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.958607    O75116\n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.920819    O75116"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -605,16 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = data.dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -626,6 +615,7 @@
     }
    ],
    "source": [
+    "data = data.dropna()\n",
     "print(f\"Current shape of data: {data.shape}\")"
    ]
   },
@@ -633,67 +623,56 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Hit or non-hit"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off."
+    "#### Kinases of interest\n",
+    "\n",
+    "We focus on the kinases of interest and map them to their uniprot IDs in accordance with the Table in the Theory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cutoff = 6.3\n",
-    "# TODO: Check if this cut-off is the same as in the paper"
+    "name_to_uniprot = {'EGFR': 'P00533',\n",
+    "                   'ErbB2': 'P04626',\n",
+    "                   'BRAF': 'P15056',\n",
+    "                   'CDK2': 'P24941',\n",
+    "                   'LCK': 'P06239',\n",
+    "                   'MET': 'P08581',\n",
+    "                   'p38a': 'Q16539',\n",
+    "                   'KDR': 'P35968',\n",
+    "                   'p110a': 'P42336'}"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 18,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "def binarize_pic50(pic50_value, threshold):\n",
-    "    \"\"\"\n",
-    "    Binarizes a scalar value given a threshold.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    pic50_value : float\n",
-    "        The measurement pIC50 value of a kinase-ligand pair.\n",
-    "    threshold : float\n",
-    "        The cutoff to determine activity.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    1 if the pIC50 value is above the threshold, which indicates activity.\n",
-    "    0 otherwise.\n",
-    "    \"\"\"\n",
-    "    if pic50_value >= threshold:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return 0"
+    "We keep data for these kinases only:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (33169, 3)\n"
+     ]
+    }
+   ],
    "source": [
-    "data[\"activity\"] = data[\"activities.standard_value\"].apply(binarize_pic50,\n",
-    "                                                           args=(cutoff,))"
+    "data = data[data[\"UniprotID\"].isin(name_to_uniprot.values())]\n",
+    "print(f\"Current shape of data: {data.shape}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -717,242 +696,181 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>compound_structures.canonical_smiles</th>\n",
-       "      <th>activities.standard_value</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>activity_value</th>\n",
        "      <th>UniprotID</th>\n",
-       "      <th>activity</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.096910</td>\n",
-       "      <td>O75116</td>\n",
-       "      <td>1</td>\n",
+       "      <th>57</th>\n",
+       "      <td>Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1</td>\n",
+       "      <td>11.522879</td>\n",
+       "      <td>P00533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>O75116</td>\n",
-       "      <td>1</td>\n",
+       "      <th>98</th>\n",
+       "      <td>CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC</td>\n",
+       "      <td>11.221849</td>\n",
+       "      <td>P00533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>O75116</td>\n",
-       "      <td>1</td>\n",
+       "      <th>101</th>\n",
+       "      <td>CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
+       "      <td>11.221849</td>\n",
+       "      <td>P00533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>13.958607</td>\n",
-       "      <td>O75116</td>\n",
-       "      <td>1</td>\n",
+       "      <th>139</th>\n",
+       "      <td>Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1</td>\n",
+       "      <td>11.096910</td>\n",
+       "      <td>P00533</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>13.920819</td>\n",
-       "      <td>O75116</td>\n",
-       "      <td>1</td>\n",
+       "      <th>140</th>\n",
+       "      <td>CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
+       "      <td>11.096910</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186925</th>\n",
+       "      <td>Nc1nc2cc(-c3cncc(S(N)(=O)=O)c3)ccn2n1</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>P42336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186927</th>\n",
+       "      <td>O=C(/C=C/c1ccc(O)c(O)c1)c1ccc(O[C@@H]2O[C@H](C...</td>\n",
+       "      <td>1.721246</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186929</th>\n",
+       "      <td>CS(=O)(=O)c1cncc(-c2ccn3nc(N)nc3c2)c1</td>\n",
+       "      <td>1.700000</td>\n",
+       "      <td>P42336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186936</th>\n",
+       "      <td>O=C(CCc1ccc(O)cc1)c1c(O)cc(O)cc1O</td>\n",
+       "      <td>1.602060</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>186957</th>\n",
+       "      <td>Nc1nc2cc(-c3cncc(S(=O)(=O)Nc4ccccc4)c3)ccn2n1</td>\n",
+       "      <td>1.400000</td>\n",
+       "      <td>P42336</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>33169 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                compound_structures.canonical_smiles  \\\n",
-       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "                                                   smiles  activity_value  \\\n",
+       "57                      Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1       11.522879   \n",
+       "98                    CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC       11.221849   \n",
+       "101                    CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.221849   \n",
+       "139                   Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1       11.096910   \n",
+       "140                       CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.096910   \n",
+       "...                                                   ...             ...   \n",
+       "186925              Nc1nc2cc(-c3cncc(S(N)(=O)=O)c3)ccn2n1        1.800000   \n",
+       "186927  O=C(/C=C/c1ccc(O)c(O)c1)c1ccc(O[C@@H]2O[C@H](C...        1.721246   \n",
+       "186929              CS(=O)(=O)c1cncc(-c2ccn3nc(N)nc3c2)c1        1.700000   \n",
+       "186936                  O=C(CCc1ccc(O)cc1)c1c(O)cc(O)cc1O        1.602060   \n",
+       "186957      Nc1nc2cc(-c3cncc(S(=O)(=O)Nc4ccccc4)c3)ccn2n1        1.400000   \n",
+       "\n",
+       "       UniprotID  \n",
+       "57        P00533  \n",
+       "98        P00533  \n",
+       "101       P00533  \n",
+       "139       P00533  \n",
+       "140       P00533  \n",
+       "...          ...  \n",
+       "186925    P42336  \n",
+       "186927    P00533  \n",
+       "186929    P42336  \n",
+       "186936    P00533  \n",
+       "186957    P42336  \n",
        "\n",
-       "   activities.standard_value UniprotID  activity  \n",
-       "0                  14.096910    O75116         1  \n",
-       "1                  14.000000    O75116         1  \n",
-       "2                  14.000000    O75116         1  \n",
-       "3                  13.958607    O75116         1  \n",
-       "4                  13.920819    O75116         1  "
+       "[33169 rows x 3 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Kinases of interest"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We focus on the kinases of interest and map them to their uniprot IDs in accordance with the Table in the Theory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "name_to_uniprot = {'EGFR': 'P00533',\n",
-    "                   'ErbB2': 'P04626',\n",
-    "                   'BRAF': 'P15056',\n",
-    "                   'CDK2': 'P24941',\n",
-    "                   'LCK': 'P06239',\n",
-    "                   'MET': 'P08581',\n",
-    "                   'p38a': 'Q16539',\n",
-    "                   'KDR': 'P35968',\n",
-    "                   'p110a': 'P42336'}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Show kinase coverage"
+    "data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now look at the kinase coverage.\n",
-    "\n",
-    "For each of them, we compute three metrics:\n",
-    "\n",
-    "1. the total number of measured compounds against the given kinase,\n",
-    "2. the number of active compounds, and\n",
-    "3. the fraction of active compouds, i.e. the ratio of active compounds and total number of measured compounds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: Use percentage instead of fraction?"
+    "Let's look at EGFR data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
-    "    \"\"\"\n",
-    "    Retrieve the three metrics for a given kinase.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    kinase_name : str\n",
-    "        The name of the kinase of interest, e.g. \"EGFR\".\n",
-    "\n",
-    "    activity_dataframe :  pd.DataFrame\n",
-    "        The dataframe with activity values for kinases.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    tuple : (int, int, float)\n",
-    "        The three metrics:\n",
-    "        1. The total number of measured compounds against the kinase\n",
-    "        2. The number of active compounds against the kinase.\n",
-    "        3. The fraction of active compounds against the kinase.\n",
-    "    \"\"\"\n",
-    "    uniprot_id = name_to_uniprot[kinase_name]\n",
-    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"]\n",
-    "                                     == uniprot_id]\n",
-    "    total_measured_compounds = len(kinase_data)\n",
-    "    active_compounds = len(kinase_data[kinase_data[\"activity\"] == 1])\n",
-    "    if total_measured_compounds > 0:\n",
-    "        fraction = active_compounds/total_measured_compounds\n",
-    "    else:\n",
-    "        print(\"No compounds were measured for this kinase.\")\n",
-    "        fraction = np.nan\n",
-    "    return (total_measured_compounds, active_compounds, fraction)"
+    "EGFR_data = data[data[\"UniprotID\"] == \"P00533\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see what information we get for EGFR."
+    "Some compounds have been tested several times against EGFR, as shown below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(6026, 3706, 0.6150016594756057)"
+       "[('COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 40),\n",
+       " ('C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 24),\n",
+       " ('C=CC(=O)Nc1cc(Nc2nccc(-c3cn(C)c4ccccc34)n2)c(OC)cc1N(C)CCN(C)C', 12),\n",
+       " ('C=CC(=O)Nc1cccc(Oc2nc(Nc3ccc(N4CCN(C)CC4)cc3OC)ncc2Cl)c1', 10),\n",
+       " ('CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4',\n",
+       "  7)]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "EGFR_metrics = kinase_to_activity_numbers(\"EGFR\", data)\n",
-    "EGFR_metrics"
+    "measured_compounds = Counter(EGFR_data[\"smiles\"])\n",
+    "measured_compounds.most_common()[0:5]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a table from this information for all kinases:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def from_numbers_to_table(activity_dataframe):\n",
-    "    \"\"\"\n",
-    "    Create a metric table for all kinases.\n",
-    "\n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    activity_dataframe :  pd.DataFrame\n",
-    "        The dataframe with activity values for kinases.\n",
-    "\n",
-    "    Returns\n",
-    "    -------\n",
-    "    table : pd.DataFrame\n",
-    "        A dataframe with the kinases as rows and metrics as columns.\n",
-    "    \"\"\"\n",
-    "    table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
-    "                         columns=[\"total\", \"actives\", \"fraction\"])\n",
-    "    for name in name_to_uniprot:\n",
-    "        metrics = kinase_to_activity_numbers(name, activity_dataframe)\n",
-    "        table.loc[name] = metrics\n",
-    "    return table"
+    "As a simple workaround, we keep the value for which the compound has the highest activity value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -976,244 +894,1040 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>total</th>\n",
-       "      <th>actives</th>\n",
-       "      <th>fraction</th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>activity_value</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>6026</td>\n",
-       "      <td>3706</td>\n",
-       "      <td>0.615002</td>\n",
+       "      <th>0</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
+       "      <td>5.336488</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>1723</td>\n",
-       "      <td>1049</td>\n",
-       "      <td>0.608822</td>\n",
+       "      <th>1</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
+       "      <td>5.996539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>3708</td>\n",
-       "      <td>3013</td>\n",
-       "      <td>0.812567</td>\n",
+       "      <th>2</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
+       "      <td>8.397940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>1513</td>\n",
-       "      <td>825</td>\n",
-       "      <td>0.545274</td>\n",
+       "      <th>3</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>7.207608</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>1550</td>\n",
-       "      <td>935</td>\n",
-       "      <td>0.603226</td>\n",
+       "      <th>4</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>8.420216</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>2867</td>\n",
-       "      <td>2279</td>\n",
-       "      <td>0.794908</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>3654</td>\n",
-       "      <td>2796</td>\n",
-       "      <td>0.765189</td>\n",
+       "      <th>32683</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1</td>\n",
+       "      <td>5.102373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>7734</td>\n",
-       "      <td>5401</td>\n",
-       "      <td>0.698345</td>\n",
+       "      <th>32684</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1</td>\n",
+       "      <td>6.292430</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>4394</td>\n",
-       "      <td>2815</td>\n",
-       "      <td>0.640646</td>\n",
+       "      <th>32685</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1</td>\n",
+       "      <td>7.522879</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32686</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1</td>\n",
+       "      <td>5.903090</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32687</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1</td>\n",
+       "      <td>6.397940</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>32688 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      total actives  fraction\n",
-       "EGFR   6026    3706  0.615002\n",
-       "ErbB2  1723    1049  0.608822\n",
-       "BRAF   3708    3013  0.812567\n",
-       "CDK2   1513     825  0.545274\n",
-       "LCK    1550     935  0.603226\n",
-       "MET    2867    2279  0.794908\n",
-       "p38a   3654    2796  0.765189\n",
-       "KDR    7734    5401  0.698345\n",
-       "p110a  4394    2815  0.640646"
+       "      UniprotID                                             smiles  \\\n",
+       "0        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
+       "1        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
+       "2        P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
+       "3        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "4        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "...         ...                                                ...   \n",
+       "32683    Q16539           c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1   \n",
+       "32684    Q16539     c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1   \n",
+       "32685    Q16539             c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1   \n",
+       "32686    Q16539        c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1   \n",
+       "32687    Q16539                c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1   \n",
+       "\n",
+       "       activity_value  \n",
+       "0            5.336488  \n",
+       "1            5.996539  \n",
+       "2            8.397940  \n",
+       "3            7.207608  \n",
+       "4            8.420216  \n",
+       "...               ...  \n",
+       "32683        5.102373  \n",
+       "32684        6.292430  \n",
+       "32685        7.522879  \n",
+       "32686        5.903090  \n",
+       "32687        6.397940  \n",
+       "\n",
+       "[32688 rows x 3 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_table = from_numbers_to_table(data)\n",
-    "kinase_table"
+    "data = data.groupby([\"UniprotID\",\n",
+    "                     \"smiles\"])['activity_value'].max().reset_index()\n",
+    "data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's beautify the table:"
+    "#### Hit or non-hit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a cut-off of $6.3$, similarly to the referenced paper."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cutoff = 6.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def binarize_pic50(pic50_value, threshold):\n",
+    "    \"\"\"\n",
+    "    Binarizes a scalar value given a threshold.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    pic50_value : float\n",
+    "        The measurement pIC50 value of a kinase-ligand pair.\n",
+    "    threshold : float\n",
+    "        The cutoff to determine activity.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    1 if the pIC50 value is above the threshold, which indicates activity.\n",
+    "    0 otherwise.\n",
+    "    \"\"\"\n",
+    "    if pic50_value >= threshold:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        return 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"activity_binary\"] = data[\"activity_value\"].apply(binarize_pic50,\n",
+    "                                                       args=(cutoff,))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (32688, 4)\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_3e895_row0_col2 {\n",
-       "  background-color: #d8d8ea;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3e895_row1_col2 {\n",
-       "  background-color: #dcdcec;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_3e895_row2_col2 {\n",
-       "  background-color: #3f007d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3e895_row3_col2 {\n",
-       "  background-color: #fcfbfd;\n",
-       "  color: #000000;\n",
-       "  background-color: yellow;\n",
-       "}\n",
-       "#T_3e895_row4_col2 {\n",
-       "  background-color: #e0dfee;\n",
-       "  color: #000000;\n",
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-       "#T_3e895_row5_col2 {\n",
-       "  background-color: #4a1486;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3e895_row6_col2 {\n",
-       "  background-color: #5d3897;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3e895_row7_col2 {\n",
-       "  background-color: #8d89c0;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_3e895_row8_col2 {\n",
-       "  background-color: #c0c1de;\n",
-       "  color: #000000;\n",
-       "}\n",
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
-       "<table id=\"T_3e895_\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >total</th>\n",
-       "      <th class=\"col_heading level0 col1\" >actives</th>\n",
-       "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>activity_value</th>\n",
+       "      <th>activity_binary</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_3e895_row0_col0\" class=\"data row0 col0\" >6026</td>\n",
-       "      <td id=\"T_3e895_row0_col1\" class=\"data row0 col1\" >3706</td>\n",
-       "      <td id=\"T_3e895_row0_col2\" class=\"data row0 col2\" >0.615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_3e895_row1_col0\" class=\"data row1 col0\" >1723</td>\n",
-       "      <td id=\"T_3e895_row1_col1\" class=\"data row1 col1\" >1049</td>\n",
-       "      <td id=\"T_3e895_row1_col2\" class=\"data row1 col2\" >0.609</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_3e895_row2_col0\" class=\"data row2 col0\" >3708</td>\n",
-       "      <td id=\"T_3e895_row2_col1\" class=\"data row2 col1\" >3013</td>\n",
-       "      <td id=\"T_3e895_row2_col2\" class=\"data row2 col2\" >0.813</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_3e895_row3_col0\" class=\"data row3 col0\" >1513</td>\n",
-       "      <td id=\"T_3e895_row3_col1\" class=\"data row3 col1\" >825</td>\n",
-       "      <td id=\"T_3e895_row3_col2\" class=\"data row3 col2\" >0.545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_3e895_row4_col0\" class=\"data row4 col0\" >1550</td>\n",
-       "      <td id=\"T_3e895_row4_col1\" class=\"data row4 col1\" >935</td>\n",
-       "      <td id=\"T_3e895_row4_col2\" class=\"data row4 col2\" >0.603</td>\n",
+       "      <th>0</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
+       "      <td>5.336488</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_3e895_row5_col0\" class=\"data row5 col0\" >2867</td>\n",
-       "      <td id=\"T_3e895_row5_col1\" class=\"data row5 col1\" >2279</td>\n",
-       "      <td id=\"T_3e895_row5_col2\" class=\"data row5 col2\" >0.795</td>\n",
+       "      <th>1</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
+       "      <td>5.996539</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_3e895_row6_col0\" class=\"data row6 col0\" >3654</td>\n",
-       "      <td id=\"T_3e895_row6_col1\" class=\"data row6 col1\" >2796</td>\n",
-       "      <td id=\"T_3e895_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th>2</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
+       "      <td>8.397940</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_3e895_row7_col0\" class=\"data row7 col0\" >7734</td>\n",
-       "      <td id=\"T_3e895_row7_col1\" class=\"data row7 col1\" >5401</td>\n",
-       "      <td id=\"T_3e895_row7_col2\" class=\"data row7 col2\" >0.698</td>\n",
+       "      <th>3</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>7.207608</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e895_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_3e895_row8_col0\" class=\"data row8 col0\" >4394</td>\n",
-       "      <td id=\"T_3e895_row8_col1\" class=\"data row8 col1\" >2815</td>\n",
-       "      <td id=\"T_3e895_row8_col2\" class=\"data row8 col2\" >0.641</td>\n",
+       "      <th>4</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>8.420216</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>\n"
+       "</table>\n",
+       "</div>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc2b52a9460>"
+       "  UniprotID                                             smiles  \\\n",
+       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
+       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
+       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
+       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "\n",
+       "   activity_value  activity_binary  \n",
+       "0        5.336488                0  \n",
+       "1        5.996539                0  \n",
+       "2        8.397940                1  \n",
+       "3        7.207608                1  \n",
+       "4        8.420216                1  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_table_styler = kinase_table.style.\\\n",
-    "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
-    "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
-    "    highlight_min(color=\"yellow\", axis=None)\n",
-    "kinase_table_styler"
+    "print(f\"Current shape of data: {data.shape}\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# x[:,\"Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(F)(F)F)cc1\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "### Show kinase promiscuity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now look at the kinase promiscuity.\n",
+    "\n",
+    "For each of them, we compute three metrics:\n",
+    "\n",
+    "1. the total number of measured compounds against the given kinase,\n",
+    "2. the number of active compounds, and\n",
+    "3. the fraction of active compouds, i.e. the ratio of active compounds and total number of measured compounds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# TODO: Use percentage instead of fraction?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
+    "    \"\"\"\n",
+    "    Retrieve the three metrics for a given kinase.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_name : str\n",
+    "        The name of the kinase of interest, e.g. \"EGFR\".\n",
+    "\n",
+    "    activity_dataframe :  pd.DataFrame\n",
+    "        The dataframe with activity values for kinases.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    tuple : (int, int, float)\n",
+    "        The three metrics:\n",
+    "        1. The total number of measured compounds against the kinase\n",
+    "        2. The number of active compounds against the kinase.\n",
+    "        3. The fraction of active compounds against the kinase.\n",
+    "    \"\"\"\n",
+    "    uniprot_id = name_to_uniprot[kinase_name]\n",
+    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"]\n",
+    "                                     == uniprot_id]\n",
+    "    total_measured_compounds = len(kinase_data)\n",
+    "    active_compounds = len(kinase_data[kinase_data[\"activity_binary\"] == 1])\n",
+    "    if total_measured_compounds > 0:\n",
+    "        fraction = active_compounds/total_measured_compounds\n",
+    "    else:\n",
+    "        print(\"No compounds were measured for this kinase.\")\n",
+    "        fraction = np.nan\n",
+    "    return (total_measured_compounds, active_compounds, fraction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what information we get for EGFR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5869, 3574, 0.6089623445220651)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "EGFR_metrics = kinase_to_activity_numbers(\"EGFR\", data)\n",
+    "EGFR_metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create a table from this information for all kinases:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def from_numbers_to_table(activity_dataframe):\n",
+    "    \"\"\"\n",
+    "    Create a metric table for all kinases.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    activity_dataframe :  pd.DataFrame\n",
+    "        The dataframe with activity values for kinases.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    table : pd.DataFrame\n",
+    "        A dataframe with the kinases as rows and metrics as columns.\n",
+    "    \"\"\"\n",
+    "    table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "                         columns=[\"total\", \"actives\", \"fraction\"])\n",
+    "    for name in name_to_uniprot:\n",
+    "        metrics = kinase_to_activity_numbers(name, activity_dataframe)\n",
+    "        table.loc[name] = metrics\n",
+    "    return table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total</th>\n",
+       "      <th>actives</th>\n",
+       "      <th>fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>5869</td>\n",
+       "      <td>3574</td>\n",
+       "      <td>0.608962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>1694</td>\n",
+       "      <td>1025</td>\n",
+       "      <td>0.605077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>3682</td>\n",
+       "      <td>2988</td>\n",
+       "      <td>0.811515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>1505</td>\n",
+       "      <td>819</td>\n",
+       "      <td>0.544186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>1537</td>\n",
+       "      <td>930</td>\n",
+       "      <td>0.605075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>2821</td>\n",
+       "      <td>2235</td>\n",
+       "      <td>0.792272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>3609</td>\n",
+       "      <td>2760</td>\n",
+       "      <td>0.764755</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>7624</td>\n",
+       "      <td>5312</td>\n",
+       "      <td>0.696747</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>4347</td>\n",
+       "      <td>2780</td>\n",
+       "      <td>0.639522</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      total actives  fraction\n",
+       "EGFR   5869    3574  0.608962\n",
+       "ErbB2  1694    1025  0.605077\n",
+       "BRAF   3682    2988  0.811515\n",
+       "CDK2   1505     819  0.544186\n",
+       "LCK    1537     930  0.605075\n",
+       "MET    2821    2235  0.792272\n",
+       "p38a   3609    2760  0.764755\n",
+       "KDR    7624    5312  0.696747\n",
+       "p110a  4347    2780  0.639522"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_table = from_numbers_to_table(data)\n",
+    "kinase_table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's beautify the table:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_1e959_row0_col2 {\n",
+       "  background-color: #dbdbec;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_1e959_row1_col2, #T_1e959_row4_col2 {\n",
+       "  background-color: #dedded;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_1e959_row2_col2 {\n",
+       "  background-color: #3f007d;\n",
+       "  color: #f1f1f1;\n",
+       "  background-color: red;\n",
+       "}\n",
+       "#T_1e959_row3_col2 {\n",
+       "  background-color: #fcfbfd;\n",
+       "  color: #000000;\n",
+       "  background-color: yellow;\n",
+       "}\n",
+       "#T_1e959_row5_col2 {\n",
+       "  background-color: #4b1687;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_1e959_row6_col2 {\n",
+       "  background-color: #5c3797;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_1e959_row7_col2 {\n",
+       "  background-color: #8d89c0;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_1e959_row8_col2 {\n",
+       "  background-color: #c0c1de;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_1e959_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >total</th>\n",
+       "      <th class=\"col_heading level0 col1\" >actives</th>\n",
+       "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1e959_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
+       "      <td id=\"T_1e959_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
+       "      <td id=\"T_1e959_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1e959_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
+       "      <td id=\"T_1e959_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
+       "      <td id=\"T_1e959_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_1e959_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
+       "      <td id=\"T_1e959_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
+       "      <td id=\"T_1e959_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_1e959_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
+       "      <td id=\"T_1e959_row3_col1\" class=\"data row3 col1\" >819</td>\n",
+       "      <td id=\"T_1e959_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_1e959_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
+       "      <td id=\"T_1e959_row4_col1\" class=\"data row4 col1\" >930</td>\n",
+       "      <td id=\"T_1e959_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_1e959_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
+       "      <td id=\"T_1e959_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
+       "      <td id=\"T_1e959_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_1e959_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
+       "      <td id=\"T_1e959_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
+       "      <td id=\"T_1e959_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_1e959_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
+       "      <td id=\"T_1e959_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
+       "      <td id=\"T_1e959_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1e959_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_1e959_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
+       "      <td id=\"T_1e959_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
+       "      <td id=\"T_1e959_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb9908e77c0>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_table_styler = kinase_table.style.\\\n",
+    "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
+    "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
+    "    highlight_min(color=\"yellow\", axis=None).\\\n",
+    "    highlight_max(color=\"red\", subset=[\"fraction\"])\n",
+    "kinase_table_styler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the table, we notice that CDK2, BRAF are the least, most promiscious kinases, respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>activity_value</th>\n",
+       "      <th>activity_binary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
+       "      <td>5.336488</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
+       "      <td>5.996539</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
+       "      <td>8.397940</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>7.207608</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>8.420216</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32683</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1</td>\n",
+       "      <td>5.102373</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32684</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1</td>\n",
+       "      <td>6.292430</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32685</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1</td>\n",
+       "      <td>7.522879</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32686</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1</td>\n",
+       "      <td>5.903090</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32687</th>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1</td>\n",
+       "      <td>6.397940</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>32688 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      UniprotID                                             smiles  \\\n",
+       "0        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
+       "1        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
+       "2        P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
+       "3        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "4        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
+       "...         ...                                                ...   \n",
+       "32683    Q16539           c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1   \n",
+       "32684    Q16539     c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1   \n",
+       "32685    Q16539             c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1   \n",
+       "32686    Q16539        c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1   \n",
+       "32687    Q16539                c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1   \n",
+       "\n",
+       "       activity_value  activity_binary  \n",
+       "0            5.336488                0  \n",
+       "1            5.996539                0  \n",
+       "2            8.397940                1  \n",
+       "3            7.207608                1  \n",
+       "4            8.420216                1  \n",
+       "...               ...              ...  \n",
+       "32683        5.102373                0  \n",
+       "32684        6.292430                0  \n",
+       "32685        7.522879                1  \n",
+       "32686        5.903090                0  \n",
+       "32687        6.397940                1  \n",
+       "\n",
+       "[32688 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('EGFR', 'CDK2')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"EGFR\", \"CDK2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      EGFR ErbB2 BRAF CDK2  LCK  MET p38a  KDR p110a\n",
+       "EGFR   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "ErbB2  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "BRAF   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "CDK2   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "LCK    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "MET    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "p38a   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "KDR    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
+       "p110a  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table_comparison = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "                                columns=name_to_uniprot.keys())\n",
+    "table_comparison"
+   ]
   },
   {
    "cell_type": "markdown",

From 16710dbd878087c3def44e327b17022102878537 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 16:45:13 +0200
Subject: [PATCH 019/158] Satisfy black-nb

---
 .../talktorial.ipynb                          | 131 ++++++++++++------
 1 file changed, 87 insertions(+), 44 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 4e04de7b..dffdce46 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -2,7 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "# T026 · Kinase similarity: Interaction fingerprints\n",
     "\n",
@@ -42,6 +44,7 @@
     "* Retrieve and preprocess data\n",
     "* Show kinase coverage\n",
     "* Compare kinases\n",
+    "* Map structure to kinase distance matrix\n",
     "* Visualize similarity as kinase matrix\n",
     "* Visualize similarity as phylogenetic tree"
    ]
@@ -217,15 +220,20 @@
    "outputs": [],
    "source": [
     "query_kinases = [\n",
-    "    'EGFR', \n",
-    "    'ErbB2', 'Her2',\n",
-    "    'PI3K', 'PI3KCA', 'p110a', \n",
-    "    'BRAF', \n",
-    "    'CDK2',\n",
-    "    'LCK', \n",
-    "    'MET', \n",
-    "    'p38a', 'MAPK14', \n",
-    "    'VEGFR2', 'KDR'\n",
+    "    \"EGFR\",\n",
+    "    \"ErbB2\",\n",
+    "    \"Her2\",\n",
+    "    \"PI3K\",\n",
+    "    \"PI3KCA\",\n",
+    "    \"p110a\",\n",
+    "    \"BRAF\",\n",
+    "    \"CDK2\",\n",
+    "    \"LCK\",\n",
+    "    \"MET\",\n",
+    "    \"p38a\",\n",
+    "    \"MAPK14\",\n",
+    "    \"VEGFR2\",\n",
+    "    \"KDR\",\n",
     "]"
    ]
   },
@@ -237,19 +245,19 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from opencadd.databases.klifs import setup_remote"
+    "#### Set up a remote KLIFS session"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "#### Set up a remote KLIFS session"
+    "from opencadd.databases.klifs import setup_remote"
    ]
   },
   {
@@ -306,7 +314,7 @@
     "We filter the structures by different criteria:\n",
     "\n",
     "- Species: human\n",
-    "- Conformation: DFG-in\n",
+    "- Conformation: DFG-in (the active kinase conformation)\n",
     "- Resolution: $\\le 3$ Angström\n",
     "- Quality score*: $\\ge 6$\n",
     "\n",
@@ -330,8 +338,8 @@
    "source": [
     "structures = structures[\n",
     "    (structures[\"species.klifs\"] == \"Human\")\n",
-    "    & (structures[\"structure.dfg\"] == \"in\") \n",
-    "    & (structures[\"structure.resolution\"] <= 3) \n",
+    "    & (structures[\"structure.dfg\"] == \"in\")\n",
+    "    & (structures[\"structure.resolution\"] <= 3)\n",
     "    & (structures[\"structure.qualityscore\"] >= 6)\n",
     "]\n",
     "print(f\"Number of structures: {len(structures)}\")\n",
@@ -1226,7 +1234,7 @@
    "source": [
     "# Select EGFR-EGFR strcture pairs only\n",
     "D = kinase_distance_matrix.loc[\"EGFR\", \"EGFR\"]\n",
-    "# Extract all pairwise distances without identical structure pairs \n",
+    "# Extract all pairwise distances without identical structure pairs\n",
     "# = lower triangular matrix without the diagonal\n",
     "D_condensed = distance.squareform(D)\n",
     "# Plot pairwise distances\n",
@@ -1438,7 +1446,9 @@
     "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
     "# We take the minimum value in each kinase pair group\n",
     "# We unstack the remaining data points\n",
-    "kinase_distance_matrix = kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    "kinase_distance_matrix = (\n",
+    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
     "kinase_distance_matrix"
    ]
   },
@@ -1456,20 +1466,9 @@
    "execution_count": 22,
    "metadata": {},
    "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'kinase_similarity_matrix' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m/tmp/ipykernel_12417/207115603.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Kinase distance\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Kinase distance matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mkinase_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinase_similarity_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m45\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkinase_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'kinase_similarity_matrix' is not defined"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1484,8 +1483,8 @@
     "plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
     "plt.colorbar(label=\"Kinase distance\")\n",
     "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_similarity_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
+    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
     "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
    ]
   },
@@ -1498,16 +1497,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "kinase_similarity_matrix = 1 - kinase_distance_matrix\n",
     "plt.imshow(kinase_similarity_matrix, vmin=0.0, vmax=1.0)\n",
     "plt.colorbar(label=\"Kinase similarity\")\n",
     "plt.title(\"Kinase distance matrix\")\n",
     "kinase_names = kinase_similarity_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45);\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
     "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
    ]
   },
@@ -1527,9 +1539,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.3       , 0.27777778, 0.39393939, 0.36363636, 0.32      ,\n",
+       "       0.36842105, 0.5       , 0.33333333, 0.11111111, 0.4516129 ,\n",
+       "       0.23809524, 0.19047619, 0.13333333, 0.34782609, 0.27777778,\n",
+       "       0.25806452, 0.23809524, 0.16666667, 0.22727273, 0.38095238,\n",
+       "       0.17391304, 0.40625   , 0.41935484, 0.38235294, 0.57142857,\n",
+       "       0.25806452, 0.05882353, 0.33333333, 0.53333333, 0.04347826,\n",
+       "       0.25      , 0.375     , 0.19047619, 0.42857143, 0.35294118,\n",
+       "       0.47368421])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "D = kinase_distance_matrix.to_numpy()\n",
     "D_condensed = distance.squareform(D)\n",
@@ -1545,7 +1575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1561,7 +1591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1577,9 +1607,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "labels = kinase_distance_matrix.columns.to_list()\n",

From 0bd8805e9443b09fbc55c872df3ba27626a9bb15 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 17:43:10 +0200
Subject: [PATCH 020/158] Fix typos

---
 .../talktorial.ipynb                          | 525 ++++++++++++------
 1 file changed, 356 insertions(+), 169 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index dffdce46..6d6ed8d3 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -46,7 +46,7 @@
     "* Compare kinases\n",
     "* Map structure to kinase distance matrix\n",
     "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
+    "* Visualize similarity as dendrogram"
    ]
   },
   {
@@ -114,7 +114,7 @@
    "source": [
     "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
-    "Interaction fingerprints encode the binding mode of a ligand in a binding site that is seen in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand intentially targets multiple proteins at once.\n",
+    "Interaction fingerprints encode the binding mode of a ligand in a binding site that is seen in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand intentionally targets multiple proteins at once.\n",
     "\n",
     "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. We will make use of pre-calculated interaction fingerprints provided by the kinase-focused resource KLIFS."
    ]
@@ -180,9 +180,10 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "from sklearn.metrics import pairwise_distances\n",
     "from scipy.spatial import distance_matrix, distance\n",
     "from scipy.cluster import hierarchy\n",
@@ -210,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define our kinases of interest and all their synoyms as described in Table 1 in the global variable `query_kinases`."
+    "Let's define our kinases of interest and all their synonyms as described in Table 1 in the global variable `query_kinases`."
    ]
   },
   {
@@ -706,7 +707,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "### Show kinase coverage\n",
     "\n",
@@ -1210,7 +1214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A kinase pair can be representated by many different structure pairs which are associated with different distance values. For example, if we compare all EGFR structure to each other, the range of distance values is already quite high because we can observe quite different binding modes of the co-crystallized ligands."
+    "A kinase pair can be represented by many different structure pairs which are associated with different distance values. For example, if we compare all EGFR structure to each other, the range of distance values is already quite high because we can observe quite different binding modes of the co-crystallized ligands."
    ]
   },
   {
@@ -1232,7 +1236,7 @@
     }
    ],
    "source": [
-    "# Select EGFR-EGFR strcture pairs only\n",
+    "# Select EGFR-EGFR structure pairs only\n",
     "D = kinase_distance_matrix.loc[\"EGFR\", \"EGFR\"]\n",
     "# Extract all pairwise distances without identical structure pairs\n",
     "# = lower triangular matrix without the diagonal\n",
@@ -1250,193 +1254,370 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
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        "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
+       "<table id=\"T_4b73a_\">\n",
        "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th>BRAF</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>ErbB2</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>LCK</th>\n",
-       "      <th>MET</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>p38a</th>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
+       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
+       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.333333</td>\n",
+       "      <th id=\"T_4b73a_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_4b73a_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
+       "      <td id=\"T_4b73a_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
+       "      <td id=\"T_4b73a_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
+       "      <td id=\"T_4b73a_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
+       "      <td id=\"T_4b73a_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
+       "      <td id=\"T_4b73a_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
+       "      <td id=\"T_4b73a_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
+       "      <td id=\"T_4b73a_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.300000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.277778</td>\n",
+       "      <th id=\"T_4b73a_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_4b73a_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
+       "      <td id=\"T_4b73a_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
+       "      <td id=\"T_4b73a_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
+       "      <td id=\"T_4b73a_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
+       "      <td id=\"T_4b73a_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
+       "      <td id=\"T_4b73a_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
+       "      <td id=\"T_4b73a_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
+       "      <td id=\"T_4b73a_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.173913</td>\n",
+       "      <th id=\"T_4b73a_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_4b73a_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
+       "      <td id=\"T_4b73a_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
+       "      <td id=\"T_4b73a_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
+       "      <td id=\"T_4b73a_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
+       "      <td id=\"T_4b73a_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
+       "      <td id=\"T_4b73a_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
+       "      <td id=\"T_4b73a_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
+       "      <td id=\"T_4b73a_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.451613</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.258065</td>\n",
+       "      <th id=\"T_4b73a_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_4b73a_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
+       "      <td id=\"T_4b73a_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
+       "      <td id=\"T_4b73a_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
+       "      <td id=\"T_4b73a_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
+       "      <td id=\"T_4b73a_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
+       "      <td id=\"T_4b73a_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
+       "      <td id=\"T_4b73a_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
+       "      <td id=\"T_4b73a_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.238095</td>\n",
-       "      <td>0.406250</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.043478</td>\n",
+       "      <th id=\"T_4b73a_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_4b73a_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
+       "      <td id=\"T_4b73a_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
+       "      <td id=\"T_4b73a_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
+       "      <td id=\"T_4b73a_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
+       "      <td id=\"T_4b73a_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
+       "      <td id=\"T_4b73a_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
+       "      <td id=\"T_4b73a_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
+       "      <td id=\"T_4b73a_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>0.320000</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.058824</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.190476</td>\n",
+       "      <th id=\"T_4b73a_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_4b73a_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
+       "      <td id=\"T_4b73a_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
+       "      <td id=\"T_4b73a_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
+       "      <td id=\"T_4b73a_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
+       "      <td id=\"T_4b73a_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
+       "      <td id=\"T_4b73a_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
+       "      <td id=\"T_4b73a_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
+       "      <td id=\"T_4b73a_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>0.368421</td>\n",
-       "      <td>0.133333</td>\n",
-       "      <td>0.227273</td>\n",
-       "      <td>0.382353</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.352941</td>\n",
+       "      <th id=\"T_4b73a_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_4b73a_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
+       "      <td id=\"T_4b73a_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
+       "      <td id=\"T_4b73a_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
+       "      <td id=\"T_4b73a_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
+       "      <td id=\"T_4b73a_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
+       "      <td id=\"T_4b73a_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
+       "      <td id=\"T_4b73a_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
+       "      <td id=\"T_4b73a_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.347826</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.571429</td>\n",
-       "      <td>0.533333</td>\n",
-       "      <td>0.375000</td>\n",
-       "      <td>0.428571</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.473684</td>\n",
+       "      <th id=\"T_4b73a_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_4b73a_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
+       "      <td id=\"T_4b73a_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
+       "      <td id=\"T_4b73a_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
+       "      <td id=\"T_4b73a_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
+       "      <td id=\"T_4b73a_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
+       "      <td id=\"T_4b73a_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
+       "      <td id=\"T_4b73a_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
+       "      <td id=\"T_4b73a_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
+       "      <td id=\"T_4b73a_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>0.277778</td>\n",
-       "      <td>0.173913</td>\n",
-       "      <td>0.258065</td>\n",
-       "      <td>0.043478</td>\n",
-       "      <td>0.190476</td>\n",
-       "      <td>0.352941</td>\n",
-       "      <td>0.473684</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th id=\"T_4b73a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_4b73a_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
+       "      <td id=\"T_4b73a_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
+       "      <td id=\"T_4b73a_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
+       "      <td id=\"T_4b73a_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
+       "      <td id=\"T_4b73a_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
+       "      <td id=\"T_4b73a_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
+       "      <td id=\"T_4b73a_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
+       "      <td id=\"T_4b73a_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
+       "      <td id=\"T_4b73a_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+       "</table>\n"
       ],
       "text/plain": [
-       "kinase.klifs_name      BRAF      CDK2      EGFR     ErbB2       KDR       LCK  \\\n",
-       "kinase.klifs_name                                                               \n",
-       "BRAF               0.000000  0.300000  0.277778  0.393939  0.363636  0.320000   \n",
-       "CDK2               0.300000  0.000000  0.111111  0.451613  0.238095  0.190476   \n",
-       "EGFR               0.277778  0.111111  0.000000  0.258065  0.238095  0.166667   \n",
-       "ErbB2              0.393939  0.451613  0.258065  0.000000  0.406250  0.419355   \n",
-       "KDR                0.363636  0.238095  0.238095  0.406250  0.000000  0.058824   \n",
-       "LCK                0.320000  0.190476  0.166667  0.419355  0.058824  0.000000   \n",
-       "MET                0.368421  0.133333  0.227273  0.382353  0.333333  0.250000   \n",
-       "p110a              0.500000  0.347826  0.380952  0.571429  0.533333  0.375000   \n",
-       "p38a               0.333333  0.277778  0.173913  0.258065  0.043478  0.190476   \n",
-       "\n",
-       "kinase.klifs_name       MET     p110a      p38a  \n",
-       "kinase.klifs_name                                \n",
-       "BRAF               0.368421  0.500000  0.333333  \n",
-       "CDK2               0.133333  0.347826  0.277778  \n",
-       "EGFR               0.227273  0.380952  0.173913  \n",
-       "ErbB2              0.382353  0.571429  0.258065  \n",
-       "KDR                0.333333  0.533333  0.043478  \n",
-       "LCK                0.250000  0.375000  0.190476  \n",
-       "MET                0.000000  0.428571  0.352941  \n",
-       "p110a              0.428571  0.000000  0.473684  \n",
-       "p38a               0.352941  0.473684  0.000000  "
+       "<pandas.io.formats.style.Styler at 0x7fc0edec6a60>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1449,7 +1630,9 @@
     "kinase_distance_matrix = (\n",
     "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     ")\n",
-    "kinase_distance_matrix"
+    "# Show matrix with background gradient\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "kinase_distance_matrix.style.background_gradient(cmap=cm)"
    ]
   },
   {
@@ -1463,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1497,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1539,7 +1722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1555,7 +1738,7 @@
        "       0.47368421])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1570,12 +1753,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can submit this condensed vector to a hierarchical clustering to extract the relationship between the different kinases. We use here `method=\"average\"`, which stands for the linkage method UPGMA (unweighted pair group method with arithmetic mean). This means that the distance between two clusters A and B is defined as the avarge of all distances between pairs of elements in clusters A and B. At each clustering step, the two clusters with the lowest average distance are combined."
+    "We can submit this condensed vector to a hierarchical clustering to extract the relationship between the different kinases. We use here `method=\"average\"`, which stands for the linkage method UPGMA (unweighted pair group method with arithmetic mean). This means that the distance between two clusters A and B is defined as the average of all distances between pairs of elements in clusters A and B. At each clustering step, the two clusters with the lowest average distance are combined."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1591,7 +1774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1607,7 +1790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1639,7 +1822,11 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity. "
+    "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity. \n",
+    "\n",
+    "The two closest binding modes are observed for the p38a-KDR (VEGFR2) and CDK2-EGFR pairs. In both cases the kinases belong to different kinase groups, i.e. CMGC and TK, a grouping based on sequence information only. Rather dissimilar binding modes are observed for p110a (PI3K), ErbB2, and BRAF &mdash; although ErbB2 belongs to the same kinase family as EGFR. The atypical kinase p110a &mdash; the most dissimilar kinase to the other kinases in the panel &mdash; is an expected finding.\n",
+    "\n",
+    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have &mdash at least for kinases such as EGFR and CDK2 &mdash much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content."
    ]
   },
   {

From d5b0b841582aef892378084f624acddce5f1ed59 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 17:53:02 +0200
Subject: [PATCH 021/158] Add frozen dataset + use it in notebook

---
 .../data/frozen_structure_klifs_ids.csv       | 1621 +++++++++++++++++
 .../talktorial.ipynb                          |  390 ++--
 2 files changed, 1809 insertions(+), 202 deletions(-)
 create mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/data/frozen_structure_klifs_ids.csv

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/frozen_structure_klifs_ids.csv b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/frozen_structure_klifs_ids.csv
new file mode 100644
index 00000000..2eae163c
--- /dev/null
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/frozen_structure_klifs_ids.csv
@@ -0,0 +1,1621 @@
+structure.klifs_id
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diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 6d6ed8d3..6078256f 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -366,6 +366,26 @@
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please uncomment the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load frozen dataset\n",
+    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\"structure.klifs_id\"].to_list()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "#### Fetch the structures' IFP (if available)\n",
     "\n",
@@ -374,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -448,7 +468,7 @@
        "4                 782  0000000000000010000001000000000000000000000000..."
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -468,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -694,7 +714,7 @@
        "[5 rows x 42 columns]"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,30 +739,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "kinase.klifs_name\n",
-       "ErbB2      4\n",
-       "KDR        6\n",
-       "LCK       30\n",
-       "p110a     45\n",
-       "BRAF      57\n",
-       "MET       95\n",
-       "p38a     127\n",
-       "EGFR     339\n",
-       "CDK2     763\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
     "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
@@ -758,22 +757,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "n_structures_per_kinase.plot(kind=\"barh\", ax=ax)\n",
@@ -810,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -825,7 +811,7 @@
        "Name: interaction.fingerprint, dtype: string"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -838,7 +824,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -853,7 +839,7 @@
        "Name: interaction.fingerprint, dtype: object"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +867,7 @@
        "Name: interaction.fingerprint, dtype: object"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -894,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -909,7 +895,7 @@
        "       [False, False, False, ..., False, False, False]])"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -929,7 +915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -950,7 +936,7 @@
        "        0.        ]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -962,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1064,7 +1050,7 @@
        "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1093,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1194,7 +1180,7 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1219,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1261,220 +1247,220 @@
      "data": {
       "text/html": [
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        "  background-color: #108810;\n",
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-       "#T_4b73a_row7_col1 {\n",
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        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4b73a_row8_col1 {\n",
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        "  background-color: #5aac5a;\n",
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        "}\n",
-       "#T_4b73a_row8_col4 {\n",
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        "  background-color: #d8ead8;\n",
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        "}\n",
-       "#T_4b73a_row8_col6 {\n",
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        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4b73a_row8_col7 {\n",
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        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_4b73a_\">\n",
+       "<table id=\"T_8602d_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
@@ -1503,118 +1489,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_4b73a_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
-       "      <td id=\"T_4b73a_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
-       "      <td id=\"T_4b73a_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
-       "      <td id=\"T_4b73a_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
-       "      <td id=\"T_4b73a_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
-       "      <td id=\"T_4b73a_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
-       "      <td id=\"T_4b73a_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
-       "      <td id=\"T_4b73a_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
+       "      <th id=\"T_8602d_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_8602d_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
+       "      <td id=\"T_8602d_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
+       "      <td id=\"T_8602d_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
+       "      <td id=\"T_8602d_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
+       "      <td id=\"T_8602d_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
+       "      <td id=\"T_8602d_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
+       "      <td id=\"T_8602d_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
+       "      <td id=\"T_8602d_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_4b73a_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
-       "      <td id=\"T_4b73a_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
-       "      <td id=\"T_4b73a_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
-       "      <td id=\"T_4b73a_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
-       "      <td id=\"T_4b73a_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
-       "      <td id=\"T_4b73a_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
-       "      <td id=\"T_4b73a_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
-       "      <td id=\"T_4b73a_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
+       "      <th id=\"T_8602d_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_8602d_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
+       "      <td id=\"T_8602d_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
+       "      <td id=\"T_8602d_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
+       "      <td id=\"T_8602d_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
+       "      <td id=\"T_8602d_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
+       "      <td id=\"T_8602d_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
+       "      <td id=\"T_8602d_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
+       "      <td id=\"T_8602d_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_4b73a_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
-       "      <td id=\"T_4b73a_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
-       "      <td id=\"T_4b73a_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
-       "      <td id=\"T_4b73a_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
-       "      <td id=\"T_4b73a_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
-       "      <td id=\"T_4b73a_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
-       "      <td id=\"T_4b73a_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
-       "      <td id=\"T_4b73a_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
+       "      <th id=\"T_8602d_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_8602d_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
+       "      <td id=\"T_8602d_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
+       "      <td id=\"T_8602d_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
+       "      <td id=\"T_8602d_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
+       "      <td id=\"T_8602d_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
+       "      <td id=\"T_8602d_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
+       "      <td id=\"T_8602d_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
+       "      <td id=\"T_8602d_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_4b73a_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
-       "      <td id=\"T_4b73a_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
-       "      <td id=\"T_4b73a_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
-       "      <td id=\"T_4b73a_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
-       "      <td id=\"T_4b73a_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
-       "      <td id=\"T_4b73a_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
-       "      <td id=\"T_4b73a_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
-       "      <td id=\"T_4b73a_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
+       "      <th id=\"T_8602d_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_8602d_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
+       "      <td id=\"T_8602d_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
+       "      <td id=\"T_8602d_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
+       "      <td id=\"T_8602d_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
+       "      <td id=\"T_8602d_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
+       "      <td id=\"T_8602d_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
+       "      <td id=\"T_8602d_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
+       "      <td id=\"T_8602d_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_4b73a_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
-       "      <td id=\"T_4b73a_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
-       "      <td id=\"T_4b73a_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
-       "      <td id=\"T_4b73a_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
-       "      <td id=\"T_4b73a_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
-       "      <td id=\"T_4b73a_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
-       "      <td id=\"T_4b73a_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
-       "      <td id=\"T_4b73a_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
+       "      <th id=\"T_8602d_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_8602d_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
+       "      <td id=\"T_8602d_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
+       "      <td id=\"T_8602d_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
+       "      <td id=\"T_8602d_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
+       "      <td id=\"T_8602d_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
+       "      <td id=\"T_8602d_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
+       "      <td id=\"T_8602d_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
+       "      <td id=\"T_8602d_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_4b73a_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
-       "      <td id=\"T_4b73a_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
-       "      <td id=\"T_4b73a_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
-       "      <td id=\"T_4b73a_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
-       "      <td id=\"T_4b73a_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
-       "      <td id=\"T_4b73a_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
-       "      <td id=\"T_4b73a_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
-       "      <td id=\"T_4b73a_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
+       "      <th id=\"T_8602d_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_8602d_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
+       "      <td id=\"T_8602d_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
+       "      <td id=\"T_8602d_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
+       "      <td id=\"T_8602d_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
+       "      <td id=\"T_8602d_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
+       "      <td id=\"T_8602d_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
+       "      <td id=\"T_8602d_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
+       "      <td id=\"T_8602d_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_4b73a_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
-       "      <td id=\"T_4b73a_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
-       "      <td id=\"T_4b73a_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
-       "      <td id=\"T_4b73a_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
-       "      <td id=\"T_4b73a_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
-       "      <td id=\"T_4b73a_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
-       "      <td id=\"T_4b73a_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
-       "      <td id=\"T_4b73a_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
+       "      <th id=\"T_8602d_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_8602d_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
+       "      <td id=\"T_8602d_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
+       "      <td id=\"T_8602d_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
+       "      <td id=\"T_8602d_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
+       "      <td id=\"T_8602d_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
+       "      <td id=\"T_8602d_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
+       "      <td id=\"T_8602d_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
+       "      <td id=\"T_8602d_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_4b73a_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
-       "      <td id=\"T_4b73a_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
-       "      <td id=\"T_4b73a_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
-       "      <td id=\"T_4b73a_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
-       "      <td id=\"T_4b73a_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
-       "      <td id=\"T_4b73a_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
-       "      <td id=\"T_4b73a_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
-       "      <td id=\"T_4b73a_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
-       "      <td id=\"T_4b73a_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
+       "      <th id=\"T_8602d_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_8602d_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
+       "      <td id=\"T_8602d_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
+       "      <td id=\"T_8602d_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
+       "      <td id=\"T_8602d_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
+       "      <td id=\"T_8602d_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
+       "      <td id=\"T_8602d_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
+       "      <td id=\"T_8602d_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
+       "      <td id=\"T_8602d_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
+       "      <td id=\"T_8602d_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4b73a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_4b73a_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
-       "      <td id=\"T_4b73a_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
-       "      <td id=\"T_4b73a_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
-       "      <td id=\"T_4b73a_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
-       "      <td id=\"T_4b73a_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
-       "      <td id=\"T_4b73a_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
-       "      <td id=\"T_4b73a_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
-       "      <td id=\"T_4b73a_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
-       "      <td id=\"T_4b73a_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
+       "      <th id=\"T_8602d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_8602d_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
+       "      <td id=\"T_8602d_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
+       "      <td id=\"T_8602d_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
+       "      <td id=\"T_8602d_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
+       "      <td id=\"T_8602d_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
+       "      <td id=\"T_8602d_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
+       "      <td id=\"T_8602d_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
+       "      <td id=\"T_8602d_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
+       "      <td id=\"T_8602d_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc0edec6a60>"
+       "<pandas.io.formats.style.Styler at 0x7f52e37666d0>"
       ]
      },
      "execution_count": 22,

From 0ac4f5743f91b21ea6373122a194cb27fe1d2635 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 23 Aug 2021 18:17:04 +0200
Subject: [PATCH 022/158] Add Jaccard distance equation

---
 .../talktorial.ipynb                          | 416 +++++++++++-------
 1 file changed, 247 insertions(+), 169 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 6078256f..015e6572 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -358,9 +358,18 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n"
+     ]
+    }
+   ],
    "source": [
-    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()"
+    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()\n",
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
    ]
   },
   {
@@ -374,10 +383,20 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n"
+     ]
+    }
+   ],
    "source": [
     "# Load frozen dataset\n",
-    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\"structure.klifs_id\"].to_list()"
+    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\"structure.klifs_id\"].to_list()\n",
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -728,7 +747,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -739,13 +757,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kinase.klifs_name\n",
+       "ErbB2      4\n",
+       "KDR        6\n",
+       "LCK       30\n",
+       "p110a     45\n",
+       "BRAF      57\n",
+       "MET       95\n",
+       "p38a     127\n",
+       "EGFR     339\n",
+       "CDK2     763\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
     "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
-    "n_structures_per_kinase"
+    "n_structures_per_kinase\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -757,9 +797,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots()\n",
     "n_structures_per_kinase.plot(kind=\"barh\", ax=ax)\n",
@@ -910,7 +963,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Calculate pairwise Jaccard distances"
+    "#### Calculate pairwise Jaccard distances\n",
+    "\n",
+    "The Jaccard distance is a common distance measure in case of binary fingerprints. \n",
+    "\n",
+    "$d_J(A,B) = 1 - J(A,B) = \\frac{\\mid A \\mid \\cup \\mid B \\mid - \\mid A \\mid \\cap \\mid B \\mid}{\\mid A \\mid + \\mid B \\mid}$"
    ]
   },
   {
@@ -950,6 +1007,27 @@
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5725586972826172"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This cell is for testing purposes only\n",
+    "np.mean(structure_distance_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1050,7 +1128,7 @@
        "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1079,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1180,7 +1258,7 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1205,7 +1283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1240,227 +1318,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_8602d_row0_col0, #T_8602d_row1_col1, #T_8602d_row2_col2, #T_8602d_row3_col3, #T_8602d_row4_col4, #T_8602d_row5_col5, #T_8602d_row6_col6, #T_8602d_row7_col7, #T_8602d_row8_col8 {\n",
+       "#T_368cf_row0_col0, #T_368cf_row1_col1, #T_368cf_row2_col2, #T_368cf_row3_col3, #T_368cf_row4_col4, #T_368cf_row5_col5, #T_368cf_row6_col6, #T_368cf_row7_col7, #T_368cf_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row0_col1, #T_8602d_row2_col7, #T_8602d_row8_col0 {\n",
+       "#T_368cf_row0_col1, #T_368cf_row2_col7, #T_368cf_row8_col0 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col2, #T_8602d_row4_col0, #T_8602d_row5_col3 {\n",
+       "#T_368cf_row0_col2, #T_368cf_row4_col0, #T_368cf_row5_col3 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col3 {\n",
+       "#T_368cf_row0_col3 {\n",
        "  background-color: #49a449;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col4 {\n",
+       "#T_368cf_row0_col4 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col5, #T_8602d_row3_col4 {\n",
+       "#T_368cf_row0_col5, #T_368cf_row3_col4 {\n",
        "  background-color: #379b37;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col6 {\n",
+       "#T_368cf_row0_col6 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col7 {\n",
+       "#T_368cf_row0_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row0_col8 {\n",
+       "#T_368cf_row0_col8 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row1_col0 {\n",
+       "#T_368cf_row1_col0 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row1_col2 {\n",
+       "#T_368cf_row1_col2 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row1_col3 {\n",
+       "#T_368cf_row1_col3 {\n",
        "  background-color: #319831;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row1_col4, #T_8602d_row2_col4 {\n",
+       "#T_368cf_row1_col4, #T_368cf_row2_col4 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row1_col5, #T_8602d_row8_col2, #T_8602d_row8_col5 {\n",
+       "#T_368cf_row1_col5, #T_368cf_row8_col2, #T_368cf_row8_col5 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row1_col6 {\n",
+       "#T_368cf_row1_col6 {\n",
        "  background-color: #a2cfa2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row1_col7 {\n",
+       "#T_368cf_row1_col7 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row1_col8 {\n",
+       "#T_368cf_row1_col8 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row2_col0 {\n",
+       "#T_368cf_row2_col0 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row2_col1 {\n",
+       "#T_368cf_row2_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row2_col3, #T_8602d_row8_col3 {\n",
+       "#T_368cf_row2_col3, #T_368cf_row8_col3 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row2_col5 {\n",
+       "#T_368cf_row2_col5 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row2_col6 {\n",
+       "#T_368cf_row2_col6 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row2_col8 {\n",
+       "#T_368cf_row2_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row3_col0 {\n",
+       "#T_368cf_row3_col0 {\n",
        "  background-color: #329832;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row3_col1, #T_8602d_row3_col5, #T_8602d_row3_col7, #T_8602d_row7_col0, #T_8602d_row7_col2, #T_8602d_row7_col3, #T_8602d_row7_col4, #T_8602d_row7_col6, #T_8602d_row7_col8 {\n",
+       "#T_368cf_row3_col1, #T_368cf_row3_col5, #T_368cf_row3_col7, #T_368cf_row7_col0, #T_368cf_row7_col2, #T_368cf_row7_col3, #T_368cf_row7_col4, #T_368cf_row7_col6, #T_368cf_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row3_col2 {\n",
+       "#T_368cf_row3_col2 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row3_col6, #T_8602d_row7_col5 {\n",
+       "#T_368cf_row3_col6, #T_368cf_row7_col5 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row3_col8 {\n",
+       "#T_368cf_row3_col8 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col1 {\n",
+       "#T_368cf_row4_col1 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col2, #T_8602d_row6_col4 {\n",
+       "#T_368cf_row4_col2, #T_368cf_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col3 {\n",
+       "#T_368cf_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col5 {\n",
+       "#T_368cf_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row4_col6 {\n",
+       "#T_368cf_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col7 {\n",
+       "#T_368cf_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row4_col8 {\n",
+       "#T_368cf_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row5_col0 {\n",
+       "#T_368cf_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row5_col1 {\n",
+       "#T_368cf_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row5_col2 {\n",
+       "#T_368cf_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row5_col4 {\n",
+       "#T_368cf_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row5_col6 {\n",
+       "#T_368cf_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row5_col7 {\n",
+       "#T_368cf_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row5_col8 {\n",
+       "#T_368cf_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row6_col0 {\n",
+       "#T_368cf_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row6_col1 {\n",
+       "#T_368cf_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row6_col2, #T_8602d_row6_col5 {\n",
+       "#T_368cf_row6_col2, #T_368cf_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row6_col3 {\n",
+       "#T_368cf_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row6_col7 {\n",
+       "#T_368cf_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row6_col8 {\n",
+       "#T_368cf_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row7_col1 {\n",
+       "#T_368cf_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row8_col1 {\n",
+       "#T_368cf_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row8_col4 {\n",
+       "#T_368cf_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8602d_row8_col6 {\n",
+       "#T_368cf_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8602d_row8_col7 {\n",
+       "#T_368cf_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_8602d_\">\n",
+       "<table id=\"T_368cf_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
@@ -1489,121 +1567,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_8602d_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
-       "      <td id=\"T_8602d_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
-       "      <td id=\"T_8602d_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
-       "      <td id=\"T_8602d_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
-       "      <td id=\"T_8602d_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
-       "      <td id=\"T_8602d_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
-       "      <td id=\"T_8602d_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
-       "      <td id=\"T_8602d_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
+       "      <th id=\"T_368cf_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_368cf_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
+       "      <td id=\"T_368cf_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
+       "      <td id=\"T_368cf_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
+       "      <td id=\"T_368cf_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
+       "      <td id=\"T_368cf_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
+       "      <td id=\"T_368cf_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
+       "      <td id=\"T_368cf_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
+       "      <td id=\"T_368cf_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_8602d_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
-       "      <td id=\"T_8602d_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
-       "      <td id=\"T_8602d_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
-       "      <td id=\"T_8602d_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
-       "      <td id=\"T_8602d_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
-       "      <td id=\"T_8602d_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
-       "      <td id=\"T_8602d_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
-       "      <td id=\"T_8602d_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
+       "      <th id=\"T_368cf_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_368cf_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
+       "      <td id=\"T_368cf_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
+       "      <td id=\"T_368cf_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
+       "      <td id=\"T_368cf_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
+       "      <td id=\"T_368cf_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
+       "      <td id=\"T_368cf_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
+       "      <td id=\"T_368cf_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
+       "      <td id=\"T_368cf_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_8602d_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
-       "      <td id=\"T_8602d_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
-       "      <td id=\"T_8602d_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
-       "      <td id=\"T_8602d_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
-       "      <td id=\"T_8602d_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
-       "      <td id=\"T_8602d_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
-       "      <td id=\"T_8602d_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
-       "      <td id=\"T_8602d_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
+       "      <th id=\"T_368cf_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_368cf_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
+       "      <td id=\"T_368cf_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
+       "      <td id=\"T_368cf_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
+       "      <td id=\"T_368cf_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
+       "      <td id=\"T_368cf_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
+       "      <td id=\"T_368cf_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
+       "      <td id=\"T_368cf_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
+       "      <td id=\"T_368cf_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_8602d_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
-       "      <td id=\"T_8602d_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
-       "      <td id=\"T_8602d_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
-       "      <td id=\"T_8602d_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
-       "      <td id=\"T_8602d_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
-       "      <td id=\"T_8602d_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
-       "      <td id=\"T_8602d_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
-       "      <td id=\"T_8602d_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
+       "      <th id=\"T_368cf_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_368cf_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
+       "      <td id=\"T_368cf_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
+       "      <td id=\"T_368cf_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
+       "      <td id=\"T_368cf_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
+       "      <td id=\"T_368cf_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
+       "      <td id=\"T_368cf_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
+       "      <td id=\"T_368cf_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
+       "      <td id=\"T_368cf_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_8602d_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
-       "      <td id=\"T_8602d_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
-       "      <td id=\"T_8602d_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
-       "      <td id=\"T_8602d_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
-       "      <td id=\"T_8602d_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
-       "      <td id=\"T_8602d_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
-       "      <td id=\"T_8602d_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
-       "      <td id=\"T_8602d_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
+       "      <th id=\"T_368cf_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_368cf_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
+       "      <td id=\"T_368cf_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
+       "      <td id=\"T_368cf_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
+       "      <td id=\"T_368cf_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
+       "      <td id=\"T_368cf_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
+       "      <td id=\"T_368cf_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
+       "      <td id=\"T_368cf_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
+       "      <td id=\"T_368cf_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_8602d_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
-       "      <td id=\"T_8602d_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
-       "      <td id=\"T_8602d_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
-       "      <td id=\"T_8602d_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
-       "      <td id=\"T_8602d_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
-       "      <td id=\"T_8602d_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
-       "      <td id=\"T_8602d_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
-       "      <td id=\"T_8602d_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
+       "      <th id=\"T_368cf_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_368cf_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
+       "      <td id=\"T_368cf_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
+       "      <td id=\"T_368cf_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
+       "      <td id=\"T_368cf_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
+       "      <td id=\"T_368cf_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
+       "      <td id=\"T_368cf_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
+       "      <td id=\"T_368cf_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
+       "      <td id=\"T_368cf_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_8602d_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
-       "      <td id=\"T_8602d_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
-       "      <td id=\"T_8602d_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
-       "      <td id=\"T_8602d_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
-       "      <td id=\"T_8602d_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
-       "      <td id=\"T_8602d_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
-       "      <td id=\"T_8602d_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
-       "      <td id=\"T_8602d_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
+       "      <th id=\"T_368cf_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_368cf_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
+       "      <td id=\"T_368cf_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
+       "      <td id=\"T_368cf_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
+       "      <td id=\"T_368cf_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
+       "      <td id=\"T_368cf_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
+       "      <td id=\"T_368cf_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
+       "      <td id=\"T_368cf_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
+       "      <td id=\"T_368cf_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_8602d_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
-       "      <td id=\"T_8602d_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
-       "      <td id=\"T_8602d_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
-       "      <td id=\"T_8602d_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
-       "      <td id=\"T_8602d_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
-       "      <td id=\"T_8602d_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
-       "      <td id=\"T_8602d_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
-       "      <td id=\"T_8602d_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
-       "      <td id=\"T_8602d_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
+       "      <th id=\"T_368cf_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_368cf_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
+       "      <td id=\"T_368cf_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
+       "      <td id=\"T_368cf_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
+       "      <td id=\"T_368cf_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
+       "      <td id=\"T_368cf_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
+       "      <td id=\"T_368cf_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
+       "      <td id=\"T_368cf_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
+       "      <td id=\"T_368cf_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
+       "      <td id=\"T_368cf_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8602d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_8602d_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
-       "      <td id=\"T_8602d_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
-       "      <td id=\"T_8602d_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
-       "      <td id=\"T_8602d_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
-       "      <td id=\"T_8602d_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
-       "      <td id=\"T_8602d_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
-       "      <td id=\"T_8602d_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
-       "      <td id=\"T_8602d_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
-       "      <td id=\"T_8602d_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
+       "      <th id=\"T_368cf_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_368cf_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
+       "      <td id=\"T_368cf_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
+       "      <td id=\"T_368cf_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
+       "      <td id=\"T_368cf_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
+       "      <td id=\"T_368cf_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
+       "      <td id=\"T_368cf_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
+       "      <td id=\"T_368cf_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
+       "      <td id=\"T_368cf_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
+       "      <td id=\"T_368cf_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f52e37666d0>"
+       "<pandas.io.formats.style.Styler at 0x7f0ab788c3a0>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1632,7 +1710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1666,7 +1744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1708,7 +1786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1724,7 +1802,7 @@
        "       0.47368421])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1744,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1760,7 +1838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1776,7 +1854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {

From f0fb54c2b01fd16a37b6fd1af9db95c9e0334710 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 23 Aug 2021 19:06:51 +0200
Subject: [PATCH 023/158] update theory and create similarity matrix.

---
 .../talktorial.ipynb                          | 788 ++++++++++--------
 1 file changed, 420 insertions(+), 368 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index ef59ae60..0d64b4ed 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "Add a short summary of this talktorial's content."
+    "The aim of this talktorial is to investigate kinase similarity through ligand profiling. We make the following assumption: if a compound was tested as active on two different kinases, then we could assume that these two same kinases may have some degree of similarity. "
    ]
   },
   {
@@ -29,7 +29,9 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor: ligand-profile\n",
+    "    * Kinase similarity\n",
+    "    * Kinase promiscuity"
    ]
   },
   {
@@ -38,8 +40,11 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Retrieve and preprocess data\n",
-    "* Show kinase coverage\n",
+    "* Retrieve the data\n",
+    "* Preprocess the data\n",
+    "    * Kinases of interest\n",
+    "    * Hit or non-hit\n",
+    "* Show kinase promiscuity\n",
     "* Compare kinases\n",
     "* Visualize similarity as kinase matrix\n",
     "* Visualize similarity as phylogenetic tree"
@@ -52,7 +57,16 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor: ligand-profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: ADD"
    ]
   },
   {
@@ -73,10 +87,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "\n",
+    " \n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined **EGFR** and **ErbB2** as targets (indicated by a ‘+’) and **BRAF** (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2** (cyclic-dependent kinase 2), **LCK** (lymphocyte-specific protein tyrosine kinase), **MET** (mesenchymal-epithelial transition factor) and **p38α** (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
    ]
   },
   {
@@ -98,56 +114,64 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: ligand-profile\n",
-    "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
-    "\n",
-    "- XXX = KLIFS pocket sequence\n",
-    "- XXX = KiSSim fingerprint\n",
-    "- XXX = KLIFS IFP\n",
-    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+    "In this talktorial, we focus on bioactivity retrieve from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database. We query the data focusing on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compound activity measures"
+    "In drug design, it is common to determine the activity of a compound against as a \"hit\" or \"non hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater to the cutoff, the compound is labeled as active, and inactive otherwise."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
+    "# TODO: add reference to other talktorial."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
+    "### Kinase similarity descriptor: ligand-profile"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    \n",
-    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
-    "</div>"
+    "As a measure of similarity, we use ligand profiling in this talktorial."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "If you place links, please link descriptive words.\n",
+    "#### Kinase similarity\n",
     "\n",
-    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
-    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+    "$$\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on $K_i$ and $K_j$}}\n",
+    "{\\#\\text{ of compounds that were tested on both $K_i$ and $K_j$}}\n",
+    "$$\n",
+    "\n",
+    "Assuming that only one compound was tested on two kinases, and that the compound was tested as active for one and inactive for the other, then the similarity between these two kinases would be null."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Kinase promiscuity\n",
+    "Computing the similarity between a kinase and itself may be interpreted as kinase promiscuity, where the similarity descrived above would therefore represent the fraction of active compounds over all tested compounds."
    ]
   },
   {
@@ -159,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,18 +194,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "import pandas as pd\n",
+    "import numpy as np\n",
     "from collections import Counter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +218,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve and preprocess data"
+    "### Retrieve the data"
    ]
   },
   {
@@ -205,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -427,7 +452,7 @@
        "4  CHEMBL3639077     2014.0          NaN    O75116  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -440,6 +465,13 @@
     "data.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Preprocess the data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -449,14 +481,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKd', 'pKi', 'pIC50'}\n",
+      "Activities: {'pKd', 'pIC50', 'pKi'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -475,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -491,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +616,7 @@
        "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.920819    O75116"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -603,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -630,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -672,7 +704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +835,7 @@
        "[33169 rows x 3 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -821,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -837,7 +869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -851,7 +883,7 @@
        "  7)]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -870,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1001,7 +1033,7 @@
        "[32688 rows x 3 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1028,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1037,7 +1069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1065,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,7 +1107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1168,7 +1200,7 @@
        "4        8.420216                1  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1178,15 +1210,6 @@
     "data.head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# x[:,\"Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(F)(F)F)cc1\"]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1207,15 +1230,6 @@
     "3. the fraction of active compouds, i.e. the ratio of active compounds and total number of measured compounds."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: Use percentage instead of fraction?"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 23,
@@ -1447,42 +1461,42 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_1e959_row0_col2 {\n",
+       "#T_140b3_row0_col2 {\n",
        "  background-color: #dbdbec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1e959_row1_col2, #T_1e959_row4_col2 {\n",
+       "#T_140b3_row1_col2, #T_140b3_row4_col2 {\n",
        "  background-color: #dedded;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1e959_row2_col2 {\n",
+       "#T_140b3_row2_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_1e959_row3_col2 {\n",
+       "#T_140b3_row3_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_1e959_row5_col2 {\n",
+       "#T_140b3_row5_col2 {\n",
        "  background-color: #4b1687;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e959_row6_col2 {\n",
+       "#T_140b3_row6_col2 {\n",
        "  background-color: #5c3797;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e959_row7_col2 {\n",
+       "#T_140b3_row7_col2 {\n",
        "  background-color: #8d89c0;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e959_row8_col2 {\n",
+       "#T_140b3_row8_col2 {\n",
        "  background-color: #c0c1de;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_1e959_\">\n",
+       "<table id=\"T_140b3_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1493,64 +1507,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_1e959_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
-       "      <td id=\"T_1e959_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
-       "      <td id=\"T_1e959_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_140b3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_140b3_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
+       "      <td id=\"T_140b3_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
+       "      <td id=\"T_140b3_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_1e959_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
-       "      <td id=\"T_1e959_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
-       "      <td id=\"T_1e959_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
+       "      <th id=\"T_140b3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_140b3_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
+       "      <td id=\"T_140b3_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
+       "      <td id=\"T_140b3_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_1e959_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
-       "      <td id=\"T_1e959_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
-       "      <td id=\"T_1e959_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <th id=\"T_140b3_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_140b3_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
+       "      <td id=\"T_140b3_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
+       "      <td id=\"T_140b3_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_1e959_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
-       "      <td id=\"T_1e959_row3_col1\" class=\"data row3 col1\" >819</td>\n",
-       "      <td id=\"T_1e959_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
+       "      <th id=\"T_140b3_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_140b3_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
+       "      <td id=\"T_140b3_row3_col1\" class=\"data row3 col1\" >819</td>\n",
+       "      <td id=\"T_140b3_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_1e959_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
-       "      <td id=\"T_1e959_row4_col1\" class=\"data row4 col1\" >930</td>\n",
-       "      <td id=\"T_1e959_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
+       "      <th id=\"T_140b3_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_140b3_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
+       "      <td id=\"T_140b3_row4_col1\" class=\"data row4 col1\" >930</td>\n",
+       "      <td id=\"T_140b3_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_1e959_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
-       "      <td id=\"T_1e959_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
-       "      <td id=\"T_1e959_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
+       "      <th id=\"T_140b3_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_140b3_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
+       "      <td id=\"T_140b3_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
+       "      <td id=\"T_140b3_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_1e959_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
-       "      <td id=\"T_1e959_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
-       "      <td id=\"T_1e959_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th id=\"T_140b3_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_140b3_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
+       "      <td id=\"T_140b3_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
+       "      <td id=\"T_140b3_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_1e959_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
-       "      <td id=\"T_1e959_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
-       "      <td id=\"T_1e959_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
+       "      <th id=\"T_140b3_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_140b3_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
+       "      <td id=\"T_140b3_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
+       "      <td id=\"T_140b3_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e959_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_1e959_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
-       "      <td id=\"T_1e959_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
-       "      <td id=\"T_1e959_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
+       "      <th id=\"T_140b3_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_140b3_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
+       "      <td id=\"T_140b3_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
+       "      <td id=\"T_140b3_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb9908e77c0>"
+       "<pandas.io.formats.style.Styler at 0x7fd1ec07a1c0>"
       ]
      },
      "execution_count": 27,
@@ -1585,179 +1599,220 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "def similarity_ligand_profile(kinase_name1, kinase_name2, activity_dataframe):\n",
+    "    \"\"\"\n",
+    "    Compute the similarity of two kinases using ligand profiling.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_name1 : str\n",
+    "        First kinase of interest.\n",
+    "    kinase_name2 : str\n",
+    "        Second kinase of interest.\n",
+    "\n",
+    "    activity_dataframe :  pd.DataFrame\n",
+    "        The dataframe with activity values for kinases.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    fraction : float\n",
+    "        The metric for kinase similariy,\n",
+    "        i.e. number of active compounds on both kinases\n",
+    "        over number of measured compounds on both kinases.\n",
+    "    \"\"\"\n",
+    "    if kinase_name1 == kinase_name2:\n",
+    "        (total_compounds,\n",
+    "         active_compounds,\n",
+    "         fraction) = kinase_to_activity_numbers(kinase_name1,\n",
+    "                                                activity_dataframe)\n",
+    "        return fraction\n",
+    "    else:\n",
+    "        uniprot1 = name_to_uniprot[kinase_name1]\n",
+    "        uniprot2 = name_to_uniprot[kinase_name2]\n",
+    "\n",
+    "        # Data for the two kinases only\n",
+    "        reduced_data = activity_dataframe[activity_dataframe\n",
+    "                                          [\"UniprotID\"].isin([uniprot1,\n",
+    "                                                              uniprot2])]\n",
+    "\n",
+    "        # Look at active compounds only\n",
+    "        active_entries = reduced_data[reduced_data[\"activity_binary\"] == 1]\n",
+    "        # Group by compounds\n",
+    "        compounds = active_entries.groupby(\"smiles\").size()\n",
+    "        # Look at the number of active compounds measured on both kinases\n",
+    "        active_compounds_on_both = compounds[compounds == 2].shape\n",
+    "\n",
+    "        # Look at all tested compounds\n",
+    "        compounds = reduced_data.groupby(\"smiles\").size()\n",
+    "        # Look at the number of compounds measured on both kinases\n",
+    "        measured_compounds_on_both = compounds[compounds == 2].shape\n",
+    "\n",
+    "        if measured_compounds_on_both[0] > 0:\n",
+    "            fraction = (active_compounds_on_both[0] /\n",
+    "                        measured_compounds_on_both[0])\n",
+    "        else:\n",
+    "            print(f\"No compounds were measured on both kinases, \"\n",
+    "                  f\"namely {kinase_name1} and {kinase_name2}.\")\n",
+    "            fraction = np.nan\n",
+    "        return fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the similarity between EGFR and MET:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UniprotID</th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>activity_value</th>\n",
-       "      <th>activity_binary</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
-       "      <td>5.336488</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
-       "      <td>5.996539</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
-       "      <td>8.397940</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>7.207608</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>8.420216</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32683</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1</td>\n",
-       "      <td>5.102373</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32684</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1</td>\n",
-       "      <td>6.292430</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32685</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1</td>\n",
-       "      <td>7.522879</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32686</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1</td>\n",
-       "      <td>5.903090</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32687</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1</td>\n",
-       "      <td>6.397940</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>32688 rows × 4 columns</p>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "      UniprotID                                             smiles  \\\n",
-       "0        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
-       "1        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
-       "2        P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
-       "3        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "4        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "...         ...                                                ...   \n",
-       "32683    Q16539           c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1   \n",
-       "32684    Q16539     c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1   \n",
-       "32685    Q16539             c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1   \n",
-       "32686    Q16539        c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1   \n",
-       "32687    Q16539                c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1   \n",
-       "\n",
-       "       activity_value  activity_binary  \n",
-       "0            5.336488                0  \n",
-       "1            5.996539                0  \n",
-       "2            8.397940                1  \n",
-       "3            7.207608                1  \n",
-       "4            8.420216                1  \n",
-       "...               ...              ...  \n",
-       "32683        5.102373                0  \n",
-       "32684        6.292430                0  \n",
-       "32685        7.522879                1  \n",
-       "32686        5.903090                0  \n",
-       "32687        6.397940                1  \n",
-       "\n",
-       "[32688 rows x 4 columns]"
+       "0.22580645161290322"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data"
+    "similarity_EGFR_MET = similarity_ligand_profile(\"EGFR\", \"MET\", data)\n",
+    "similarity_EGFR_MET"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "('EGFR', 'CDK2')"
+       "0.22580645161290322"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"EGFR\", \"CDK2\""
+    "similarity_MET_EGFR = similarity_ligand_profile(\"MET\", \"EGFR\", data)\n",
+    "similarity_MET_EGFR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the promiscuity for EGFR:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6089623445220651"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "promiscuity_EGFR = similarity_ligand_profile(\"EGFR\", \"EGFR\", data)\n",
+    "promiscuity_EGFR"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "a = data[data[\"UniprotID\"].isin([\"P00533\", \"Q16539\"])]\n",
+    "a\n",
+    "c = a[a[\"activity_binary\"]==1]\n",
+    "d = c.groupby(\"smiles\").size()\n",
+    "d\n",
+    "d[d==2].shape\n",
+    "b = a.groupby(\"smiles\").size()\n",
+    "b\n",
+    "b[b==2].shape\n",
+    "data.groupby(\"smiles\").size().unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No compounds were measured on both kinases, namely ErbB2 and p110a.\n",
+      "No compounds were measured on both kinases, namely p110a and ErbB2.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.60896234, 0.55691769, 0.41935484, 0.11627907, 0.23846154,\n",
+       "        0.22580645, 0.31578947, 0.34821429, 0.07142857],\n",
+       "       [0.55691769, 0.60507674, 0.25      , 0.15384615, 0.17647059,\n",
+       "        0.03571429, 0.125     , 0.39779006,        nan],\n",
+       "       [0.41935484, 0.25      , 0.81151548, 0.06666667, 0.51162791,\n",
+       "        0.11111111, 0.64444444, 0.7481203 , 0.16666667],\n",
+       "       [0.11627907, 0.15384615, 0.06666667, 0.54418605, 0.16666667,\n",
+       "        0.08695652, 0.08333333, 0.60169492, 0.26666667],\n",
+       "       [0.23846154, 0.17647059, 0.51162791, 0.16666667, 0.60507482,\n",
+       "        0.28787879, 0.4893617 , 0.43269231, 0.        ],\n",
+       "       [0.22580645, 0.03571429, 0.11111111, 0.08695652, 0.28787879,\n",
+       "        0.79227224, 0.04761905, 0.5393586 , 0.        ],\n",
+       "       [0.31578947, 0.125     , 0.64444444, 0.08333333, 0.4893617 ,\n",
+       "        0.04761905, 0.76475478, 0.5       , 0.        ],\n",
+       "       [0.34821429, 0.39779006, 0.7481203 , 0.60169492, 0.43269231,\n",
+       "        0.5393586 , 0.5       , 0.69674711, 0.18079096],\n",
+       "       [0.07142857,        nan, 0.16666667, 0.26666667, 0.        ,\n",
+       "        0.        , 0.        , 0.18079096, 0.63952151]])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = np.zeros((len(name_to_uniprot), len(name_to_uniprot)))\n",
+    "for i, kinase_name1 in enumerate(name_to_uniprot):\n",
+    "    for j, kinase_name2 in enumerate(name_to_uniprot):\n",
+    "        table[i, j] = similarity_ligand_profile(kinase_name1,\n",
+    "                                                kinase_name2,\n",
+    "                                                data)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1795,154 +1850,151 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>EGFR</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.608962</td>\n",
+       "      <td>0.556918</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.116279</td>\n",
+       "      <td>0.238462</td>\n",
+       "      <td>0.225806</td>\n",
+       "      <td>0.315789</td>\n",
+       "      <td>0.348214</td>\n",
+       "      <td>0.071429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ErbB2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.556918</td>\n",
+       "      <td>0.605077</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.176471</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.397790</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BRAF</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.419355</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.811515</td>\n",
+       "      <td>0.066667</td>\n",
+       "      <td>0.511628</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.644444</td>\n",
+       "      <td>0.748120</td>\n",
+       "      <td>0.166667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDK2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.116279</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.066667</td>\n",
+       "      <td>0.544186</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.601695</td>\n",
+       "      <td>0.266667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LCK</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.238462</td>\n",
+       "      <td>0.176471</td>\n",
+       "      <td>0.511628</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.605075</td>\n",
+       "      <td>0.287879</td>\n",
+       "      <td>0.489362</td>\n",
+       "      <td>0.432692</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MET</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.225806</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.086957</td>\n",
+       "      <td>0.287879</td>\n",
+       "      <td>0.792272</td>\n",
+       "      <td>0.047619</td>\n",
+       "      <td>0.539359</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p38a</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.315789</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.644444</td>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.489362</td>\n",
+       "      <td>0.047619</td>\n",
+       "      <td>0.764755</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KDR</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.348214</td>\n",
+       "      <td>0.397790</td>\n",
+       "      <td>0.748120</td>\n",
+       "      <td>0.601695</td>\n",
+       "      <td>0.432692</td>\n",
+       "      <td>0.539359</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696747</td>\n",
+       "      <td>0.180791</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p110a</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.266667</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.180791</td>\n",
+       "      <td>0.639522</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      EGFR ErbB2 BRAF CDK2  LCK  MET p38a  KDR p110a\n",
-       "EGFR   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "ErbB2  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "BRAF   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "CDK2   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "LCK    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "MET    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "p38a   NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "KDR    NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN\n",
-       "p110a  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN   NaN"
+       "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
+       "EGFR   0.608962  0.556918  0.419355  0.116279  0.238462  0.225806  0.315789   \n",
+       "ErbB2  0.556918  0.605077  0.250000  0.153846  0.176471  0.035714  0.125000   \n",
+       "BRAF   0.419355  0.250000  0.811515  0.066667  0.511628  0.111111  0.644444   \n",
+       "CDK2   0.116279  0.153846  0.066667  0.544186  0.166667  0.086957  0.083333   \n",
+       "LCK    0.238462  0.176471  0.511628  0.166667  0.605075  0.287879  0.489362   \n",
+       "MET    0.225806  0.035714  0.111111  0.086957  0.287879  0.792272  0.047619   \n",
+       "p38a   0.315789  0.125000  0.644444  0.083333  0.489362  0.047619  0.764755   \n",
+       "KDR    0.348214  0.397790  0.748120  0.601695  0.432692  0.539359  0.500000   \n",
+       "p110a  0.071429       NaN  0.166667  0.266667  0.000000  0.000000  0.000000   \n",
+       "\n",
+       "            KDR     p110a  \n",
+       "EGFR   0.348214  0.071429  \n",
+       "ErbB2  0.397790       NaN  \n",
+       "BRAF   0.748120  0.166667  \n",
+       "CDK2   0.601695  0.266667  \n",
+       "LCK    0.432692  0.000000  \n",
+       "MET    0.539359  0.000000  \n",
+       "p38a   0.500000  0.000000  \n",
+       "KDR    0.696747  0.180791  \n",
+       "p110a  0.180791  0.639522  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table_comparison = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "table_comparison = pd.DataFrame(data=table, index=name_to_uniprot.keys(),\n",
     "                                columns=name_to_uniprot.keys())\n",
     "table_comparison"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as kinase matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 965d57ec573c70a0fb1cf92375d6c2aedf34ce04 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 23 Aug 2021 22:37:14 +0200
Subject: [PATCH 024/158] retrieve klifs pocket sequence.

---
 .../T024_kinase_similarity_sequence/README.md |  78 ++++
 .../data/README.md                            |   6 +
 .../images/README.md                          |   5 +
 .../talktorial.ipynb                          | 345 ++++++++++++++++++
 4 files changed, 434 insertions(+)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/README.md
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/images/README.md
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md b/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
new file mode 100644
index 00000000..a38d14a4
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
@@ -0,0 +1,78 @@
+<div class="alert alert-block alert-info">
+
+<b>Thank you for contributing to TeachOpenCADD!</b>
+
+</div>
+
+
+<div class="alert alert-block alert-info">
+
+<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
+
+</div>
+
+
+# T000 · Talktorial topic title
+
+Authors:
+
+- First and last name, year(s) of contribution, lab, institution
+- First and last name, year(s) of contribution, lab, institution
+
+
+*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
+
+
+<div class="alert alert-block alert-info">
+
+<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
+
+</div>
+
+
+## Aim of this talktorial
+
+Add a short summary of this talktorial's content.
+
+
+### Contents in *Theory*
+
+_Add Table of Contents (TOC) for Theory section._
+
+* ChEMBL database
+* Compound activity measures
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+
+</div>
+
+
+### Contents in *Practical*
+
+_Add Table of Contents (TOC) for Practical section._
+
+* Connect to ChEMBL database
+* Load and draw molecules
+
+
+<div class="alert alert-block alert-info">
+
+<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
+
+</div>
+
+
+### References
+
+* Paper 
+* Tutorial links
+* Other useful resources
+
+*We suggest the following citation style:*
+* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
+
+*Example:*
+* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/README.md b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/README.md
new file mode 100644
index 00000000..cc6d1e4c
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/README.md
@@ -0,0 +1,6 @@
+# Data
+
+This folder stores input and output data for the Jupyter notebook.
+
+- `xxx.csv`: Describe data.
+- `xxx.sdf`: Describe data.
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/images/README.md b/teachopencadd/talktorials/T024_kinase_similarity_sequence/images/README.md
new file mode 100644
index 00000000..d4ebaa47
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/images/README.md
@@ -0,0 +1,5 @@
+# Talktorial title
+
+## Images
+
+This folder stores images used in the Jupyter notebook.
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
new file mode 100644
index 00000000..d5f4557d
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# T024 · Kinase similarity: sequence\n",
+    "\n",
+    "Authors:\n",
+    "\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aim of this talktorial\n",
+    "\n",
+    "Add a short summary of this talktorial's content."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Theory*\n",
+    "\n",
+    "* Kinase dataset\n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Practical*\n",
+    "\n",
+    "* Retrieve and preprocess data\n",
+    "* Show kinase coverage\n",
+    "* Compare kinases\n",
+    "* Visualize similarity as kinase matrix\n",
+    "* Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Kinase similarity descriptor: XXX"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+   ]
+  },
+  {
+   "attachments": {
+    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: sequence\n",
+    "\n",
+    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "\n",
+    "- XXX = KLIFS pocket sequence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install flake8 pycodestyle_magic\n",
+    "%load_ext pycodestyle_magic\n",
+    "%pycodestyle_on"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HERE = Path(_dh[-1])\n",
+    "DATA = HERE / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_kinases = ['EGFR',\n",
+    "                 'ErbB2',\n",
+    "                 'BRAF',\n",
+    "                 'CDK2',\n",
+    "                 'LCK',\n",
+    "                 'MET',\n",
+    "                 'p38a',\n",
+    "                 'KDR',\n",
+    "                 'p110a']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def klifs_pocket_sequence(kinase_name):\n",
+    "    \"\"\"\n",
+    "    Retrieves the pocket sequence from KLIFS using the API.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_name : str\n",
+    "        The name of the kinase of interest.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    str :\n",
+    "        The 85 residues pocket sequence from KLIFS,\n",
+    "        if the kinase name is valid, None otherwise.\n",
+    "    \"\"\"\n",
+    "    response = requests.get(f\"https://klifs.net/api/\"\n",
+    "                            f\"kinase_ID?kinase_name={kinase_name}\"\n",
+    "                            f\"&species=HUMAN\")\n",
+    "\n",
+    "    if response.status_code == 200:\n",
+    "        return response.json()[0]['pocket']\n",
+    "    else:\n",
+    "        print(f'KLIFS failed for kinase {kinase_name}')\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
+      "KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA\n",
+      "QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA\n",
+      "EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA\n",
+      "ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA\n",
+      "EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n",
+      "SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA\n",
+      "KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA\n",
+      "CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF\n"
+     ]
+    }
+   ],
+   "source": [
+    "for kinase in query_kinases:\n",
+    "    print(klifs_pocket_sequence(kinase))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as kinase matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize similarity as phylogenetic tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quiz\n",
+    "\n",
+    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
+    "\n",
+    "1. Question\n",
+    "2. Question\n",
+    "3. Question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  },
+  "toc-autonumbering": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 16b0c73af61fac3471264f9147bc702c5b3543dc Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 09:49:25 +0200
Subject: [PATCH 025/158] Add identity sequence comparison.

---
 .../talktorial.ipynb                          | 286 ++++++++++++++++--
 1 file changed, 263 insertions(+), 23 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index d5f4557d..291f9eac 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -132,7 +132,9 @@
     "from pathlib import Path\n",
     "\n",
     "import pandas as pd\n",
-    "import requests"
+    "import numpy as np\n",
+    "import requests\n",
+    "import biotite.sequence.align as align"
    ]
   },
   {
@@ -149,7 +151,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve and preprocess data"
+    "### Retrieve sequences from KLIFS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We start by listing the kinases of interest."
    ]
   },
   {
@@ -169,6 +178,13 @@
     "                 'p110a']"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use KLIFS' API to retrieve the 85-long pocket sequence for each kinase."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -207,53 +223,131 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
-      "KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA\n",
-      "QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA\n",
-      "EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA\n",
-      "ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA\n",
-      "EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n",
-      "SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA\n",
-      "KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA\n",
-      "CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'EGFR': 'KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA',\n",
+       " 'ErbB2': 'KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA',\n",
+       " 'BRAF': 'QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA',\n",
+       " 'CDK2': 'EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA',\n",
+       " 'LCK': 'ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA',\n",
+       " 'MET': 'EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA',\n",
+       " 'p38a': 'SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA',\n",
+       " 'KDR': 'KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA',\n",
+       " 'p110a': 'CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "kinase_sequences = {}\n",
     "for kinase in query_kinases:\n",
-    "    print(klifs_pocket_sequence(kinase))"
+    "    kinase_sequences[kinase] = klifs_pocket_sequence(kinase)\n",
+    "kinase_sequences"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Show kinase coverage"
+    "### Compare kinases"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def sequence_similarity(kinase_name1, kinase_name2, type_=\"identity\"):\n",
+    "    \"\"\"\n",
+    "    Compares two sequences using a given metric.\n",
+    "\n",
+    "    kinase_name1, kinase_name2 : str\n",
+    "        The two names of the kinases for comparison.\n",
+    "\n",
+    "    type_ : str ? default = identity\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    float :\n",
+    "        The similarity between the pocket sequences of the two kinases.\n",
+    "    \"\"\"\n",
+    "    sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
+    "    sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
+    "\n",
+    "    if len(sequence_1) != len(sequence_1):\n",
+    "        print(\"Mismatch in sequence lengths.\")\n",
+    "        return None\n",
+    "    else:\n",
+    "        if type_ == \"identity\":\n",
+    "            # True is the character is the same, False otherwise\n",
+    "            is_match = np.compare_chararrays(np.array(list(sequence_1)),\n",
+    "                                             np.array(list(sequence_2)),\n",
+    "                                             cmp=\"==\",\n",
+    "                                             rstrip=True)\n",
+    "            similarity_normed = sum(is_match)/len(sequence_1)\n",
+    "            return similarity_normed\n",
+    "        else:\n",
+    "            print(\"type not implemented yet.\")\n",
+    "            return None"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "Let's look at the sequence similarity between EGFR and MET:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4588235294117647"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_similarity(\"EGFR\", \"MET\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, the similarity between a kinase and itself leads the highest possible score:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_similarity(\"EGFR\", \"EGFR\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -262,6 +356,152 @@
     "### Visualize similarity as kinase matrix"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.        , 0.89411765, 0.37647059, 0.31764706, 0.44705882,\n",
+       "        0.45882353, 0.38823529, 0.47058824, 0.11764706],\n",
+       "       [0.89411765, 1.        , 0.4       , 0.32941176, 0.42352941,\n",
+       "        0.47058824, 0.4       , 0.43529412, 0.11764706],\n",
+       "       [0.37647059, 0.4       , 1.        , 0.32941176, 0.38823529,\n",
+       "        0.37647059, 0.37647059, 0.4       , 0.15294118],\n",
+       "       [0.31764706, 0.32941176, 0.32941176, 1.        , 0.37647059,\n",
+       "        0.36470588, 0.47058824, 0.34117647, 0.10588235],\n",
+       "       [0.44705882, 0.42352941, 0.38823529, 0.37647059, 1.        ,\n",
+       "        0.4       , 0.38823529, 0.43529412, 0.14117647],\n",
+       "       [0.45882353, 0.47058824, 0.37647059, 0.36470588, 0.4       ,\n",
+       "        1.        , 0.36470588, 0.47058824, 0.10588235],\n",
+       "       [0.38823529, 0.4       , 0.37647059, 0.47058824, 0.38823529,\n",
+       "        0.36470588, 1.        , 0.38823529, 0.14117647],\n",
+       "       [0.47058824, 0.43529412, 0.4       , 0.34117647, 0.43529412,\n",
+       "        0.47058824, 0.38823529, 1.        , 0.15294118],\n",
+       "       [0.11764706, 0.11764706, 0.15294118, 0.10588235, 0.14117647,\n",
+       "        0.10588235, 0.14117647, 0.15294118, 1.        ]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = np.zeros((len(query_kinases), len(query_kinases)))\n",
+    "for i, kinase_name1 in enumerate(query_kinases):\n",
+    "    for j, kinase_name2 in enumerate(query_kinases):\n",
+    "        table[i, j] = sequence_similarity(kinase_name1,\n",
+    "                                          kinase_name2)\n",
+    "table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    A   C   D   E   F   G   H   I   K   L   M   N   P   Q   R   S   T   V   W   Y   B   Z   X   *\n",
+      "A   4   0  -2  -1  -2   0  -2  -1  -1  -1  -1  -2  -1  -1  -1   1   0   0  -3  -2  -2  -1   0  -4\n",
+      "C   0   9  -3  -4  -2  -3  -3  -1  -3  -1  -1  -3  -3  -3  -3  -1  -1  -1  -2  -2  -3  -3  -2  -4\n",
+      "D  -2  -3   6   2  -3  -1  -1  -3  -1  -4  -3   1  -1   0  -2   0  -1  -3  -4  -3   4   1  -1  -4\n",
+      "E  -1  -4   2   5  -3  -2   0  -3   1  -3  -2   0  -1   2   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
+      "F  -2  -2  -3  -3   6  -3  -1   0  -3   0   0  -3  -4  -3  -3  -2  -2  -1   1   3  -3  -3  -1  -4\n",
+      "G   0  -3  -1  -2  -3   6  -2  -4  -2  -4  -3   0  -2  -2  -2   0  -2  -3  -2  -3  -1  -2  -1  -4\n",
+      "H  -2  -3  -1   0  -1  -2   8  -3  -1  -3  -2   1  -2   0   0  -1  -2  -3  -2   2   0   0  -1  -4\n",
+      "I  -1  -1  -3  -3   0  -4  -3   4  -3   2   1  -3  -3  -3  -3  -2  -1   3  -3  -1  -3  -3  -1  -4\n",
+      "K  -1  -3  -1   1  -3  -2  -1  -3   5  -2  -1   0  -1   1   2   0  -1  -2  -3  -2   0   1  -1  -4\n",
+      "L  -1  -1  -4  -3   0  -4  -3   2  -2   4   2  -3  -3  -2  -2  -2  -1   1  -2  -1  -4  -3  -1  -4\n",
+      "M  -1  -1  -3  -2   0  -3  -2   1  -1   2   5  -2  -2   0  -1  -1  -1   1  -1  -1  -3  -1  -1  -4\n",
+      "N  -2  -3   1   0  -3   0   1  -3   0  -3  -2   6  -2   0   0   1   0  -3  -4  -2   3   0  -1  -4\n",
+      "P  -1  -3  -1  -1  -4  -2  -2  -3  -1  -3  -2  -2   7  -1  -2  -1  -1  -2  -4  -3  -2  -1  -2  -4\n",
+      "Q  -1  -3   0   2  -3  -2   0  -3   1  -2   0   0  -1   5   1   0  -1  -2  -2  -1   0   3  -1  -4\n",
+      "R  -1  -3  -2   0  -3  -2   0  -3   2  -2  -1   0  -2   1   5  -1  -1  -3  -3  -2  -1   0  -1  -4\n",
+      "S   1  -1   0   0  -2   0  -1  -2   0  -2  -1   1  -1   0  -1   4   1  -2  -3  -2   0   0   0  -4\n",
+      "T   0  -1  -1  -1  -2  -2  -2  -1  -1  -1  -1   0  -1  -1  -1   1   5   0  -2  -2  -1  -1   0  -4\n",
+      "V   0  -1  -3  -2  -1  -3  -3   3  -2   1   1  -3  -2  -2  -3  -2   0   4  -3  -1  -3  -2  -1  -4\n",
+      "W  -3  -2  -4  -3   1  -2  -2  -3  -3  -2  -1  -4  -4  -2  -3  -3  -2  -3  11   2  -4  -3  -2  -4\n",
+      "Y  -2  -2  -3  -2   3  -3   2  -1  -2  -1  -1  -2  -3  -1  -2  -2  -2  -1   2   7  -3  -2  -1  -4\n",
+      "B  -2  -3   4   1  -3  -1   0  -3   0  -4  -3   3  -2   0  -1   0  -1  -3  -4  -3   4   1  -1  -4\n",
+      "Z  -1  -3   1   4  -3  -2   0  -3   1  -3  -1   0  -1   3   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
+      "X   0  -2  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -2  -1  -1   0   0  -1  -2  -1  -1  -1  -1  -4\n",
+      "*  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4   1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Obtain BLOSUM62\n",
+    "substitution_matrix = align.SubstitutionMatrix.std_protein_matrix()\n",
+    "print(substitution_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "biotite.sequence.align.SubstitutionMatrix"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(substitution_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "substitution_matrix.is_symmetric()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Show kinase coverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,

From 23c0225c9f817122a570cee29292fca0c871ba0a Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 12:37:11 +0200
Subject: [PATCH 026/158] add substitution metric.

---
 .../talktorial.ipynb                          | 427 ++++++++++++++++--
 1 file changed, 389 insertions(+), 38 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 291f9eac..fc3eab00 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -73,25 +73,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
-   ]
-  },
-  {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    " \n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
+    "\n",
+    " \n",
+    "\n",
+    "*Table 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "\n",
+    " \n",
+    "\n",
+    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
+    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
+    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
+    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
+    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
+    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
+    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
+    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
+    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
+    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
+    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
    ]
   },
   {
@@ -217,6 +223,13 @@
     "        return None"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how these pocket sequence look like."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -252,7 +265,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "### Sequence similarity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given two kinases, we create a function which computes the sequence similarity between them using one of the two measures discussed in the theory, namely the identity or the substitution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Identity score\n",
+    "We first define a function which compares element wise elements as described in the theory."
    ]
   },
   {
@@ -260,6 +288,98 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def identity_score(sequence1, sequence2):\n",
+    "    \"\"\"\n",
+    "    sequence1 :\n",
+    "    sequence2 :\n",
+    "    \"\"\"\n",
+    "    # True is the character is the same, False otherwise\n",
+    "    return np.compare_chararrays(sequence1,\n",
+    "                                 sequence2,\n",
+    "                                 cmp=\"==\",\n",
+    "                                 rstrip=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Substitution score\n",
+    "We now define the function which is more specific to amino acids grouping and use the `biotite` library and retrieve the substitution matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (90 > 79 characters)\n",
+      "6:1: W293 blank line contains whitespace\n",
+      "11:25: W291 trailing whitespace\n"
+     ]
+    }
+   ],
+   "source": [
+    "def substitution_score(sequence1,\n",
+    "                       sequence2,\n",
+    "                       substitution_matrix=align.SubstitutionMatrix.std_protein_matrix()):\n",
+    "    \"\"\"\n",
+    "    ADD\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    sequence1 :\n",
+    "    sequence2 :\n",
+    "    substitution_matrix: \n",
+    "        Default align.SubstitutionMatrix.std_protein_matrix() from biotite\n",
+    "        Obtain BLOSUM62\n",
+    "    Returns\n",
+    "    -------\n",
+    "    \"\"\"\n",
+    "    # Retrieve np.array from substitution matrix\n",
+    "    score_matrix = substitution_matrix.score_matrix()\n",
+    "\n",
+    "    # Retireve the letter\n",
+    "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
+    "\n",
+    "    # Map letter to index\n",
+    "    dict_letters = {}\n",
+    "    for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
+    "        dict_letters[letter] = i\n",
+    "\n",
+    "    match_score = match_score = np.zeros(len(sequence1))\n",
+    "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1,\n",
+    "                                                             sequence2)):\n",
+    "        ind1 = dict_letters[character_seq1]\n",
+    "        ind2 = dict_letters[character_seq2]\n",
+    "        match_score[i] = score_matrix[ind1, ind2]\n",
+    "    return match_score"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Kinase comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given two kinases, we create a function which computes the sequence similarity between them using one of the two measures discussed in the theory, namely the identity or the substitution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def sequence_similarity(kinase_name1, kinase_name2, type_=\"identity\"):\n",
     "    \"\"\"\n",
@@ -282,16 +402,18 @@
     "        print(\"Mismatch in sequence lengths.\")\n",
     "        return None\n",
     "    else:\n",
+    "        seq_array1 = np.array(list(sequence_1))\n",
+    "        seq_array2 = np.array(list(sequence_2))\n",
     "        if type_ == \"identity\":\n",
-    "            # True is the character is the same, False otherwise\n",
-    "            is_match = np.compare_chararrays(np.array(list(sequence_1)),\n",
-    "                                             np.array(list(sequence_2)),\n",
-    "                                             cmp=\"==\",\n",
-    "                                             rstrip=True)\n",
+    "            is_match = identity_score(seq_array1, seq_array2)\n",
     "            similarity_normed = sum(is_match)/len(sequence_1)\n",
     "            return similarity_normed\n",
+    "        elif type_ == \"substitution\":\n",
+    "            match_score = substitution_score(seq_array1,\n",
+    "                                             seq_array2)\n",
+    "            return match_score\n",
     "        else:\n",
-    "            print(\"type not implemented yet.\")\n",
+    "            print(\"Type not defined.\")\n",
     "            return None"
    ]
   },
@@ -304,7 +426,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  4.,  2.,  6., -1.,  6., -2.,  6.,  6., -1.,  4.,  7., -1.,\n",
+       "       -1.,  4.,  3.,  5.,  0.,  4.,  2.,  0.,  4., -1.,  5.,  0., -1.,\n",
+       "        3.,  5., -1.,  0., -1.,  0.,  7.,  1.,  4., -1., -1.,  4.,  4.,\n",
+       "        6.,  4., -2.,  1.,  3., -1., -1., -1.,  5., -1., -1.,  6., -3.,\n",
+       "        4., -2.,  1.,  3.,  3.,  5.,  0.,  7.,  4., -1.,  0.,  2.,  2.,\n",
+       "        0.,  4.,  8.,  5.,  6.,  4.,  4.,  4.,  5.,  6., -1.,  2.,  1.,\n",
+       "        3.,  0.,  6.,  6.,  6.,  4.,  4.])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = sequence_similarity(\"EGFR\", \"MET\", \"substitution\")\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -313,13 +462,14 @@
        "0.4588235294117647"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sequence_similarity(\"EGFR\", \"MET\")"
+    "a = sequence_similarity(\"EGFR\", \"MET\", \"identity\")\n",
+    "a"
    ]
   },
   {
@@ -331,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -340,7 +490,7 @@
        "1.0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,6 +499,30 @@
     "sequence_similarity(\"EGFR\", \"EGFR\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5., 4., 4., 6., 4., 6., 4., 6., 6., 5., 4., 7., 5., 4., 4., 4., 5.,\n",
+       "       5., 4., 5., 4., 4., 6., 5., 4., 7., 4., 5., 4., 4., 4., 6., 7., 8.,\n",
+       "       4., 9., 5., 4., 4., 6., 4., 5., 4., 4., 5., 5., 4., 5., 7., 6., 6.,\n",
+       "       9., 4., 4., 6., 7., 4., 5., 5., 7., 4., 5., 6., 5., 5., 4., 4., 8.,\n",
+       "       5., 6., 4., 4., 4., 5., 6., 4., 4., 4., 4., 5., 6., 6., 6., 4., 4.])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sequence_similarity(\"EGFR\", \"EGFR\", type_=\"substitution\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -358,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +558,7 @@
        "        0.10588235, 0.14117647, 0.15294118, 1.        ]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -398,9 +572,70 @@
     "table"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Substitution"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['K', 'V', 'L', 'G', 'S', 'G', 'A', 'F', 'G', 'T', 'V', 'Y', 'K',\n",
+       "       'V', 'A', 'I', 'K', 'E', 'L', 'E', 'I', 'L', 'D', 'E', 'A', 'Y',\n",
+       "       'V', 'M', 'A', 'S', 'V', 'D', 'P', 'H', 'V', 'C', 'R', 'L', 'L',\n",
+       "       'G', 'I', 'Q', 'L', 'I', 'T', 'Q', 'L', 'M', 'P', 'F', 'G', 'C',\n",
+       "       'L', 'L', 'D', 'Y', 'V', 'R', 'E', 'Y', 'L', 'E', 'D', 'R', 'R',\n",
+       "       'L', 'V', 'H', 'R', 'D', 'L', 'A', 'A', 'R', 'N', 'V', 'L', 'V',\n",
+       "       'I', 'T', 'D', 'F', 'G', 'L', 'A'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.array(list(kinase_sequences[\"EGFR\"]))\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['E', 'V', 'I', 'G', 'R', 'G', 'H', 'F', 'G', 'C', 'V', 'Y', 'H',\n",
+       "       'C', 'A', 'V', 'K', 'S', 'L', 'Q', 'F', 'L', 'T', 'E', 'G', 'I',\n",
+       "       'I', 'M', 'K', 'D', 'F', 'S', 'P', 'N', 'V', 'L', 'S', 'L', 'L',\n",
+       "       'G', 'I', 'L', 'V', 'V', 'L', 'P', 'Y', 'M', 'K', 'H', 'G', 'D',\n",
+       "       'L', 'R', 'N', 'F', 'I', 'R', 'N', 'Y', 'L', 'A', 'S', 'K', 'K',\n",
+       "       'F', 'V', 'H', 'R', 'D', 'L', 'A', 'A', 'R', 'N', 'C', 'M', 'L',\n",
+       "       'V', 'A', 'D', 'F', 'G', 'L', 'A'], dtype='<U1')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = np.array(list(kinase_sequences[\"MET\"]))\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -443,27 +678,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "biotite.sequence.align.SubstitutionMatrix"
+       "(numpy.ndarray, (24, 24))"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(substitution_matrix)"
+    "score_matrix = substitution_matrix.score_matrix()\n",
+    "type(score_matrix), score_matrix.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "letter_alphabet = substitution_matrix.get_alphabet1()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'A': 0,\n",
+       " 'C': 1,\n",
+       " 'D': 2,\n",
+       " 'E': 3,\n",
+       " 'F': 4,\n",
+       " 'G': 5,\n",
+       " 'H': 6,\n",
+       " 'I': 7,\n",
+       " 'K': 8,\n",
+       " 'L': 9,\n",
+       " 'M': 10,\n",
+       " 'N': 11,\n",
+       " 'P': 12,\n",
+       " 'Q': 13,\n",
+       " 'R': 14,\n",
+       " 'S': 15,\n",
+       " 'T': 16,\n",
+       " 'V': 17,\n",
+       " 'W': 18,\n",
+       " 'Y': 19,\n",
+       " 'B': 20,\n",
+       " 'Z': 21,\n",
+       " 'X': 22,\n",
+       " '*': 23}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict_letters = {}\n",
+    "for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
+    "    dict_letters[letter] = i\n",
+    "dict_letters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "match_score = np.zeros(len(a))\n",
+    "for i, (character_seq1, character_seq2) in enumerate(zip(a, b)):\n",
+    "    # print(character_seq1, character_seq2)#, type(character_seq1))\n",
+    "    ind1 = dict_letters[character_seq1]\n",
+    "    ind2 = dict_letters[character_seq2]\n",
+    "    match_score[i] = score_matrix[ind1, ind2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1.,  4.,  2.,  6., -1.,  6., -2.,  6.,  6., -1.,  4.,  7., -1.,\n",
+       "       -1.,  4.,  3.,  5.,  0.,  4.,  2.,  0.,  4., -1.,  5.,  0., -1.,\n",
+       "        3.,  5., -1.,  0., -1.,  0.,  7.,  1.,  4., -1., -1.,  4.,  4.,\n",
+       "        6.,  4., -2.,  1.,  3., -1., -1., -1.,  5., -1., -1.,  6., -3.,\n",
+       "        4., -2.,  1.,  3.,  3.,  5.,  0.,  7.,  4., -1.,  0.,  2.,  2.,\n",
+       "        0.,  4.,  8.,  5.,  6.,  4.,  4.,  4.,  5.,  6., -1.,  2.,  1.,\n",
+       "        3.,  0.,  6.,  6.,  6.,  4.,  4.])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "match_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(24, 24)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "substitution_matrix.shape()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +823,7 @@
        "True"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }

From b30bcadaa9e4cfdaf3692ad1a495563187d0dbc8 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 14:59:23 +0200
Subject: [PATCH 027/158] finalize similarity matrix.

---
 .../talktorial.ipynb                          | 891 +++++++++++-------
 1 file changed, 525 insertions(+), 366 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index fc3eab00..30ab25e0 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -139,6 +139,7 @@
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
+    "import seaborn as sns\n",
     "import requests\n",
     "import biotite.sequence.align as align"
    ]
@@ -272,7 +273,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Given two kinases, we create a function which computes the sequence similarity between them using one of the two measures discussed in the theory, namely the identity or the substitution."
+    "Given two kinases, we create functions which account for identity or subsituition similarity, as described in the theory."
    ]
   },
   {
@@ -280,7 +281,7 @@
    "metadata": {},
    "source": [
     "#### Identity score\n",
-    "We first define a function which compares element wise elements as described in the theory."
+    "We first define a function which compares element-wise characters in two sequences."
    ]
   },
   {
@@ -291,8 +292,21 @@
    "source": [
     "def identity_score(sequence1, sequence2):\n",
     "    \"\"\"\n",
-    "    sequence1 :\n",
+    "    Computes the element-wise binary similarity between two sequences.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    sequence1 : np.array\n",
+    "        An array of character describing the first sequence.\n",
     "    sequence2 :\n",
+    "        An array of character describing the second sequence.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    np.array :\n",
+    "        The bool array for each character.\n",
+    "        1 if the elements are identical,\n",
+    "        0 otherwise.\n",
     "    \"\"\"\n",
     "    # True is the character is the same, False otherwise\n",
     "    return np.compare_chararrays(sequence1,\n",
@@ -313,31 +327,26 @@
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (90 > 79 characters)\n",
-      "6:1: W293 blank line contains whitespace\n",
-      "11:25: W291 trailing whitespace\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def substitution_score(sequence1,\n",
     "                       sequence2,\n",
-    "                       substitution_matrix=align.SubstitutionMatrix.std_protein_matrix()):\n",
+    "                       substitution_matrix=align.\n",
+    "                       SubstitutionMatrix.std_protein_matrix()):\n",
     "    \"\"\"\n",
-    "    ADD\n",
-    "    \n",
+    "    Retrieve #TODO\n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    sequence1 :\n",
+    "    sequence1 : np.array\n",
+    "        An array of character describing the first sequence.\n",
     "    sequence2 :\n",
-    "    substitution_matrix: \n",
-    "        Default align.SubstitutionMatrix.std_protein_matrix() from biotite\n",
-    "        Obtain BLOSUM62\n",
+    "        An array of character describing the second sequence.\n",
+    "    substitution_matrix:\n",
+    "        A substituition matrix specific to amino acids.\n",
+    "        The default is align.SubstitutionMatrix.std_protein_matrix()\n",
+    "        from biotite, which represents BLOSUM62.\n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    \"\"\"\n",
@@ -358,6 +367,8 @@
     "        ind1 = dict_letters[character_seq1]\n",
     "        ind2 = dict_letters[character_seq2]\n",
     "        match_score[i] = score_matrix[ind1, ind2]\n",
+    "    # TODO normalize?\n",
+    "    # TODO check for * VS -\n",
     "    return match_score"
    ]
   },
@@ -372,7 +383,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Given two kinases, we create a function which computes the sequence similarity between them using one of the two measures discussed in the theory, namely the identity or the substitution."
+    "Given two kinases, we create a function which computes the sequence similarity between them using one of the two measures, the identity or the substitution."
    ]
   },
   {
@@ -388,7 +399,9 @@
     "    kinase_name1, kinase_name2 : str\n",
     "        The two names of the kinases for comparison.\n",
     "\n",
-    "    type_ : str ? default = identity\n",
+    "    type_ : str\n",
+    "        The type of metric to compute the similarity.\n",
+    "        The default is `identity`.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -411,7 +424,8 @@
     "        elif type_ == \"substitution\":\n",
     "            match_score = substitution_score(seq_array1,\n",
     "                                             seq_array2)\n",
-    "            return match_score\n",
+    "            similarity = sum(match_score)\n",
+    "            return similarity\n",
     "        else:\n",
     "            print(\"Type not defined.\")\n",
     "            return None"
@@ -430,25 +444,19 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  4.,  2.,  6., -1.,  6., -2.,  6.,  6., -1.,  4.,  7., -1.,\n",
-       "       -1.,  4.,  3.,  5.,  0.,  4.,  2.,  0.,  4., -1.,  5.,  0., -1.,\n",
-       "        3.,  5., -1.,  0., -1.,  0.,  7.,  1.,  4., -1., -1.,  4.,  4.,\n",
-       "        6.,  4., -2.,  1.,  3., -1., -1., -1.,  5., -1., -1.,  6., -3.,\n",
-       "        4., -2.,  1.,  3.,  3.,  5.,  0.,  7.,  4., -1.,  0.,  2.,  2.,\n",
-       "        0.,  4.,  8.,  5.,  6.,  4.,  4.,  4.,  5.,  6., -1.,  2.,  1.,\n",
-       "        3.,  0.,  6.,  6.,  6.,  4.,  4.])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pocket sequence similarity between EGFR and MET kinases: 205.0 using substitution.\n"
+     ]
     }
    ],
    "source": [
-    "a = sequence_similarity(\"EGFR\", \"MET\", \"substitution\")\n",
-    "a"
+    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "                                              \"MET\",\n",
+    "                                              \"substitution\")\n",
+    "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
+    "      f\"{EGFR_MET_seq_similarity} using substitution.\")"
    ]
   },
   {
@@ -457,19 +465,19 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.4588235294117647"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pocket sequence similarity between EGFR and MET kinases: 0.46 using identity.\n"
+     ]
     }
    ],
    "source": [
-    "a = sequence_similarity(\"EGFR\", \"MET\", \"identity\")\n",
-    "a"
+    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "                                              \"MET\",\n",
+    "                                              \"identity\")\n",
+    "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
+    "      f\"{EGFR_MET_seq_similarity:.2f} using identity.\")"
    ]
   },
   {
@@ -485,18 +493,17 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pocket sequence similarity between EGFR itself: 1.00 using identity.\n"
+     ]
     }
    ],
    "source": [
-    "sequence_similarity(\"EGFR\", \"EGFR\")"
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \"EGFR\")\n",
+    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
+    "      f\"{EGFR_seq_similarity:.2f} using identity.\")"
    ]
   },
   {
@@ -505,22 +512,18 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([5., 4., 4., 6., 4., 6., 4., 6., 6., 5., 4., 7., 5., 4., 4., 4., 5.,\n",
-       "       5., 4., 5., 4., 4., 6., 5., 4., 7., 4., 5., 4., 4., 4., 6., 7., 8.,\n",
-       "       4., 9., 5., 4., 4., 6., 4., 5., 4., 4., 5., 5., 4., 5., 7., 6., 6.,\n",
-       "       9., 4., 4., 6., 7., 4., 5., 5., 7., 4., 5., 6., 5., 5., 4., 4., 8.,\n",
-       "       5., 6., 4., 4., 4., 5., 6., 4., 4., 4., 4., 5., 6., 6., 6., 4., 4.])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pocket sequence similarity between EGFR itself: 429.00 using substitution.\n"
+     ]
     }
    ],
    "source": [
-    "sequence_similarity(\"EGFR\", \"EGFR\", type_=\"substitution\")"
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \"EGFR\", type_=\"substitution\")\n",
+    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
+    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")\n",
+    "# TODO: normalize"
    ]
   },
   {
@@ -534,49 +537,13 @@
    "cell_type": "code",
    "execution_count": 14,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1.        , 0.89411765, 0.37647059, 0.31764706, 0.44705882,\n",
-       "        0.45882353, 0.38823529, 0.47058824, 0.11764706],\n",
-       "       [0.89411765, 1.        , 0.4       , 0.32941176, 0.42352941,\n",
-       "        0.47058824, 0.4       , 0.43529412, 0.11764706],\n",
-       "       [0.37647059, 0.4       , 1.        , 0.32941176, 0.38823529,\n",
-       "        0.37647059, 0.37647059, 0.4       , 0.15294118],\n",
-       "       [0.31764706, 0.32941176, 0.32941176, 1.        , 0.37647059,\n",
-       "        0.36470588, 0.47058824, 0.34117647, 0.10588235],\n",
-       "       [0.44705882, 0.42352941, 0.38823529, 0.37647059, 1.        ,\n",
-       "        0.4       , 0.38823529, 0.43529412, 0.14117647],\n",
-       "       [0.45882353, 0.47058824, 0.37647059, 0.36470588, 0.4       ,\n",
-       "        1.        , 0.36470588, 0.47058824, 0.10588235],\n",
-       "       [0.38823529, 0.4       , 0.37647059, 0.47058824, 0.38823529,\n",
-       "        0.36470588, 1.        , 0.38823529, 0.14117647],\n",
-       "       [0.47058824, 0.43529412, 0.4       , 0.34117647, 0.43529412,\n",
-       "        0.47058824, 0.38823529, 1.        , 0.15294118],\n",
-       "       [0.11764706, 0.11764706, 0.15294118, 0.10588235, 0.14117647,\n",
-       "        0.10588235, 0.14117647, 0.15294118, 1.        ]])"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "table = np.zeros((len(query_kinases), len(query_kinases)))\n",
+    "kinase_similarity_matrix = np.zeros((len(query_kinases), len(query_kinases)))\n",
     "for i, kinase_name1 in enumerate(query_kinases):\n",
     "    for j, kinase_name2 in enumerate(query_kinases):\n",
-    "        table[i, j] = sequence_similarity(kinase_name1,\n",
-    "                                          kinase_name2)\n",
-    "table"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Substitution"
+    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
+    "                                                             kinase_name2)"
    ]
   },
   {
@@ -586,14 +553,171 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.894118</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.317647</td>\n",
+       "      <td>0.447059</td>\n",
+       "      <td>0.458824</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.117647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.894118</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.423529</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.117647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.152941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.317647</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.341176</td>\n",
+       "      <td>0.105882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.447059</td>\n",
+       "      <td>0.423529</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.141176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.458824</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.105882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.141176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.341176</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.152941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "array(['K', 'V', 'L', 'G', 'S', 'G', 'A', 'F', 'G', 'T', 'V', 'Y', 'K',\n",
-       "       'V', 'A', 'I', 'K', 'E', 'L', 'E', 'I', 'L', 'D', 'E', 'A', 'Y',\n",
-       "       'V', 'M', 'A', 'S', 'V', 'D', 'P', 'H', 'V', 'C', 'R', 'L', 'L',\n",
-       "       'G', 'I', 'Q', 'L', 'I', 'T', 'Q', 'L', 'M', 'P', 'F', 'G', 'C',\n",
-       "       'L', 'L', 'D', 'Y', 'V', 'R', 'E', 'Y', 'L', 'E', 'D', 'R', 'R',\n",
-       "       'L', 'V', 'H', 'R', 'D', 'L', 'A', 'A', 'R', 'N', 'V', 'L', 'V',\n",
-       "       'I', 'T', 'D', 'F', 'G', 'L', 'A'], dtype='<U1')"
+       "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
+       "EGFR   1.000000  0.894118  0.376471  0.317647  0.447059  0.458824  0.388235   \n",
+       "ErbB2  0.894118  1.000000  0.400000  0.329412  0.423529  0.470588  0.400000   \n",
+       "BRAF   0.376471  0.400000  1.000000  0.329412  0.388235  0.376471  0.376471   \n",
+       "CDK2   0.317647  0.329412  0.329412  1.000000  0.376471  0.364706  0.470588   \n",
+       "LCK    0.447059  0.423529  0.388235  0.376471  1.000000  0.400000  0.388235   \n",
+       "MET    0.458824  0.470588  0.376471  0.364706  0.400000  1.000000  0.364706   \n",
+       "p38a   0.388235  0.400000  0.376471  0.470588  0.388235  0.364706  1.000000   \n",
+       "KDR    0.470588  0.435294  0.400000  0.341176  0.435294  0.470588  0.388235   \n",
+       "p110a  0.117647  0.117647  0.152941  0.105882  0.141176  0.105882  0.141176   \n",
+       "\n",
+       "            KDR     p110a  \n",
+       "EGFR   0.470588  0.117647  \n",
+       "ErbB2  0.435294  0.117647  \n",
+       "BRAF   0.400000  0.152941  \n",
+       "CDK2   0.341176  0.105882  \n",
+       "LCK    0.435294  0.141176  \n",
+       "MET    0.470588  0.105882  \n",
+       "p38a   0.388235  0.141176  \n",
+       "KDR    1.000000  0.152941  \n",
+       "p110a  0.152941  1.000000  "
       ]
      },
      "execution_count": 15,
@@ -602,8 +726,10 @@
     }
    ],
    "source": [
-    "a = np.array(list(kinase_sequences[\"EGFR\"]))\n",
-    "a"
+    "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
+    "                                        index=query_kinases,\n",
+    "                                        columns=query_kinases)\n",
+    "kinase_similarity_matrix"
    ]
   },
   {
@@ -613,14 +739,274 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_9913a_row0_col0, #T_9913a_row1_col1, #T_9913a_row2_col2, #T_9913a_row3_col3, #T_9913a_row4_col4, #T_9913a_row5_col5, #T_9913a_row6_col6, #T_9913a_row7_col7, #T_9913a_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9913a_row0_col1, #T_9913a_row1_col0 {\n",
+       "  background-color: #1c8e1c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9913a_row0_col2, #T_9913a_row5_col2, #T_9913a_row6_col2, #T_9913a_row7_col3 {\n",
+       "  background-color: #add5ad;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col3 {\n",
+       "  background-color: #b3d8b3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col4 {\n",
+       "  background-color: #97ca97;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col5 {\n",
+       "  background-color: #8ec58e;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col6, #T_9913a_row2_col4, #T_9913a_row4_col6, #T_9913a_row6_col4, #T_9913a_row7_col6 {\n",
+       "  background-color: #a7d2a7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col7, #T_9913a_row5_col7 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row0_col8, #T_9913a_row1_col8 {\n",
+       "  background-color: #e8f2e8;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col2, #T_9913a_row2_col7, #T_9913a_row3_col5, #T_9913a_row5_col3, #T_9913a_row6_col5, #T_9913a_row7_col2 {\n",
+       "  background-color: #a6d2a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col3, #T_9913a_row2_col3 {\n",
+       "  background-color: #b1d7b1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col4, #T_9913a_row4_col5 {\n",
+       "  background-color: #9dcd9d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col5, #T_9913a_row6_col3, #T_9913a_row7_col5 {\n",
+       "  background-color: #8bc48b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col6, #T_9913a_row2_col5, #T_9913a_row4_col3, #T_9913a_row5_col4 {\n",
+       "  background-color: #a4d0a4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row1_col7, #T_9913a_row4_col7 {\n",
+       "  background-color: #9ccd9c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row2_col0 {\n",
+       "  background-color: #a6d1a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row2_col1, #T_9913a_row6_col1 {\n",
+       "  background-color: #a0cea0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row2_col6, #T_9913a_row3_col4 {\n",
+       "  background-color: #aad3aa;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row2_col8, #T_9913a_row7_col8 {\n",
+       "  background-color: #dfeddf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col0 {\n",
+       "  background-color: #b5d9b5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col1 {\n",
+       "  background-color: #b2d7b2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col2 {\n",
+       "  background-color: #badbba;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col6, #T_9913a_row5_col0 {\n",
+       "  background-color: #90c790;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col7 {\n",
+       "  background-color: #b7dab7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row3_col8, #T_9913a_row5_col8, #T_9913a_row8_col0, #T_9913a_row8_col1, #T_9913a_row8_col2, #T_9913a_row8_col3, #T_9913a_row8_col4, #T_9913a_row8_col5, #T_9913a_row8_col6, #T_9913a_row8_col7 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row4_col0 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row4_col1 {\n",
+       "  background-color: #9acb9a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row4_col2, #T_9913a_row6_col7 {\n",
+       "  background-color: #a9d3a9;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row4_col8, #T_9913a_row6_col8 {\n",
+       "  background-color: #e1eee1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row5_col1, #T_9913a_row7_col0 {\n",
+       "  background-color: #8dc58d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row5_col6 {\n",
+       "  background-color: #aed5ae;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row6_col0 {\n",
+       "  background-color: #a3d0a3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row7_col1 {\n",
+       "  background-color: #96c996;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9913a_row7_col4 {\n",
+       "  background-color: #9bcc9b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_9913a_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_9913a_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_9913a_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
+       "      <td id=\"T_9913a_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
+       "      <td id=\"T_9913a_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
+       "      <td id=\"T_9913a_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_9913a_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_9913a_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
+       "      <td id=\"T_9913a_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
+       "      <td id=\"T_9913a_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
+       "      <td id=\"T_9913a_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_9913a_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
+       "      <td id=\"T_9913a_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_9913a_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
+       "      <td id=\"T_9913a_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
+       "      <td id=\"T_9913a_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
+       "      <td id=\"T_9913a_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
+       "      <td id=\"T_9913a_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
+       "      <td id=\"T_9913a_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_9913a_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
+       "      <td id=\"T_9913a_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
+       "      <td id=\"T_9913a_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
+       "      <td id=\"T_9913a_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_9913a_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
+       "      <td id=\"T_9913a_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
+       "      <td id=\"T_9913a_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
+       "      <td id=\"T_9913a_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_9913a_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
+       "      <td id=\"T_9913a_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
+       "      <td id=\"T_9913a_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_9913a_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
+       "      <td id=\"T_9913a_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
+       "      <td id=\"T_9913a_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
+       "      <td id=\"T_9913a_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
+       "      <td id=\"T_9913a_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
+       "      <td id=\"T_9913a_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
+       "      <td id=\"T_9913a_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_9913a_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9913a_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_9913a_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
+       "      <td id=\"T_9913a_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
+       "      <td id=\"T_9913a_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
+       "      <td id=\"T_9913a_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
+       "      <td id=\"T_9913a_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
+       "      <td id=\"T_9913a_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
+       "      <td id=\"T_9913a_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
+       "      <td id=\"T_9913a_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
+       "      <td id=\"T_9913a_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
       "text/plain": [
-       "array(['E', 'V', 'I', 'G', 'R', 'G', 'H', 'F', 'G', 'C', 'V', 'Y', 'H',\n",
-       "       'C', 'A', 'V', 'K', 'S', 'L', 'Q', 'F', 'L', 'T', 'E', 'G', 'I',\n",
-       "       'I', 'M', 'K', 'D', 'F', 'S', 'P', 'N', 'V', 'L', 'S', 'L', 'L',\n",
-       "       'G', 'I', 'L', 'V', 'V', 'L', 'P', 'Y', 'M', 'K', 'H', 'G', 'D',\n",
-       "       'L', 'R', 'N', 'F', 'I', 'R', 'N', 'Y', 'L', 'A', 'S', 'K', 'K',\n",
-       "       'F', 'V', 'H', 'R', 'D', 'L', 'A', 'A', 'R', 'N', 'C', 'M', 'L',\n",
-       "       'V', 'A', 'D', 'F', 'G', 'L', 'A'], dtype='<U1')"
+       "<pandas.io.formats.style.Styler at 0x7fe0bbf693a0>"
       ]
      },
      "execution_count": 16,
@@ -629,258 +1015,38 @@
     }
    ],
    "source": [
-    "b = np.array(list(kinase_sequences[\"MET\"]))\n",
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    A   C   D   E   F   G   H   I   K   L   M   N   P   Q   R   S   T   V   W   Y   B   Z   X   *\n",
-      "A   4   0  -2  -1  -2   0  -2  -1  -1  -1  -1  -2  -1  -1  -1   1   0   0  -3  -2  -2  -1   0  -4\n",
-      "C   0   9  -3  -4  -2  -3  -3  -1  -3  -1  -1  -3  -3  -3  -3  -1  -1  -1  -2  -2  -3  -3  -2  -4\n",
-      "D  -2  -3   6   2  -3  -1  -1  -3  -1  -4  -3   1  -1   0  -2   0  -1  -3  -4  -3   4   1  -1  -4\n",
-      "E  -1  -4   2   5  -3  -2   0  -3   1  -3  -2   0  -1   2   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
-      "F  -2  -2  -3  -3   6  -3  -1   0  -3   0   0  -3  -4  -3  -3  -2  -2  -1   1   3  -3  -3  -1  -4\n",
-      "G   0  -3  -1  -2  -3   6  -2  -4  -2  -4  -3   0  -2  -2  -2   0  -2  -3  -2  -3  -1  -2  -1  -4\n",
-      "H  -2  -3  -1   0  -1  -2   8  -3  -1  -3  -2   1  -2   0   0  -1  -2  -3  -2   2   0   0  -1  -4\n",
-      "I  -1  -1  -3  -3   0  -4  -3   4  -3   2   1  -3  -3  -3  -3  -2  -1   3  -3  -1  -3  -3  -1  -4\n",
-      "K  -1  -3  -1   1  -3  -2  -1  -3   5  -2  -1   0  -1   1   2   0  -1  -2  -3  -2   0   1  -1  -4\n",
-      "L  -1  -1  -4  -3   0  -4  -3   2  -2   4   2  -3  -3  -2  -2  -2  -1   1  -2  -1  -4  -3  -1  -4\n",
-      "M  -1  -1  -3  -2   0  -3  -2   1  -1   2   5  -2  -2   0  -1  -1  -1   1  -1  -1  -3  -1  -1  -4\n",
-      "N  -2  -3   1   0  -3   0   1  -3   0  -3  -2   6  -2   0   0   1   0  -3  -4  -2   3   0  -1  -4\n",
-      "P  -1  -3  -1  -1  -4  -2  -2  -3  -1  -3  -2  -2   7  -1  -2  -1  -1  -2  -4  -3  -2  -1  -2  -4\n",
-      "Q  -1  -3   0   2  -3  -2   0  -3   1  -2   0   0  -1   5   1   0  -1  -2  -2  -1   0   3  -1  -4\n",
-      "R  -1  -3  -2   0  -3  -2   0  -3   2  -2  -1   0  -2   1   5  -1  -1  -3  -3  -2  -1   0  -1  -4\n",
-      "S   1  -1   0   0  -2   0  -1  -2   0  -2  -1   1  -1   0  -1   4   1  -2  -3  -2   0   0   0  -4\n",
-      "T   0  -1  -1  -1  -2  -2  -2  -1  -1  -1  -1   0  -1  -1  -1   1   5   0  -2  -2  -1  -1   0  -4\n",
-      "V   0  -1  -3  -2  -1  -3  -3   3  -2   1   1  -3  -2  -2  -3  -2   0   4  -3  -1  -3  -2  -1  -4\n",
-      "W  -3  -2  -4  -3   1  -2  -2  -3  -3  -2  -1  -4  -4  -2  -3  -3  -2  -3  11   2  -4  -3  -2  -4\n",
-      "Y  -2  -2  -3  -2   3  -3   2  -1  -2  -1  -1  -2  -3  -1  -2  -2  -2  -1   2   7  -3  -2  -1  -4\n",
-      "B  -2  -3   4   1  -3  -1   0  -3   0  -4  -3   3  -2   0  -1   0  -1  -3  -4  -3   4   1  -1  -4\n",
-      "Z  -1  -3   1   4  -3  -2   0  -3   1  -3  -1   0  -1   3   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
-      "X   0  -2  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -2  -1  -1   0   0  -1  -2  -1  -1  -1  -1  -4\n",
-      "*  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4   1\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Obtain BLOSUM62\n",
-    "substitution_matrix = align.SubstitutionMatrix.std_protein_matrix()\n",
-    "print(substitution_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(numpy.ndarray, (24, 24))"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "score_matrix = substitution_matrix.score_matrix()\n",
-    "type(score_matrix), score_matrix.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "letter_alphabet = substitution_matrix.get_alphabet1()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'A': 0,\n",
-       " 'C': 1,\n",
-       " 'D': 2,\n",
-       " 'E': 3,\n",
-       " 'F': 4,\n",
-       " 'G': 5,\n",
-       " 'H': 6,\n",
-       " 'I': 7,\n",
-       " 'K': 8,\n",
-       " 'L': 9,\n",
-       " 'M': 10,\n",
-       " 'N': 11,\n",
-       " 'P': 12,\n",
-       " 'Q': 13,\n",
-       " 'R': 14,\n",
-       " 'S': 15,\n",
-       " 'T': 16,\n",
-       " 'V': 17,\n",
-       " 'W': 18,\n",
-       " 'Y': 19,\n",
-       " 'B': 20,\n",
-       " 'Z': 21,\n",
-       " 'X': 22,\n",
-       " '*': 23}"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict_letters = {}\n",
-    "for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
-    "    dict_letters[letter] = i\n",
-    "dict_letters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "match_score = np.zeros(len(a))\n",
-    "for i, (character_seq1, character_seq2) in enumerate(zip(a, b)):\n",
-    "    # print(character_seq1, character_seq2)#, type(character_seq1))\n",
-    "    ind1 = dict_letters[character_seq1]\n",
-    "    ind2 = dict_letters[character_seq2]\n",
-    "    match_score[i] = score_matrix[ind1, ind2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 1.,  4.,  2.,  6., -1.,  6., -2.,  6.,  6., -1.,  4.,  7., -1.,\n",
-       "       -1.,  4.,  3.,  5.,  0.,  4.,  2.,  0.,  4., -1.,  5.,  0., -1.,\n",
-       "        3.,  5., -1.,  0., -1.,  0.,  7.,  1.,  4., -1., -1.,  4.,  4.,\n",
-       "        6.,  4., -2.,  1.,  3., -1., -1., -1.,  5., -1., -1.,  6., -3.,\n",
-       "        4., -2.,  1.,  3.,  3.,  5.,  0.,  7.,  4., -1.,  0.,  2.,  2.,\n",
-       "        0.,  4.,  8.,  5.,  6.,  4.,  4.,  4.,  5.,  6., -1.,  2.,  1.,\n",
-       "        3.,  0.,  6.,  6.,  6.,  4.,  4.])"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "match_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(24, 24)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "substitution_matrix.shape()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "substitution_matrix.is_symmetric()"
+    "# Show matrix with background gradient\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "kinase_similarity_matrix.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Show kinase coverage"
+    "### Save kinase distance matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
-    "### Visualize similarity as phylogenetic tree"
+    "kinase_similarity_matrix.to_csv(DATA / \"kinase_similarity_matrix_sequence.csv\")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n",
+    "\n",
+    "The kinase similarity matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket sequence perspective we have talked about in this talktorial."
    ]
   },
   {
@@ -895,13 +1061,6 @@
     "2. Question\n",
     "3. Question"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 601309f328aa995263716ac508ae1256d72bfc40 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 14:59:39 +0200
Subject: [PATCH 028/158] add csv for similarity matrix.

---
 .../data/kinase_similarity_matrix_sequence.csv         | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
new file mode 100644
index 00000000..ce6a35bf
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,1.0,0.8941176470588236,0.3764705882352941,0.3176470588235294,0.4470588235294118,0.4588235294117647,0.38823529411764707,0.47058823529411764,0.11764705882352941
+ErbB2,0.8941176470588236,1.0,0.4,0.32941176470588235,0.4235294117647059,0.47058823529411764,0.4,0.43529411764705883,0.11764705882352941
+BRAF,0.3764705882352941,0.4,1.0,0.32941176470588235,0.38823529411764707,0.3764705882352941,0.3764705882352941,0.4,0.15294117647058825
+CDK2,0.3176470588235294,0.32941176470588235,0.32941176470588235,1.0,0.3764705882352941,0.36470588235294116,0.47058823529411764,0.3411764705882353,0.10588235294117647
+LCK,0.4470588235294118,0.4235294117647059,0.38823529411764707,0.3764705882352941,1.0,0.4,0.38823529411764707,0.43529411764705883,0.1411764705882353
+MET,0.4588235294117647,0.47058823529411764,0.3764705882352941,0.36470588235294116,0.4,1.0,0.36470588235294116,0.47058823529411764,0.10588235294117647
+p38a,0.38823529411764707,0.4,0.3764705882352941,0.47058823529411764,0.38823529411764707,0.36470588235294116,1.0,0.38823529411764707,0.1411764705882353
+KDR,0.47058823529411764,0.43529411764705883,0.4,0.3411764705882353,0.43529411764705883,0.47058823529411764,0.38823529411764707,1.0,0.15294117647058825
+p110a,0.11764705882352941,0.11764705882352941,0.15294117647058825,0.10588235294117647,0.1411764705882353,0.10588235294117647,0.1411764705882353,0.15294117647058825,1.0

From 198dd016e3891773fcab3a942e3f214df20e1612 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 15:12:21 +0200
Subject: [PATCH 029/158] add csv table for similarity matrix.

---
 ...inase_similarity_matrix_ligand_profile.csv |  10 +
 .../talktorial.ipynb                          | 630 +++++++++++++-----
 2 files changed, 480 insertions(+), 160 deletions(-)
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
new file mode 100644
index 00000000..0a003404
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,0.6089623445220651,0.5569176882661997,0.41935483870967744,0.11627906976744186,0.23846153846153847,0.22580645161290322,0.3157894736842105,0.3482142857142857,0.07142857142857142
+ErbB2,0.5569176882661997,0.6050767414403778,0.25,0.15384615384615385,0.17647058823529413,0.03571428571428571,0.125,0.39779005524861877,
+BRAF,0.41935483870967744,0.25,0.8115154807170016,0.06666666666666667,0.5116279069767442,0.1111111111111111,0.6444444444444445,0.7481203007518797,0.16666666666666666
+CDK2,0.11627906976744186,0.15384615384615385,0.06666666666666667,0.5441860465116279,0.16666666666666666,0.08695652173913043,0.08333333333333333,0.6016949152542372,0.26666666666666666
+LCK,0.23846153846153847,0.17647058823529413,0.5116279069767442,0.16666666666666666,0.605074821080026,0.2878787878787879,0.48936170212765956,0.4326923076923077,0.0
+MET,0.22580645161290322,0.03571428571428571,0.1111111111111111,0.08695652173913043,0.2878787878787879,0.7922722438851472,0.047619047619047616,0.5393586005830904,0.0
+p38a,0.3157894736842105,0.125,0.6444444444444445,0.08333333333333333,0.48936170212765956,0.047619047619047616,0.7647547797173733,0.5,0.0
+KDR,0.3482142857142857,0.39779005524861877,0.7481203007518797,0.6016949152542372,0.4326923076923077,0.5393586005830904,0.5,0.6967471143756558,0.1807909604519774
+p110a,0.07142857142857142,,0.16666666666666666,0.26666666666666666,0.0,0.0,0.0,0.1807909604519774,0.6395215090867264
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 0d64b4ed..a16b035d 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -91,23 +91,27 @@
     "\n",
     " \n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined **EGFR** and **ErbB2** as targets (indicated by a ‘+’) and **BRAF** (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2** (cyclic-dependent kinase 2), **LCK** (lymphocyte-specific protein tyrosine kinase), **MET** (mesenchymal-epithelial transition factor) and **p38α** (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
-   ]
-  },
-  {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
+    "\n",
+    " \n",
+    "\n",
+    "*Table 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+    " \n",
+    "\n",
+    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
+    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
+    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
+    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
+    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
+    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
+    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
+    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
+    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
+    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
+    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
    ]
   },
   {
@@ -201,6 +205,7 @@
     "from pathlib import Path\n",
     "import pandas as pd\n",
     "import numpy as np\n",
+    "import seaborn as sns\n",
     "from collections import Counter"
    ]
   },
@@ -488,7 +493,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKd', 'pIC50', 'pKi'}\n",
+      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -1461,42 +1466,42 @@
      "data": {
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        "}\n",
-       "#T_140b3_row1_col2, #T_140b3_row4_col2 {\n",
+       "#T_28336_row1_col2, #T_28336_row4_col2 {\n",
        "  background-color: #dedded;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_140b3_row2_col2 {\n",
+       "#T_28336_row2_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_140b3_row3_col2 {\n",
+       "#T_28336_row3_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_140b3_row5_col2 {\n",
+       "#T_28336_row5_col2 {\n",
        "  background-color: #4b1687;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_140b3_row6_col2 {\n",
+       "#T_28336_row6_col2 {\n",
        "  background-color: #5c3797;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_140b3_row7_col2 {\n",
+       "#T_28336_row7_col2 {\n",
        "  background-color: #8d89c0;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_140b3_row8_col2 {\n",
+       "#T_28336_row8_col2 {\n",
        "  background-color: #c0c1de;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_140b3_\">\n",
+       "<table id=\"T_28336_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1507,64 +1512,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_140b3_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
-       "      <td id=\"T_140b3_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
-       "      <td id=\"T_140b3_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_28336_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_28336_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
+       "      <td id=\"T_28336_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
+       "      <td id=\"T_28336_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_140b3_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
-       "      <td id=\"T_140b3_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
-       "      <td id=\"T_140b3_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
+       "      <th id=\"T_28336_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_28336_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
+       "      <td id=\"T_28336_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
+       "      <td id=\"T_28336_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_140b3_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
-       "      <td id=\"T_140b3_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
-       "      <td id=\"T_140b3_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <th id=\"T_28336_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_28336_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
+       "      <td id=\"T_28336_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
+       "      <td id=\"T_28336_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_140b3_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
-       "      <td id=\"T_140b3_row3_col1\" class=\"data row3 col1\" >819</td>\n",
-       "      <td id=\"T_140b3_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
+       "      <th id=\"T_28336_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_28336_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
+       "      <td id=\"T_28336_row3_col1\" class=\"data row3 col1\" >819</td>\n",
+       "      <td id=\"T_28336_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_140b3_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
-       "      <td id=\"T_140b3_row4_col1\" class=\"data row4 col1\" >930</td>\n",
-       "      <td id=\"T_140b3_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
+       "      <th id=\"T_28336_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_28336_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
+       "      <td id=\"T_28336_row4_col1\" class=\"data row4 col1\" >930</td>\n",
+       "      <td id=\"T_28336_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_140b3_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
-       "      <td id=\"T_140b3_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
-       "      <td id=\"T_140b3_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
+       "      <th id=\"T_28336_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_28336_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
+       "      <td id=\"T_28336_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
+       "      <td id=\"T_28336_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_140b3_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
-       "      <td id=\"T_140b3_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
-       "      <td id=\"T_140b3_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th id=\"T_28336_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_28336_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
+       "      <td id=\"T_28336_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
+       "      <td id=\"T_28336_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_140b3_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
-       "      <td id=\"T_140b3_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
-       "      <td id=\"T_140b3_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
+       "      <th id=\"T_28336_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_28336_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
+       "      <td id=\"T_28336_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
+       "      <td id=\"T_28336_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_140b3_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_140b3_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
-       "      <td id=\"T_140b3_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
-       "      <td id=\"T_140b3_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
+       "      <th id=\"T_28336_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_28336_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
+       "      <td id=\"T_28336_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
+       "      <td id=\"T_28336_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd1ec07a1c0>"
+       "<pandas.io.formats.style.Styler at 0x7fb372a2e2e0>"
       ]
      },
      "execution_count": 27,
@@ -1672,40 +1677,17 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.22580645161290322"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ligand profile similarity between EGFR and MET: 0.23.\n"
+     ]
     }
    ],
    "source": [
     "similarity_EGFR_MET = similarity_ligand_profile(\"EGFR\", \"MET\", data)\n",
-    "similarity_EGFR_MET"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.22580645161290322"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "similarity_MET_EGFR = similarity_ligand_profile(\"MET\", \"EGFR\", data)\n",
-    "similarity_MET_EGFR"
+    "print(f\"Ligand profile similarity between EGFR and MET: \"\n",
+    "      f\"{similarity_EGFR_MET:.2f}.\")"
    ]
   },
   {
@@ -1717,39 +1699,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.6089623445220651"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EGRF promiscuity: 0.61.\n"
+     ]
     }
    ],
    "source": [
     "promiscuity_EGFR = similarity_ligand_profile(\"EGFR\", \"EGFR\", data)\n",
-    "promiscuity_EGFR"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "a = data[data[\"UniprotID\"].isin([\"P00533\", \"Q16539\"])]\n",
-    "a\n",
-    "c = a[a[\"activity_binary\"]==1]\n",
-    "d = c.groupby(\"smiles\").size()\n",
-    "d\n",
-    "d[d==2].shape\n",
-    "b = a.groupby(\"smiles\").size()\n",
-    "b\n",
-    "b[b==2].shape\n",
-    "data.groupby(\"smiles\").size().unique()"
+    "print(f\"EGRF promiscuity: \"\n",
+    "      f\"{promiscuity_EGFR:.2f}.\")"
    ]
   },
   {
@@ -1761,7 +1725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1771,48 +1735,22 @@
       "No compounds were measured on both kinases, namely ErbB2 and p110a.\n",
       "No compounds were measured on both kinases, namely p110a and ErbB2.\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.60896234, 0.55691769, 0.41935484, 0.11627907, 0.23846154,\n",
-       "        0.22580645, 0.31578947, 0.34821429, 0.07142857],\n",
-       "       [0.55691769, 0.60507674, 0.25      , 0.15384615, 0.17647059,\n",
-       "        0.03571429, 0.125     , 0.39779006,        nan],\n",
-       "       [0.41935484, 0.25      , 0.81151548, 0.06666667, 0.51162791,\n",
-       "        0.11111111, 0.64444444, 0.7481203 , 0.16666667],\n",
-       "       [0.11627907, 0.15384615, 0.06666667, 0.54418605, 0.16666667,\n",
-       "        0.08695652, 0.08333333, 0.60169492, 0.26666667],\n",
-       "       [0.23846154, 0.17647059, 0.51162791, 0.16666667, 0.60507482,\n",
-       "        0.28787879, 0.4893617 , 0.43269231, 0.        ],\n",
-       "       [0.22580645, 0.03571429, 0.11111111, 0.08695652, 0.28787879,\n",
-       "        0.79227224, 0.04761905, 0.5393586 , 0.        ],\n",
-       "       [0.31578947, 0.125     , 0.64444444, 0.08333333, 0.4893617 ,\n",
-       "        0.04761905, 0.76475478, 0.5       , 0.        ],\n",
-       "       [0.34821429, 0.39779006, 0.7481203 , 0.60169492, 0.43269231,\n",
-       "        0.5393586 , 0.5       , 0.69674711, 0.18079096],\n",
-       "       [0.07142857,        nan, 0.16666667, 0.26666667, 0.        ,\n",
-       "        0.        , 0.        , 0.18079096, 0.63952151]])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "table = np.zeros((len(name_to_uniprot), len(name_to_uniprot)))\n",
+    "kinase_similarity_matrix = np.zeros((len(name_to_uniprot),\n",
+    "                                     len(name_to_uniprot)))\n",
     "for i, kinase_name1 in enumerate(name_to_uniprot):\n",
     "    for j, kinase_name2 in enumerate(name_to_uniprot):\n",
-    "        table[i, j] = similarity_ligand_profile(kinase_name1,\n",
-    "                                                kinase_name2,\n",
-    "                                                data)\n",
-    "table"
+    "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(\n",
+    "            kinase_name1,\n",
+    "            kinase_name2,\n",
+    "            data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1984,30 +1922,400 @@
        "p110a  0.180791  0.639522  "
       ]
      },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
+    "                                        index=name_to_uniprot.keys(),\n",
+    "                                        columns=name_to_uniprot.keys())\n",
+    "kinase_similarity_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_567e4_row0_col0, #T_567e4_row1_col1, #T_567e4_row2_col2, #T_567e4_row2_col7, #T_567e4_row4_col4, #T_567e4_row5_col5, #T_567e4_row6_col6, #T_567e4_row7_col3, #T_567e4_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row0_col1, #T_567e4_row7_col2 {\n",
+       "  background-color: #138913;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row0_col2, #T_567e4_row5_col4 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col3 {\n",
+       "  background-color: #d5e9d5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col4 {\n",
+       "  background-color: #8fc68f;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col5, #T_567e4_row7_col8 {\n",
+       "  background-color: #a8d2a8;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col6 {\n",
+       "  background-color: #8ac48a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col7 {\n",
+       "  background-color: #a6d1a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row0_col8, #T_567e4_row3_col5 {\n",
+       "  background-color: #d1e6d1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col0 {\n",
+       "  background-color: #168b16;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row1_col2, #T_567e4_row4_col1 {\n",
+       "  background-color: #b1d7b1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col3, #T_567e4_row1_col6 {\n",
+       "  background-color: #c5e0c5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col4 {\n",
+       "  background-color: #a6d2a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col5 {\n",
+       "  background-color: #e0eee0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col7 {\n",
+       "  background-color: #91c791;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row1_col8, #T_567e4_row8_col1 {\n",
+       "  background-color: #000000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row2_col0 {\n",
+       "  background-color: #53a953;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row2_col1 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row2_col3, #T_567e4_row3_col2, #T_567e4_row4_col8, #T_567e4_row5_col1, #T_567e4_row5_col8, #T_567e4_row6_col8, #T_567e4_row8_col0, #T_567e4_row8_col4, #T_567e4_row8_col5, #T_567e4_row8_col6, #T_567e4_row8_col7 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row2_col4 {\n",
+       "  background-color: #249224;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row2_col5 {\n",
+       "  background-color: #cae3ca;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row2_col6 {\n",
+       "  background-color: #259225;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row2_col8 {\n",
+       "  background-color: #aed5ae;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row3_col0 {\n",
+       "  background-color: #d7e9d7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row3_col1 {\n",
+       "  background-color: #badbba;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row3_col3 {\n",
+       "  background-color: #198c19;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row3_col4 {\n",
+       "  background-color: #aad3aa;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row3_col6 {\n",
+       "  background-color: #d2e7d2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row3_col7 {\n",
+       "  background-color: #3d9e3d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row3_col8 {\n",
+       "  background-color: #89c389;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row4_col0 {\n",
+       "  background-color: #a2cfa2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row4_col2 {\n",
+       "  background-color: #5fae5f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row4_col3 {\n",
+       "  background-color: #bfdebf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row4_col5 {\n",
+       "  background-color: #95c995;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row4_col6 {\n",
+       "  background-color: #55a955;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row4_col7 {\n",
+       "  background-color: #83c083;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row5_col0 {\n",
+       "  background-color: #a7d2a7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row5_col2, #T_567e4_row5_col6, #T_567e4_row6_col5 {\n",
+       "  background-color: #ddecdd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row5_col3 {\n",
+       "  background-color: #e2efe2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row5_col7, #T_567e4_row7_col1 {\n",
+       "  background-color: #56aa56;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row6_col0 {\n",
+       "  background-color: #80bf80;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row6_col1 {\n",
+       "  background-color: #c6e1c6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row6_col2 {\n",
+       "  background-color: #349a34;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row6_col3 {\n",
+       "  background-color: #e4f0e4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row6_col4 {\n",
+       "  background-color: #2c962c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row6_col7 {\n",
+       "  background-color: #66b266;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row7_col0 {\n",
+       "  background-color: #72b872;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row7_col4 {\n",
+       "  background-color: #42a042;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row7_col5 {\n",
+       "  background-color: #4ba54a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row7_col6 {\n",
+       "  background-color: #51a851;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row7_col7 {\n",
+       "  background-color: #158a15;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_567e4_row8_col2 {\n",
+       "  background-color: #cbe4cb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_567e4_row8_col3 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_567e4_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_567e4_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_567e4_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "      <td id=\"T_567e4_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "      <td id=\"T_567e4_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "      <td id=\"T_567e4_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "      <td id=\"T_567e4_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "      <td id=\"T_567e4_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "      <td id=\"T_567e4_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "      <td id=\"T_567e4_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_567e4_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "      <td id=\"T_567e4_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "      <td id=\"T_567e4_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "      <td id=\"T_567e4_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "      <td id=\"T_567e4_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "      <td id=\"T_567e4_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "      <td id=\"T_567e4_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "      <td id=\"T_567e4_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "      <td id=\"T_567e4_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_567e4_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "      <td id=\"T_567e4_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "      <td id=\"T_567e4_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <td id=\"T_567e4_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "      <td id=\"T_567e4_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "      <td id=\"T_567e4_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "      <td id=\"T_567e4_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "      <td id=\"T_567e4_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "      <td id=\"T_567e4_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_567e4_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "      <td id=\"T_567e4_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "      <td id=\"T_567e4_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "      <td id=\"T_567e4_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "      <td id=\"T_567e4_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "      <td id=\"T_567e4_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "      <td id=\"T_567e4_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "      <td id=\"T_567e4_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "      <td id=\"T_567e4_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_567e4_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "      <td id=\"T_567e4_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "      <td id=\"T_567e4_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "      <td id=\"T_567e4_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "      <td id=\"T_567e4_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "      <td id=\"T_567e4_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "      <td id=\"T_567e4_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "      <td id=\"T_567e4_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "      <td id=\"T_567e4_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_567e4_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "      <td id=\"T_567e4_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "      <td id=\"T_567e4_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "      <td id=\"T_567e4_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "      <td id=\"T_567e4_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "      <td id=\"T_567e4_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "      <td id=\"T_567e4_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "      <td id=\"T_567e4_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "      <td id=\"T_567e4_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_567e4_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "      <td id=\"T_567e4_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "      <td id=\"T_567e4_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "      <td id=\"T_567e4_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "      <td id=\"T_567e4_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "      <td id=\"T_567e4_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "      <td id=\"T_567e4_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "      <td id=\"T_567e4_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "      <td id=\"T_567e4_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_567e4_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "      <td id=\"T_567e4_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "      <td id=\"T_567e4_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "      <td id=\"T_567e4_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "      <td id=\"T_567e4_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "      <td id=\"T_567e4_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "      <td id=\"T_567e4_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "      <td id=\"T_567e4_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "      <td id=\"T_567e4_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_567e4_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_567e4_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "      <td id=\"T_567e4_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
+       "      <td id=\"T_567e4_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "      <td id=\"T_567e4_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "      <td id=\"T_567e4_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "      <td id=\"T_567e4_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "      <td id=\"T_567e4_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "      <td id=\"T_567e4_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "      <td id=\"T_567e4_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fb372e418b0>"
+      ]
+     },
      "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "table_comparison = pd.DataFrame(data=table, index=name_to_uniprot.keys(),\n",
-    "                                columns=name_to_uniprot.keys())\n",
-    "table_comparison"
+    "# Show matrix with background gradient\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "kinase_similarity_matrix.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as phylogenetic tree"
+    "### Save kinase distance matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "kinase_similarity_matrix.to_csv(DATA /\n",
+    "                                \"kinase_similarity_matrix_ligand_profile.csv\")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2015,7 +2323,9 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n",
+    "\n",
+    "The kinase similarity matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
   },
   {

From 23e47d7891a3715520dc8340ff2b79c8bb94e96a Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 15:26:43 +0200
Subject: [PATCH 030/158] Add talktorial folder skeleton

---
 .../README.md                                 |   0
 .../data/README.md                            |   0
 .../images/README.md                          |   0
 .../talktorial.ipynb                          | 318 ------------------
 4 files changed, 318 deletions(-)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T025_kinase_similarity_kissim}/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T025_kinase_similarity_kissim}/data/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T025_kinase_similarity_kissim}/images/README.md (100%)
 delete mode 100644 teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb

diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md b/teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
rename to teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
rename to teachopencadd/talktorials/T025_kinase_similarity_kissim/data/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md b/teachopencadd/talktorials/T025_kinase_similarity_kissim/images/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
rename to teachopencadd/talktorials/T025_kinase_similarity_kissim/images/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb b/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
deleted file mode 100644
index 0fa255f7..00000000
--- a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
+++ /dev/null
@@ -1,318 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# T000 · Kinase similarity: XXX\n",
-    "\n",
-    "Authors:\n",
-    "\n",
-    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
-    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
-    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Aim of this talktorial\n",
-    "\n",
-    "Add a short summary of this talktorial's content."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contents in *Theory*\n",
-    "\n",
-    "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Contents in *Practical*\n",
-    "\n",
-    "* Retrieve and preprocess data\n",
-    "* Show kinase coverage\n",
-    "* Compare kinases\n",
-    "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### References\n",
-    "\n",
-    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Theory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Kinase dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
-   ]
-  },
-  {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
-    "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Kinase similarity descriptor: XXX\n",
-    "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
-    "\n",
-    "- XXX = KLIFS pocket sequence\n",
-    "- XXX = KiSSim fingerprint\n",
-    "- XXX = KLIFS IFP\n",
-    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compound activity measures"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    \n",
-    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you place links, please link descriptive words.\n",
-    "\n",
-    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
-    "\n",
-    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Practical"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pathlib import Path\n",
-    "\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "HERE = Path(_dh[-1])\n",
-    "DATA = HERE / \"data\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve and preprocess data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Show kinase coverage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compare kinases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as kinase matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as phylogenetic tree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Discussion\n",
-    "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quiz\n",
-    "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.5"
-  },
-  "toc-autonumbering": true,
-  "widgets": {
-   "application/vnd.jupyter.widget-state+json": {
-    "state": {},
-    "version_major": 2,
-    "version_minor": 0
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 9dad580ce9b1b0095552dca26910f2f6de0a85b7 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 15:27:00 +0200
Subject: [PATCH 031/158] Add talktorial (Theory + Practical)

---
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diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
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+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -0,0 +1,1383 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# T025 · Kinase similarity: Kinase pocket (KiSSim fingerprint)\n",
+    "\n",
+    "Authors:\n",
+    "\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Aim of this talktorial\n",
+    "\n",
+    "We will assess the similarity between a set of kinases based on the KiSSim fingerprint, which describes the physicochemical and spatial properties in structurally resolved kinases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Theory*\n",
+    "\n",
+    "* Kinase dataset\n",
+    "* Kinase similarity descriptor: Kinase pockets (KiSSim fingerprint)\n",
+    "* Fetching KLIFS data with `opencadd.databases.klifs`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Contents in *Practical*\n",
+    "\n",
+    "* Retrieve and preprocess data\n",
+    "* Show kinase coverage\n",
+    "* Encode and compare structures\n",
+    "* Map structure to kinase distance matrix\n",
+    "* Save kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Protein Data Bank\n",
+    "  * PDB URL: http://www.rcsb.org/\n",
+    "  * PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)\n",
+    "* KLIFS\n",
+    "  * KLIFS URL: https://klifs.net/\n",
+    "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "  * KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
+    " * Binding site comparison reviews: \n",
+    "   * [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites)\n",
+    "    * [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)\n",
+    "* KiSSim: Kinase Structural Similarity\n",
+    "  * GitHub repository: https://github.com/volkamerlab/kissim\n",
+    "  * Documentation: https://kissim.readthedocs.io\n",
+    "* `opencadd`, a Python library for structural cheminformatics\n",
+    "  * GitHub repository: https://github.com/volkamerlab/opencadd\n",
+    "  * Documentation: https://opencadd.readthedocs.io"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Theory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
+    "\n",
+    "*Table 1:* \n",
+    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "\n",
+    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
+    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
+    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
+    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
+    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
+    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
+    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
+    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
+    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
+    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
+    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: Kinase pockets (KiSSim fingerprint)\n",
+    "\n",
+    "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets. We here use the KiSSim (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
+    "\n",
+    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinase binding site residues as defined by KLIFS.\n",
+    "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The KiSSim fingerprint encodes each of the 85 residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 2). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase.\n",
+    "\n",
+    "![KiSSim fingerprint](https://raw.githubusercontent.com/volkamerlab/kissim/main/docs/_static/kissim_toc.png)\n",
+    "\n",
+    "*Figure 2:* \n",
+    "The KiSSim fingerprint encodes physicochemical and spatial properties of a kinase binding site.\n",
+    "Figure taken from: https://github.com/volkamerlab/kissim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fetching KLIFS data with `opencadd.databases.klifs`\n",
+    "\n",
+    "`opencadd` is a Python library for structural cheminformatics developed by the Volkamer lab at the Charité in Berlin. \n",
+    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS IFP as `pandas` DataFrame.\n",
+    "\n",
+    "- GitHub repository: https://github.com/volkamerlab/opencadd\n",
+    "- Documentation: https://opencadd.readthedocs.io\n",
+    "\n",
+    "For more information about this library and the KLIFS OpenAPI, please refer to __Talktorial T012__."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f42d460875d94dd983366bf029b9fe7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import pairwise\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from opencadd.databases.klifs import setup_remote\n",
+    "from kissim.encoding import FingerprintGenerator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "HERE = Path(_dh[-1])\n",
+    "DATA = HERE / \"data\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define our kinases of interest and all their synonyms as described in Table 1 in the global variable `query_kinases`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_kinases = [\n",
+    "    \"EGFR\",\n",
+    "    \"ErbB2\",\n",
+    "    \"Her2\",\n",
+    "    \"PI3K\",\n",
+    "    \"PI3KCA\",\n",
+    "    \"p110a\",\n",
+    "    \"BRAF\",\n",
+    "    \"CDK2\",\n",
+    "    \"LCK\",\n",
+    "    \"MET\",\n",
+    "    \"p38a\",\n",
+    "    \"MAPK14\",\n",
+    "    \"VEGFR2\",\n",
+    "    \"KDR\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we query the KLIFS database using the `opencadd.databases.klifs` module to generate our IFP dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set up a remote KLIFS session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from opencadd.databases.klifs import setup_remote"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "klifs_session = setup_remote()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Fetch all structures describing these kinases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (80 > 79 characters)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 2466\n",
+      "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
+     ]
+    }
+   ],
+   "source": [
+    "structures = klifs_session.structures.by_kinase_name(kinase_names=query_kinases)\n",
+    "print(f\"Number of structures: {len(structures)}\")\n",
+    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Filter structures\n",
+    "\n",
+    "We filter the structures by different criteria:\n",
+    "\n",
+    "- Species: human\n",
+    "- Conformation: DFG-in (the active kinase conformation)\n",
+    "- Resolution: $\\le 3$ Angström\n",
+    "- Quality score*: $\\ge 6$\n",
+    "\n",
+    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms (the higher the better the structure quality)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n",
+      "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
+     ]
+    }
+   ],
+   "source": [
+    "structures = structures[\n",
+    "    (structures[\"species.klifs\"] == \"Human\")\n",
+    "    & (structures[\"structure.dfg\"] == \"in\")\n",
+    "    & (structures[\"structure.resolution\"] <= 3)\n",
+    "    & (structures[\"structure.qualityscore\"] >= 6)\n",
+    "]\n",
+    "print(f\"Number of structures: {len(structures)}\")\n",
+    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the structure KLIFS IDs for the next step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n"
+     ]
+    }
+   ],
+   "source": [
+    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()\n",
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please uncomment the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1620\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load frozen dataset\n",
+    "structure_klifs_ids = pd.read_csv(\n",
+    "    DATA / \"frozen_structure_klifs_ids.csv\"\n",
+    ")[\"structure.klifs_id\"].to_list()\n",
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Show kinase coverage\n",
+    "\n",
+    "Let's get the number of structures that describe our kinases (kinase coverage)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "kinase.klifs_name\n",
+       "ErbB2      4\n",
+       "KDR        6\n",
+       "LCK       32\n",
+       "p110a     49\n",
+       "BRAF      72\n",
+       "MET       99\n",
+       "p38a     151\n",
+       "EGFR     357\n",
+       "CDK2     850\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (80 > 79 characters)\n",
+      "2:80: E501 line too long (86 > 79 characters)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
+    "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
+    "n_structures_per_kinase\n",
+    "# NBVAL_CHECK_OUTPUT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's plot our results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "n_structures_per_kinase.plot(kind=\"barh\", ax=ax)\n",
+    "ax.set_xlabel(\"Number of structures\")\n",
+    "ax.set_ylabel(\"Kinase name\")\n",
+    "for i, value in enumerate(n_structures_per_kinase):\n",
+    "    ax.text(value, i, str(value), va=\"center\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not: See e.g. CDK2 and EGFR vs. KDR and ErbB2. We will have to keep this in mind when interpreting our results later."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load KiSSim fingerprints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use in this notebook pre-calculated KiSSim fingerprints for our kinase set. If you wanted to calculate the fingerprints yourself, you can use the following `kissim` API: \n",
+    "\n",
+    "```python\n",
+    "from kissim.api import encode\n",
+    "kissim_fingerprints = encode(structure_klifs_ids, n_cores=1)\n",
+    "kissim_fingerprints.to_json(DATA / \"kissim_fingerprints.json\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's load the pre-calculated KiSSim fingerprints!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (87 > 79 characters)\n"
+     ]
+    }
+   ],
+   "source": [
+    "kissim_fingerprints = FingerprintGenerator.from_json(DATA / \"kissim_fingerprints.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The fingerprints are stored in a `FingerprintGenerator` objects; you can access the fingerprints with the `data` property: `kissim_fingerprints.data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2:80: E501 line too long (112 > 79 characters)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures: 1611\n",
+      "Kinases: p110a CDK2 BRAF LCK p38a EGFR ErbB2 KDR MET\n"
+     ]
+    }
+   ],
+   "source": [
+    "structure_klifs_ids = list(kissim_fingerprints.data.keys())\n",
+    "kinase_names = [fingerprint.kinase_name for structure_klifs_id, fingerprint in kissim_fingerprints.data.items()]\n",
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
+    "print(\"Kinases:\", *list(set(kinase_names)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might notice that we have fewer fingerprints than structures. This can happen during the `kissim` fingerprinting when some structures cannot be encoded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's extract all fingerprints as `numpy` array where each row corresponds to a structure's fingerprint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1611, 1032)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2:40: W291 trailing whitespace\n",
+      "3:40: W291 trailing whitespace\n"
+     ]
+    }
+   ],
+   "source": [
+    "kissim_fingerprints_array = [\n",
+    "    fingerprint.values_array().tolist()\n",
+    "    for structure_klifs_id, fingerprint\n",
+    "    in kissim_fingerprints.data.items()\n",
+    "]\n",
+    "kissim_fingerprints_array = np.array(kissim_fingerprints_array)\n",
+    "kissim_fingerprints_array.shape\n",
+    "# NBVAL_CHECK_OUTPUT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's delete the KiSSim variable because we do not need it any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del kissim_fingerprints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare structures\n",
+    "\n",
+    "Let's compare the KiSSim fingerprints pairwise. We use `sklearn`'s `nan_euclidean_distances` method, which calculates the Euclidean distance between all pairwise vectors of a matrix. \n",
+    "\n",
+    "If two vectors have NaN values (which can happen if residues are not resolved in a structure), the following procedure is applied:\n",
+    "\n",
+    ">  When calculating the distance between a pair of samples, this formulation ignores feature coordinates with a missing value in either sample and scales up the weight of the remaining coordinates.\n",
+    "\n",
+    "Please find more information in their documentation:\n",
+    "\n",
+    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.        , 13.25694052, 14.00147438, ..., 50.66954295,\n",
+       "        50.7622906 , 50.9508342 ],\n",
+       "       [13.25694052,  0.        , 10.37977891, ..., 50.76260217,\n",
+       "        51.20730054, 51.4472163 ],\n",
+       "       [14.00147438, 10.37977891,  0.        , ..., 51.94849587,\n",
+       "        52.88538294, 52.73268888],\n",
+       "       ...,\n",
+       "       [50.66954295, 50.76260217, 51.94849587, ...,  0.        ,\n",
+       "         8.24900205,  6.08783522],\n",
+       "       [50.7622906 , 51.20730054, 52.88538294, ...,  8.24900205,\n",
+       "         0.        ,  7.94854801],\n",
+       "       [50.9508342 , 51.4472163 , 52.73268888, ...,  6.08783522,\n",
+       "         7.94854801,  0.        ]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (87 > 79 characters)\n"
+     ]
+    }
+   ],
+   "source": [
+    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints_array)\n",
+    "structure_distance_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure distance matrix size: (1611, 1611)\n",
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>6285</th>\n",
+       "      <th>10568</th>\n",
+       "      <th>11187</th>\n",
+       "      <th>4060</th>\n",
+       "      <th>10566</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6285</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>13.256941</td>\n",
+       "      <td>14.001474</td>\n",
+       "      <td>26.391543</td>\n",
+       "      <td>14.307291</td>\n",
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+       "    <tr>\n",
+       "      <th>10568</th>\n",
+       "      <td>13.256941</td>\n",
+       "      <td>0.000000</td>\n",
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+       "      <td>27.882193</td>\n",
+       "      <td>16.833932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11187</th>\n",
+       "      <td>14.001474</td>\n",
+       "      <td>10.379779</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>30.962221</td>\n",
+       "      <td>18.338492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4060</th>\n",
+       "      <td>26.391543</td>\n",
+       "      <td>27.882193</td>\n",
+       "      <td>30.962221</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>28.905189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10566</th>\n",
+       "      <td>14.307291</td>\n",
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+       "      <td>18.338492</td>\n",
+       "      <td>28.905189</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           6285       10568      11187      4060       10566\n",
+       "6285    0.000000  13.256941  14.001474  26.391543  14.307291\n",
+       "10568  13.256941   0.000000  10.379779  27.882193  16.833932\n",
+       "11187  14.001474  10.379779   0.000000  30.962221  18.338492\n",
+       "4060   26.391543  27.882193  30.962221   0.000000  28.905189\n",
+       "10566  14.307291  16.833932  18.338492  28.905189   0.000000"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (85 > 79 characters)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create DataFrame with structure KLIFS IDs as index/columns\n",
+    "structure_distance_matrix = pd.DataFrame(\n",
+    "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
+    ")\n",
+    "print(f\"Structure distance matrix size: {structure_distance_matrix.shape}\")\n",
+    "print(\"Show matrix subset:\")\n",
+    "structure_distance_matrix.iloc[:5, :5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Map structure to kinase distance matrix\n",
+    "\n",
+    "First &mdash; as an intermediate step &mdash; we will create a structure distance matrix but instead of labeling the data with structure KLIFS IDs we add the corresponding kinase name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>CDK2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>13.256941</td>\n",
+       "      <td>14.001474</td>\n",
+       "      <td>26.391543</td>\n",
+       "      <td>14.307291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>13.256941</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.379779</td>\n",
+       "      <td>27.882193</td>\n",
+       "      <td>16.833932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>14.001474</td>\n",
+       "      <td>10.379779</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>30.962221</td>\n",
+       "      <td>18.338492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>26.391543</td>\n",
+       "      <td>27.882193</td>\n",
+       "      <td>30.962221</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>28.905189</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>14.307291</td>\n",
+       "      <td>16.833932</td>\n",
+       "      <td>18.338492</td>\n",
+       "      <td>28.905189</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           CDK2       CDK2       CDK2       CDK2       CDK2\n",
+       "CDK2   0.000000  13.256941  14.001474  26.391543  14.307291\n",
+       "CDK2  13.256941   0.000000  10.379779  27.882193  16.833932\n",
+       "CDK2  14.001474  10.379779   0.000000  30.962221  18.338492\n",
+       "CDK2  26.391543  27.882193  30.962221   0.000000  28.905189\n",
+       "CDK2  14.307291  16.833932  18.338492  28.905189   0.000000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Copy distance matrix to kinase matrix\n",
+    "kinase_distance_matrix = structure_distance_matrix\n",
+    "# Replace structure KLIFS IDs with the structures' kinase names\n",
+    "kinase_distance_matrix.index = kinase_names\n",
+    "kinase_distance_matrix.columns = kinase_names\n",
+    "print(\"Show matrix subset:\")\n",
+    "kinase_distance_matrix.iloc[:5, :5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this talktorial, we will consider per kinase pair the two structures that show the most similar binding mode for their co-crystallized ligands. Hence, we select the structure pair with the minimum distance as representative for a kinase pair."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
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+       "#T_78045_row8_col7 {\n",
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+       "</style>\n",
+       "<table id=\"T_78045_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_78045_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_78045_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_78045_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_78045_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_78045_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_78045_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_78045_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_78045_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_78045_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_78045_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_78045_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_78045_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_78045_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_78045_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_78045_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_78045_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_78045_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_78045_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_78045_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_78045_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_78045_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_78045_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_78045_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_78045_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_78045_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_78045_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_78045_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_78045_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_78045_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_78045_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_78045_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_78045_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_78045_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_78045_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_78045_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_78045_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_78045_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_78045_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_78045_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_78045_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_78045_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_78045_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_78045_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_78045_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_78045_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_78045_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_78045_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_78045_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_78045_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_78045_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_78045_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_78045_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_78045_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_78045_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_78045_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_78045_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_78045_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_78045_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_78045_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_78045_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_78045_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_78045_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_78045_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_78045_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_78045_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_78045_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_78045_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_78045_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_78045_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_78045_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_78045_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_78045_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_78045_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_78045_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_78045_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_78045_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_78045_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_78045_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_78045_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_78045_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_78045_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_78045_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f635a1e6e80>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2:80: E501 line too long (89 > 79 characters)\n",
+      "6:80: E501 line too long (81 > 79 characters)\n",
+      "11:75: W291 trailing whitespace\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We unstack the matrix (each pairwise comparison in a single row)\n",
+    "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
+    "# We take the minimum value in each kinase pair group\n",
+    "# We unstack the remaining data points\n",
+    "kinase_distance_matrix = (\n",
+    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
+    "# Show matrix with background gradient\n",
+    "# Show matrix with background gradient\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Save kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix.index.name = None\n",
+    "kinase_distance_matrix.columns.name = None\n",
+    "kinase_distance_matrix.to_csv(DATA / \"kinase_distance_matrix_kissim.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Discussion\n",
+    "\n",
+    "In this talktorial, we have assessed kinase similarity used KiSSim fingerprints, which describe physicochemical and spatial properties of pockets in kinase structures. We have reduced a structure distance matrix to a kinase distance matrix by selecting for each kinase pair the corresponding closest structure pair. \n",
+    "\n",
+    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quiz\n",
+    "\n",
+    "1. Can you think of reasons why it is important to include physicochemical *and* spatial properties in a fingerprint?\n",
+    "2. Can you think of a reason why the side chain orientation and solvent exposure may be interesting features to consider in a fingerprint?\n",
+    "2. Can you think of research questions for which you could make use of the structure distance matrix?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.6"
+  },
+  "toc-autonumbering": true,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 7f1c16c56c0a527053b895259776361676505647 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 15:27:11 +0200
Subject: [PATCH 032/158] Add frozen set of structure KLIFS IDs

---
 .../data/frozen_structure_klifs_ids.csv       | 1621 +++++++++++++++++
 1 file changed, 1621 insertions(+)
 create mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/frozen_structure_klifs_ids.csv

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/frozen_structure_klifs_ids.csv b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/frozen_structure_klifs_ids.csv
new file mode 100644
index 00000000..2eae163c
--- /dev/null
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/frozen_structure_klifs_ids.csv
@@ -0,0 +1,1621 @@
+structure.klifs_id
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From 9710bc7327ab76d4602d6e6a6b00fe338d7392a9 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:02:04 +0200
Subject: [PATCH 033/158] Update talktorial (use fingerprints from CSV instead
 of JSON)

---
 .../talktorial.ipynb                          | 522 ++++++++----------
 1 file changed, 216 insertions(+), 306 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 8a8d1ce4..782c8b79 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -174,13 +174,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f42d460875d94dd983366bf029b9fe7c",
+       "model_id": "f7ed13c4ebba4baeaffdad2223c902e9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -275,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -291,16 +291,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (80 > 79 characters)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -334,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -366,7 +359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -391,7 +384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -404,9 +397,9 @@
    ],
    "source": [
     "# Load frozen dataset\n",
-    "structure_klifs_ids = pd.read_csv(\n",
-    "    DATA / \"frozen_structure_klifs_ids.csv\"\n",
-    ")[\"structure.klifs_id\"].to_list()\n",
+    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\n",
+    "    \"structure.klifs_id\"\n",
+    "].to_list()\n",
     "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -424,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -443,17 +436,9 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (80 > 79 characters)\n",
-      "2:80: E501 line too long (86 > 79 characters)\n"
-     ]
     }
    ],
    "source": [
@@ -472,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -518,9 +503,20 @@
     "We will use in this notebook pre-calculated KiSSim fingerprints for our kinase set. If you wanted to calculate the fingerprints yourself, you can use the following `kissim` API: \n",
     "\n",
     "```python\n",
+    "# Generate fingerprints\n",
     "from kissim.api import encode\n",
     "kissim_fingerprints = encode(structure_klifs_ids, n_cores=1)\n",
-    "kissim_fingerprints.to_json(DATA / \"kissim_fingerprints.json\")\n",
+    "\n",
+    "# Save fingerprints as csv files\n",
+    "structure_klifs_ids = list(kissim_fingerprints.data.keys())\n",
+    "kissim_fingerprints_array = [\n",
+    "    fingerprint.values_array().tolist()\n",
+    "    for structure_klifs_id, fingerprint\n",
+    "    in kissim_fingerprints.data.items()\n",
+    "]\n",
+    "kissim_fingerprints_array = np.array(kissim_fingerprints_array)\n",
+    "kissim_fingerprints_df = pd.DataFrame(kissim_fingerprints_array, index=structure_klifs_ids)\n",
+    "kissim_fingerprints_df.to_csv(DATA / \"kissim_fingerprints.csv\")\n",
     "```"
    ]
   },
@@ -533,54 +529,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (87 > 79 characters)\n"
-     ]
-    }
-   ],
-   "source": [
-    "kissim_fingerprints = FingerprintGenerator.from_json(DATA / \"kissim_fingerprints.json\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The fingerprints are stored in a `FingerprintGenerator` objects; you can access the fingerprints with the `data` property: `kissim_fingerprints.data`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2:80: E501 line too long (112 > 79 characters)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 1611\n",
-      "Kinases: p110a CDK2 BRAF LCK p38a EGFR ErbB2 KDR MET\n"
+      "Matrix shape: (1611, 1032)\n",
+      "Number of fingerprints: 1611\n",
+      "Number of fingerprint bits: 1032\n"
      ]
     }
    ],
    "source": [
-    "structure_klifs_ids = list(kissim_fingerprints.data.keys())\n",
-    "kinase_names = [fingerprint.kinase_name for structure_klifs_id, fingerprint in kissim_fingerprints.data.items()]\n",
-    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
-    "print(\"Kinases:\", *list(set(kinase_names)))"
+    "kissim_fingerprints = pd.read_csv(DATA / \"kissim_fingerprints.csv\", index_col=0)\n",
+    "print(f\"Matrix shape: {kissim_fingerprints.shape}\")\n",
+    "print(f\"Number of fingerprints: {kissim_fingerprints.shape[0]}\")\n",
+    "print(f\"Number of fingerprint bits: {kissim_fingerprints.shape[1]}\")"
    ]
   },
   {
@@ -590,64 +556,6 @@
     "You might notice that we have fewer fingerprints than structures. This can happen during the `kissim` fingerprinting when some structures cannot be encoded."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's extract all fingerprints as `numpy` array where each row corresponds to a structure's fingerprint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1611, 1032)"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2:40: W291 trailing whitespace\n",
-      "3:40: W291 trailing whitespace\n"
-     ]
-    }
-   ],
-   "source": [
-    "kissim_fingerprints_array = [\n",
-    "    fingerprint.values_array().tolist()\n",
-    "    for structure_klifs_id, fingerprint\n",
-    "    in kissim_fingerprints.data.items()\n",
-    "]\n",
-    "kissim_fingerprints_array = np.array(kissim_fingerprints_array)\n",
-    "kissim_fingerprints_array.shape\n",
-    "# NBVAL_CHECK_OUTPUT"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's delete the KiSSim variable because we do not need it any more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "del kissim_fingerprints"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -667,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -688,26 +596,19 @@
        "         7.94854801,  0.        ]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (87 > 79 characters)\n"
-     ]
     }
    ],
    "source": [
-    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints_array)\n",
+    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints.values)\n",
     "structure_distance_matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -800,20 +701,14 @@
        "10566  14.307291  16.833932  18.338492  28.905189   0.000000"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (85 > 79 characters)\n"
-     ]
     }
    ],
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
+    "structure_klifs_ids = kissim_fingerprints.index.to_list()\n",
     "structure_distance_matrix = pd.DataFrame(\n",
     "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
     ")\n",
@@ -835,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -865,13 +760,21 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
+       "      <th>kinase.klifs_name</th>\n",
        "      <th>CDK2</th>\n",
        "      <th>CDK2</th>\n",
        "      <th>CDK2</th>\n",
        "      <th>CDK2</th>\n",
        "      <th>CDK2</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -919,15 +822,16 @@
        "</div>"
       ],
       "text/plain": [
-       "           CDK2       CDK2       CDK2       CDK2       CDK2\n",
-       "CDK2   0.000000  13.256941  14.001474  26.391543  14.307291\n",
-       "CDK2  13.256941   0.000000  10.379779  27.882193  16.833932\n",
-       "CDK2  14.001474  10.379779   0.000000  30.962221  18.338492\n",
-       "CDK2  26.391543  27.882193  30.962221   0.000000  28.905189\n",
-       "CDK2  14.307291  16.833932  18.338492  28.905189   0.000000"
+       "kinase.klifs_name       CDK2       CDK2       CDK2       CDK2       CDK2\n",
+       "kinase.klifs_name                                                       \n",
+       "CDK2                0.000000  13.256941  14.001474  26.391543  14.307291\n",
+       "CDK2               13.256941   0.000000  10.379779  27.882193  16.833932\n",
+       "CDK2               14.001474  10.379779   0.000000  30.962221  18.338492\n",
+       "CDK2               26.391543  27.882193  30.962221   0.000000  28.905189\n",
+       "CDK2               14.307291  16.833932  18.338492  28.905189   0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,6 +840,9 @@
     "# Copy distance matrix to kinase matrix\n",
     "kinase_distance_matrix = structure_distance_matrix\n",
     "# Replace structure KLIFS IDs with the structures' kinase names\n",
+    "kinase_names = structures.set_index(\"structure.klifs_id\").loc[\n",
+    "    structure_klifs_ids, \"kinase.klifs_name\"\n",
+    "]\n",
     "kinase_distance_matrix.index = kinase_names\n",
     "kinase_distance_matrix.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
@@ -951,198 +858,198 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
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        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row6_col7 {\n",
+       "#T_1e2f0_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row8_col1 {\n",
+       "#T_1e2f0_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row8_col2 {\n",
+       "#T_1e2f0_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row8_col3 {\n",
+       "#T_1e2f0_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row8_col6 {\n",
+       "#T_1e2f0_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_78045_row8_col7 {\n",
+       "#T_1e2f0_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_78045_\">\n",
+       "<table id=\"T_1e2f0_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
        "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
        "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
        "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
@@ -1153,135 +1060,138 @@
        "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_78045_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_78045_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_78045_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_78045_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_78045_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_78045_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_78045_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_78045_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_78045_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_1e2f0_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_1e2f0_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_1e2f0_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_1e2f0_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_1e2f0_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_1e2f0_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_1e2f0_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_1e2f0_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_1e2f0_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_78045_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_78045_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_78045_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_78045_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_78045_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_78045_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_78045_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_78045_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_78045_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_1e2f0_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_1e2f0_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_1e2f0_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_1e2f0_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_1e2f0_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_1e2f0_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_1e2f0_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_1e2f0_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_1e2f0_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_78045_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_78045_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_78045_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_78045_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_78045_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_78045_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_78045_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_78045_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_78045_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_1e2f0_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_1e2f0_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_1e2f0_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_1e2f0_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_1e2f0_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_1e2f0_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_1e2f0_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_1e2f0_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_1e2f0_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_78045_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_78045_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_78045_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_78045_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_78045_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_78045_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_78045_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_78045_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_78045_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_1e2f0_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_1e2f0_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_1e2f0_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_1e2f0_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_1e2f0_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_1e2f0_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_1e2f0_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_1e2f0_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_1e2f0_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_78045_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_78045_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_78045_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_78045_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_78045_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_78045_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_78045_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_78045_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_78045_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_1e2f0_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_1e2f0_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_1e2f0_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_1e2f0_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_1e2f0_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_1e2f0_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_1e2f0_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_1e2f0_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_1e2f0_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_78045_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_78045_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_78045_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_78045_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_78045_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_78045_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_78045_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_78045_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_78045_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_1e2f0_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_1e2f0_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_1e2f0_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_1e2f0_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_1e2f0_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_1e2f0_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_1e2f0_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_1e2f0_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_1e2f0_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_78045_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_78045_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_78045_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_78045_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_78045_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_78045_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_78045_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_78045_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_78045_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_1e2f0_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_1e2f0_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_1e2f0_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_1e2f0_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_1e2f0_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_1e2f0_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_1e2f0_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_1e2f0_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_1e2f0_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_78045_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_78045_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_78045_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_78045_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_78045_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_78045_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_78045_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_78045_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_78045_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_1e2f0_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_1e2f0_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_1e2f0_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_1e2f0_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_1e2f0_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_1e2f0_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_1e2f0_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_1e2f0_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_1e2f0_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_1e2f0_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_78045_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_78045_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_78045_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_78045_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_78045_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_78045_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_78045_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_78045_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_78045_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_78045_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_1e2f0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_1e2f0_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_1e2f0_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_1e2f0_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_1e2f0_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_1e2f0_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_1e2f0_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_1e2f0_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_1e2f0_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_1e2f0_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f635a1e6e80>"
+       "<pandas.io.formats.style.Styler at 0x7ff4942344f0>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2:80: E501 line too long (89 > 79 characters)\n",
-      "6:80: E501 line too long (81 > 79 characters)\n",
-      "11:75: W291 trailing whitespace\n"
-     ]
     }
    ],
    "source": [
@@ -1309,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [

From 1b63b8c51330c4acb08919a09c478c6295e02dde Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:02:17 +0200
Subject: [PATCH 034/158] Add fingerprints CSV file to data

---
 .../data/kissim_fingerprints.csv              | 1612 +++++++++++++++++
 1 file changed, 1612 insertions(+)
 create mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kissim_fingerprints.csv

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kissim_fingerprints.csv b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kissim_fingerprints.csv
new file mode 100644
index 00000000..239852f4
--- /dev/null
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kissim_fingerprints.csv
@@ -0,0 +1,1612 @@
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From 7c3e160ce2c375e53f43d042afb7d26765c37b92 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:02:29 +0200
Subject: [PATCH 035/158] Add kinase distance matrix (output)

---
 .../data/kinase_distance_matrix_kissim.csv             | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
new file mode 100644
index 00000000..1e6d82eb
--- /dev/null
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
@@ -0,0 +1,10 @@
+,BRAF,CDK2,EGFR,ErbB2,KDR,LCK,MET,p110a,p38a
+BRAF,0.0,17.156338686753887,19.515299484883034,21.382787595771614,21.089164552869708,21.582681844181142,20.297483020671486,37.61086252438263,21.73142143717456
+CDK2,17.156338686753887,0.0,18.146634315220403,21.106349112675172,19.880466907012618,17.972924805354655,18.304287545534248,36.78007102947219,19.480656386198007
+EGFR,19.515299484883034,18.146634315220403,0.0,16.391994226150114,17.28165001676286,16.467224894268252,17.498394567945827,36.04620732877459,22.12761688356271
+ErbB2,21.382787595771614,21.106349112675172,16.391994226150114,0.0,23.850591780152264,23.88097026329463,22.563235671308735,41.277474125565234,24.68170843658602
+KDR,21.089164552869708,19.880466907012618,17.28165001676286,23.850591780152264,0.0,19.25489849284598,20.430532117581084,41.104454960305326,20.26254051064599
+LCK,21.582681844181142,17.972924805354655,16.467224894268252,23.88097026329463,19.25489849284598,0.0,19.221161302580736,39.02201780223324,22.45669271712331
+MET,20.297483020671486,18.304287545534248,17.498394567945827,22.563235671308735,20.430532117581084,19.221161302580736,0.0,39.41427751663535,21.982654782895654
+p110a,37.61086252438264,36.78007102947219,36.04620732877459,41.277474125565234,41.104454960305326,39.02201780223324,39.41427751663535,0.0,38.52974786964488
+p38a,21.73142143717456,19.480656386198007,22.127616883562684,24.68170843658602,20.26254051064599,22.45669271712331,21.982654782895654,38.52974786964488,0.0

From 54756169b9d9732b34cce233f8211277d87d3c97 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:11:22 +0200
Subject: [PATCH 036/158] Update Table1, remove plots, save matrix

---
 .../talktorial.ipynb                          | 636 +++++-------------
 1 file changed, 187 insertions(+), 449 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 015e6572..ea25ec69 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -43,7 +43,7 @@
     "\n",
     "* Retrieve and preprocess data\n",
     "* Show kinase coverage\n",
-    "* Compare kinases\n",
+    "* Compare structures\n",
     "* Map structure to kinase distance matrix\n",
     "* Visualize similarity as kinase matrix\n",
     "* Visualize similarity as dendrogram"
@@ -57,16 +57,16 @@
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
     "* Protein Data Bank\n",
-    "  - PDB URL: http://www.rcsb.org/\n",
-    "  - PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)\n",
+    "  * PDB URL: http://www.rcsb.org/\n",
+    "  * PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)\n",
     "* KLIFS\n",
-    "  - KLIFS URL: https://klifs.net/\n",
-    "  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
-    "  - KLIFS binding site definition and interaction fingerprint calculation: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
+    "  * KLIFS URL: https://klifs.net/\n",
+    "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "  * KLIFS binding site definition and interaction fingerprint calculation: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
     "* Interaction fingerprint (IFP): [<i>J. Chem. Inf. Model.</i> (2007), <b>71(1)</b>, 195-207](https://doi.org/10.1021/ci600342e)\n",
     "* `opencadd`, a Python library for structural cheminformatics\n",
-    "  - GitHub repository: https://github.com/volkamerlab/opencadd\n",
-    "  - Documentation: https://opencadd.readthedocs.io"
+    "  * GitHub repository: https://github.com/volkamerlab/opencadd\n",
+    "  * Documentation: https://opencadd.readthedocs.io"
    ]
   },
   {
@@ -89,23 +89,23 @@
    "source": [
     "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined **EGFR** and **ErbB2** as targets (indicated by a ‘+’) and **BRAF** (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2** (cyclic-dependent kinase 2), **LCK** (lymphocyte-specific protein tyrosine kinase), **MET** (mesenchymal-epithelial transition factor) and **p38α** (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
-   ]
-  },
-  {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+    "*Table 1:* \n",
+    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "\n",
+    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
+    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
+    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
+    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
+    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
+    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
+    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
+    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
+    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
+    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
+    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
    ]
   },
   {
@@ -168,15 +168,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -265,16 +257,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -413,85 +396,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Number of structures with IFPs: 1466\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>structure.klifs_id</th>\n",
-       "      <th>interaction.fingerprint</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>775</td>\n",
-       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>777</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>778</td>\n",
-       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>779</td>\n",
-       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>782</td>\n",
-       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   structure.klifs_id                            interaction.fingerprint\n",
-       "0                 775  0000000000000010000000000000000000000000000000...\n",
-       "1                 777  0000000000000010000001000000000000000000000000...\n",
-       "2                 778  0000000000000010000000000000000000000000000000...\n",
-       "3                 779  0000000000000010000001000000000000000000000000...\n",
-       "4                 782  0000000000000010000001000000000000000000000000..."
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ifps = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
     "print(f\"Number of structures with IFPs: {len(ifps)}\")\n",
@@ -842,7 +749,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 1. Prepare IFPs as `numpy` array\n",
+    "#### Prepare IFPs as `numpy` array\n",
     "\n",
     "KLIFS deposits the IFP as a string of 0 and 1. We have to prepare our IFPs as an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
    ]
@@ -1325,223 +1232,223 @@
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-       "#T_368cf_row3_col8 {\n",
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        "  background-color: #6bb46b;\n",
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-       "#T_368cf_row4_col1 {\n",
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-       "#T_368cf_row4_col2, #T_368cf_row6_col4 {\n",
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-       "#T_368cf_row4_col3 {\n",
+       "#T_71dcf_row4_col3 {\n",
        "  background-color: #43a143;\n",
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-       "#T_368cf_row4_col5 {\n",
+       "#T_71dcf_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
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        "}\n",
-       "#T_368cf_row4_col6 {\n",
+       "#T_71dcf_row4_col6 {\n",
        "  background-color: #349934;\n",
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        "}\n",
-       "#T_368cf_row4_col7 {\n",
+       "#T_71dcf_row4_col7 {\n",
        "  background-color: #108810;\n",
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-       "#T_368cf_row4_col8 {\n",
+       "#T_71dcf_row4_col8 {\n",
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-       "#T_368cf_row5_col0 {\n",
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+       "#T_71dcf_row5_col2 {\n",
        "  background-color: #84c084;\n",
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-       "#T_368cf_row5_col4 {\n",
+       "#T_71dcf_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
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-       "#T_368cf_row5_col6 {\n",
+       "#T_71dcf_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
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-       "#T_368cf_row5_col7 {\n",
+       "#T_71dcf_row5_col7 {\n",
        "  background-color: #50a750;\n",
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        "}\n",
-       "#T_368cf_row5_col8 {\n",
+       "#T_71dcf_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
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        "}\n",
-       "#T_368cf_row6_col0 {\n",
+       "#T_71dcf_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
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-       "#T_368cf_row6_col1 {\n",
+       "#T_71dcf_row6_col1 {\n",
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        "}\n",
-       "#T_368cf_row6_col2, #T_368cf_row6_col5 {\n",
+       "#T_71dcf_row6_col2, #T_71dcf_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
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        "}\n",
-       "#T_368cf_row6_col3 {\n",
+       "#T_71dcf_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row6_col7 {\n",
+       "#T_71dcf_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row6_col8 {\n",
+       "#T_71dcf_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row7_col1 {\n",
+       "#T_71dcf_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row8_col1 {\n",
+       "#T_71dcf_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row8_col4 {\n",
+       "#T_71dcf_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_368cf_row8_col6 {\n",
+       "#T_71dcf_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_368cf_row8_col7 {\n",
+       "#T_71dcf_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_368cf_\">\n",
+       "<table id=\"T_71dcf_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
        "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
        "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
@@ -1552,133 +1459,121 @@
        "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_368cf_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row0_col1\" class=\"data row0 col1\" >0.300000</td>\n",
-       "      <td id=\"T_368cf_row0_col2\" class=\"data row0 col2\" >0.277778</td>\n",
-       "      <td id=\"T_368cf_row0_col3\" class=\"data row0 col3\" >0.393939</td>\n",
-       "      <td id=\"T_368cf_row0_col4\" class=\"data row0 col4\" >0.363636</td>\n",
-       "      <td id=\"T_368cf_row0_col5\" class=\"data row0 col5\" >0.320000</td>\n",
-       "      <td id=\"T_368cf_row0_col6\" class=\"data row0 col6\" >0.368421</td>\n",
-       "      <td id=\"T_368cf_row0_col7\" class=\"data row0 col7\" >0.500000</td>\n",
-       "      <td id=\"T_368cf_row0_col8\" class=\"data row0 col8\" >0.333333</td>\n",
+       "      <th id=\"T_71dcf_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_71dcf_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_71dcf_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_71dcf_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_71dcf_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_71dcf_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_71dcf_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_71dcf_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_71dcf_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_368cf_row1_col0\" class=\"data row1 col0\" >0.300000</td>\n",
-       "      <td id=\"T_368cf_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row1_col2\" class=\"data row1 col2\" >0.111111</td>\n",
-       "      <td id=\"T_368cf_row1_col3\" class=\"data row1 col3\" >0.451613</td>\n",
-       "      <td id=\"T_368cf_row1_col4\" class=\"data row1 col4\" >0.238095</td>\n",
-       "      <td id=\"T_368cf_row1_col5\" class=\"data row1 col5\" >0.190476</td>\n",
-       "      <td id=\"T_368cf_row1_col6\" class=\"data row1 col6\" >0.133333</td>\n",
-       "      <td id=\"T_368cf_row1_col7\" class=\"data row1 col7\" >0.347826</td>\n",
-       "      <td id=\"T_368cf_row1_col8\" class=\"data row1 col8\" >0.277778</td>\n",
+       "      <th id=\"T_71dcf_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_71dcf_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_71dcf_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_71dcf_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_71dcf_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_71dcf_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_71dcf_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_71dcf_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_71dcf_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_368cf_row2_col0\" class=\"data row2 col0\" >0.277778</td>\n",
-       "      <td id=\"T_368cf_row2_col1\" class=\"data row2 col1\" >0.111111</td>\n",
-       "      <td id=\"T_368cf_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row2_col3\" class=\"data row2 col3\" >0.258065</td>\n",
-       "      <td id=\"T_368cf_row2_col4\" class=\"data row2 col4\" >0.238095</td>\n",
-       "      <td id=\"T_368cf_row2_col5\" class=\"data row2 col5\" >0.166667</td>\n",
-       "      <td id=\"T_368cf_row2_col6\" class=\"data row2 col6\" >0.227273</td>\n",
-       "      <td id=\"T_368cf_row2_col7\" class=\"data row2 col7\" >0.380952</td>\n",
-       "      <td id=\"T_368cf_row2_col8\" class=\"data row2 col8\" >0.173913</td>\n",
+       "      <th id=\"T_71dcf_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_71dcf_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_71dcf_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_71dcf_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_71dcf_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_71dcf_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_71dcf_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_71dcf_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_71dcf_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_368cf_row3_col0\" class=\"data row3 col0\" >0.393939</td>\n",
-       "      <td id=\"T_368cf_row3_col1\" class=\"data row3 col1\" >0.451613</td>\n",
-       "      <td id=\"T_368cf_row3_col2\" class=\"data row3 col2\" >0.258065</td>\n",
-       "      <td id=\"T_368cf_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row3_col4\" class=\"data row3 col4\" >0.406250</td>\n",
-       "      <td id=\"T_368cf_row3_col5\" class=\"data row3 col5\" >0.419355</td>\n",
-       "      <td id=\"T_368cf_row3_col6\" class=\"data row3 col6\" >0.382353</td>\n",
-       "      <td id=\"T_368cf_row3_col7\" class=\"data row3 col7\" >0.571429</td>\n",
-       "      <td id=\"T_368cf_row3_col8\" class=\"data row3 col8\" >0.258065</td>\n",
+       "      <th id=\"T_71dcf_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_71dcf_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_71dcf_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_71dcf_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_71dcf_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_71dcf_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_71dcf_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_71dcf_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_71dcf_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_368cf_row4_col0\" class=\"data row4 col0\" >0.363636</td>\n",
-       "      <td id=\"T_368cf_row4_col1\" class=\"data row4 col1\" >0.238095</td>\n",
-       "      <td id=\"T_368cf_row4_col2\" class=\"data row4 col2\" >0.238095</td>\n",
-       "      <td id=\"T_368cf_row4_col3\" class=\"data row4 col3\" >0.406250</td>\n",
-       "      <td id=\"T_368cf_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row4_col5\" class=\"data row4 col5\" >0.058824</td>\n",
-       "      <td id=\"T_368cf_row4_col6\" class=\"data row4 col6\" >0.333333</td>\n",
-       "      <td id=\"T_368cf_row4_col7\" class=\"data row4 col7\" >0.533333</td>\n",
-       "      <td id=\"T_368cf_row4_col8\" class=\"data row4 col8\" >0.043478</td>\n",
+       "      <th id=\"T_71dcf_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_71dcf_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_71dcf_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_71dcf_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_71dcf_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_71dcf_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_71dcf_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_71dcf_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_71dcf_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_368cf_row5_col0\" class=\"data row5 col0\" >0.320000</td>\n",
-       "      <td id=\"T_368cf_row5_col1\" class=\"data row5 col1\" >0.190476</td>\n",
-       "      <td id=\"T_368cf_row5_col2\" class=\"data row5 col2\" >0.166667</td>\n",
-       "      <td id=\"T_368cf_row5_col3\" class=\"data row5 col3\" >0.419355</td>\n",
-       "      <td id=\"T_368cf_row5_col4\" class=\"data row5 col4\" >0.058824</td>\n",
-       "      <td id=\"T_368cf_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row5_col6\" class=\"data row5 col6\" >0.250000</td>\n",
-       "      <td id=\"T_368cf_row5_col7\" class=\"data row5 col7\" >0.375000</td>\n",
-       "      <td id=\"T_368cf_row5_col8\" class=\"data row5 col8\" >0.190476</td>\n",
+       "      <th id=\"T_71dcf_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_71dcf_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_71dcf_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_71dcf_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_71dcf_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_71dcf_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_71dcf_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_71dcf_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_71dcf_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_368cf_row6_col0\" class=\"data row6 col0\" >0.368421</td>\n",
-       "      <td id=\"T_368cf_row6_col1\" class=\"data row6 col1\" >0.133333</td>\n",
-       "      <td id=\"T_368cf_row6_col2\" class=\"data row6 col2\" >0.227273</td>\n",
-       "      <td id=\"T_368cf_row6_col3\" class=\"data row6 col3\" >0.382353</td>\n",
-       "      <td id=\"T_368cf_row6_col4\" class=\"data row6 col4\" >0.333333</td>\n",
-       "      <td id=\"T_368cf_row6_col5\" class=\"data row6 col5\" >0.250000</td>\n",
-       "      <td id=\"T_368cf_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row6_col7\" class=\"data row6 col7\" >0.428571</td>\n",
-       "      <td id=\"T_368cf_row6_col8\" class=\"data row6 col8\" >0.352941</td>\n",
+       "      <th id=\"T_71dcf_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_71dcf_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_71dcf_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_71dcf_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_71dcf_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_71dcf_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_71dcf_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_71dcf_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_71dcf_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_368cf_row7_col0\" class=\"data row7 col0\" >0.500000</td>\n",
-       "      <td id=\"T_368cf_row7_col1\" class=\"data row7 col1\" >0.347826</td>\n",
-       "      <td id=\"T_368cf_row7_col2\" class=\"data row7 col2\" >0.380952</td>\n",
-       "      <td id=\"T_368cf_row7_col3\" class=\"data row7 col3\" >0.571429</td>\n",
-       "      <td id=\"T_368cf_row7_col4\" class=\"data row7 col4\" >0.533333</td>\n",
-       "      <td id=\"T_368cf_row7_col5\" class=\"data row7 col5\" >0.375000</td>\n",
-       "      <td id=\"T_368cf_row7_col6\" class=\"data row7 col6\" >0.428571</td>\n",
-       "      <td id=\"T_368cf_row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
-       "      <td id=\"T_368cf_row7_col8\" class=\"data row7 col8\" >0.473684</td>\n",
+       "      <th id=\"T_71dcf_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_71dcf_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_71dcf_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_71dcf_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_71dcf_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_71dcf_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_71dcf_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_71dcf_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_71dcf_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_71dcf_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_368cf_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_368cf_row8_col0\" class=\"data row8 col0\" >0.333333</td>\n",
-       "      <td id=\"T_368cf_row8_col1\" class=\"data row8 col1\" >0.277778</td>\n",
-       "      <td id=\"T_368cf_row8_col2\" class=\"data row8 col2\" >0.173913</td>\n",
-       "      <td id=\"T_368cf_row8_col3\" class=\"data row8 col3\" >0.258065</td>\n",
-       "      <td id=\"T_368cf_row8_col4\" class=\"data row8 col4\" >0.043478</td>\n",
-       "      <td id=\"T_368cf_row8_col5\" class=\"data row8 col5\" >0.190476</td>\n",
-       "      <td id=\"T_368cf_row8_col6\" class=\"data row8 col6\" >0.352941</td>\n",
-       "      <td id=\"T_368cf_row8_col7\" class=\"data row8 col7\" >0.473684</td>\n",
-       "      <td id=\"T_368cf_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
+       "      <th id=\"T_71dcf_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_71dcf_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_71dcf_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_71dcf_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_71dcf_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_71dcf_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_71dcf_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_71dcf_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_71dcf_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_71dcf_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f0ab788c3a0>"
+       "<pandas.io.formats.style.Styler at 0x7f1ef14b5a30>"
       ]
      },
      "execution_count": 23,
@@ -1694,187 +1589,30 @@
     "kinase_distance_matrix = (\n",
     "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     ")\n",
+    "kinase_distance_matrix.index.name = None\n",
+    "kinase_distance_matrix.columns.name = None\n",
+    "\n",
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix.style.background_gradient(cmap=cm)"
+    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\") "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "### Visualize similarity as kinase matrix\n",
-    "\n",
-    "We visualize the kinase matrix in the form of a heatmap."
+    "### Save kinase distance matrix"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
-    "plt.colorbar(label=\"Kinase distance\")\n",
-    "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
-    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have calculated distances between fingerprints that range from 0 to 1 (identical to dissimilar). The term similarity is often defined between 0 and 1 (dissimilar to identical), hence for the following heatmap, we will use the transformation $similarity = 1 - distance$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "kinase_similarity_matrix = 1 - kinase_distance_matrix\n",
-    "plt.imshow(kinase_similarity_matrix, vmin=0.0, vmax=1.0)\n",
-    "plt.colorbar(label=\"Kinase similarity\")\n",
-    "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_similarity_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
-    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as dendrogram"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We use the `scipy` function `squareform` to create a condensed vector of the distance matrix of shape $n*(n-1)/2$, where $n$ is the shape of the quadratic distance matrix. The values in this vector correspond to the values of the lower triangular matrix."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.3       , 0.27777778, 0.39393939, 0.36363636, 0.32      ,\n",
-       "       0.36842105, 0.5       , 0.33333333, 0.11111111, 0.4516129 ,\n",
-       "       0.23809524, 0.19047619, 0.13333333, 0.34782609, 0.27777778,\n",
-       "       0.25806452, 0.23809524, 0.16666667, 0.22727273, 0.38095238,\n",
-       "       0.17391304, 0.40625   , 0.41935484, 0.38235294, 0.57142857,\n",
-       "       0.25806452, 0.05882353, 0.33333333, 0.53333333, 0.04347826,\n",
-       "       0.25      , 0.375     , 0.19047619, 0.42857143, 0.35294118,\n",
-       "       0.47368421])"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "D = kinase_distance_matrix.to_numpy()\n",
-    "D_condensed = distance.squareform(D)\n",
-    "D_condensed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can submit this condensed vector to a hierarchical clustering to extract the relationship between the different kinases. We use here `method=\"average\"`, which stands for the linkage method UPGMA (unweighted pair group method with arithmetic mean). This means that the distance between two clusters A and B is defined as the average of all distances between pairs of elements in clusters A and B. At each clustering step, the two clusters with the lowest average distance are combined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
    "outputs": [],
    "source": [
-    "hclust = hierarchy.linkage(D_condensed, method=\"average\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now generate a phylogenetic tree based on our clustering."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tree = hierarchy.to_tree(hclust)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We visualize this tree as dendrogram."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "labels = kinase_distance_matrix.columns.to_list()\n",
-    "hierarchy.dendrogram(hclust, labels=labels, orientation=\"left\", ax=ax)\n",
-    "ax.set_xlabel(\"Distance\");"
+    "kinase_distance_matrix.to_csv(DATA / \"kinase_distance_matrix_ifp.csv\")"
    ]
   },
   {
@@ -1886,11 +1624,11 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity. \n",
+    "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity.\n",
     "\n",
-    "The two closest binding modes are observed for the p38a-KDR (VEGFR2) and CDK2-EGFR pairs. In both cases the kinases belong to different kinase groups, i.e. CMGC and TK, a grouping based on sequence information only. Rather dissimilar binding modes are observed for p110a (PI3K), ErbB2, and BRAF &mdash; although ErbB2 belongs to the same kinase family as EGFR. The atypical kinase p110a &mdash; the most dissimilar kinase to the other kinases in the panel &mdash; is an expected finding.\n",
+    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have &mdash at least for kinases such as EGFR and CDK2 &mdash much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
     "\n",
-    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have &mdash at least for kinases such as EGFR and CDK2 &mdash much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content."
+    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
    ]
   },
   {

From 0012a41ee7383330438807940a4b809fa93a4d31 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:11:34 +0200
Subject: [PATCH 037/158] Add kinase distance matrix (output)

---
 .../data/kinase_distance_matrix_ifp.csv                | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv
new file mode 100644
index 00000000..752de190
--- /dev/null
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv
@@ -0,0 +1,10 @@
+,BRAF,CDK2,EGFR,ErbB2,KDR,LCK,MET,p110a,p38a
+BRAF,0.0,0.3,0.2777777777777778,0.3939393939393939,0.36363636363636365,0.32,0.3684210526315789,0.5,0.3333333333333333
+CDK2,0.3,0.0,0.1111111111111111,0.45161290322580644,0.23809523809523808,0.19047619047619047,0.13333333333333333,0.34782608695652173,0.2777777777777778
+EGFR,0.2777777777777778,0.1111111111111111,0.0,0.25806451612903225,0.23809523809523808,0.16666666666666666,0.22727272727272727,0.38095238095238093,0.17391304347826086
+ErbB2,0.3939393939393939,0.45161290322580644,0.25806451612903225,0.0,0.40625,0.41935483870967744,0.38235294117647056,0.5714285714285714,0.25806451612903225
+KDR,0.36363636363636365,0.23809523809523808,0.23809523809523808,0.40625,0.0,0.058823529411764705,0.3333333333333333,0.5333333333333333,0.043478260869565216
+LCK,0.32,0.19047619047619047,0.16666666666666666,0.41935483870967744,0.058823529411764705,0.0,0.25,0.375,0.19047619047619047
+MET,0.3684210526315789,0.13333333333333333,0.22727272727272727,0.38235294117647056,0.3333333333333333,0.25,0.0,0.42857142857142855,0.35294117647058826
+p110a,0.5,0.34782608695652173,0.38095238095238093,0.5714285714285714,0.5333333333333333,0.375,0.42857142857142855,0.0,0.47368421052631576
+p38a,0.3333333333333333,0.2777777777777778,0.17391304347826086,0.25806451612903225,0.043478260869565216,0.19047619047619047,0.35294117647058826,0.47368421052631576,0.0

From 764636cc0cb8c05d7c12f827627aac789e3088e9 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:16:08 +0200
Subject: [PATCH 038/158] Remove unused imports

---
 .../talktorial.ipynb                          | 293 +++++++++---------
 1 file changed, 146 insertions(+), 147 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index ea25ec69..eb1b7a64 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -177,8 +177,7 @@
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "from sklearn.metrics import pairwise_distances\n",
-    "from scipy.spatial import distance_matrix, distance\n",
-    "from scipy.cluster import hierarchy\n",
+    "from scipy.spatial import distance\n",
     "from opencadd.databases.klifs import setup_remote"
    ]
   },
@@ -1232,220 +1231,220 @@
      "data": {
       "text/html": [
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+       "#T_57db4_row0_col0, #T_57db4_row1_col1, #T_57db4_row2_col2, #T_57db4_row3_col3, #T_57db4_row4_col4, #T_57db4_row5_col5, #T_57db4_row6_col6, #T_57db4_row7_col7, #T_57db4_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71dcf_row0_col1, #T_71dcf_row2_col7, #T_71dcf_row8_col0 {\n",
+       "#T_57db4_row0_col1, #T_57db4_row2_col7, #T_57db4_row8_col0 {\n",
        "  background-color: #4ea64e;\n",
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        "}\n",
-       "#T_71dcf_row0_col2, #T_71dcf_row4_col0, #T_71dcf_row5_col3 {\n",
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-       "#T_71dcf_row2_col3, #T_71dcf_row8_col3 {\n",
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-       "#T_71dcf_row2_col8 {\n",
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-       "#T_71dcf_row4_col2, #T_71dcf_row6_col4 {\n",
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        "}\n",
-       "#T_71dcf_row6_col7 {\n",
+       "#T_57db4_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71dcf_row6_col8 {\n",
+       "#T_57db4_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71dcf_row7_col1 {\n",
+       "#T_57db4_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71dcf_row8_col1 {\n",
+       "#T_57db4_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71dcf_row8_col4 {\n",
+       "#T_57db4_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71dcf_row8_col6 {\n",
+       "#T_57db4_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71dcf_row8_col7 {\n",
+       "#T_57db4_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_71dcf_\">\n",
+       "<table id=\"T_57db4_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1462,118 +1461,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_71dcf_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_71dcf_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_71dcf_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_71dcf_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_71dcf_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_71dcf_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_71dcf_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_71dcf_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_57db4_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_57db4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_57db4_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_57db4_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_57db4_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_57db4_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_57db4_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_57db4_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_57db4_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_71dcf_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_71dcf_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_71dcf_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_71dcf_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_71dcf_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_71dcf_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_71dcf_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_71dcf_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_57db4_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_57db4_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_57db4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_57db4_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_57db4_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_57db4_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_57db4_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_57db4_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_57db4_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_71dcf_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_71dcf_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_71dcf_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_71dcf_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_71dcf_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_71dcf_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_71dcf_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_71dcf_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_57db4_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_57db4_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_57db4_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_57db4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_57db4_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_57db4_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_57db4_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_57db4_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_57db4_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_71dcf_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_71dcf_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_71dcf_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_71dcf_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_71dcf_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_71dcf_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_71dcf_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_71dcf_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_57db4_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_57db4_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_57db4_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_57db4_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_57db4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_57db4_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_57db4_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_57db4_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_57db4_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_71dcf_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_71dcf_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_71dcf_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_71dcf_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_71dcf_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_71dcf_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_71dcf_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_71dcf_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_57db4_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_57db4_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_57db4_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_57db4_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_57db4_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_57db4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_57db4_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_57db4_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_57db4_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_71dcf_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_71dcf_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_71dcf_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_71dcf_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_71dcf_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_71dcf_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_71dcf_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_71dcf_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_57db4_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_57db4_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_57db4_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_57db4_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_57db4_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_57db4_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_57db4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_57db4_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_57db4_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_71dcf_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_71dcf_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_71dcf_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_71dcf_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_71dcf_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_71dcf_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_71dcf_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_71dcf_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_57db4_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_57db4_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_57db4_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_57db4_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_57db4_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_57db4_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_57db4_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_57db4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_57db4_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_71dcf_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_71dcf_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_71dcf_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_71dcf_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_71dcf_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_71dcf_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_71dcf_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_71dcf_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_71dcf_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_57db4_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_57db4_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_57db4_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_57db4_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_57db4_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_57db4_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_57db4_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_57db4_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_57db4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_57db4_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71dcf_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_71dcf_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_71dcf_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_71dcf_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_71dcf_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_71dcf_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_71dcf_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_71dcf_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_71dcf_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_71dcf_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_57db4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_57db4_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_57db4_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_57db4_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_57db4_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_57db4_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_57db4_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_57db4_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_57db4_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_57db4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f1ef14b5a30>"
+       "<pandas.io.formats.style.Styler at 0x7fa1edf30f70>"
       ]
      },
      "execution_count": 23,

From 2d3f384be96d9e8518bd1a510a3f6a21d4a02bd4 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:19:03 +0200
Subject: [PATCH 039/158] Remove unused imports

---
 .../talktorials/T025_kinase_similarity_kissim/talktorial.ipynb | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 782c8b79..26686db8 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -198,8 +198,7 @@
     "from sklearn.metrics import pairwise\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
-    "from opencadd.databases.klifs import setup_remote\n",
-    "from kissim.encoding import FingerprintGenerator"
+    "from opencadd.databases.klifs import setup_remote"
    ]
   },
   {

From 43c71a24dd0df5f89d729dddfffccb197dbe119d Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 16:21:34 +0200
Subject: [PATCH 040/158] finalize notebook.

---
 .../talktorial.ipynb                          | 659 ++++++++----------
 1 file changed, 303 insertions(+), 356 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index a16b035d..82b63b10 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "The aim of this talktorial is to investigate kinase similarity through ligand profiling. We make the following assumption: if a compound was tested as active on two different kinases, then we could assume that these two same kinases may have some degree of similarity. "
+    "The aim of this talktorial is to investigate kinase similarity through ligand profiling. In the context of drug design, the following assumption can be made: if a compound was tested as active on two different kinases, it is suspected that these two kinases may have some degree of similarity. The concept of kinase promiscuity is also covered."
    ]
   },
   {
@@ -29,6 +29,7 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
+    "* Bioactivity data\n",
     "* Kinase similarity descriptor: ligand-profile\n",
     "    * Kinase similarity\n",
     "    * Kinase promiscuity"
@@ -44,10 +45,10 @@
     "* Preprocess the data\n",
     "    * Kinases of interest\n",
     "    * Hit or non-hit\n",
-    "* Show kinase promiscuity\n",
+    "* Kinase promiscuity\n",
     "* Compare kinases\n",
     "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
+    "* Save kinase distance matrix"
    ]
   },
   {
@@ -57,16 +58,7 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: ligand-profile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: ADD"
+    "* [ChEMBL](https://www.ebi.ac.uk/chembl/) database"
    ]
   },
   {
@@ -97,7 +89,7 @@
     " \n",
     "\n",
     "*Table 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
     "\n",
     " \n",
     "\n",
@@ -118,23 +110,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, we focus on bioactivity retrieve from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database. We query the data focusing on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome."
+    "### Bioactivity data"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In drug design, it is common to determine the activity of a compound against as a \"hit\" or \"non hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater to the cutoff, the compound is labeled as active, and inactive otherwise."
+    "In order to measure kinase similarity through ligand profiling, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome.\n",
+    "For more details on querying the ChEMBL database, please refer to [Talktorial T001](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb)."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# TODO: add reference to other talktorial."
+    "In drug design, it is common to determine the activity of a compound against a kinase of interest as a \"hit\" or \"non hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active, and inactive otherwise."
    ]
   },
   {
@@ -163,8 +154,8 @@
     "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on $K_i$ and $K_j$}}\n",
-    "{\\#\\text{ of compounds that were tested on both $K_i$ and $K_j$}}\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on } K_i \\text{ and } K_j}\n",
+    "{\\#\\text{ of compounds that were tested on both } K_i \\text{ and } K_j}.\n",
     "$$\n",
     "\n",
     "Assuming that only one compound was tested on two kinases, and that the compound was tested as active for one and inactive for the other, then the similarity between these two kinases would be null."
@@ -175,7 +166,7 @@
    "metadata": {},
    "source": [
     "#### Kinase promiscuity\n",
-    "Computing the similarity between a kinase and itself may be interpreted as kinase promiscuity, where the similarity descrived above would therefore represent the fraction of active compounds over all tested compounds."
+    "Computing the similarity between a kinase and itself may be interpreted as kinase promiscuity, where the similarity described above would therefore represent the fraction of active compounds over all tested compounds."
    ]
   },
   {
@@ -187,18 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# !pip install flake8 pycodestyle_magic\n",
-    "%load_ext pycodestyle_magic\n",
-    "%pycodestyle_on"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -235,16 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: Take v.29 instead of v.28."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -457,7 +428,7 @@
        "4  CHEMBL3639077     2014.0          NaN    O75116  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -486,14 +457,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
+      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -512,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,12 +494,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the uniprot ID of the kinase. We rename these columns for readability."
+    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the UniProt ID of the kinase. These columns are renamed for readability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +592,7 @@
        "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.920819    O75116"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -640,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -662,12 +633,12 @@
    "source": [
     "#### Kinases of interest\n",
     "\n",
-    "We focus on the kinases of interest and map them to their uniprot IDs in accordance with the Table in the Theory."
+    "We focus on the kinases of interest and map them to their UniProt IDs, using Table 1 from the theory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -691,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -709,7 +680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -840,7 +811,7 @@
        "[33169 rows x 3 columns]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -858,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -874,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -888,7 +859,7 @@
        "  7)]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,7 +878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1038,7 +1009,7 @@
        "[32688 rows x 3 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1060,12 +1031,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a cut-off of $6.3$, similarly to the referenced paper."
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a cut-off of $6.3$, similarly to the [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1074,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1102,17 +1073,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
     "data[\"activity_binary\"] = data[\"activity_value\"].apply(binarize_pic50,\n",
-    "                                                       args=(cutoff,))"
+    "                                                       args=(cutoff, ))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1205,7 +1176,7 @@
        "4        8.420216                1  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1219,7 +1190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Show kinase promiscuity"
+    "### Kinase promiscuity"
    ]
   },
   {
@@ -1228,22 +1199,22 @@
    "source": [
     "We now look at the kinase promiscuity.\n",
     "\n",
-    "For each of them, we compute three metrics:\n",
+    "For a given kinase, three values are computed:\n",
     "\n",
     "1. the total number of measured compounds against the given kinase,\n",
-    "2. the number of active compounds, and\n",
-    "3. the fraction of active compouds, i.e. the ratio of active compounds and total number of measured compounds."
+    "2. the number of active compounds against the kinase, and\n",
+    "3. the fraction of active compounds, i.e. the ratio of active compounds over the total number of measured compounds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
     "    \"\"\"\n",
-    "    Retrieve the three metrics for a given kinase.\n",
+    "    Retrieve the three values for a given kinase.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -1283,41 +1254,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "(5869, 3574, 0.6089623445220651)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of measured compounds: \t5869 \n",
+      "Number of active compounds: \t\t3574 \n",
+      "Fraction of active compounds: \t\t0.61 \n",
+      "\n"
+     ]
     }
    ],
    "source": [
     "EGFR_metrics = kinase_to_activity_numbers(\"EGFR\", data)\n",
-    "EGFR_metrics"
+    "print(f\"Total number of measured compounds: \\t\"\n",
+    "      f\"{EGFR_metrics[0]} \\n\"\n",
+    "      f\"Number of active compounds: \\t\\t\"\n",
+    "      f\"{EGFR_metrics[1]} \\n\"\n",
+    "      f\"Fraction of active compounds: \\t\\t\"\n",
+    "      f\"{EGFR_metrics[2]:.2f} \\n\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's create a table from this information for all kinases:"
+    "Let's create a table from these values for all kinases:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "def from_numbers_to_table(activity_dataframe):\n",
     "    \"\"\"\n",
-    "    Create a metric table for all kinases.\n",
+    "    Create a table with all three values for all kinases.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -1326,20 +1302,20 @@
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    table : pd.DataFrame\n",
-    "        A dataframe with the kinases as rows and metrics as columns.\n",
+    "    promiscuity_table : pd.DataFrame\n",
+    "        A dataframe with the kinases as rows and values as columns.\n",
     "    \"\"\"\n",
-    "    table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
-    "                         columns=[\"total\", \"actives\", \"fraction\"])\n",
+    "    promiscuity_table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
     "    for name in name_to_uniprot:\n",
-    "        metrics = kinase_to_activity_numbers(name, activity_dataframe)\n",
-    "        table.loc[name] = metrics\n",
-    "    return table"
+    "        values = kinase_to_activity_numbers(name, activity_dataframe)\n",
+    "        promiscuity_table.loc[name] = values\n",
+    "    return promiscuity_table"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1440,14 +1416,14 @@
        "p110a  4347    2780  0.639522"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_table = from_numbers_to_table(data)\n",
-    "kinase_table"
+    "kinase_promiscuity_table = from_numbers_to_table(data)\n",
+    "kinase_promiscuity_table"
    ]
   },
   {
@@ -1459,49 +1435,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_28336_row0_col2 {\n",
+       "#T_6660b_row0_col2 {\n",
        "  background-color: #dbdbec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_28336_row1_col2, #T_28336_row4_col2 {\n",
+       "#T_6660b_row1_col2, #T_6660b_row4_col2 {\n",
        "  background-color: #dedded;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_28336_row2_col2 {\n",
+       "#T_6660b_row2_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_28336_row3_col2 {\n",
+       "#T_6660b_row3_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_28336_row5_col2 {\n",
+       "#T_6660b_row5_col2 {\n",
        "  background-color: #4b1687;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_28336_row6_col2 {\n",
+       "#T_6660b_row6_col2 {\n",
        "  background-color: #5c3797;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_28336_row7_col2 {\n",
+       "#T_6660b_row7_col2 {\n",
        "  background-color: #8d89c0;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_28336_row8_col2 {\n",
+       "#T_6660b_row8_col2 {\n",
        "  background-color: #c0c1de;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_28336_\">\n",
+       "<table id=\"T_6660b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1512,85 +1488,84 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_28336_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
-       "      <td id=\"T_28336_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
-       "      <td id=\"T_28336_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_6660b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_6660b_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
+       "      <td id=\"T_6660b_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
+       "      <td id=\"T_6660b_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_28336_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
-       "      <td id=\"T_28336_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
-       "      <td id=\"T_28336_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
+       "      <th id=\"T_6660b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_6660b_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
+       "      <td id=\"T_6660b_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
+       "      <td id=\"T_6660b_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_28336_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
-       "      <td id=\"T_28336_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
-       "      <td id=\"T_28336_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <th id=\"T_6660b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_6660b_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
+       "      <td id=\"T_6660b_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
+       "      <td id=\"T_6660b_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_28336_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
-       "      <td id=\"T_28336_row3_col1\" class=\"data row3 col1\" >819</td>\n",
-       "      <td id=\"T_28336_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
+       "      <th id=\"T_6660b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_6660b_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
+       "      <td id=\"T_6660b_row3_col1\" class=\"data row3 col1\" >819</td>\n",
+       "      <td id=\"T_6660b_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_28336_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
-       "      <td id=\"T_28336_row4_col1\" class=\"data row4 col1\" >930</td>\n",
-       "      <td id=\"T_28336_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
+       "      <th id=\"T_6660b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_6660b_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
+       "      <td id=\"T_6660b_row4_col1\" class=\"data row4 col1\" >930</td>\n",
+       "      <td id=\"T_6660b_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_28336_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
-       "      <td id=\"T_28336_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
-       "      <td id=\"T_28336_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
+       "      <th id=\"T_6660b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_6660b_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
+       "      <td id=\"T_6660b_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
+       "      <td id=\"T_6660b_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_28336_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
-       "      <td id=\"T_28336_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
-       "      <td id=\"T_28336_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th id=\"T_6660b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_6660b_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
+       "      <td id=\"T_6660b_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
+       "      <td id=\"T_6660b_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_28336_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
-       "      <td id=\"T_28336_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
-       "      <td id=\"T_28336_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
+       "      <th id=\"T_6660b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_6660b_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
+       "      <td id=\"T_6660b_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
+       "      <td id=\"T_6660b_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_28336_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_28336_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
-       "      <td id=\"T_28336_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
-       "      <td id=\"T_28336_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
+       "      <th id=\"T_6660b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_6660b_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
+       "      <td id=\"T_6660b_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
+       "      <td id=\"T_6660b_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb372a2e2e0>"
+       "<pandas.io.formats.style.Styler at 0x7ffc6f75b9a0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_table_styler = kinase_table.style.\\\n",
+    "kinase_promiscuity_table.style.\\\n",
     "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
     "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
     "    highlight_min(color=\"yellow\", axis=None).\\\n",
-    "    highlight_max(color=\"red\", subset=[\"fraction\"])\n",
-    "kinase_table_styler"
+    "    highlight_max(color=\"red\", subset=[\"fraction\"])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the table, we notice that CDK2, BRAF are the least, most promiscious kinases, respectively."
+    "From the table, we notice that CDK2, BRAF are the least, most promiscuous kinases, respectively."
    ]
   },
   {
@@ -1602,13 +1577,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "def similarity_ligand_profile(kinase_name1, kinase_name2, activity_dataframe):\n",
     "    \"\"\"\n",
-    "    Compute the similarity of two kinases using ligand profiling.\n",
+    "    Compute the similarity between two kinases using ligand profiling.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -1673,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1690,32 +1665,6 @@
     "      f\"{similarity_EGFR_MET:.2f}.\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at the promiscuity for EGFR:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EGRF promiscuity: 0.61.\n"
-     ]
-    }
-   ],
-   "source": [
-    "promiscuity_EGFR = similarity_ligand_profile(\"EGFR\", \"EGFR\", data)\n",
-    "print(f\"EGRF promiscuity: \"\n",
-    "      f\"{promiscuity_EGFR:.2f}.\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1725,7 +1674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1750,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1922,7 +1871,7 @@
        "p110a  0.180791  0.639522  "
       ]
      },
-     "execution_count": 32,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1936,227 +1885,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       "#T_12a3d_row0_col0, #T_12a3d_row1_col1, #T_12a3d_row2_col2, #T_12a3d_row2_col7, #T_12a3d_row4_col4, #T_12a3d_row5_col5, #T_12a3d_row6_col6, #T_12a3d_row7_col3, #T_12a3d_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row0_col1, #T_567e4_row7_col2 {\n",
+       "#T_12a3d_row0_col1, #T_12a3d_row7_col2 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row0_col2, #T_567e4_row5_col4 {\n",
+       "#T_12a3d_row0_col2, #T_12a3d_row5_col4 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row0_col3 {\n",
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        "  background-color: #d5e9d5;\n",
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-       "#T_567e4_row0_col4 {\n",
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-       "#T_567e4_row0_col5, #T_567e4_row7_col8 {\n",
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        "  background-color: #8ac48a;\n",
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        "}\n",
-       "#T_567e4_row0_col8, #T_567e4_row3_col5 {\n",
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        "  background-color: #d1e6d1;\n",
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-       "#T_567e4_row1_col0 {\n",
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-       "#T_567e4_row1_col2, #T_567e4_row4_col1 {\n",
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        "  background-color: #c5e0c5;\n",
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        "  background-color: #e0eee0;\n",
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        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row1_col8, #T_567e4_row8_col1 {\n",
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        "  background-color: #000000;\n",
        "  color: #f1f1f1;\n",
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-       "#T_567e4_row2_col0 {\n",
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        "  background-color: #53a953;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row2_col1 {\n",
+       "#T_12a3d_row2_col1 {\n",
        "  background-color: #92c892;\n",
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        "}\n",
-       "#T_567e4_row2_col3, #T_567e4_row3_col2, #T_567e4_row4_col8, #T_567e4_row5_col1, #T_567e4_row5_col8, #T_567e4_row6_col8, #T_567e4_row8_col0, #T_567e4_row8_col4, #T_567e4_row8_col5, #T_567e4_row8_col6, #T_567e4_row8_col7 {\n",
+       "#T_12a3d_row2_col3, #T_12a3d_row3_col2, #T_12a3d_row4_col8, #T_12a3d_row5_col1, #T_12a3d_row5_col8, #T_12a3d_row6_col8, #T_12a3d_row8_col0, #T_12a3d_row8_col4, #T_12a3d_row8_col5, #T_12a3d_row8_col6, #T_12a3d_row8_col7 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row2_col4 {\n",
+       "#T_12a3d_row2_col4 {\n",
        "  background-color: #249224;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row2_col5 {\n",
+       "#T_12a3d_row2_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row2_col6 {\n",
+       "#T_12a3d_row2_col6 {\n",
        "  background-color: #259225;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row2_col8 {\n",
+       "#T_12a3d_row2_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row3_col0 {\n",
+       "#T_12a3d_row3_col0 {\n",
        "  background-color: #d7e9d7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row3_col1 {\n",
+       "#T_12a3d_row3_col1 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row3_col3 {\n",
+       "#T_12a3d_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row3_col4 {\n",
+       "#T_12a3d_row3_col4 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row3_col6 {\n",
+       "#T_12a3d_row3_col6 {\n",
        "  background-color: #d2e7d2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row3_col7 {\n",
+       "#T_12a3d_row3_col7 {\n",
        "  background-color: #3d9e3d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row3_col8 {\n",
+       "#T_12a3d_row3_col8 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row4_col0 {\n",
+       "#T_12a3d_row4_col0 {\n",
        "  background-color: #a2cfa2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row4_col2 {\n",
+       "#T_12a3d_row4_col2 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row4_col3 {\n",
+       "#T_12a3d_row4_col3 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row4_col5 {\n",
+       "#T_12a3d_row4_col5 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row4_col6 {\n",
+       "#T_12a3d_row4_col6 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row4_col7 {\n",
+       "#T_12a3d_row4_col7 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row5_col0 {\n",
+       "#T_12a3d_row5_col0 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row5_col2, #T_567e4_row5_col6, #T_567e4_row6_col5 {\n",
+       "#T_12a3d_row5_col2, #T_12a3d_row5_col6, #T_12a3d_row6_col5 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row5_col3 {\n",
+       "#T_12a3d_row5_col3 {\n",
        "  background-color: #e2efe2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row5_col7, #T_567e4_row7_col1 {\n",
+       "#T_12a3d_row5_col7, #T_12a3d_row7_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row6_col0 {\n",
+       "#T_12a3d_row6_col0 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row6_col1 {\n",
+       "#T_12a3d_row6_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row6_col2 {\n",
+       "#T_12a3d_row6_col2 {\n",
        "  background-color: #349a34;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row6_col3 {\n",
+       "#T_12a3d_row6_col3 {\n",
        "  background-color: #e4f0e4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row6_col4 {\n",
+       "#T_12a3d_row6_col4 {\n",
        "  background-color: #2c962c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row6_col7 {\n",
+       "#T_12a3d_row6_col7 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row7_col0 {\n",
+       "#T_12a3d_row7_col0 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row7_col4 {\n",
+       "#T_12a3d_row7_col4 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row7_col5 {\n",
+       "#T_12a3d_row7_col5 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row7_col6 {\n",
+       "#T_12a3d_row7_col6 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row7_col7 {\n",
+       "#T_12a3d_row7_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_567e4_row8_col2 {\n",
+       "#T_12a3d_row8_col2 {\n",
        "  background-color: #cbe4cb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_567e4_row8_col3 {\n",
+       "#T_12a3d_row8_col3 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_567e4_\">\n",
+       "<table id=\"T_12a3d_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2173,121 +2122,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_567e4_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_567e4_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "      <td id=\"T_567e4_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "      <td id=\"T_567e4_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "      <td id=\"T_567e4_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "      <td id=\"T_567e4_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "      <td id=\"T_567e4_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "      <td id=\"T_567e4_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "      <td id=\"T_567e4_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_567e4_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "      <td id=\"T_567e4_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "      <td id=\"T_567e4_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "      <td id=\"T_567e4_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "      <td id=\"T_567e4_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "      <td id=\"T_567e4_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "      <td id=\"T_567e4_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "      <td id=\"T_567e4_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "      <td id=\"T_567e4_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_567e4_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "      <td id=\"T_567e4_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "      <td id=\"T_567e4_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "      <td id=\"T_567e4_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "      <td id=\"T_567e4_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "      <td id=\"T_567e4_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "      <td id=\"T_567e4_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "      <td id=\"T_567e4_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "      <td id=\"T_567e4_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_567e4_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "      <td id=\"T_567e4_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "      <td id=\"T_567e4_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "      <td id=\"T_567e4_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "      <td id=\"T_567e4_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "      <td id=\"T_567e4_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "      <td id=\"T_567e4_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "      <td id=\"T_567e4_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "      <td id=\"T_567e4_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_567e4_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "      <td id=\"T_567e4_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "      <td id=\"T_567e4_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "      <td id=\"T_567e4_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "      <td id=\"T_567e4_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "      <td id=\"T_567e4_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "      <td id=\"T_567e4_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "      <td id=\"T_567e4_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "      <td id=\"T_567e4_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_567e4_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "      <td id=\"T_567e4_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "      <td id=\"T_567e4_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "      <td id=\"T_567e4_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "      <td id=\"T_567e4_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "      <td id=\"T_567e4_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "      <td id=\"T_567e4_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "      <td id=\"T_567e4_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "      <td id=\"T_567e4_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_567e4_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "      <td id=\"T_567e4_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "      <td id=\"T_567e4_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "      <td id=\"T_567e4_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "      <td id=\"T_567e4_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "      <td id=\"T_567e4_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "      <td id=\"T_567e4_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "      <td id=\"T_567e4_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "      <td id=\"T_567e4_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_567e4_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "      <td id=\"T_567e4_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "      <td id=\"T_567e4_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "      <td id=\"T_567e4_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "      <td id=\"T_567e4_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "      <td id=\"T_567e4_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "      <td id=\"T_567e4_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "      <td id=\"T_567e4_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "      <td id=\"T_567e4_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_567e4_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_567e4_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "      <td id=\"T_567e4_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
-       "      <td id=\"T_567e4_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "      <td id=\"T_567e4_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "      <td id=\"T_567e4_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "      <td id=\"T_567e4_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "      <td id=\"T_567e4_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "      <td id=\"T_567e4_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "      <td id=\"T_567e4_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "      <th id=\"T_12a3d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_12a3d_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_12a3d_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "      <td id=\"T_12a3d_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "      <td id=\"T_12a3d_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "      <td id=\"T_12a3d_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "      <td id=\"T_12a3d_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "      <td id=\"T_12a3d_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "      <td id=\"T_12a3d_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "      <td id=\"T_12a3d_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_12a3d_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "      <td id=\"T_12a3d_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "      <td id=\"T_12a3d_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "      <td id=\"T_12a3d_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "      <td id=\"T_12a3d_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "      <td id=\"T_12a3d_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "      <td id=\"T_12a3d_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "      <td id=\"T_12a3d_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "      <td id=\"T_12a3d_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_12a3d_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "      <td id=\"T_12a3d_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "      <td id=\"T_12a3d_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <td id=\"T_12a3d_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "      <td id=\"T_12a3d_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "      <td id=\"T_12a3d_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "      <td id=\"T_12a3d_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "      <td id=\"T_12a3d_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "      <td id=\"T_12a3d_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_12a3d_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "      <td id=\"T_12a3d_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "      <td id=\"T_12a3d_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "      <td id=\"T_12a3d_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "      <td id=\"T_12a3d_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "      <td id=\"T_12a3d_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "      <td id=\"T_12a3d_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "      <td id=\"T_12a3d_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "      <td id=\"T_12a3d_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_12a3d_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "      <td id=\"T_12a3d_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "      <td id=\"T_12a3d_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "      <td id=\"T_12a3d_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "      <td id=\"T_12a3d_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "      <td id=\"T_12a3d_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "      <td id=\"T_12a3d_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "      <td id=\"T_12a3d_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "      <td id=\"T_12a3d_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_12a3d_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "      <td id=\"T_12a3d_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "      <td id=\"T_12a3d_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "      <td id=\"T_12a3d_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "      <td id=\"T_12a3d_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "      <td id=\"T_12a3d_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "      <td id=\"T_12a3d_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "      <td id=\"T_12a3d_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "      <td id=\"T_12a3d_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_12a3d_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "      <td id=\"T_12a3d_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "      <td id=\"T_12a3d_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "      <td id=\"T_12a3d_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "      <td id=\"T_12a3d_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "      <td id=\"T_12a3d_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "      <td id=\"T_12a3d_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "      <td id=\"T_12a3d_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "      <td id=\"T_12a3d_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_12a3d_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "      <td id=\"T_12a3d_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "      <td id=\"T_12a3d_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "      <td id=\"T_12a3d_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "      <td id=\"T_12a3d_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "      <td id=\"T_12a3d_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "      <td id=\"T_12a3d_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "      <td id=\"T_12a3d_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "      <td id=\"T_12a3d_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_12a3d_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_12a3d_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "      <td id=\"T_12a3d_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
+       "      <td id=\"T_12a3d_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "      <td id=\"T_12a3d_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "      <td id=\"T_12a3d_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "      <td id=\"T_12a3d_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "      <td id=\"T_12a3d_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "      <td id=\"T_12a3d_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "      <td id=\"T_12a3d_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb372e418b0>"
+       "<pandas.io.formats.style.Styler at 0x7ffc6f75ba30>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2309,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2323,7 +2272,7 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n",
+    "In this talktorial, we investigate how activity can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
     "The kinase similarity matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
@@ -2334,11 +2283,9 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
+    "1. Is there be an optimal way to deal with multiple kinase-ligand measurements?\n",
+    "2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few? \n",
+    "3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of $0$, as is the case for p110a and LCK?"
    ]
   }
  ],

From 68d9039d35f5957c34ee88d3c8008f59604a1535 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:30:02 +0200
Subject: [PATCH 041/158] Remove numpy output, fix TOC contents

---
 .../talktorial.ipynb                          | 353 ++++++++----------
 1 file changed, 154 insertions(+), 199 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 26686db8..f85a137d 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -18,7 +18,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -46,7 +45,8 @@
     "\n",
     "* Retrieve and preprocess data\n",
     "* Show kinase coverage\n",
-    "* Encode and compare structures\n",
+    "* Load KiSSim fingerprints\n",
+    "* Compare structures\n",
     "* Map structure to kinase distance matrix\n",
     "* Save kinase distance matrix"
    ]
@@ -121,7 +121,9 @@
    "source": [
     "### Kinase similarity descriptor: Kinase pockets (KiSSim fingerprint)\n",
     "\n",
-    "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets. We here use the KiSSim (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
+    "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
+    "\n",
+    "We here use the novel KiSSim (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
    ]
   },
   {
@@ -176,20 +178,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f7ed13c4ebba4baeaffdad2223c902e9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -545,7 +534,8 @@
     "kissim_fingerprints = pd.read_csv(DATA / \"kissim_fingerprints.csv\", index_col=0)\n",
     "print(f\"Matrix shape: {kissim_fingerprints.shape}\")\n",
     "print(f\"Number of fingerprints: {kissim_fingerprints.shape[0]}\")\n",
-    "print(f\"Number of fingerprint bits: {kissim_fingerprints.shape[1]}\")"
+    "print(f\"Number of fingerprint bits: {kissim_fingerprints.shape[1]}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -576,33 +566,9 @@
    "cell_type": "code",
    "execution_count": 13,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.        , 13.25694052, 14.00147438, ..., 50.66954295,\n",
-       "        50.7622906 , 50.9508342 ],\n",
-       "       [13.25694052,  0.        , 10.37977891, ..., 50.76260217,\n",
-       "        51.20730054, 51.4472163 ],\n",
-       "       [14.00147438, 10.37977891,  0.        , ..., 51.94849587,\n",
-       "        52.88538294, 52.73268888],\n",
-       "       ...,\n",
-       "       [50.66954295, 50.76260217, 51.94849587, ...,  0.        ,\n",
-       "         8.24900205,  6.08783522],\n",
-       "       [50.7622906 , 51.20730054, 52.88538294, ...,  8.24900205,\n",
-       "         0.        ,  7.94854801],\n",
-       "       [50.9508342 , 51.4472163 , 52.73268888, ...,  6.08783522,\n",
-       "         7.94854801,  0.        ]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints.values)\n",
-    "structure_distance_matrix"
+    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints.values)"
    ]
   },
   {
@@ -852,7 +818,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, we will consider per kinase pair the two structures that show the most similar binding mode for their co-crystallized ligands. Hence, we select the structure pair with the minimum distance as representative for a kinase pair."
+    "In this talktorial, we will consider per kinase pair the two structures that show the most similar pockets. Hence, we select the structure pair with the minimum distance as representative for a kinase pair."
    ]
   },
   {
@@ -864,191 +830,191 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
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-       "#T_1e2f0_row3_col4, #T_1e2f0_row5_col3 {\n",
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        "  background-color: #62b062;\n",
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-       "#T_1e2f0_row3_col5 {\n",
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        "  background-color: #008000;\n",
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-       "#T_1e2f0_row3_col8 {\n",
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        "  background-color: #55a955;\n",
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        "  background-color: #67b267;\n",
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-       "#T_1e2f0_row4_col2 {\n",
+       "#T_c9147_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1e2f0_row4_col7 {\n",
+       "#T_c9147_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row4_col8 {\n",
+       "#T_c9147_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
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-       "#T_1e2f0_row5_col1 {\n",
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        "  background-color: #78bb78;\n",
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-       "#T_1e2f0_row5_col6, #T_1e2f0_row6_col2 {\n",
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        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
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-       "#T_1e2f0_row5_col7 {\n",
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        "  background-color: #0c860c;\n",
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        "}\n",
-       "#T_1e2f0_row5_col8 {\n",
+       "#T_c9147_row5_col8 {\n",
        "  background-color: #61b061;\n",
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        "}\n",
-       "#T_1e2f0_row6_col1, #T_1e2f0_row6_col4 {\n",
+       "#T_c9147_row6_col1, #T_c9147_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row6_col3 {\n",
+       "#T_c9147_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row6_col7 {\n",
+       "#T_c9147_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row8_col1 {\n",
+       "#T_c9147_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row8_col2 {\n",
+       "#T_c9147_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row8_col3 {\n",
+       "#T_c9147_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row8_col6 {\n",
+       "#T_c9147_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1e2f0_row8_col7 {\n",
+       "#T_c9147_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_1e2f0_\">\n",
+       "<table id=\"T_c9147_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
        "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
        "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
@@ -1059,133 +1025,121 @@
        "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase.klifs_name</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_1e2f0_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_1e2f0_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_1e2f0_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_1e2f0_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_1e2f0_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_1e2f0_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_1e2f0_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_1e2f0_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_c9147_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_c9147_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_c9147_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_c9147_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_c9147_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_c9147_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_c9147_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_c9147_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_c9147_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_1e2f0_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_1e2f0_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_1e2f0_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_1e2f0_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_1e2f0_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_1e2f0_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_1e2f0_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_1e2f0_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_c9147_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_c9147_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_c9147_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_c9147_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_c9147_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_c9147_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_c9147_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_c9147_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_c9147_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_1e2f0_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_1e2f0_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_1e2f0_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_1e2f0_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_1e2f0_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_1e2f0_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_1e2f0_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_1e2f0_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_c9147_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_c9147_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_c9147_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_c9147_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_c9147_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_c9147_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_c9147_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_c9147_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_c9147_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_1e2f0_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_1e2f0_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_1e2f0_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_1e2f0_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_1e2f0_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_1e2f0_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_1e2f0_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_1e2f0_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_c9147_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_c9147_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_c9147_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_c9147_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_c9147_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_c9147_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_c9147_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_c9147_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_c9147_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_1e2f0_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_1e2f0_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_1e2f0_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_1e2f0_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_1e2f0_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_1e2f0_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_1e2f0_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_1e2f0_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_c9147_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_c9147_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_c9147_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_c9147_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_c9147_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_c9147_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_c9147_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_c9147_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_c9147_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_1e2f0_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_1e2f0_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_1e2f0_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_1e2f0_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_1e2f0_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_1e2f0_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_1e2f0_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_1e2f0_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_c9147_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_c9147_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_c9147_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_c9147_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_c9147_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_c9147_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_c9147_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_c9147_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_c9147_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_1e2f0_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_1e2f0_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_1e2f0_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_1e2f0_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_1e2f0_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_1e2f0_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_1e2f0_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_1e2f0_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_c9147_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_c9147_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_c9147_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_c9147_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_c9147_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_c9147_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_c9147_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_c9147_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_c9147_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_1e2f0_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_1e2f0_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_1e2f0_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_1e2f0_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_1e2f0_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_1e2f0_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_1e2f0_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_1e2f0_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_1e2f0_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_c9147_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_c9147_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_c9147_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_c9147_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_c9147_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_c9147_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_c9147_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_c9147_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_c9147_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_c9147_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_1e2f0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_1e2f0_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_1e2f0_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_1e2f0_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_1e2f0_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_1e2f0_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_1e2f0_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_1e2f0_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_1e2f0_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_1e2f0_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_c9147_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c9147_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_c9147_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_c9147_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_c9147_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_c9147_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_c9147_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_c9147_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_c9147_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_c9147_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff4942344f0>"
+       "<pandas.io.formats.style.Styler at 0x7ff6700192e0>"
       ]
      },
      "execution_count": 16,
@@ -1201,10 +1155,13 @@
     "kinase_distance_matrix = (\n",
     "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     ")\n",
-    "# Show matrix with background gradient\n",
+    "kinase_distance_matrix.index.name = None\n",
+    "kinase_distance_matrix.columns.name = None\n",
+    "\n",
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1222,8 +1179,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix.index.name = None\n",
-    "kinase_distance_matrix.columns.name = None\n",
     "kinase_distance_matrix.to_csv(DATA / \"kinase_distance_matrix_kissim.csv\")"
    ]
   },

From e74e05451dd109eb8663176bfec887265046dd87 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:34:17 +0200
Subject: [PATCH 042/158] Remove numpy output, fix TOC contents

---
 .../talktorial.ipynb                          | 359 ++++++++----------
 1 file changed, 157 insertions(+), 202 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index eb1b7a64..2578f585 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -45,8 +45,7 @@
     "* Show kinase coverage\n",
     "* Compare structures\n",
     "* Map structure to kinase distance matrix\n",
-    "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as dendrogram"
+    "* Save kinase distance matrix"
    ]
   },
   {
@@ -880,60 +879,15 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.        , 0.72      , 0.54285714, ..., 0.40740741, 0.73333333,\n",
-       "        0.73333333],\n",
-       "       [0.72      , 0.        , 0.74193548, ..., 0.70833333, 0.76190476,\n",
-       "        0.76190476],\n",
-       "       [0.54285714, 0.74193548, 0.        , ..., 0.61111111, 0.78378378,\n",
-       "        0.78378378],\n",
-       "       ...,\n",
-       "       [0.40740741, 0.70833333, 0.61111111, ..., 0.        , 0.57692308,\n",
-       "        0.57692308],\n",
-       "       [0.73333333, 0.76190476, 0.78378378, ..., 0.57692308, 0.        ,\n",
-       "        0.        ],\n",
-       "       [0.73333333, 0.76190476, 0.78378378, ..., 0.57692308, 0.        ,\n",
-       "        0.        ]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "structure_distance_matrix = pairwise_distances(ifps_array, metric=\"jaccard\")\n",
-    "structure_distance_matrix"
+    "structure_distance_matrix = pairwise_distances(ifps_array, metric=\"jaccard\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 19,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5725586972826172"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This cell is for testing purposes only\n",
-    "np.mean(structure_distance_matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1034,7 +988,7 @@
        "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1063,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1164,7 +1118,7 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1189,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1224,227 +1178,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row1_col6 {\n",
+       "#T_e3880_row1_col6 {\n",
        "  background-color: #a2cfa2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row1_col7 {\n",
+       "#T_e3880_row1_col7 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row1_col8 {\n",
+       "#T_e3880_row1_col8 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row2_col0 {\n",
+       "#T_e3880_row2_col0 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row2_col1 {\n",
+       "#T_e3880_row2_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row2_col3, #T_57db4_row8_col3 {\n",
+       "#T_e3880_row2_col3, #T_e3880_row8_col3 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row2_col5 {\n",
+       "#T_e3880_row2_col5 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row2_col6 {\n",
+       "#T_e3880_row2_col6 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row2_col8 {\n",
+       "#T_e3880_row2_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row3_col0 {\n",
+       "#T_e3880_row3_col0 {\n",
        "  background-color: #329832;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row3_col1, #T_57db4_row3_col5, #T_57db4_row3_col7, #T_57db4_row7_col0, #T_57db4_row7_col2, #T_57db4_row7_col3, #T_57db4_row7_col4, #T_57db4_row7_col6, #T_57db4_row7_col8 {\n",
+       "#T_e3880_row3_col1, #T_e3880_row3_col5, #T_e3880_row3_col7, #T_e3880_row7_col0, #T_e3880_row7_col2, #T_e3880_row7_col3, #T_e3880_row7_col4, #T_e3880_row7_col6, #T_e3880_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row3_col2 {\n",
+       "#T_e3880_row3_col2 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row3_col6, #T_57db4_row7_col5 {\n",
+       "#T_e3880_row3_col6, #T_e3880_row7_col5 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row3_col8 {\n",
+       "#T_e3880_row3_col8 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col1 {\n",
+       "#T_e3880_row4_col1 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col2, #T_57db4_row6_col4 {\n",
+       "#T_e3880_row4_col2, #T_e3880_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col3 {\n",
+       "#T_e3880_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col5 {\n",
+       "#T_e3880_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row4_col6 {\n",
+       "#T_e3880_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col7 {\n",
+       "#T_e3880_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row4_col8 {\n",
+       "#T_e3880_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row5_col0 {\n",
+       "#T_e3880_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row5_col1 {\n",
+       "#T_e3880_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row5_col2 {\n",
+       "#T_e3880_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row5_col4 {\n",
+       "#T_e3880_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row5_col6 {\n",
+       "#T_e3880_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row5_col7 {\n",
+       "#T_e3880_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row5_col8 {\n",
+       "#T_e3880_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row6_col0 {\n",
+       "#T_e3880_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row6_col1 {\n",
+       "#T_e3880_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row6_col2, #T_57db4_row6_col5 {\n",
+       "#T_e3880_row6_col2, #T_e3880_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row6_col3 {\n",
+       "#T_e3880_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row6_col7 {\n",
+       "#T_e3880_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row6_col8 {\n",
+       "#T_e3880_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row7_col1 {\n",
+       "#T_e3880_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row8_col1 {\n",
+       "#T_e3880_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row8_col4 {\n",
+       "#T_e3880_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_57db4_row8_col6 {\n",
+       "#T_e3880_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_57db4_row8_col7 {\n",
+       "#T_e3880_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_57db4_\">\n",
+       "<table id=\"T_e3880_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1461,121 +1415,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_57db4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_57db4_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_57db4_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_57db4_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_57db4_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_57db4_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_57db4_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_57db4_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_e3880_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_e3880_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_e3880_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_e3880_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_e3880_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_e3880_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_e3880_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_e3880_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_e3880_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_57db4_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_57db4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_57db4_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_57db4_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_57db4_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_57db4_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_57db4_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_57db4_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_e3880_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_e3880_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_e3880_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_e3880_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_e3880_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_e3880_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_e3880_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_e3880_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_e3880_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_57db4_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_57db4_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_57db4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_57db4_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_57db4_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_57db4_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_57db4_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_57db4_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_e3880_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_e3880_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_e3880_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_e3880_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_e3880_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_e3880_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_e3880_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_e3880_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_e3880_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_57db4_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_57db4_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_57db4_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_57db4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_57db4_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_57db4_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_57db4_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_57db4_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_e3880_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_e3880_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_e3880_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_e3880_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_e3880_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_e3880_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_e3880_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_e3880_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_e3880_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_57db4_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_57db4_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_57db4_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_57db4_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_57db4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_57db4_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_57db4_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_57db4_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_e3880_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_e3880_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_e3880_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_e3880_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_e3880_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_e3880_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_e3880_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_e3880_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_e3880_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_57db4_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_57db4_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_57db4_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_57db4_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_57db4_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_57db4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_57db4_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_57db4_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_e3880_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_e3880_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_e3880_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_e3880_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_e3880_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_e3880_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_e3880_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_e3880_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_e3880_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_57db4_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_57db4_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_57db4_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_57db4_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_57db4_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_57db4_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_57db4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_57db4_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_e3880_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_e3880_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_e3880_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_e3880_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_e3880_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_e3880_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_e3880_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_e3880_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_e3880_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_57db4_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_57db4_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_57db4_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_57db4_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_57db4_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_57db4_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_57db4_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_57db4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_57db4_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_e3880_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_e3880_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_e3880_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_e3880_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_e3880_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_e3880_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_e3880_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_e3880_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_e3880_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_e3880_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_57db4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_57db4_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_57db4_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_57db4_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_57db4_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_57db4_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_57db4_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_57db4_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_57db4_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_57db4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_e3880_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_e3880_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_e3880_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_e3880_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_e3880_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_e3880_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_e3880_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_e3880_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_e3880_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_e3880_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa1edf30f70>"
+       "<pandas.io.formats.style.Styler at 0x7f2651232340>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1593,7 +1547,8 @@
     "\n",
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\") "
+    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\") \n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1607,7 +1562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [

From 7bec0fc63a6aea6d7a03d91efe35192d8a3c02af Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:43:10 +0200
Subject: [PATCH 043/158] Add folder skeleton + Aim of this talktorial

---
 .../README.md                                       |  0
 .../data/README.md                                  |  0
 .../images/README.md                                |  0
 .../talktorial.ipynb                                | 13 +++++++++----
 4 files changed, 9 insertions(+), 4 deletions(-)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T028_kinase_similarity_compare_perspectives}/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T028_kinase_similarity_compare_perspectives}/data/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T028_kinase_similarity_compare_perspectives}/images/README.md (100%)
 rename teachopencadd/talktorials/{T0xx_kinase_similarity_xxx => T028_kinase_similarity_compare_perspectives}/talktorial.ipynb (99%)

diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/README.md
rename to teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/data/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/data/README.md
rename to teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/data/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/images/README.md
similarity index 100%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/images/README.md
rename to teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/images/README.md
diff --git a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
similarity index 99%
rename from teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
rename to teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 0fa255f7..d766af9a 100644
--- a/teachopencadd/talktorials/T0xx_kinase_similarity_xxx/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# T000 · Kinase similarity: XXX\n",
+    "# T028 · Kinase similarity: Compare different perspectives\n",
     "\n",
     "Authors:\n",
     "\n",
@@ -19,7 +19,12 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "Add a short summary of this talktorial's content."
+    "We will compare different perspectives on kinase similarity, which were discussed in detail in previous notebooks:\n",
+    "\n",
+    "* **Talktorial T024**: Kinase pocket sequences (KLIFS pocket sequences)\n",
+    "* **Talktorial T025**: Kinase pocket structures (KiSSim fingerprint based on KLIFS pocket residues)\n",
+    "* **Talktorial T026**: Kinase-ligand interaction profiles (KLIFS IFPs based on KLIFS pocket residues)\n",
+    "* **Talktorial T027**: Ligand profiles"
    ]
   },
   {
@@ -288,7 +293,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -302,7 +307,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.9.6"
   },
   "toc-autonumbering": true,
   "widgets": {

From 4a69474c79015563e5f32bd24961d27e4db13c10 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 16:49:52 +0200
Subject: [PATCH 044/158] Add kinase matrices

---
 .../data/kinase_distance_matrix_kissim.csv             | 10 ++++++++++
 .../data/kinase_distance_matrix_ifp.csv                | 10 ++++++++++
 2 files changed, 20 insertions(+)
 create mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
 create mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
new file mode 100644
index 00000000..1e6d82eb
--- /dev/null
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
@@ -0,0 +1,10 @@
+,BRAF,CDK2,EGFR,ErbB2,KDR,LCK,MET,p110a,p38a
+BRAF,0.0,17.156338686753887,19.515299484883034,21.382787595771614,21.089164552869708,21.582681844181142,20.297483020671486,37.61086252438263,21.73142143717456
+CDK2,17.156338686753887,0.0,18.146634315220403,21.106349112675172,19.880466907012618,17.972924805354655,18.304287545534248,36.78007102947219,19.480656386198007
+EGFR,19.515299484883034,18.146634315220403,0.0,16.391994226150114,17.28165001676286,16.467224894268252,17.498394567945827,36.04620732877459,22.12761688356271
+ErbB2,21.382787595771614,21.106349112675172,16.391994226150114,0.0,23.850591780152264,23.88097026329463,22.563235671308735,41.277474125565234,24.68170843658602
+KDR,21.089164552869708,19.880466907012618,17.28165001676286,23.850591780152264,0.0,19.25489849284598,20.430532117581084,41.104454960305326,20.26254051064599
+LCK,21.582681844181142,17.972924805354655,16.467224894268252,23.88097026329463,19.25489849284598,0.0,19.221161302580736,39.02201780223324,22.45669271712331
+MET,20.297483020671486,18.304287545534248,17.498394567945827,22.563235671308735,20.430532117581084,19.221161302580736,0.0,39.41427751663535,21.982654782895654
+p110a,37.61086252438264,36.78007102947219,36.04620732877459,41.277474125565234,41.104454960305326,39.02201780223324,39.41427751663535,0.0,38.52974786964488
+p38a,21.73142143717456,19.480656386198007,22.127616883562684,24.68170843658602,20.26254051064599,22.45669271712331,21.982654782895654,38.52974786964488,0.0
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv
new file mode 100644
index 00000000..752de190
--- /dev/null
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv
@@ -0,0 +1,10 @@
+,BRAF,CDK2,EGFR,ErbB2,KDR,LCK,MET,p110a,p38a
+BRAF,0.0,0.3,0.2777777777777778,0.3939393939393939,0.36363636363636365,0.32,0.3684210526315789,0.5,0.3333333333333333
+CDK2,0.3,0.0,0.1111111111111111,0.45161290322580644,0.23809523809523808,0.19047619047619047,0.13333333333333333,0.34782608695652173,0.2777777777777778
+EGFR,0.2777777777777778,0.1111111111111111,0.0,0.25806451612903225,0.23809523809523808,0.16666666666666666,0.22727272727272727,0.38095238095238093,0.17391304347826086
+ErbB2,0.3939393939393939,0.45161290322580644,0.25806451612903225,0.0,0.40625,0.41935483870967744,0.38235294117647056,0.5714285714285714,0.25806451612903225
+KDR,0.36363636363636365,0.23809523809523808,0.23809523809523808,0.40625,0.0,0.058823529411764705,0.3333333333333333,0.5333333333333333,0.043478260869565216
+LCK,0.32,0.19047619047619047,0.16666666666666666,0.41935483870967744,0.058823529411764705,0.0,0.25,0.375,0.19047619047619047
+MET,0.3684210526315789,0.13333333333333333,0.22727272727272727,0.38235294117647056,0.3333333333333333,0.25,0.0,0.42857142857142855,0.35294117647058826
+p110a,0.5,0.34782608695652173,0.38095238095238093,0.5714285714285714,0.5333333333333333,0.375,0.42857142857142855,0.0,0.47368421052631576
+p38a,0.3333333333333333,0.2777777777777778,0.17391304347826086,0.25806451612903225,0.043478260869565216,0.19047619047619047,0.35294117647058826,0.47368421052631576,0.0

From b593e0263d32e6bdd30713f4b54cf6ce607e603d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 17:00:42 +0200
Subject: [PATCH 045/158] Add visualization code for example matrix

---
 .../talktorial.ipynb                          | 302 +++++++++++++++---
 1 file changed, 255 insertions(+), 47 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index d766af9a..e91c3243 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -78,25 +78,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
-   ]
-  },
-  {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+    "*Table 1:* \n",
+    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "\n",
+    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
+    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
+    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
+    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
+    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
+    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
+    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
+    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
+    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
+    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
+    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
    ]
   },
   {
@@ -113,13 +113,6 @@
     "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compound activity measures"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -166,19 +159,14 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.spatial import distance_matrix, distance\n",
+    "from scipy.cluster import hierarchy"
    ]
   },
   {
@@ -195,63 +183,283 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve and preprocess data"
+    "### Load kinase matrices"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "kinase_distance_matrix_paths = {\n",
+    "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
+    "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'kissim': PosixPath('/home/dominique/Documents/Work/GitHub/teachopencadd/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv'),\n",
+       " 'ifp': PosixPath('/home/dominique/Documents/Work/GitHub/teachopencadd/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv')}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrix_paths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrices = {}\n",
+    "for kinsim_perspective, path in kinase_distance_matrix_paths.items():\n",
+    "    kinase_distance_matrices[kinsim_perspective] = pd.read_csv(path, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
+       " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
+       " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
+       " EGFR   19.515299  18.146634   0.000000  16.391994  17.281650  16.467225   \n",
+       " ErbB2  21.382788  21.106349  16.391994   0.000000  23.850592  23.880970   \n",
+       " KDR    21.089165  19.880467  17.281650  23.850592   0.000000  19.254898   \n",
+       " LCK    21.582682  17.972925  16.467225  23.880970  19.254898   0.000000   \n",
+       " MET    20.297483  18.304288  17.498395  22.563236  20.430532  19.221161   \n",
+       " p110a  37.610863  36.780071  36.046207  41.277474  41.104455  39.022018   \n",
+       " p38a   21.731421  19.480656  22.127617  24.681708  20.262541  22.456693   \n",
+       " \n",
+       "              MET      p110a       p38a  \n",
+       " BRAF   20.297483  37.610863  21.731421  \n",
+       " CDK2   18.304288  36.780071  19.480656  \n",
+       " EGFR   17.498395  36.046207  22.127617  \n",
+       " ErbB2  22.563236  41.277474  24.681708  \n",
+       " KDR    20.430532  41.104455  20.262541  \n",
+       " LCK    19.221161  39.022018  22.456693  \n",
+       " MET     0.000000  39.414278  21.982655  \n",
+       " p110a  39.414278   0.000000  38.529748  \n",
+       " p38a   21.982655  38.529748   0.000000  ,\n",
+       " 'ifp':            BRAF      CDK2      EGFR     ErbB2       KDR       LCK       MET  \\\n",
+       " BRAF   0.000000  0.300000  0.277778  0.393939  0.363636  0.320000  0.368421   \n",
+       " CDK2   0.300000  0.000000  0.111111  0.451613  0.238095  0.190476  0.133333   \n",
+       " EGFR   0.277778  0.111111  0.000000  0.258065  0.238095  0.166667  0.227273   \n",
+       " ErbB2  0.393939  0.451613  0.258065  0.000000  0.406250  0.419355  0.382353   \n",
+       " KDR    0.363636  0.238095  0.238095  0.406250  0.000000  0.058824  0.333333   \n",
+       " LCK    0.320000  0.190476  0.166667  0.419355  0.058824  0.000000  0.250000   \n",
+       " MET    0.368421  0.133333  0.227273  0.382353  0.333333  0.250000  0.000000   \n",
+       " p110a  0.500000  0.347826  0.380952  0.571429  0.533333  0.375000  0.428571   \n",
+       " p38a   0.333333  0.277778  0.173913  0.258065  0.043478  0.190476  0.352941   \n",
+       " \n",
+       "           p110a      p38a  \n",
+       " BRAF   0.500000  0.333333  \n",
+       " CDK2   0.347826  0.277778  \n",
+       " EGFR   0.380952  0.173913  \n",
+       " ErbB2  0.571429  0.258065  \n",
+       " KDR    0.533333  0.043478  \n",
+       " LCK    0.375000  0.190476  \n",
+       " MET    0.428571  0.352941  \n",
+       " p110a  0.000000  0.473684  \n",
+       " p38a   0.473684  0.000000  }"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrices"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Show kinase coverage"
+    "### Visualize similarity for example perspective"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "kinase_distance_matrix = kinase_distance_matrices[\"ifp\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Visualize kinase matrix\n",
+    "\n",
+    "We visualize the kinase matrix in the form of a heatmap."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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PgNsKqM4CcjOcZvYh8FvgYkl9YEElemWQ8TSh2f+NvPRyHKe+tKJURwdsAUw3s+fM7BNgIjCmQrkvA9cDb+Rbi0Wp1nD2jc3w0nFOTP828CrwP0kPA/cAVwKvZJB9KbC9pNWq1M1xnEbBcmuqrwTMSJzPpGwQWdJKwP4EG1IoVQ0OmVWO92lm84Az4lEpf0CFtLuAuxLnc/FRdcfpEhgwvzW1fzZU0tTE+XgzGx8/V7Ks5SECzge+YWYtUrEDUr5yyHGcwsgYrG2WmW3WRt5MYFjifGUWb8luBkyMRnMosJek+WZ2Y3qN0+GG03GcQrF8piNNAUbELryXgUOBwxa9jy3o3pM0gTBD58Y8bl5O15uZ6jhOQ5HH4JCZzQdOIoyWPwFcZ2bTJB0v6fhq9JK0j6SqbKB7nI7jFIZZfhPgzWwSMKksreJAkJmNSyHyUOACSdcDV5jZE2l1cY/TcZxCMVOqo/P1ss8BGwPPAldImizp2NJc9PZww+k4ToGIltZuqY56YGazCfM+JwIrEKYzPSTpy+1d17RN9V795jFsZJbpodnYdNcXC5MNcOPNWxcqH2D4dyYXKr/nXSsUKr9fj08KlT/j/cGFygeYvNH1hcpf68ETCpPd0rt2GY28Vl3SfsDRwBrA74EtzOyNuGHRE8BFbV3btIbTcZwmwEI/Z4NyIHCemS2yqZCZfSjp8+1d6E11x3EKJacll0XwarnRlPQTADO7o70L3XA6jlMYRuMODgG7VUjbM82F3lR3HKdAREtrY/VxSjoBOBFYI25nWWIpIFXkCTecjuMUSp28yfa4GriFsBNbcl+N983s7TQC3HA6jlMYZg1pOM3MXpD0pfIMSUPSGE83nI7jFEoDTke6GtgHeJDQDZtU0IDVOxLghtNxnEJptOlIMaKugB3M7KVqZKQeVY8xPJKbF1fcc1PSXZIW2xpK0jhJb8Zrp0n6U5xoiqRTJT0u6VFJd0hatZrKOI7TeDTiqLqZGfDnaq/PMh2pPEDbOeUFYqiM9rg2Xrse8AlwSEx/GNgsxh36E3BuBr0cx2lQ8oo5VBAPSNq8mgtrnscp6QVJ35V0L3BQTP6cpPsl/U/SFhWu6QH0B94BMLM7Y8wigAcIm5Q6jtPsWGN6nJGdgMmSno2t3cfKpie1SZY+zsViqSfCAn9kZqMA4t54/c1sG0nbA5cD68dyh0gaRVhM/zTw1wr3+QJhqoDjOF2BBuvjTJBqsnslshjOuTHcbyXK46pfA2Bmd0saKGlwqZyZnRQ7Zi8BvgYsaPJL+hxh+/sdKt1E0rHAsQC9l+1w5yfHcRqABpyOBICZvQggaVmgT5Zr81py+UG5Tu2dx47ZvwLbl9Ik7UqIkrmfmX1c6SZmNt7MNjOzzXoO7le71o7jFI5ZuqOzkbSfpGeA54F/AS+QsrVb1Fr1Q6Jio4D3zOy9CmVGETYQRdLGwK8JRrPQeMiO43QeDb5W/YfAVsDTMV7RLhSw5LK8j/NWM6s4JQl4R9L9wEAguT1TqY+zGyFq3biY/lNgAPDHGKHuJTPbL4NujuM0IgbWYGvVE8wzs7ckdZPUzczuLO2O1BGpDWc7sdSHl53v2Ea5CcCENvJ2TauH4zhNRuMODr0raQBwN3CVpDeA+Wku9G3lHMcpkHTN9Do11ccAc4FTgFsJXYf7prnQl1w6jlMsDepxmllyUPvKLNe64XQcpzgacHckSe9T2ZyLMOlnYEcy3HA6jlMsDWY4zazmSeBuOB3HKZYGa6pLGmhmsyUNqZTv+3E6jlN/GsxwsiTvxzm/pRuz5vQvTP7Nd25TmGzonC++6Ljn83Z8tVD5c9ZcrVD5b3yvw66smpn2ydxC5a956YuFyX7jzRzi2huN2FTfJ/6t+gFrWsPpOE5z0GgbGSeRtCEwnIQtNLMbOrrODafjOMXSoIZT0uXAhsA0oDUmG+CG03Gc+qKcllxKGg1cAHQHflO+mbqkMYT1562EFUBfNbN72xG5lZmtW40uvnLIcZzisAxHO8ToEpcQ9tBcFxgrqdzo3QFsFLe//Dzwmw60m1xBRirc43Qcp0CU1+DQFsB0M3sOQNJEwpLJx0sFzGxOonx/Ou4kuJJgPF8DPmbhBPgNO1LGDafjOMWSTx/nSsCMxPlMYMvyQpL2B34MLAvs3YHMy4EjgMdY2MeZCjecjuMUS3rDOVTS1MT5eDMbHz9XclsXk2xmfwb+HMP2/BBob+e1l8zsptTaJXDD6ThOcRiQfnBolpktFlo8MhMYljhfGXilzduGsD1rSBpqZrPaKPakpKsJ0Sg+Tlzb4ah6roNDkuYkPu8l6RlJq0g6S9LLMab6M5JuSHbKxljsT0n6r6QpkkbmqZfjOPVDlu7ogCnACEmrSeoFHAos4i1KWjPGM0PSJkAv4K12ZPYlGMzdCdvJ7UtYUdQhhXicknYBLgJ2N7OXYl3OM7OfxfxDgH9K2sDM3oyXHW5mUyUdTdgRfrcidHMcp5PJoY/TzOZLOgm4jTAd6XIzmxaj6mJmlwIHAEdKmkfYZ/OQGN+sLZlHV6tP7oZT0nbAZcBeZvZspTJmdq2kvYHDCPOykkwmRL90HMdZgJlNAiaVpV2a+PwToMPQF5K+bmbnSrqIyv2kJ3ckI2/D2Rv4C7CjmT3ZQdmHgLUrpI8GbsxZL8dx6kSKZnhn80T8O7XdUu2Qt+GcB9wPfAH4Sgdly3uMr5LUn+CGb1LxgkRc9Z6fGlSbpo7jdA6Nt8nHX+PfBbu+S+oGDDCz2Wlk5L1yqBU4GNhc0rc6KLsxCy0/wOHAaoQtny6pdEEyrnr3gR5X3XEaHiNYhTRHJyPpakkDo8P2OPCUpFTdhLkvuTSzDwkjU4dL+kKlMpIOIIxkXVN27TzgTGArSevkrZvjOJ1PTqPqRbBu9DA/Q+g7XYUwIb5DChlVN7O344L8uyWV5lCdIulzhKVQ/wN2ToyoJ6+dK+nnwOmEJr/jOM1M4/VxlugpqSfBcF5sZvOkdCY8V8NpZgMSn2cQmt4QBozOaue6HcvOf56nXo7j1JHGNZy/Bl4A/ktw8lYFUvVx+sohx3EKo47N8A4xswuBC0vnkl4CdkpzrRtOx3GKpcFG1dsiTpafn6asG07HcQpFdRgxLxo3nI7jFEuDNtVrwXeAdxynOFJORapHP6ikfpK+I+myeD5CUqpNPtxwOo5TLDmEziiIKwi7I20dz2cCZ6e5sGmb6q3zuzHn7eJWDw2ZVewv2fOD4p+Ufj1yiIvdDkXHPW+Z/nyh8ns9u02h8gGOHHxUsTfYe5nCRM+7vlc+ghq3qb6GmR0iaSwsmEOeaiSraQ2n4zjNQaNORwI+kdSXaNolrUFiQ+P2cMPpOE6xNK7h/B5wKzBM0lXAtsC4NBe64XQcpzgaewL87ZIeArYi7Nb2lXbCbCyCDw45jlMsDTo4JGlb4CMz+xswGPhWXHbZIW44HccplgY1nMCvgA8lbUSIOvEi8Ls0F7rhdBynMETjzuME5sdllmOAC83sAmCpNBd6H6fjOMVhDb3k8n1J3wQ+B2wvqTvQM82F7nE6jlMsjdtUP4Qw/egLZvYasBIhwm6HFGI4k/HVy9KPlPQ/SdMkPS7p9Jg+QdKB8fMQSQ/HMMGO4zQ7DWo4zew1M/uFmd0Tz18ys8bq45S0J/BVQqz19QgB2d4rKzOIEDd5vJld0Vm6OY5THI3axylpK0lTJM2R9ImkFknvdXxl5zbVvwmcbmavAJjZR2Z2WSJ/AHALcLWZ/aoT9XIcp0ga1OMELgbGAs8AfYFjaCNQZDmdaTjXBx5sJ/8XwL1mdl4n6eM4TtGkNZp1miRvZtOB7mbWElu5O6a5rpEGh/4JjJG0bFsFJB0raaqkqS1zPuhE1RzHqRa1pjvqwIeSegGPSDpX0imEYJId0pmGcxqwaTv5EwkTUidJqjiXapG46gNS1c9xnDrTqH2chFDA3YCTgA+AYcABaS7sTMP5Y+BcScsDSOot6eRkATM7H7gD+HN8EziO0+w0aFPdzF6MYy2zzez7ZnZqbLp3SFGGs5+kmYnjVDObROh4/YekaYT+zsUm4JvZN4AZwO8lNVJXguM4WWngPk5J20q6XdLTkp4rHWmuLWTlkJlVNHix83WxaUZmNq7s3OdwOk4XQPFoUH4LnEJw4lqyXOgeneM4hZLX4JCk0ZKekjRd0hkV8g+X9Gg87o+bd7THe2Z2i5m9YWZvlY40dfK16o7jFEsOzfC4jvwSYDdCbKApkm4ys8cTxZ4HdjCzd+KCm/HAlu2IvVPST4EbSOz8bmYPdaSPG07HcYoln/7LLYDpZvYcgKSJhF2NFhhOM7s/Uf4BYOUOZJaM6maJNAN27kgZN5yO4xRHtqlGQyVNTZyPN7Px8fNKhEHjEjNp35v8AmElYtuqme2UWrMy3HA6jlMs6Q3nLDPbrI28SmNMFSVL2olgOEe1kf85M/uDpFMr5ZvZLzpS1A2n4ziFktPk9pmECeolVgZeWexe0obAb4A92xnoKa2eqbTQJpW2TWs4u/dsZfDQirvX5cJSL6faCLpq3lov1X6pNTHj/cGFyn/jewMLlV903PNVzrq/40I1stzkYr+jKbv1Lkx26+35rIPMaTnlFGCEpNWAl4FDgcMWuY+0CmGg5wgze7odWX8DMLPvL6artG8aZXw6kuM4xZHTBHgzm09YGnkb8ARwnZlNk3S8pONjse8CywC/lPRIWX9pkjskDS9PjHsAn5+mWk3rcTqO0yTktCoorj6cVJZ2aeLzMYSt4TriFOB2SXuZ2TMAMYTGYcAOaXRxw+k4TmGUgrU1EmY2SdLHwC2SPkMwtpsD25vZO2lkeFPdcZxiacC16mZ2BzAOuAtYHdglrdEE9zgdxykYWWO5nJLeJ5hqAb2BXYA3JAkwM+twRM8Np+M4xWGNFx7YzGqeMuOG03GcYmkshzMX3HA6jlMojTY4lAe5Dg5JMkm/T5z3kPSmpJvj+bh4/kji2Cjx+W1Jz8fP/8hTN8dx6kQDDg7VSt4e5wfA+pL6mtlcwhZQL5eVudbMTipLGwkgaQJws5n9KWe9HMepB/WLJ1QoRUxHugXYO34eC1xTwD0cx2kCRENHuayaIgznROBQSX2ADYF/l+UfUtZU71uADo7jNApm6Y4mIvfBITN7NK4DHUvZ8qhIpaZ6KiQdCxwL0PNTxW6e4DhOPnhTPT03AT8j52Z6Mq56j0EeV91xGp4GjnJZC0VNR7qcEAjpMUk7FnQPx3GagGbrv0xDIR6nmc00swvayC7v4yx200XHceqLe5ztY2YDKqTdRVhIj5lNACa0c/24PPVxHKfOGKi1yaxiCnzlkOM4hdIVB4fccDqOUyxuOB3HcdLTiBsZ54EbTsdxiqMJJ7enwQ2n4ziF0hWnI7nhdBynULyp7jiOkwUDfDpS47Bin3f53ro3Fyb/60d8tjDZAEMGpo4LVTWTN7q+UPnTPplbqPwjBx9VqPzlJhe/38HrW88uVP5x0+4pTPZ5fd/PR1DXs5vNazgdx2kOvKnuOI6TFR9VdxzHyUADRrnMAzecjuMURpgA7x6n4zhONtzjdBzHyUZX9DiL2gHecRwn1x3gJY2W9JSk6ZLOqJC/tqTJkj6WdHp+lVicqg2npJNiBUzS0ER6m8p3VHHHcboaKQO1deCVSuoOXALsCawLjJW0blmxt4GTCWF7CqUWj/M+YFfgxbL0isqnrLjjOF0MtVqqowO2AKab2XNm9gkhmu6YZAEze8PMpgDziqnJQjo0nJKGS3pS0pWSHpX0J0n9zOxhM3uhvHw7yrdZcUlflDRF0n8lXS+pX+1Vcxyn7limuOpDJU1NHMcmJK0EzEicz4xpdSGtx7kWMN7MNgRmAydWca/2Kn6DmW1uZhsBTwBfqEK+4ziNSPqm+qxSFNt4jE9IUSXJnVOBxUlrOGeY2X3x8x+AUVXcq72Kry/pHkmPAYcD61UUIB1behu99/b8KlRwHKfTyWdwaCYwLHG+MvBKzpqmJq3hLK9WNZa+vYpPAE4ysw2A7wN9KiqRiKs+aIjPpHKcZkBmqY4OmAKMkLSapF7AocBNhSvfBmmtzyqStjazycBY4N4q7rWg4sDLhIofFvOWAl6V1JPgcb5chXzHcRoNA1pqb1Gb2XxJJwG3Ad2By81smqTjY/6lkpYHpgIDgVZJXwXWNbPct6hKazifAI6S9GvgGeBXkk4Gvg4sDzwqaZKZHdOe8pUqHuV/B/g3YYT+MYIhdRynyRGpvMlUmNkkYFJZ2qWJz68RWrKFk9ZwtprZ8WVpF8ZjEdpTvlLFY/qvgF+l1MVxnGaiC64c8o5Cx3GKZUk0nHGu5vrFq+I4TpfD8E0+HMdxstIVN/lww+k4ToEYtHY9l9MNp+M4xWEsmX2cjuM4NdH1HE43nI7jFIv3cTYQMz5YmtMeOKgw+Xqt4qrP3LDH+hcqH2CtB08oVP6al5bvKJgzey9TqPgpu/UuVD4UG/cc4Jb1Bhcm+z3LyTy44XQcx8mAAR3vtdl0uOF0HKdAfFTdcRwnO95UdxzHyYA31R3HcbJiYN5UdxzHyYY31R3HcTJgQIt7nI7jONnogh5nLXHVKyLptzHMbymU8ICYPkjSX2PeNElH531vx3EajZQRLpvMuOZuOIFTzGyjGEr4JeCkmP4l4PEYAnhH4Ocx6JLjOF0VI8zjTHM0EVUbTknDJT0p6cqEd9mvFBhJkoC+LIyIacBSMX0A8DYwP5a9UdKD0RM9tsLtHMdpVtzjXIy1gPHRu5wNnAgg6QrgNWBt4KJY9mJgHUJI4MeAr5gtmKfweTPbFNgMOFlSxUXKybjqLbM/qFF1x3E6BTecizHDzO6Ln/8AjAIws6OBFQnRMQ+J+XsAj8T0kcDFkgbGvJMl/Rd4gBB7fUSlmyXjqncfWPwmGY7j1IgZ1tKS6mgmajWc5a+JBedm1gJcCxwQk44GbrDAdOB5YG1JOwK7AlvH/s+HgWK3JnIcp/NotXRHE1Gr4VxF0tbx81jgXklrwoI+zn2BJ2P+S8AuMW85QjP/OWAQ8I6ZfShpbWCrGnVyHKeR6IJN9VrncT4BHCXp18AzhNjot8cmuID/AqVNIX8ITJD0WMz7hpnNknQrcLykR4GnCM11x3G6Aua7I1Wi1cyOL0vbtlJBM3sF2L1C+sfAnjXq4ThOo9Jk3mQafOWQ4ziFYu5xLsTMXgDWz08Vx3G6HGa+Vt1xHCczXXBbuSKWXDqO4wAxrHqrpTo6QtJoSU9Jmi7pjAr5knRhzH9U0iZF1AnccDqOUyQWNzJOc7SDpO7AJYSB5HWBsZLWLSu2J2HxzAjgWMIsn0Jww+k4TqHk5HFuAUw3s+fM7BNgIjCmrMwY4Hdxkc0DwGBJK+RfI5A16VQBSW8CWQJ7DwVmFaSOy6+//M64x5Imf1Uz+1QtN4zztIemLN4H+ChxPt7Mxkc5BwKjzeyYeH4EsKWZlXZfQ9LNwDlmdm88v4MwX3xqLXWoRNMODmX9QSVNNbPNitLH5ddXfmfcw+Vnx8xG5yRKlcRXUSYXvKnuOE4zMJOwAVCJlQk7rWUtkwtuOB3HaQamACMkrRY3QD8UuKmszE3AkXF0fSvgPTN7tQhlmrapXgXjXX6Xlt8Z93D5dcLM5ks6CbgN6A5cbmbTJB0f8y8FJgF7AdOBDwk7shVC0w4OOY7j1AtvqjuO42TEDafjOE5G3HA2KXGjaKcOSNqg3jo49WWJNZySNpHUqXPacqbw0MqS1pC0nKSaJkGXyewjqWde8irI31bSbvFz7i8XSXsAE2O0AmcJZYk0nJJGE4LLfVyQ/C0kHSNpqzyNTkL+7sBlks6WtFHe8uM9RgM3AD8GrpP06RxlXiZpXK3yymSXjOQ3gWUBLOeRz2g0LwK+ZGZPSir8/6eolkVJrqRBkjzGV0aWOMMZjc45wIlm9pikwTnL3xu4Etgc+AawX0zP5R9A0p4EY/Z3wn6oR+ftwUnaBriQEO75FOAfwBExr6p6RKPzf8DVwO3AVyRtn4vCgd7x70cU8EKM+l9JmOryuqReifDWhSBJZmaSRkk6LP6tuaWRkPsZQp2ulbSrG9D0LFGGU9IA4EzgbjO7S9Iw4G5JW+Qk/9PAT4FxZnYccA1wrKQBeXg/klYnRA79rZn9gTBPbSNiWOZEuWqNm6IXtTrwfTO7z8zeI8SWWhuq8+IkrQb8ErjQzP5gZlcRPP7u1ehZQf4GwIHx9GXiMjtJvUteYS3eoaSRwK+Bw4A7gS8BW8cdewojGrd9CS+x1YGvAadL6peD3N2AbwPHAXMIOwntL6l3uxc7wBJmOM1sDvAtYJ24n991wGVm9p9aZStE95wJ/AB4MN7vOuB1YNUc5A8C3iZ4y9tJ2tjM3gHeAU6UdImkQyQNrcFIl7yoicA/E+n/AZZK6DIgi1Azex74G7CzpLVi8i7ATyXdJOk0LYyWWg1rAgdFIzOM6H2a2ccJr7Dal8nWBEO8j5n9k9BUf49gqLct0nhKWhU4BtgHeBpYA1gN+LqkvhllDYqOQokRwMmEqLLDgQsIARWPiM+a0x5m1uUPoH/Z+bbAXcBViTTVIH9NgicyEOgZ07rHv/8AdoyfNwA+VYX8fQmGpz+wHPBV4HqC13Y3sAfBK7keuBfoXcU9dgf+CJwOjCzLWw14Mn4+GvhdqZ4dyOwBDEicn014Wf0WuAPYCdif0O1wGbBcRp1HRX16AAcAVwAvEIzMn+N3cw2he+BnQLcqvpMZwGbxvFv82yvW5aKoQ4+cntNVgYOB9YB+sV5rE7ZUezg+Z58H/kd4gaaqD2H/yn8D/wKuSaQvHb+n9eL59fG3yPQ7LIlH3RUovIKwazQ6x8XzHvHvNtHYHQcsVeM9VooPZf+E/F7x7zWEGPJjCMvFMhnOaBTvB/ZOpPUh9J++RthaK/lPnfmhB0YTvOTjozE+qSx/aeBP8Z/236V/tBR6X0kI9/w74DMx/WuEpv+GibIDgH4Zdd4zGsjjgI1j2t7xH//nBI9wT+Ag4MvA6hnl701YH71tPF8eGJzI7wV8n2Cst8nhOV2b0M1wGcHg/xjYPubtQ9guDYIx/xWwfkq5K8bf4OB4/h9gjUT+b4AfEV4AN5SeJz86+F7rrUDhFQxv8DcJnshFBG9tSMzbijBQcWrWf9x4/VqEZs7g+LAv5nnEh/LvwGRgg4zyNwJaga3j+ZrAuQRPZChh4OZaYFTimkyeM7AJ8C6wbzw/iPASOBI4KFHuRYKns24KmXsA0wje5DbAd6IR/VrMPxv4fbx3Ji8wofMzJcNSlncIcHk0nH2qfGZWAB4Bzo/nywNPAYfG86TneSawfA7P6XHAtxL1Oyka0VGEZnVL/O1fAHbKIHcEcBXR8wceI+ykfhmwCqHL5Ir42+5daz2WlKPuChRSqdCfVVqHPzD+444mNOe+H43AwcAywDqEXVUGZ7zHIMIo8S8JTf9rSTRfE/f/NTAPWLPKutxL8Fo/ReheOCWR9ynC9JurqaJ5HmXsAEwAvkhoIj5EeMGcSWjGfSl+n+OBtVLI25awldd2ibQewGcJnuceMW18NHC9qtB5P+CCsu+5eyJ/DHAz4SXQgwwvE2BQ/Pt5gud6KuGleFxZucx6d3DfLwN3JM5Xjcbz5/F8s2hcd8godwXCS+syQmvhSkILaSJho+BSuZXzrE9XP+quQKdUMng4N8bP6wKfxAdpWjSgHfbXtSF3C+B78Y39NHAGMDYaj9HRKA0jezNxbRJNJkITvxU4Np4njcRQoged8R4lgyNCd8YlhIGsklc4IBrTH5Xfsx2ZPYETCC+R7Ul4k4QR9HOAKxJpy1b5ve9G6CvtxkLvr1usy27RWO4NrJhR7h6ELovt4vkR8XxiWbkxwI5ZDHJ7v0Hi/HLg24nzDeJvv02l8u3IXQ04Kj5/vaOcHYEbgeGJcv8B1qmlDkvq0eVG1SXtJOkiSUfFaSSY2ZnAB5IuJXiX+5vZF4FxwKNmNi+D/BUkfTqOeE4leHvzgL6EQZytCfMfvwl8l9B8fy6D/NEEz+zwhP57ELbM2i+et5TmbprZLDN7O638eA9Z/M+xwD8I/7S3xfwBFmYgDAHWjHMH252zKGknggc+kdAt8VlCU52SzoRukWUl9Yhpb6TVtyxpFsEjW8/MWiV1M7PWWKetgM+a2d/MLOsmtp8meN3flTTazH4PnA+8oxCqAUkHEZrML5e+w2qQtAYwTtIy8bw7oX95RUnfBDCzxwhdBpvE8w7vF1c0/ZHQxD8BOMLMHjOzuwjP6zqSekhah/Cim1NtHZZkutR+nHGS8jnAXwn78q0u6XlgNmF6zfcJ/VT3SuppZlMyyt+bMCjTSjCWLQTj8H3gDcJI6I/N7M2EYctilPcCfkLwLCcn88xsH0n/kPR3M9s9i9yyeyxHmNY0T2EvwxUJ3uCZhObzQYS5p3Ni3T5vIThWW/JKRnhIUNPekXQFoak7ShJmdn0svg5hlLolo9rdgfnxN5tnZg9Lugm4NS4IeAaYG43bWMILrBquIcyXnAEcF+/3+2i3t4jP15qEF+8zVd6jNK/1YULTuZ+k6+Iz80Cs6yGSbiE0q8cRupjSyF2B8BteEPX+CuFFNZCwMOB/wGmE72hj4DtmNqPaeizR1NvlzesANiQYtNJI5BaEQZnh8XwF4EnCfDzIPohSasbtRBg9X57gITxN8DZHEDyu8cAqGWWL0DS+lhCQKpl3HnBe4nwKsduhinssRxj42Z/Q3PwP8BnClKkbCKP1WxI8lpmkGz0vTb86BvhNIn0gYfDqPEKz/XDg0TQyy+QPJQyIlAb0eifyvkXoA76FMMXpiSrkb0gc4Sc0939CGGnenfgCTtTvHlKOZndwz52j7mMI8ye/TJxtQXBm+gBnxTL7ZJA7OPn8EAzzvYR+9gnxud0Q2A7YvJr/Az/id1tvBXKrSDA8N7Do3MxbCf2PXwZGEt6048k42kroc0wa5WQf4x/ifUVopp9JlfPg4sO9ReL88/Gf9R4W7chftQrZpT7NgwkvlAkEz6mUfwNwXfy8NTAshcySURtE8PKuLctfOn73fwWeI8WIfBv32Zfw0ls6nieN5+oEY78j2V9Yy8Tf9SXCKPzm0XBdQnhRlsIzfCaWH5jTs9oX6Bs/H5AwnhVH59MYN8pmdBD6rX9Qel7ic39YHvr70QX6OCUNlTTEQp/coYBJ+qOkcwn/uE8R+q6uJfQRftfMPmpb4mLyNyY0LW8irAvvbqGPsbQ07SyC0R5qoXn9MzN7PYP8TSVtE/v9liEYgBJPmNl2ZrYd8GlJpb6uLGGRiX2Apf6xpwiGcx1gy8QqkcOB7gprsCdbiiacmc0irD65l7Cy6CFJK0saLGkZCyubJhFGuHc1s8ez6J24z18J3utUSUub2ceJ739V4H0zu8vMXsoo9y2CgVmZ4ImV+pc/JHiAEwkzC8ZK6m9ms6vRHxZdbWVmc4lhcC10Y9xDaLHsLOlgSWeV6dlh36aF0BLbSjpa0ijgPjP7bsx7kTDlbFB7MpwM1Nty13IQ+jH/QxhhLY3+DiAM2LxPYsoI4aHJNOWCMDL7OKHfbzWCl3ZjIl9R7t+BlarQf09C839kPN+YMBgwrqzcZwnN6aE1fl/bA7cn6nYnYeR4DcL8x/9ShVdFaNa2AnMJcwYfJswXvJPwwhpQi95l39ezLPQ8TyRsupHJ06wgdxfCFLVlCFN+/hXr0YvwQqjJ0yRsxvIWcFRZenLWwSjCy3k2cb5oStmllsRWhK6QCQTj/4fS8xKfq0dJTBHzo8Znsd4KVK148A7uJfQTjSRMqC41f3rFB+dqqp9qtEP8p0xOCxoQH8obEw/sOMJo8aAq9L8P2D2eDyVMpt83GutvxzLHRYOWuZkLbApsFT8fEg3Z4Yn8Awjezt8Jczer7r8j9JvNjvXoTvBolwNWy/l33zMagVMJfZojc5K7VzT2pYniuehN6OudSFh59Xjy+4/5pedoa2A+cRI6GfoeCf35d7BwFdlwwhS8HxK6IJ4jQ1+pHym+83orUJXSYQS3ldhHFx+cVwmT0cfHtF6EN/jvq7zHqcBX4ufkxPb+hNHOK4DPETzerCuCSvp/Jp6vGQ3YqHi+YTT6l8b7ZDZoLPRmSwMfw6PBubys3P6E5nTmNfQV7rkXYeS20LXOBG+5FdgoZ7l7RWM8JJFW61zNPgljuAvwfMl4sqjHuTMwpnTPjIZzN0J3UmnlUY9Yl4sIe5PWvCTUj7LvvN4KVK14+Od5mLAs8XbClKBhhJHEibFMf7JPgi55ABcBZyfTEmXWAv5C6AurdsBjb8IqnQ2j/qfH9G5lelSzsqbcm12WMFVqZcKk/zPLyufSlI6yxsTfJfNSyoz3ybxENoP+DxEn1Ncoa7FFA4RujecI8ytLz9LQRH4mo1mm93RgbDzfPr44Bxf5OyypR90VqEn5YCBagTMSaQMIzZZlapS9M6FfcdN4nlylcgLBg6vJsyrXn4U7Ku1JGExZzGinkFnuza5B6NLYJZ6vRuhHPafA3yU3Q1yn5yrPF8m2hB2cdmLhlKrRhGb7uYRWwMY53WtfQnfJtYSWijfPi3pG6q1AzRUIzZSnSm/W+JDeT+07HvUnjJifWzKeMf2Q6FHlsrY36v9kQv9xhOZ/1X1sLO7NnhbTS4Z5OKFroKbBJj/a/P4rDdhMIAw4LR/zvpV8weV47/3ib39qSZesL18/UnzP9VYgl0oED+1/hFHWu8lhknKUuxJh2eS/CPs5nh2NXC7yy/R/LHqy95BxEncbMtvyZvchrNcvtCm9pB+0P2AznLC89YCYl6thI3QHzCQsPa37d9EVj9KbsemRtA9hEvfGZjYtR7l9CaPTuxIGoO40s6fzkp+4T+76x/AIFxFG1t9VCJB2ImG6S+r180524nd/K2FZ4//Febq7E1ZqnUzY4OSl0jp8y/kfMd7/Wf+di6HLGE4ASf3M7MN661EtRegf13KfS5hxcBhwfJ4vFqdtJI0hbE33HTO7RiE43c8JyyLfqq92Ti10KcPpVKYob9zpmBgH6SrCevoPgevN7Ob6auXUihvOJYRm98abGUn7EQYa/2Bmvyiqee50Hl1qWzmnbdxo1g8zu0nSR8Dlkl4wsxvqrZNTG+5xOk4n4QM2XQc3nI7jOBlp+m3lHMdxOhs3nI7jOBlxw+k4jpMRN5yO4zgZccPpOI6TETecjuM4Gfl/cfQ9B9M5MU0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
+    "plt.imshow(kinase_distance_matrix)\n",
+    "plt.colorbar(label=\"Kinase similarity\")\n",
+    "plt.title(\"Kinase distance matrix\")\n",
+    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
+    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
+    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "#### Visualize similarity as dendrogram"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "We use the `scipy` function `squareform` to create a condensed vector of the distance matrix of shape $n*(n-1)/2$, where $n$ is the shape of the quadratic distance matrix. The values in this vector correspond to the values of the lower triangular matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.3       , 0.27777778, 0.39393939, 0.36363636, 0.32      ,\n",
+       "       0.36842105, 0.5       , 0.33333333, 0.11111111, 0.4516129 ,\n",
+       "       0.23809524, 0.19047619, 0.13333333, 0.34782609, 0.27777778,\n",
+       "       0.25806452, 0.23809524, 0.16666667, 0.22727273, 0.38095238,\n",
+       "       0.17391304, 0.40625   , 0.41935484, 0.38235294, 0.57142857,\n",
+       "       0.25806452, 0.05882353, 0.33333333, 0.53333333, 0.04347826,\n",
+       "       0.25      , 0.375     , 0.19047619, 0.42857143, 0.35294118,\n",
+       "       0.47368421])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "D = kinase_distance_matrix.values\n",
+    "D_condensed = distance.squareform(D)\n",
+    "D_condensed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can submit this condensed vector to a hierarchical clustering to extract the relationship between the different kinases. We use here `method=\"average\"`, which stands for the linkage method UPGMA (unweighted pair group method with arithmetic mean). This means that the distance between two clusters A and B is defined as the average of all distances between pairs of elements in clusters A and B. At each clustering step, the two clusters with the lowest average distance are combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "hclust = hierarchy.linkage(D_condensed, method=\"average\")"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as kinase matrix"
+    "We now generate a phylogenetic tree based on our clustering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "tree = hierarchy.to_tree(hclust)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We visualize this tree as dendrogram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "labels = kinase_distance_matrix.columns.to_list()\n",
+    "hierarchy.dendrogram(hclust, labels=labels, orientation=\"left\", ax=ax)\n",
+    "ax.set_xlabel(\"Distance\");"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as phylogenetic tree"
+    "### Visualize similarities from different perspectives"
    ]
   },
   {

From 8d312872dbc9a2b8e44660aab2a11865e4b2e000 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 24 Aug 2021 17:21:43 +0200
Subject: [PATCH 046/158] Add draft for perspective comparison

---
 .../talktorial.ipynb                          | 334 +++++++++++++++---
 1 file changed, 293 insertions(+), 41 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index e91c3243..0a54a73c 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -202,37 +202,16 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'kissim': PosixPath('/home/dominique/Documents/Work/GitHub/teachopencadd/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv'),\n",
-       " 'ifp': PosixPath('/home/dominique/Documents/Work/GitHub/teachopencadd/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv')}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kinase_distance_matrix_paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
    "outputs": [],
    "source": [
     "kinase_distance_matrices = {}\n",
     "for kinsim_perspective, path in kinase_distance_matrix_paths.items():\n",
-    "    kinase_distance_matrices[kinsim_perspective] = pd.read_csv(path, index_col=0)"
+    "    kinase_distance_matrices[kinsim_perspective] = pd.read_csv(path, index_col=0).round(6)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +261,7 @@
        " p38a   0.473684  0.000000  }"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,14 +272,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
     "### Visualize similarity for example perspective"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -320,12 +302,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -364,23 +346,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.3       , 0.27777778, 0.39393939, 0.36363636, 0.32      ,\n",
-       "       0.36842105, 0.5       , 0.33333333, 0.11111111, 0.4516129 ,\n",
-       "       0.23809524, 0.19047619, 0.13333333, 0.34782609, 0.27777778,\n",
-       "       0.25806452, 0.23809524, 0.16666667, 0.22727273, 0.38095238,\n",
-       "       0.17391304, 0.40625   , 0.41935484, 0.38235294, 0.57142857,\n",
-       "       0.25806452, 0.05882353, 0.33333333, 0.53333333, 0.04347826,\n",
-       "       0.25      , 0.375     , 0.19047619, 0.42857143, 0.35294118,\n",
-       "       0.47368421])"
+       "array([0.3     , 0.277778, 0.393939, 0.363636, 0.32    , 0.368421,\n",
+       "       0.5     , 0.333333, 0.111111, 0.451613, 0.238095, 0.190476,\n",
+       "       0.133333, 0.347826, 0.277778, 0.258065, 0.238095, 0.166667,\n",
+       "       0.227273, 0.380952, 0.173913, 0.40625 , 0.419355, 0.382353,\n",
+       "       0.571429, 0.258065, 0.058824, 0.333333, 0.533333, 0.043478,\n",
+       "       0.25    , 0.375   , 0.190476, 0.428571, 0.352941, 0.473684])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -400,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -432,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -462,6 +442,13 @@
     "### Visualize similarities from different perspectives"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Normalize different matrices [0,1]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -469,6 +456,271 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualize stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def heatmap(distance_matrix, ax=None, title=\"\"):\n",
+    "    \"\"\"TODO\"\"\"\n",
+    "    #plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
+    "    ax.imshow(distance_matrix)\n",
+    "    #ax.colorbar(label=\"Kinase similarity\")\n",
+    "    ax.set_title(title)\n",
+    "    kinase_names = distance_matrix.columns.to_list()\n",
+    "    ax.set_xticks(ticks=range(0, len(kinase_names)))\n",
+    "    ax.set_xticklabels(labels=kinase_names, rotation=45)\n",
+    "    ax.set_yticks(ticks=range(0, len(kinase_names)))\n",
+    "    ax.set_yticklabels(labels=kinase_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dendrogram(distance_matrix, ax=None, title=\"\"):\n",
+    "    \"\"\"TODO\"\"\"\n",
+    "    D = distance_matrix.values\n",
+    "    print(title)\n",
+    "    D_condensed = distance.squareform(D)\n",
+    "    hclust = hierarchy.linkage(D_condensed, method=\"average\")\n",
+    "    tree = hierarchy.to_tree(hclust)\n",
+    "    labels = distance_matrix.columns.to_list()\n",
+    "    hierarchy.dendrogram(hclust, labels=labels, orientation=\"left\", ax=ax)\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_xlabel(\"Distance\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>17.156339</td>\n",
+       "      <td>19.515299</td>\n",
+       "      <td>21.382788</td>\n",
+       "      <td>21.089165</td>\n",
+       "      <td>21.582682</td>\n",
+       "      <td>20.297483</td>\n",
+       "      <td>37.610863</td>\n",
+       "      <td>21.731421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>17.156339</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>18.146634</td>\n",
+       "      <td>21.106349</td>\n",
+       "      <td>19.880467</td>\n",
+       "      <td>17.972925</td>\n",
+       "      <td>18.304288</td>\n",
+       "      <td>36.780071</td>\n",
+       "      <td>19.480656</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>19.515299</td>\n",
+       "      <td>18.146634</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>16.391994</td>\n",
+       "      <td>17.281650</td>\n",
+       "      <td>16.467225</td>\n",
+       "      <td>17.498395</td>\n",
+       "      <td>36.046207</td>\n",
+       "      <td>22.127617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>21.382788</td>\n",
+       "      <td>21.106349</td>\n",
+       "      <td>16.391994</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>23.850592</td>\n",
+       "      <td>23.880970</td>\n",
+       "      <td>22.563236</td>\n",
+       "      <td>41.277474</td>\n",
+       "      <td>24.681708</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>21.089165</td>\n",
+       "      <td>19.880467</td>\n",
+       "      <td>17.281650</td>\n",
+       "      <td>23.850592</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.254898</td>\n",
+       "      <td>20.430532</td>\n",
+       "      <td>41.104455</td>\n",
+       "      <td>20.262541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>21.582682</td>\n",
+       "      <td>17.972925</td>\n",
+       "      <td>16.467225</td>\n",
+       "      <td>23.880970</td>\n",
+       "      <td>19.254898</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>19.221161</td>\n",
+       "      <td>39.022018</td>\n",
+       "      <td>22.456693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>20.297483</td>\n",
+       "      <td>18.304288</td>\n",
+       "      <td>17.498395</td>\n",
+       "      <td>22.563236</td>\n",
+       "      <td>20.430532</td>\n",
+       "      <td>19.221161</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.414278</td>\n",
+       "      <td>21.982655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>37.610863</td>\n",
+       "      <td>36.780071</td>\n",
+       "      <td>36.046207</td>\n",
+       "      <td>41.277474</td>\n",
+       "      <td>41.104455</td>\n",
+       "      <td>39.022018</td>\n",
+       "      <td>39.414278</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>38.529748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>21.731421</td>\n",
+       "      <td>19.480656</td>\n",
+       "      <td>22.127617</td>\n",
+       "      <td>24.681708</td>\n",
+       "      <td>20.262541</td>\n",
+       "      <td>22.456693</td>\n",
+       "      <td>21.982655</td>\n",
+       "      <td>38.529748</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
+       "BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
+       "CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
+       "EGFR   19.515299  18.146634   0.000000  16.391994  17.281650  16.467225   \n",
+       "ErbB2  21.382788  21.106349  16.391994   0.000000  23.850592  23.880970   \n",
+       "KDR    21.089165  19.880467  17.281650  23.850592   0.000000  19.254898   \n",
+       "LCK    21.582682  17.972925  16.467225  23.880970  19.254898   0.000000   \n",
+       "MET    20.297483  18.304288  17.498395  22.563236  20.430532  19.221161   \n",
+       "p110a  37.610863  36.780071  36.046207  41.277474  41.104455  39.022018   \n",
+       "p38a   21.731421  19.480656  22.127617  24.681708  20.262541  22.456693   \n",
+       "\n",
+       "             MET      p110a       p38a  \n",
+       "BRAF   20.297483  37.610863  21.731421  \n",
+       "CDK2   18.304288  36.780071  19.480656  \n",
+       "EGFR   17.498395  36.046207  22.127617  \n",
+       "ErbB2  22.563236  41.277474  24.681708  \n",
+       "KDR    20.430532  41.104455  20.262541  \n",
+       "LCK    19.221161  39.022018  22.456693  \n",
+       "MET     0.000000  39.414278  21.982655  \n",
+       "p110a  39.414278   0.000000  38.529748  \n",
+       "p38a   21.982655  38.529748   0.000000  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrices[\"kissim\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1751.566954\n",
+      "kissim\n",
+      "22.258792\n",
+      "ifp\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_perspectives = len(kinase_distance_matrices)\n",
+    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*4, 10))\n",
+    "for i, (perspective, matrix) in enumerate(kinase_distance_matrices.items()):\n",
+    "    print(matrix.sum().sum())\n",
+    "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
+    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 53619b3fd45147c9f6c50767b9366d463686439d Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 18:18:58 +0200
Subject: [PATCH 047/158] Add discussion and quiz

---
 .../talktorial.ipynb                          | 345 ++++++++++--------
 1 file changed, 189 insertions(+), 156 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 30ab25e0..fdf4ed02 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "Add a short summary of this talktorial's content."
+    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the 85 residues pocket sequence for each kinase. Two similarity measures are implemented: 1. the identity similarity which is based on character-wise discrepancy and 2. the substitution similarity which is amino acid specific."
    ]
   },
   {
@@ -29,7 +29,9 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor: sequence\n",
+    "    * Identity score\n",
+    "    * Substitution score"
    ]
   },
   {
@@ -38,11 +40,13 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Retrieve and preprocess data\n",
-    "* Show kinase coverage\n",
-    "* Compare kinases\n",
+    "* Retrieve sequences from KLIFS\n",
+    "* Sequence similarity\n",
+    "    * Identity score\n",
+    "    * Substitution score\n",
+    "* Kinase comparison\n",
     "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
+    "* Save kinase distance matrix"
    ]
   },
   {
@@ -52,7 +56,10 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
+    "* KLIFS\n",
+    "  * KLIFS URL: https://klifs.net/\n",
+    "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "* Substitution matrix: [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915)"
    ]
   },
   {
@@ -82,8 +89,8 @@
     "\n",
     " \n",
     "\n",
-    "*Table 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "*Table 1:*\n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
     "\n",
     " \n",
     "\n",
@@ -106,16 +113,33 @@
    "source": [
     "### Kinase similarity descriptor: sequence\n",
     "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
+    "1. The sequence is of fixed length (it contains 85 residues), which makes computation for pairwise similarity between two sequences easy.\n",
+    "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an 85 residues sequence contains most relevant information?\n",
     "\n",
-    "- XXX = KLIFS pocket sequence"
+    "We now describe two ways to compare pocket sequences."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Practical"
+    "#### Identity score\n",
+    "A simple way of assessing the similarity between two sequences is to use the so-called identity score.\n",
+    "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If there are, the entry is set to $1$, and $0$ otherwise.\n",
+    "\n",
+    "The identity score is computed by sum the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Substitution score\n",
+    "Although the identity score is an easy measure of similarity, it does not take into account the rate at which an amino acid may change into another and treats all residues uniformly.\n",
+    "\n",
+    "The substitution score takes the changes of the amino acids over evolutionary time into account. It makes use of a substitution matrix, where each entry gives a score between two amino acids.\n",
+    "In this talktorial, we use the BLOSUM substitution matrix [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915), implemented in biotite."
    ]
   },
   {
@@ -124,9 +148,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !pip install flake8 pycodestyle_magic\n",
-    "%load_ext pycodestyle_magic\n",
-    "%pycodestyle_on"
+    "# TODO: add aggregation and normalization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Practical"
    ]
   },
   {
@@ -189,7 +218,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use KLIFS' API to retrieve the 85-long pocket sequence for each kinase."
+    "We use KLIFS' API to retrieve the $85$-long pocket sequence for each kinase."
    ]
   },
   {
@@ -228,7 +257,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see how these pocket sequence look like."
+    "Let's look at these pocket sequences."
    ]
   },
   {
@@ -273,7 +302,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Given two kinases, we create functions which account for identity or subsituition similarity, as described in the theory."
+    "Given two kinases, we create functions which account for identity or substitution similarity, as described in the theory."
    ]
   },
   {
@@ -320,7 +349,7 @@
    "metadata": {},
    "source": [
     "#### Substitution score\n",
-    "We now define the function which is more specific to amino acids grouping and use the `biotite` library and retrieve the substitution matrix."
+    "We now define the function which is more specific to amino acids grouping and use the `biotite` library for retrieving the BLOSUM substitution matrix."
    ]
   },
   {
@@ -334,7 +363,7 @@
     "                       substitution_matrix=align.\n",
     "                       SubstitutionMatrix.std_protein_matrix()):\n",
     "    \"\"\"\n",
-    "    Retrieve #TODO\n",
+    "    Retrieve the match score given the substitution matrix\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -349,6 +378,8 @@
     "\n",
     "    Returns\n",
     "    -------\n",
+    "    np.array :\n",
+    "        The vector of match score given the substitution matrix.\n",
     "    \"\"\"\n",
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
@@ -367,8 +398,6 @@
     "        ind1 = dict_letters[character_seq1]\n",
     "        ind2 = dict_letters[character_seq2]\n",
     "        match_score[i] = score_matrix[ind1, ind2]\n",
-    "    # TODO normalize?\n",
-    "    # TODO check for * VS -\n",
     "    return match_score"
    ]
   },
@@ -376,7 +405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Kinase comparison"
+    "### Kinase comparison"
    ]
   },
   {
@@ -411,6 +440,12 @@
     "    sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
     "    sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
     "\n",
+    "    # Replace possible unavailable residue\n",
+    "    # noted in KLIFS with \"-\"\n",
+    "    # by the symbol \"*\" for biotite\n",
+    "    sequence_1 = sequence_1.replace(\"-\", \"*\")\n",
+    "    sequence_2 = sequence_2.replace(\"-\", \"*\")\n",
+    "\n",
     "    if len(sequence_1) != len(sequence_1):\n",
     "        print(\"Mismatch in sequence lengths.\")\n",
     "        return None\n",
@@ -484,7 +519,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As expected, the similarity between a kinase and itself leads the highest possible score:"
+    "As expected, the similarity between a kinase and itself leads to the highest possible score:"
    ]
   },
   {
@@ -741,144 +776,144 @@
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+       "#T_4f6a5_row7_col1 {\n",
        "  background-color: #96c996;\n",
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        "</style>\n",
-       "<table id=\"T_9913a_\">\n",
+       "<table id=\"T_4f6a5_\">\n",
        "  <thead>\n",
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@@ -895,118 +930,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_9913a_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
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-       "      <td id=\"T_9913a_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
+       "      <th id=\"T_4f6a5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_4f6a5_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_4f6a5_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
+       "      <td id=\"T_4f6a5_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
+       "      <td id=\"T_4f6a5_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
+       "      <td id=\"T_4f6a5_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_9913a_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_9913a_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
-       "      <td id=\"T_9913a_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
-       "      <td id=\"T_9913a_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
-       "      <td id=\"T_9913a_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
+       "      <th id=\"T_4f6a5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_4f6a5_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_4f6a5_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
+       "      <td id=\"T_4f6a5_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
+       "      <td id=\"T_4f6a5_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
+       "      <td id=\"T_4f6a5_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_9913a_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
-       "      <td id=\"T_9913a_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
+       "      <th id=\"T_4f6a5_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_4f6a5_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
+       "      <td id=\"T_4f6a5_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_9913a_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
-       "      <td id=\"T_9913a_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
-       "      <td id=\"T_9913a_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
-       "      <td id=\"T_9913a_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
-       "      <td id=\"T_9913a_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
-       "      <td id=\"T_9913a_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
+       "      <th id=\"T_4f6a5_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_4f6a5_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
+       "      <td id=\"T_4f6a5_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
+       "      <td id=\"T_4f6a5_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
+       "      <td id=\"T_4f6a5_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
+       "      <td id=\"T_4f6a5_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
+       "      <td id=\"T_4f6a5_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_9913a_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
-       "      <td id=\"T_9913a_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
-       "      <td id=\"T_9913a_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
-       "      <td id=\"T_9913a_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
+       "      <th id=\"T_4f6a5_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_4f6a5_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
+       "      <td id=\"T_4f6a5_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
+       "      <td id=\"T_4f6a5_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
+       "      <td id=\"T_4f6a5_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_9913a_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
-       "      <td id=\"T_9913a_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
-       "      <td id=\"T_9913a_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
-       "      <td id=\"T_9913a_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
+       "      <th id=\"T_4f6a5_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_4f6a5_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
+       "      <td id=\"T_4f6a5_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
+       "      <td id=\"T_4f6a5_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
+       "      <td id=\"T_4f6a5_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_9913a_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
-       "      <td id=\"T_9913a_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
-       "      <td id=\"T_9913a_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
+       "      <th id=\"T_4f6a5_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_4f6a5_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
+       "      <td id=\"T_4f6a5_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
+       "      <td id=\"T_4f6a5_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_9913a_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
-       "      <td id=\"T_9913a_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
-       "      <td id=\"T_9913a_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
-       "      <td id=\"T_9913a_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
-       "      <td id=\"T_9913a_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
-       "      <td id=\"T_9913a_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
-       "      <td id=\"T_9913a_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_9913a_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
+       "      <th id=\"T_4f6a5_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_4f6a5_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
+       "      <td id=\"T_4f6a5_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
+       "      <td id=\"T_4f6a5_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
+       "      <td id=\"T_4f6a5_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
+       "      <td id=\"T_4f6a5_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
+       "      <td id=\"T_4f6a5_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
+       "      <td id=\"T_4f6a5_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_4f6a5_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9913a_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_9913a_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
-       "      <td id=\"T_9913a_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
-       "      <td id=\"T_9913a_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
-       "      <td id=\"T_9913a_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
-       "      <td id=\"T_9913a_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
-       "      <td id=\"T_9913a_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
-       "      <td id=\"T_9913a_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
-       "      <td id=\"T_9913a_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
-       "      <td id=\"T_9913a_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_4f6a5_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_4f6a5_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
+       "      <td id=\"T_4f6a5_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
+       "      <td id=\"T_4f6a5_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
+       "      <td id=\"T_4f6a5_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
+       "      <td id=\"T_4f6a5_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
+       "      <td id=\"T_4f6a5_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
+       "      <td id=\"T_4f6a5_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
+       "      <td id=\"T_4f6a5_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
+       "      <td id=\"T_4f6a5_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe0bbf693a0>"
+       "<pandas.io.formats.style.Styler at 0x7fd4d39443d0>"
       ]
      },
      "execution_count": 16,
@@ -1044,7 +1079,7 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n",
+    "In this talktorial, we investigate how sequences can be used to measure similarity between kinases. The focus is made of the pocket sequence, which is retrieve from KLIFS. Sequence similarity can be assessed using two scores: 1. the identity, which treats all amino acids uniformly, and 2. the substitution, which takes into account the rate of change of residues over evolutionary time.\n",
     "\n",
     "The kinase similarity matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket sequence perspective we have talked about in this talktorial."
    ]
@@ -1055,11 +1090,9 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
+    "1. Should the full kinase sequence be used instead of the pocket sequence?\n",
+    "2. How does the similarity using identity behave with respect to mutations?\n",
+    "3. How does similarity using identity compare to similarity using substitution?"
    ]
   }
  ],

From 61c6dcca2db50f30f70d4304765d85830a54afa8 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 18:30:32 +0200
Subject: [PATCH 048/158] add seq and ligand profile info.

---
 .../talktorial.ipynb                          | 89 +++++--------------
 1 file changed, 24 insertions(+), 65 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 0a54a73c..fec970ac 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -83,8 +83,8 @@
     "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
     "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
     "\n",
-    "*Table 1:* \n",
-    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
+    "*Table 1:*\n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
     "\n",
     "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
     "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
@@ -113,41 +113,6 @@
     "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    \n",
-    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you place links, please link descriptive words.\n",
-    "\n",
-    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
-    "\n",
-    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -188,19 +153,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
+    "    # \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv\",\n",
     "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
     "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
+    "    # \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv\"\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +228,7 @@
        " p38a   0.473684  0.000000  }"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -273,7 +240,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -282,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,12 +268,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -346,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +326,7 @@
        "       0.25    , 0.375   , 0.190476, 0.428571, 0.352941, 0.473684])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -380,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,12 +378,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -465,7 +431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -484,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,7 +469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -675,7 +641,7 @@
        "p38a   21.982655  38.529748   0.000000  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -686,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -701,7 +667,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 576x720 with 4 Axes>"
       ]
@@ -742,13 +708,6 @@
     "2. Question\n",
     "3. Question"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -767,7 +726,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From d7e2b43d72bd242ae05d858e496554c6f4f96d3e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 24 Aug 2021 18:39:53 +0200
Subject: [PATCH 049/158] add all perspective matrices

---
 .../talktorial.ipynb                          | 124 ++++++++++++++----
 1 file changed, 100 insertions(+), 24 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index fec970ac..fd206295 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -130,6 +130,7 @@
     "\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
     "from scipy.spatial import distance_matrix, distance\n",
     "from scipy.cluster import hierarchy"
    ]
@@ -153,21 +154,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
-    "    # \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv\",\n",
+    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv\",\n",
     "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
     "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
-    "    # \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv\"\n",
+    "    \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv\"\n",
     "}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -178,13 +179,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
+       "{'sequence':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
+       " EGFR   1.000000  0.894118  0.376471  0.317647  0.447059  0.458824  0.388235   \n",
+       " ErbB2  0.894118  1.000000  0.400000  0.329412  0.423529  0.470588  0.400000   \n",
+       " BRAF   0.376471  0.400000  1.000000  0.329412  0.388235  0.376471  0.376471   \n",
+       " CDK2   0.317647  0.329412  0.329412  1.000000  0.376471  0.364706  0.470588   \n",
+       " LCK    0.447059  0.423529  0.388235  0.376471  1.000000  0.400000  0.388235   \n",
+       " MET    0.458824  0.470588  0.376471  0.364706  0.400000  1.000000  0.364706   \n",
+       " p38a   0.388235  0.400000  0.376471  0.470588  0.388235  0.364706  1.000000   \n",
+       " KDR    0.470588  0.435294  0.400000  0.341176  0.435294  0.470588  0.388235   \n",
+       " p110a  0.117647  0.117647  0.152941  0.105882  0.141176  0.105882  0.141176   \n",
+       " \n",
+       "             KDR     p110a  \n",
+       " EGFR   0.470588  0.117647  \n",
+       " ErbB2  0.435294  0.117647  \n",
+       " BRAF   0.400000  0.152941  \n",
+       " CDK2   0.341176  0.105882  \n",
+       " LCK    0.435294  0.141176  \n",
+       " MET    0.470588  0.105882  \n",
+       " p38a   0.388235  0.141176  \n",
+       " KDR    1.000000  0.152941  \n",
+       " p110a  0.152941  1.000000  ,\n",
+       " 'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
        " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
        " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
        " EGFR   19.515299  18.146634   0.000000  16.391994  17.281650  16.467225   \n",
@@ -225,10 +247,31 @@
        " LCK    0.375000  0.190476  \n",
        " MET    0.428571  0.352941  \n",
        " p110a  0.000000  0.473684  \n",
-       " p38a   0.473684  0.000000  }"
+       " p38a   0.473684  0.000000  ,\n",
+       " 'ligand-profile':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
+       " EGFR   0.608962  0.556918  0.419355  0.116279  0.238462  0.225806  0.315789   \n",
+       " ErbB2  0.556918  0.605077  0.250000  0.153846  0.176471  0.035714  0.125000   \n",
+       " BRAF   0.419355  0.250000  0.811515  0.066667  0.511628  0.111111  0.644444   \n",
+       " CDK2   0.116279  0.153846  0.066667  0.544186  0.166667  0.086957  0.083333   \n",
+       " LCK    0.238462  0.176471  0.511628  0.166667  0.605075  0.287879  0.489362   \n",
+       " MET    0.225806  0.035714  0.111111  0.086957  0.287879  0.792272  0.047619   \n",
+       " p38a   0.315789  0.125000  0.644444  0.083333  0.489362  0.047619  0.764755   \n",
+       " KDR    0.348214  0.397790  0.748120  0.601695  0.432692  0.539359  0.500000   \n",
+       " p110a  0.071429       NaN  0.166667  0.266667  0.000000  0.000000  0.000000   \n",
+       " \n",
+       "             KDR     p110a  \n",
+       " EGFR   0.348214  0.071429  \n",
+       " ErbB2  0.397790       NaN  \n",
+       " BRAF   0.748120  0.166667  \n",
+       " CDK2   0.601695  0.266667  \n",
+       " LCK    0.432692  0.000000  \n",
+       " MET    0.539359  0.000000  \n",
+       " p38a   0.500000  0.000000  \n",
+       " KDR    0.696747  0.180791  \n",
+       " p110a  0.180791  0.639522  }"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -326,7 +369,7 @@
        "       0.25    , 0.375   , 0.190476, 0.428571, 0.352941, 0.473684])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -346,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -362,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -431,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -469,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +684,7 @@
        "p38a   21.982655  38.529748   0.000000  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -652,24 +695,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: lifand profile similarity matrix not considered symmetric due to nan value.\n",
+    "# kinase_distance_matrices[\"ligand-profile\"].values == kinase_distance_matrices[\"ligand-profile\"].values.transpose()\n",
+    "# kinase_distance_matrices[\"ligand-profile\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "34.435289999999995\n",
+      "sequence\n",
       "1751.566954\n",
       "kissim\n",
       "22.258792\n",
-      "ifp\n"
+      "ifp\n",
+      "24.793573\n",
+      "ligand-profile\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "Distance matrix 'X' must be symmetric.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_73855/77165837.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mperspective\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"sequence\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ligand-profile\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;31m# From similarity to distance matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mdendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperspective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mdendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperspective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_73855/3193762658.py\u001b[0m in \u001b[0;36mdendrogram\u001b[0;34m(distance_matrix, ax, title)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistance_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mD_condensed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquareform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mhclust\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhierarchy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_condensed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"average\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhierarchy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhclust\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36msquareform\u001b[0;34m(X, force, checks)\u001b[0m\n\u001b[1;32m   2360\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The matrix argument must be square.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchecks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2362\u001b[0;31m             \u001b[0mis_valid_dm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2364\u001b[0m         \u001b[0;31m# One-side of the dimensions is set here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36mis_valid_dm\u001b[0;34m(D, tol, throw, name, warning)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m                     raise ValueError(('Distance matrix \\'%s\\' must be '\n\u001b[0m\u001b[1;32m   2438\u001b[0m                                      'symmetric.') % name)\n\u001b[1;32m   2439\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Distance matrix 'X' must be symmetric."
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 576x720 with 4 Axes>"
+       "<Figure size 1152x720 with 8 Axes>"
       ]
      },
      "metadata": {
@@ -684,7 +756,11 @@
     "for i, (perspective, matrix) in enumerate(kinase_distance_matrices.items()):\n",
     "    print(matrix.sum().sum())\n",
     "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
-    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
+    "    if perspective in [\"sequence\", \"ligand-profile\"]:\n",
+    "        # From similarity to distance matrix\n",
+    "        dendrogram(np.ones(matrix.shape) - matrix, ax=axes[1][i], title=perspective)\n",
+    "    else:\n",
+    "        dendrogram(matrix, ax=axes[1][i], title=perspective)"
    ]
   },
   {

From b98504609f92c3b10d3605dd21010291d51ca7cc Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:28:54 +0200
Subject: [PATCH 050/158] add distance matrix (and not only similarity matrix)

---
 .../data/kinase_distance_matrix_ligand_profile.csv     | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
new file mode 100644
index 00000000..dbba4d8f
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,0.0,0.4430823117338003,0.5806451612903225,0.8837209302325582,0.7615384615384615,0.7741935483870968,0.6842105263157895,0.6517857142857143,0.9285714285714286
+ErbB2,0.4430823117338003,0.0,0.75,0.8461538461538461,0.8235294117647058,0.9642857142857143,0.875,0.6022099447513812,1.0
+BRAF,0.5806451612903225,0.75,0.0,0.9333333333333333,0.4883720930232558,0.8888888888888888,0.3555555555555555,0.25187969924812026,0.8333333333333334
+CDK2,0.8837209302325582,0.8461538461538461,0.9333333333333333,0.0,0.8333333333333334,0.9130434782608696,0.9166666666666666,0.39830508474576276,0.7333333333333334
+LCK,0.7615384615384615,0.8235294117647058,0.4883720930232558,0.8333333333333334,0.0,0.7121212121212122,0.5106382978723405,0.5673076923076923,1.0
+MET,0.7741935483870968,0.9642857142857143,0.8888888888888888,0.9130434782608696,0.7121212121212122,0.0,0.9523809523809523,0.4606413994169096,1.0
+p38a,0.6842105263157895,0.875,0.3555555555555555,0.9166666666666666,0.5106382978723405,0.9523809523809523,0.0,0.5,1.0
+KDR,0.6517857142857143,0.6022099447513812,0.25187969924812026,0.39830508474576276,0.5673076923076923,0.4606413994169096,0.5,0.0,0.8192090395480226
+p110a,0.9285714285714286,1.0,0.8333333333333334,0.7333333333333334,1.0,1.0,1.0,0.8192090395480226,0.0

From 1aba707dce48d22e26e8f8c4445625b541dee174 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:29:29 +0200
Subject: [PATCH 051/158] replace np.nan by 0

---
 .../data/kinase_similarity_matrix_ligand_profile.csv          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
index 0a003404..ba4d62d2 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
@@ -1,10 +1,10 @@
 ,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
 EGFR,0.6089623445220651,0.5569176882661997,0.41935483870967744,0.11627906976744186,0.23846153846153847,0.22580645161290322,0.3157894736842105,0.3482142857142857,0.07142857142857142
-ErbB2,0.5569176882661997,0.6050767414403778,0.25,0.15384615384615385,0.17647058823529413,0.03571428571428571,0.125,0.39779005524861877,
+ErbB2,0.5569176882661997,0.6050767414403778,0.25,0.15384615384615385,0.17647058823529413,0.03571428571428571,0.125,0.39779005524861877,0.0
 BRAF,0.41935483870967744,0.25,0.8115154807170016,0.06666666666666667,0.5116279069767442,0.1111111111111111,0.6444444444444445,0.7481203007518797,0.16666666666666666
 CDK2,0.11627906976744186,0.15384615384615385,0.06666666666666667,0.5441860465116279,0.16666666666666666,0.08695652173913043,0.08333333333333333,0.6016949152542372,0.26666666666666666
 LCK,0.23846153846153847,0.17647058823529413,0.5116279069767442,0.16666666666666666,0.605074821080026,0.2878787878787879,0.48936170212765956,0.4326923076923077,0.0
 MET,0.22580645161290322,0.03571428571428571,0.1111111111111111,0.08695652173913043,0.2878787878787879,0.7922722438851472,0.047619047619047616,0.5393586005830904,0.0
 p38a,0.3157894736842105,0.125,0.6444444444444445,0.08333333333333333,0.48936170212765956,0.047619047619047616,0.7647547797173733,0.5,0.0
 KDR,0.3482142857142857,0.39779005524861877,0.7481203007518797,0.6016949152542372,0.4326923076923077,0.5393586005830904,0.5,0.6967471143756558,0.1807909604519774
-p110a,0.07142857142857142,,0.16666666666666666,0.26666666666666666,0.0,0.0,0.0,0.1807909604519774,0.6395215090867264
+p110a,0.07142857142857142,0.0,0.16666666666666666,0.26666666666666666,0.0,0.0,0.0,0.1807909604519774,0.6395215090867264

From b7dd74e7d21ff5e1282bc814cf196993e0154036 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:29:59 +0200
Subject: [PATCH 052/158] from similarity to distance matrix.

---
 .../talktorial.ipynb                          | 1235 ++++++++++++++---
 1 file changed, 1022 insertions(+), 213 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 82b63b10..cf8e8192 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -46,9 +46,11 @@
     "    * Kinases of interest\n",
     "    * Hit or non-hit\n",
     "* Kinase promiscuity\n",
-    "* Compare kinases\n",
-    "* Visualize similarity as kinase matrix\n",
-    "* Save kinase distance matrix"
+    "* Kinase similarity\n",
+    "    * Visualize similarity as kinase matrix\n",
+    "    * Save kinase similarity matrix\n",
+    "* Kinase distance matrix\n",
+    "    * Save kinase distance matrix"
    ]
   },
   {
@@ -1442,42 +1444,42 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_6660b_row0_col2 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_6660b_row1_col2, #T_6660b_row4_col2 {\n",
+       "#T_2731f_row1_col2, #T_2731f_row4_col2 {\n",
        "  background-color: #dedded;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_6660b_row2_col2 {\n",
+       "#T_2731f_row2_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_6660b_row3_col2 {\n",
+       "#T_2731f_row3_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_6660b_row5_col2 {\n",
+       "#T_2731f_row5_col2 {\n",
        "  background-color: #4b1687;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_6660b_row6_col2 {\n",
+       "#T_2731f_row6_col2 {\n",
        "  background-color: #5c3797;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_6660b_row7_col2 {\n",
+       "#T_2731f_row7_col2 {\n",
        "  background-color: #8d89c0;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_6660b_row8_col2 {\n",
+       "#T_2731f_row8_col2 {\n",
        "  background-color: #c0c1de;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_6660b_\">\n",
+       "<table id=\"T_2731f_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1488,64 +1490,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_6660b_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
-       "      <td id=\"T_6660b_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
-       "      <td id=\"T_6660b_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_2731f_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_2731f_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
+       "      <td id=\"T_2731f_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
+       "      <td id=\"T_2731f_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_6660b_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
-       "      <td id=\"T_6660b_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
-       "      <td id=\"T_6660b_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
+       "      <th id=\"T_2731f_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_2731f_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
+       "      <td id=\"T_2731f_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
+       "      <td id=\"T_2731f_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_6660b_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
-       "      <td id=\"T_6660b_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
-       "      <td id=\"T_6660b_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <th id=\"T_2731f_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_2731f_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
+       "      <td id=\"T_2731f_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
+       "      <td id=\"T_2731f_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_6660b_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
-       "      <td id=\"T_6660b_row3_col1\" class=\"data row3 col1\" >819</td>\n",
-       "      <td id=\"T_6660b_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
+       "      <th id=\"T_2731f_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_2731f_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
+       "      <td id=\"T_2731f_row3_col1\" class=\"data row3 col1\" >819</td>\n",
+       "      <td id=\"T_2731f_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_6660b_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
-       "      <td id=\"T_6660b_row4_col1\" class=\"data row4 col1\" >930</td>\n",
-       "      <td id=\"T_6660b_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
+       "      <th id=\"T_2731f_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_2731f_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
+       "      <td id=\"T_2731f_row4_col1\" class=\"data row4 col1\" >930</td>\n",
+       "      <td id=\"T_2731f_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_6660b_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
-       "      <td id=\"T_6660b_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
-       "      <td id=\"T_6660b_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
+       "      <th id=\"T_2731f_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_2731f_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
+       "      <td id=\"T_2731f_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
+       "      <td id=\"T_2731f_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_6660b_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
-       "      <td id=\"T_6660b_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
-       "      <td id=\"T_6660b_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
+       "      <th id=\"T_2731f_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_2731f_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
+       "      <td id=\"T_2731f_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
+       "      <td id=\"T_2731f_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_6660b_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
-       "      <td id=\"T_6660b_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
-       "      <td id=\"T_6660b_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
+       "      <th id=\"T_2731f_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_2731f_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
+       "      <td id=\"T_2731f_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
+       "      <td id=\"T_2731f_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_6660b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_6660b_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
-       "      <td id=\"T_6660b_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
-       "      <td id=\"T_6660b_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
+       "      <th id=\"T_2731f_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_2731f_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
+       "      <td id=\"T_2731f_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
+       "      <td id=\"T_2731f_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ffc6f75b9a0>"
+       "<pandas.io.formats.style.Styler at 0x7f9a161977f0>"
       ]
      },
      "execution_count": 23,
@@ -1572,7 +1574,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compare kinases"
+    "## Kinase similarity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now investigate how we can use the similarity measure discussed in the theory to compare kinases. "
    ]
   },
   {
@@ -1892,220 +1901,220 @@
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        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row2_col3, #T_12a3d_row3_col2, #T_12a3d_row4_col8, #T_12a3d_row5_col1, #T_12a3d_row5_col8, #T_12a3d_row6_col8, #T_12a3d_row8_col0, #T_12a3d_row8_col4, #T_12a3d_row8_col5, #T_12a3d_row8_col6, #T_12a3d_row8_col7 {\n",
+       "#T_ad0a6_row2_col3, #T_ad0a6_row3_col2, #T_ad0a6_row4_col8, #T_ad0a6_row5_col1, #T_ad0a6_row5_col8, #T_ad0a6_row6_col8, #T_ad0a6_row8_col0, #T_ad0a6_row8_col4, #T_ad0a6_row8_col5, #T_ad0a6_row8_col6, #T_ad0a6_row8_col7 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row2_col4 {\n",
+       "#T_ad0a6_row2_col4 {\n",
        "  background-color: #249224;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row2_col5 {\n",
+       "#T_ad0a6_row2_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row2_col6 {\n",
+       "#T_ad0a6_row2_col6 {\n",
        "  background-color: #259225;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row2_col8 {\n",
+       "#T_ad0a6_row2_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row3_col0 {\n",
+       "#T_ad0a6_row3_col0 {\n",
        "  background-color: #d7e9d7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row3_col1 {\n",
+       "#T_ad0a6_row3_col1 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row3_col3 {\n",
+       "#T_ad0a6_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row3_col4 {\n",
+       "#T_ad0a6_row3_col4 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row3_col6 {\n",
+       "#T_ad0a6_row3_col6 {\n",
        "  background-color: #d2e7d2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row3_col7 {\n",
+       "#T_ad0a6_row3_col7 {\n",
        "  background-color: #3d9e3d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row3_col8 {\n",
+       "#T_ad0a6_row3_col8 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row4_col0 {\n",
+       "#T_ad0a6_row4_col0 {\n",
        "  background-color: #a2cfa2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row4_col2 {\n",
+       "#T_ad0a6_row4_col2 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row4_col3 {\n",
+       "#T_ad0a6_row4_col3 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row4_col5 {\n",
+       "#T_ad0a6_row4_col5 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row4_col6 {\n",
+       "#T_ad0a6_row4_col6 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row4_col7 {\n",
+       "#T_ad0a6_row4_col7 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row5_col0 {\n",
+       "#T_ad0a6_row5_col0 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row5_col2, #T_12a3d_row5_col6, #T_12a3d_row6_col5 {\n",
+       "#T_ad0a6_row5_col2, #T_ad0a6_row5_col6, #T_ad0a6_row6_col5 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row5_col3 {\n",
+       "#T_ad0a6_row5_col3 {\n",
        "  background-color: #e2efe2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row5_col7, #T_12a3d_row7_col1 {\n",
+       "#T_ad0a6_row5_col7, #T_ad0a6_row7_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row6_col0 {\n",
+       "#T_ad0a6_row6_col0 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row6_col1 {\n",
+       "#T_ad0a6_row6_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row6_col2 {\n",
+       "#T_ad0a6_row6_col2 {\n",
        "  background-color: #349a34;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row6_col3 {\n",
+       "#T_ad0a6_row6_col3 {\n",
        "  background-color: #e4f0e4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row6_col4 {\n",
+       "#T_ad0a6_row6_col4 {\n",
        "  background-color: #2c962c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row6_col7 {\n",
+       "#T_ad0a6_row6_col7 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row7_col0 {\n",
+       "#T_ad0a6_row7_col0 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row7_col4 {\n",
+       "#T_ad0a6_row7_col4 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row7_col5 {\n",
+       "#T_ad0a6_row7_col5 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row7_col6 {\n",
+       "#T_ad0a6_row7_col6 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row7_col7 {\n",
+       "#T_ad0a6_row7_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_12a3d_row8_col2 {\n",
+       "#T_ad0a6_row8_col2 {\n",
        "  background-color: #cbe4cb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_12a3d_row8_col3 {\n",
+       "#T_ad0a6_row8_col3 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_12a3d_\">\n",
+       "<table id=\"T_ad0a6_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2122,118 +2131,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_12a3d_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_12a3d_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "      <td id=\"T_12a3d_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "      <td id=\"T_12a3d_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "      <td id=\"T_12a3d_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "      <td id=\"T_12a3d_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "      <td id=\"T_12a3d_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "      <td id=\"T_12a3d_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "      <td id=\"T_12a3d_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_12a3d_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "      <td id=\"T_12a3d_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "      <td id=\"T_12a3d_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "      <td id=\"T_12a3d_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "      <td id=\"T_12a3d_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "      <td id=\"T_12a3d_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "      <td id=\"T_12a3d_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "      <td id=\"T_12a3d_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "      <td id=\"T_12a3d_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_12a3d_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "      <td id=\"T_12a3d_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "      <td id=\"T_12a3d_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "      <td id=\"T_12a3d_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "      <td id=\"T_12a3d_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "      <td id=\"T_12a3d_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "      <td id=\"T_12a3d_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "      <td id=\"T_12a3d_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "      <td id=\"T_12a3d_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_12a3d_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "      <td id=\"T_12a3d_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "      <td id=\"T_12a3d_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "      <td id=\"T_12a3d_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "      <td id=\"T_12a3d_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "      <td id=\"T_12a3d_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "      <td id=\"T_12a3d_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "      <td id=\"T_12a3d_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "      <td id=\"T_12a3d_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_12a3d_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "      <td id=\"T_12a3d_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "      <td id=\"T_12a3d_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "      <td id=\"T_12a3d_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "      <td id=\"T_12a3d_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "      <td id=\"T_12a3d_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "      <td id=\"T_12a3d_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "      <td id=\"T_12a3d_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "      <td id=\"T_12a3d_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_12a3d_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "      <td id=\"T_12a3d_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "      <td id=\"T_12a3d_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "      <td id=\"T_12a3d_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "      <td id=\"T_12a3d_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "      <td id=\"T_12a3d_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "      <td id=\"T_12a3d_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "      <td id=\"T_12a3d_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "      <td id=\"T_12a3d_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_12a3d_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "      <td id=\"T_12a3d_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "      <td id=\"T_12a3d_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "      <td id=\"T_12a3d_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "      <td id=\"T_12a3d_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "      <td id=\"T_12a3d_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "      <td id=\"T_12a3d_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "      <td id=\"T_12a3d_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "      <td id=\"T_12a3d_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_12a3d_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "      <td id=\"T_12a3d_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "      <td id=\"T_12a3d_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "      <td id=\"T_12a3d_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "      <td id=\"T_12a3d_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "      <td id=\"T_12a3d_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "      <td id=\"T_12a3d_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "      <td id=\"T_12a3d_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "      <td id=\"T_12a3d_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_12a3d_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_12a3d_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "      <td id=\"T_12a3d_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
-       "      <td id=\"T_12a3d_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "      <td id=\"T_12a3d_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "      <td id=\"T_12a3d_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "      <td id=\"T_12a3d_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "      <td id=\"T_12a3d_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "      <td id=\"T_12a3d_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "      <td id=\"T_12a3d_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "      <th id=\"T_ad0a6_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_ad0a6_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_ad0a6_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "      <td id=\"T_ad0a6_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "      <td id=\"T_ad0a6_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "      <td id=\"T_ad0a6_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "      <td id=\"T_ad0a6_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "      <td id=\"T_ad0a6_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "      <td id=\"T_ad0a6_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "      <td id=\"T_ad0a6_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_ad0a6_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "      <td id=\"T_ad0a6_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "      <td id=\"T_ad0a6_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "      <td id=\"T_ad0a6_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "      <td id=\"T_ad0a6_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "      <td id=\"T_ad0a6_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "      <td id=\"T_ad0a6_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "      <td id=\"T_ad0a6_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "      <td id=\"T_ad0a6_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_ad0a6_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "      <td id=\"T_ad0a6_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "      <td id=\"T_ad0a6_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <td id=\"T_ad0a6_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "      <td id=\"T_ad0a6_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "      <td id=\"T_ad0a6_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "      <td id=\"T_ad0a6_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "      <td id=\"T_ad0a6_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "      <td id=\"T_ad0a6_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_ad0a6_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "      <td id=\"T_ad0a6_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "      <td id=\"T_ad0a6_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "      <td id=\"T_ad0a6_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "      <td id=\"T_ad0a6_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "      <td id=\"T_ad0a6_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "      <td id=\"T_ad0a6_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "      <td id=\"T_ad0a6_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "      <td id=\"T_ad0a6_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_ad0a6_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "      <td id=\"T_ad0a6_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "      <td id=\"T_ad0a6_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "      <td id=\"T_ad0a6_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "      <td id=\"T_ad0a6_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "      <td id=\"T_ad0a6_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "      <td id=\"T_ad0a6_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "      <td id=\"T_ad0a6_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "      <td id=\"T_ad0a6_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_ad0a6_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "      <td id=\"T_ad0a6_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "      <td id=\"T_ad0a6_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "      <td id=\"T_ad0a6_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "      <td id=\"T_ad0a6_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "      <td id=\"T_ad0a6_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "      <td id=\"T_ad0a6_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "      <td id=\"T_ad0a6_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "      <td id=\"T_ad0a6_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_ad0a6_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "      <td id=\"T_ad0a6_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "      <td id=\"T_ad0a6_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "      <td id=\"T_ad0a6_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "      <td id=\"T_ad0a6_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "      <td id=\"T_ad0a6_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "      <td id=\"T_ad0a6_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "      <td id=\"T_ad0a6_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "      <td id=\"T_ad0a6_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_ad0a6_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "      <td id=\"T_ad0a6_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "      <td id=\"T_ad0a6_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "      <td id=\"T_ad0a6_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "      <td id=\"T_ad0a6_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "      <td id=\"T_ad0a6_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "      <td id=\"T_ad0a6_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "      <td id=\"T_ad0a6_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "      <td id=\"T_ad0a6_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ad0a6_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_ad0a6_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "      <td id=\"T_ad0a6_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
+       "      <td id=\"T_ad0a6_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "      <td id=\"T_ad0a6_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "      <td id=\"T_ad0a6_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "      <td id=\"T_ad0a6_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "      <td id=\"T_ad0a6_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "      <td id=\"T_ad0a6_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "      <td id=\"T_ad0a6_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ffc6f75ba30>"
+       "<pandas.io.formats.style.Styler at 0x7f9a15a26e80>"
       ]
      },
      "execution_count": 28,
@@ -2253,19 +2262,819 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase distance matrix"
+    "As mentioned above, no compounds were measured on both ErbB2 and p110a and therefore create a `np.nan` entry which can be problematic for algorithmic reason.\n",
+    "\n",
+    "As a simple workaround, we will fill the NA values with zero."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 29,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "#T_f829b_row7_col4 {\n",
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+       "}\n",
+       "#T_f829b_row7_col5 {\n",
+       "  background-color: #4ba54a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_f829b_row7_col6 {\n",
+       "  background-color: #51a851;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_f829b_row7_col7 {\n",
+       "  background-color: #158a15;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_f829b_row8_col2 {\n",
+       "  background-color: #cbe4cb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_f829b_row8_col3 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_f829b_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_f829b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_f829b_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "      <td id=\"T_f829b_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "      <td id=\"T_f829b_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "      <td id=\"T_f829b_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "      <td id=\"T_f829b_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "      <td id=\"T_f829b_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "      <td id=\"T_f829b_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "      <td id=\"T_f829b_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_f829b_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "      <td id=\"T_f829b_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "      <td id=\"T_f829b_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "      <td id=\"T_f829b_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "      <td id=\"T_f829b_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "      <td id=\"T_f829b_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "      <td id=\"T_f829b_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "      <td id=\"T_f829b_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "      <td id=\"T_f829b_row1_col8\" class=\"data row1 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_f829b_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "      <td id=\"T_f829b_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "      <td id=\"T_f829b_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "      <td id=\"T_f829b_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "      <td id=\"T_f829b_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "      <td id=\"T_f829b_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "      <td id=\"T_f829b_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "      <td id=\"T_f829b_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "      <td id=\"T_f829b_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_f829b_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "      <td id=\"T_f829b_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "      <td id=\"T_f829b_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "      <td id=\"T_f829b_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "      <td id=\"T_f829b_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "      <td id=\"T_f829b_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "      <td id=\"T_f829b_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "      <td id=\"T_f829b_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "      <td id=\"T_f829b_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_f829b_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "      <td id=\"T_f829b_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "      <td id=\"T_f829b_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "      <td id=\"T_f829b_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "      <td id=\"T_f829b_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "      <td id=\"T_f829b_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "      <td id=\"T_f829b_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "      <td id=\"T_f829b_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "      <td id=\"T_f829b_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_f829b_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "      <td id=\"T_f829b_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "      <td id=\"T_f829b_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "      <td id=\"T_f829b_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "      <td id=\"T_f829b_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "      <td id=\"T_f829b_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "      <td id=\"T_f829b_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "      <td id=\"T_f829b_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "      <td id=\"T_f829b_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_f829b_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "      <td id=\"T_f829b_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "      <td id=\"T_f829b_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "      <td id=\"T_f829b_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "      <td id=\"T_f829b_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "      <td id=\"T_f829b_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "      <td id=\"T_f829b_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "      <td id=\"T_f829b_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "      <td id=\"T_f829b_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_f829b_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "      <td id=\"T_f829b_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "      <td id=\"T_f829b_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "      <td id=\"T_f829b_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "      <td id=\"T_f829b_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "      <td id=\"T_f829b_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "      <td id=\"T_f829b_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "      <td id=\"T_f829b_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "      <td id=\"T_f829b_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f829b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_f829b_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "      <td id=\"T_f829b_row8_col1\" class=\"data row8 col1\" >0.000</td>\n",
+       "      <td id=\"T_f829b_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "      <td id=\"T_f829b_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "      <td id=\"T_f829b_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "      <td id=\"T_f829b_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "      <td id=\"T_f829b_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "      <td id=\"T_f829b_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "      <td id=\"T_f829b_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9a16197d90>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_similarity_matrix = kinase_similarity_matrix.fillna(0)\n",
+    "\n",
+    "kinase_similarity_matrix.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save kinase similarity matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [],
    "source": [
     "kinase_similarity_matrix.to_csv(DATA /\n",
     "                                \"kinase_similarity_matrix_ligand_profile.csv\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. In the case of the similarity matrix above, this matrix is not a distance matrix. For example, the diagonal elements are not zero.\n",
+    "\n",
+    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The values of the similarity matrix lie between: 0.00 and 0.81\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The values of the similarity matrix lie between: \"\n",
+    "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
+    "      f\" and {kinase_similarity_matrix.max().max():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix = 1 - kinase_similarity_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we set the diagonal values to $0$ and we obtain the kinase distance matrix:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.fill_diagonal(kinase_distance_matrix.values, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
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+       "<table id=\"T_1a2d4_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1a2d4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row0_col1\" class=\"data row0 col1\" >0.443</td>\n",
+       "      <td id=\"T_1a2d4_row0_col2\" class=\"data row0 col2\" >0.581</td>\n",
+       "      <td id=\"T_1a2d4_row0_col3\" class=\"data row0 col3\" >0.884</td>\n",
+       "      <td id=\"T_1a2d4_row0_col4\" class=\"data row0 col4\" >0.762</td>\n",
+       "      <td id=\"T_1a2d4_row0_col5\" class=\"data row0 col5\" >0.774</td>\n",
+       "      <td id=\"T_1a2d4_row0_col6\" class=\"data row0 col6\" >0.684</td>\n",
+       "      <td id=\"T_1a2d4_row0_col7\" class=\"data row0 col7\" >0.652</td>\n",
+       "      <td id=\"T_1a2d4_row0_col8\" class=\"data row0 col8\" >0.929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1a2d4_row1_col0\" class=\"data row1 col0\" >0.443</td>\n",
+       "      <td id=\"T_1a2d4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row1_col2\" class=\"data row1 col2\" >0.750</td>\n",
+       "      <td id=\"T_1a2d4_row1_col3\" class=\"data row1 col3\" >0.846</td>\n",
+       "      <td id=\"T_1a2d4_row1_col4\" class=\"data row1 col4\" >0.824</td>\n",
+       "      <td id=\"T_1a2d4_row1_col5\" class=\"data row1 col5\" >0.964</td>\n",
+       "      <td id=\"T_1a2d4_row1_col6\" class=\"data row1 col6\" >0.875</td>\n",
+       "      <td id=\"T_1a2d4_row1_col7\" class=\"data row1 col7\" >0.602</td>\n",
+       "      <td id=\"T_1a2d4_row1_col8\" class=\"data row1 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_1a2d4_row2_col0\" class=\"data row2 col0\" >0.581</td>\n",
+       "      <td id=\"T_1a2d4_row2_col1\" class=\"data row2 col1\" >0.750</td>\n",
+       "      <td id=\"T_1a2d4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row2_col3\" class=\"data row2 col3\" >0.933</td>\n",
+       "      <td id=\"T_1a2d4_row2_col4\" class=\"data row2 col4\" >0.488</td>\n",
+       "      <td id=\"T_1a2d4_row2_col5\" class=\"data row2 col5\" >0.889</td>\n",
+       "      <td id=\"T_1a2d4_row2_col6\" class=\"data row2 col6\" >0.356</td>\n",
+       "      <td id=\"T_1a2d4_row2_col7\" class=\"data row2 col7\" >0.252</td>\n",
+       "      <td id=\"T_1a2d4_row2_col8\" class=\"data row2 col8\" >0.833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_1a2d4_row3_col0\" class=\"data row3 col0\" >0.884</td>\n",
+       "      <td id=\"T_1a2d4_row3_col1\" class=\"data row3 col1\" >0.846</td>\n",
+       "      <td id=\"T_1a2d4_row3_col2\" class=\"data row3 col2\" >0.933</td>\n",
+       "      <td id=\"T_1a2d4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row3_col4\" class=\"data row3 col4\" >0.833</td>\n",
+       "      <td id=\"T_1a2d4_row3_col5\" class=\"data row3 col5\" >0.913</td>\n",
+       "      <td id=\"T_1a2d4_row3_col6\" class=\"data row3 col6\" >0.917</td>\n",
+       "      <td id=\"T_1a2d4_row3_col7\" class=\"data row3 col7\" >0.398</td>\n",
+       "      <td id=\"T_1a2d4_row3_col8\" class=\"data row3 col8\" >0.733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_1a2d4_row4_col0\" class=\"data row4 col0\" >0.762</td>\n",
+       "      <td id=\"T_1a2d4_row4_col1\" class=\"data row4 col1\" >0.824</td>\n",
+       "      <td id=\"T_1a2d4_row4_col2\" class=\"data row4 col2\" >0.488</td>\n",
+       "      <td id=\"T_1a2d4_row4_col3\" class=\"data row4 col3\" >0.833</td>\n",
+       "      <td id=\"T_1a2d4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row4_col5\" class=\"data row4 col5\" >0.712</td>\n",
+       "      <td id=\"T_1a2d4_row4_col6\" class=\"data row4 col6\" >0.511</td>\n",
+       "      <td id=\"T_1a2d4_row4_col7\" class=\"data row4 col7\" >0.567</td>\n",
+       "      <td id=\"T_1a2d4_row4_col8\" class=\"data row4 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_1a2d4_row5_col0\" class=\"data row5 col0\" >0.774</td>\n",
+       "      <td id=\"T_1a2d4_row5_col1\" class=\"data row5 col1\" >0.964</td>\n",
+       "      <td id=\"T_1a2d4_row5_col2\" class=\"data row5 col2\" >0.889</td>\n",
+       "      <td id=\"T_1a2d4_row5_col3\" class=\"data row5 col3\" >0.913</td>\n",
+       "      <td id=\"T_1a2d4_row5_col4\" class=\"data row5 col4\" >0.712</td>\n",
+       "      <td id=\"T_1a2d4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row5_col6\" class=\"data row5 col6\" >0.952</td>\n",
+       "      <td id=\"T_1a2d4_row5_col7\" class=\"data row5 col7\" >0.461</td>\n",
+       "      <td id=\"T_1a2d4_row5_col8\" class=\"data row5 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_1a2d4_row6_col0\" class=\"data row6 col0\" >0.684</td>\n",
+       "      <td id=\"T_1a2d4_row6_col1\" class=\"data row6 col1\" >0.875</td>\n",
+       "      <td id=\"T_1a2d4_row6_col2\" class=\"data row6 col2\" >0.356</td>\n",
+       "      <td id=\"T_1a2d4_row6_col3\" class=\"data row6 col3\" >0.917</td>\n",
+       "      <td id=\"T_1a2d4_row6_col4\" class=\"data row6 col4\" >0.511</td>\n",
+       "      <td id=\"T_1a2d4_row6_col5\" class=\"data row6 col5\" >0.952</td>\n",
+       "      <td id=\"T_1a2d4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "      <td id=\"T_1a2d4_row6_col8\" class=\"data row6 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_1a2d4_row7_col0\" class=\"data row7 col0\" >0.652</td>\n",
+       "      <td id=\"T_1a2d4_row7_col1\" class=\"data row7 col1\" >0.602</td>\n",
+       "      <td id=\"T_1a2d4_row7_col2\" class=\"data row7 col2\" >0.252</td>\n",
+       "      <td id=\"T_1a2d4_row7_col3\" class=\"data row7 col3\" >0.398</td>\n",
+       "      <td id=\"T_1a2d4_row7_col4\" class=\"data row7 col4\" >0.567</td>\n",
+       "      <td id=\"T_1a2d4_row7_col5\" class=\"data row7 col5\" >0.461</td>\n",
+       "      <td id=\"T_1a2d4_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "      <td id=\"T_1a2d4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_1a2d4_row7_col8\" class=\"data row7 col8\" >0.819</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a2d4_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_1a2d4_row8_col0\" class=\"data row8 col0\" >0.929</td>\n",
+       "      <td id=\"T_1a2d4_row8_col1\" class=\"data row8 col1\" >1.000</td>\n",
+       "      <td id=\"T_1a2d4_row8_col2\" class=\"data row8 col2\" >0.833</td>\n",
+       "      <td id=\"T_1a2d4_row8_col3\" class=\"data row8 col3\" >0.733</td>\n",
+       "      <td id=\"T_1a2d4_row8_col4\" class=\"data row8 col4\" >1.000</td>\n",
+       "      <td id=\"T_1a2d4_row8_col5\" class=\"data row8 col5\" >1.000</td>\n",
+       "      <td id=\"T_1a2d4_row8_col6\" class=\"data row8 col6\" >1.000</td>\n",
+       "      <td id=\"T_1a2d4_row8_col7\" class=\"data row8 col7\" >0.819</td>\n",
+       "      <td id=\"T_1a2d4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9a15b05130>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrix.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix.to_csv(DATA /\n",
+    "                              \"kinase_distance_matrix_ligand_profile.csv\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2274,7 +3083,7 @@
     "\n",
     "In this talktorial, we investigate how activity can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
-    "The kinase similarity matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
+    "The kinase distance matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
   },
   {

From 527f54431b0a87e99d80d03a789c501eb8d915ad Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:48:33 +0200
Subject: [PATCH 053/158] add distance matrix (and not only similarity matrix)

---
 .../data/kinase_distance_matrix_sequence.csv           | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
new file mode 100644
index 00000000..e972d41d
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,0.0,0.10588235294117643,0.6235294117647059,0.6823529411764706,0.5529411764705883,0.5411764705882354,0.611764705882353,0.5294117647058824,0.8823529411764706
+ErbB2,0.10588235294117643,0.0,0.6,0.6705882352941177,0.5764705882352941,0.5294117647058824,0.6,0.5647058823529412,0.8823529411764706
+BRAF,0.6235294117647059,0.6,0.0,0.6705882352941177,0.611764705882353,0.6235294117647059,0.6235294117647059,0.6,0.8470588235294118
+CDK2,0.6823529411764706,0.6705882352941177,0.6705882352941177,0.0,0.6235294117647059,0.6352941176470588,0.5294117647058824,0.6588235294117647,0.8941176470588236
+LCK,0.5529411764705883,0.5764705882352941,0.611764705882353,0.6235294117647059,0.0,0.6,0.611764705882353,0.5647058823529412,0.8588235294117648
+MET,0.5411764705882354,0.5294117647058824,0.6235294117647059,0.6352941176470588,0.6,0.0,0.6352941176470588,0.5294117647058824,0.8941176470588236
+p38a,0.611764705882353,0.6,0.6235294117647059,0.5294117647058824,0.611764705882353,0.6352941176470588,0.0,0.611764705882353,0.8588235294117648
+KDR,0.5294117647058824,0.5647058823529412,0.6,0.6588235294117647,0.5647058823529412,0.5294117647058824,0.611764705882353,0.0,0.8470588235294118
+p110a,0.8823529411764706,0.8823529411764706,0.8470588235294118,0.8941176470588236,0.8588235294117648,0.8941176470588236,0.8588235294117648,0.8470588235294118,0.0

From 2fe92b3ed8d7e8ffe32767e766bd867720fc95c9 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:48:47 +0200
Subject: [PATCH 054/158] add distance matrix (and not only similarity matrix)

---
 .../talktorial.ipynb                          | 601 ++++++++++++++----
 1 file changed, 473 insertions(+), 128 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index fdf4ed02..74b48783 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -405,7 +405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase comparison"
+    "## Kinase similarity"
    ]
   },
   {
@@ -776,144 +776,144 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
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+       "#T_3e429_row0_col0, #T_3e429_row1_col1, #T_3e429_row2_col2, #T_3e429_row3_col3, #T_3e429_row4_col4, #T_3e429_row5_col5, #T_3e429_row6_col6, #T_3e429_row7_col7, #T_3e429_row8_col8 {\n",
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-       "#T_4f6a5_row0_col1, #T_4f6a5_row1_col0 {\n",
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        "}\n",
-       "#T_4f6a5_row2_col8, #T_4f6a5_row7_col8 {\n",
+       "#T_3e429_row2_col8, #T_3e429_row7_col8 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col0 {\n",
+       "#T_3e429_row3_col0 {\n",
        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col1 {\n",
+       "#T_3e429_row3_col1 {\n",
        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col2 {\n",
+       "#T_3e429_row3_col2 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col6, #T_4f6a5_row5_col0 {\n",
+       "#T_3e429_row3_col6, #T_3e429_row5_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col7 {\n",
+       "#T_3e429_row3_col7 {\n",
        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row3_col8, #T_4f6a5_row5_col8, #T_4f6a5_row8_col0, #T_4f6a5_row8_col1, #T_4f6a5_row8_col2, #T_4f6a5_row8_col3, #T_4f6a5_row8_col4, #T_4f6a5_row8_col5, #T_4f6a5_row8_col6, #T_4f6a5_row8_col7 {\n",
+       "#T_3e429_row3_col8, #T_3e429_row5_col8, #T_3e429_row8_col0, #T_3e429_row8_col1, #T_3e429_row8_col2, #T_3e429_row8_col3, #T_3e429_row8_col4, #T_3e429_row8_col5, #T_3e429_row8_col6, #T_3e429_row8_col7 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row4_col0 {\n",
+       "#T_3e429_row4_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row4_col1 {\n",
+       "#T_3e429_row4_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row4_col2, #T_4f6a5_row6_col7 {\n",
+       "#T_3e429_row4_col2, #T_3e429_row6_col7 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row4_col8, #T_4f6a5_row6_col8 {\n",
+       "#T_3e429_row4_col8, #T_3e429_row6_col8 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row5_col1, #T_4f6a5_row7_col0 {\n",
+       "#T_3e429_row5_col1, #T_3e429_row7_col0 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row5_col6 {\n",
+       "#T_3e429_row5_col6 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row6_col0 {\n",
+       "#T_3e429_row6_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row7_col1 {\n",
+       "#T_3e429_row7_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4f6a5_row7_col4 {\n",
+       "#T_3e429_row7_col4 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_4f6a5_\">\n",
+       "<table id=\"T_3e429_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -930,118 +930,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_4f6a5_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_4f6a5_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
-       "      <td id=\"T_4f6a5_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
-       "      <td id=\"T_4f6a5_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
-       "      <td id=\"T_4f6a5_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
+       "      <th id=\"T_3e429_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_3e429_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_3e429_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
+       "      <td id=\"T_3e429_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
+       "      <td id=\"T_3e429_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
+       "      <td id=\"T_3e429_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_4f6a5_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_4f6a5_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
-       "      <td id=\"T_4f6a5_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
-       "      <td id=\"T_4f6a5_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
-       "      <td id=\"T_4f6a5_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
+       "      <th id=\"T_3e429_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_3e429_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_3e429_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
+       "      <td id=\"T_3e429_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
+       "      <td id=\"T_3e429_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
+       "      <td id=\"T_3e429_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_4f6a5_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
-       "      <td id=\"T_4f6a5_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
+       "      <th id=\"T_3e429_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_3e429_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
+       "      <td id=\"T_3e429_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_4f6a5_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
-       "      <td id=\"T_4f6a5_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
-       "      <td id=\"T_4f6a5_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
-       "      <td id=\"T_4f6a5_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
-       "      <td id=\"T_4f6a5_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
-       "      <td id=\"T_4f6a5_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
+       "      <th id=\"T_3e429_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_3e429_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
+       "      <td id=\"T_3e429_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
+       "      <td id=\"T_3e429_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
+       "      <td id=\"T_3e429_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
+       "      <td id=\"T_3e429_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
+       "      <td id=\"T_3e429_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_4f6a5_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
-       "      <td id=\"T_4f6a5_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
-       "      <td id=\"T_4f6a5_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
-       "      <td id=\"T_4f6a5_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
+       "      <th id=\"T_3e429_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_3e429_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
+       "      <td id=\"T_3e429_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
+       "      <td id=\"T_3e429_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
+       "      <td id=\"T_3e429_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_4f6a5_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
-       "      <td id=\"T_4f6a5_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
-       "      <td id=\"T_4f6a5_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
-       "      <td id=\"T_4f6a5_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
+       "      <th id=\"T_3e429_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_3e429_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
+       "      <td id=\"T_3e429_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
+       "      <td id=\"T_3e429_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
+       "      <td id=\"T_3e429_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_4f6a5_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
-       "      <td id=\"T_4f6a5_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
-       "      <td id=\"T_4f6a5_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
+       "      <th id=\"T_3e429_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_3e429_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
+       "      <td id=\"T_3e429_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
+       "      <td id=\"T_3e429_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_4f6a5_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
-       "      <td id=\"T_4f6a5_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
-       "      <td id=\"T_4f6a5_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
-       "      <td id=\"T_4f6a5_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
-       "      <td id=\"T_4f6a5_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
-       "      <td id=\"T_4f6a5_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
-       "      <td id=\"T_4f6a5_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_4f6a5_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
+       "      <th id=\"T_3e429_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_3e429_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
+       "      <td id=\"T_3e429_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
+       "      <td id=\"T_3e429_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
+       "      <td id=\"T_3e429_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
+       "      <td id=\"T_3e429_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
+       "      <td id=\"T_3e429_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
+       "      <td id=\"T_3e429_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_3e429_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4f6a5_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_4f6a5_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
-       "      <td id=\"T_4f6a5_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
-       "      <td id=\"T_4f6a5_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
-       "      <td id=\"T_4f6a5_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
-       "      <td id=\"T_4f6a5_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
-       "      <td id=\"T_4f6a5_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
-       "      <td id=\"T_4f6a5_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
-       "      <td id=\"T_4f6a5_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
-       "      <td id=\"T_4f6a5_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_3e429_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_3e429_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
+       "      <td id=\"T_3e429_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
+       "      <td id=\"T_3e429_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
+       "      <td id=\"T_3e429_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
+       "      <td id=\"T_3e429_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
+       "      <td id=\"T_3e429_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
+       "      <td id=\"T_3e429_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
+       "      <td id=\"T_3e429_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
+       "      <td id=\"T_3e429_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd4d39443d0>"
+       "<pandas.io.formats.style.Styler at 0x7feae981fd30>"
       ]
      },
      "execution_count": 16,
@@ -1061,7 +1061,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase distance matrix"
+    "### Save kinase similarity matrix"
    ]
   },
   {
@@ -1073,6 +1073,351 @@
     "kinase_similarity_matrix.to_csv(DATA / \"kinase_similarity_matrix_sequence.csv\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. In the case of the similarity matrix above, this matrix is not a distance matrix. For example, the diagonal elements are not zero.\n",
+    "\n",
+    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The values of the similarity matrix lie between: 0.11 and 1.00\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The values of the similarity matrix lie between: \"\n",
+    "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
+    "      f\" and {kinase_similarity_matrix.max().max():.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix = 1 - kinase_similarity_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
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+       "</style>\n",
+       "<table id=\"T_8ac29_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_8ac29_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
+       "      <td id=\"T_8ac29_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
+       "      <td id=\"T_8ac29_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
+       "      <td id=\"T_8ac29_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
+       "      <td id=\"T_8ac29_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_8ac29_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
+       "      <td id=\"T_8ac29_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
+       "      <td id=\"T_8ac29_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
+       "      <td id=\"T_8ac29_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
+       "      <td id=\"T_8ac29_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_8ac29_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
+       "      <td id=\"T_8ac29_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_8ac29_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
+       "      <td id=\"T_8ac29_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
+       "      <td id=\"T_8ac29_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
+       "      <td id=\"T_8ac29_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
+       "      <td id=\"T_8ac29_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
+       "      <td id=\"T_8ac29_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_8ac29_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
+       "      <td id=\"T_8ac29_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
+       "      <td id=\"T_8ac29_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
+       "      <td id=\"T_8ac29_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_8ac29_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
+       "      <td id=\"T_8ac29_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
+       "      <td id=\"T_8ac29_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
+       "      <td id=\"T_8ac29_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_8ac29_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
+       "      <td id=\"T_8ac29_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
+       "      <td id=\"T_8ac29_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_8ac29_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
+       "      <td id=\"T_8ac29_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
+       "      <td id=\"T_8ac29_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
+       "      <td id=\"T_8ac29_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
+       "      <td id=\"T_8ac29_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
+       "      <td id=\"T_8ac29_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
+       "      <td id=\"T_8ac29_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_8ac29_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8ac29_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_8ac29_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
+       "      <td id=\"T_8ac29_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
+       "      <td id=\"T_8ac29_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
+       "      <td id=\"T_8ac29_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
+       "      <td id=\"T_8ac29_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
+       "      <td id=\"T_8ac29_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
+       "      <td id=\"T_8ac29_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
+       "      <td id=\"T_8ac29_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
+       "      <td id=\"T_8ac29_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7feae5fad580>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrix.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix.to_csv(DATA /\n",
+    "                              \"kinase_distance_matrix_sequence.csv\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From 177595ba3d4e421eb266777ceb0217e8fc44e950 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 15:52:32 +0200
Subject: [PATCH 055/158] consider only distance matrices for the 4
 perspectives (and not similarity).

---
 .../talktorial.ipynb                          | 117 +++++++-----------
 1 file changed, 44 insertions(+), 73 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index fd206295..e137c26d 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -159,10 +159,10 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
-    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv\",\n",
+    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv\",\n",
     "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
     "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
-    "    \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv\"\n",
+    "    \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv\"\n",
     "}"
    ]
   },
@@ -186,26 +186,26 @@
      "data": {
       "text/plain": [
        "{'sequence':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       " EGFR   1.000000  0.894118  0.376471  0.317647  0.447059  0.458824  0.388235   \n",
-       " ErbB2  0.894118  1.000000  0.400000  0.329412  0.423529  0.470588  0.400000   \n",
-       " BRAF   0.376471  0.400000  1.000000  0.329412  0.388235  0.376471  0.376471   \n",
-       " CDK2   0.317647  0.329412  0.329412  1.000000  0.376471  0.364706  0.470588   \n",
-       " LCK    0.447059  0.423529  0.388235  0.376471  1.000000  0.400000  0.388235   \n",
-       " MET    0.458824  0.470588  0.376471  0.364706  0.400000  1.000000  0.364706   \n",
-       " p38a   0.388235  0.400000  0.376471  0.470588  0.388235  0.364706  1.000000   \n",
-       " KDR    0.470588  0.435294  0.400000  0.341176  0.435294  0.470588  0.388235   \n",
-       " p110a  0.117647  0.117647  0.152941  0.105882  0.141176  0.105882  0.141176   \n",
+       " EGFR   0.000000  0.105882  0.623529  0.682353  0.552941  0.541176  0.611765   \n",
+       " ErbB2  0.105882  0.000000  0.600000  0.670588  0.576471  0.529412  0.600000   \n",
+       " BRAF   0.623529  0.600000  0.000000  0.670588  0.611765  0.623529  0.623529   \n",
+       " CDK2   0.682353  0.670588  0.670588  0.000000  0.623529  0.635294  0.529412   \n",
+       " LCK    0.552941  0.576471  0.611765  0.623529  0.000000  0.600000  0.611765   \n",
+       " MET    0.541176  0.529412  0.623529  0.635294  0.600000  0.000000  0.635294   \n",
+       " p38a   0.611765  0.600000  0.623529  0.529412  0.611765  0.635294  0.000000   \n",
+       " KDR    0.529412  0.564706  0.600000  0.658824  0.564706  0.529412  0.611765   \n",
+       " p110a  0.882353  0.882353  0.847059  0.894118  0.858824  0.894118  0.858824   \n",
        " \n",
        "             KDR     p110a  \n",
-       " EGFR   0.470588  0.117647  \n",
-       " ErbB2  0.435294  0.117647  \n",
-       " BRAF   0.400000  0.152941  \n",
-       " CDK2   0.341176  0.105882  \n",
-       " LCK    0.435294  0.141176  \n",
-       " MET    0.470588  0.105882  \n",
-       " p38a   0.388235  0.141176  \n",
-       " KDR    1.000000  0.152941  \n",
-       " p110a  0.152941  1.000000  ,\n",
+       " EGFR   0.529412  0.882353  \n",
+       " ErbB2  0.564706  0.882353  \n",
+       " BRAF   0.600000  0.847059  \n",
+       " CDK2   0.658824  0.894118  \n",
+       " LCK    0.564706  0.858824  \n",
+       " MET    0.529412  0.894118  \n",
+       " p38a   0.611765  0.858824  \n",
+       " KDR    0.000000  0.847059  \n",
+       " p110a  0.847059  0.000000  ,\n",
        " 'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
        " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
        " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
@@ -249,26 +249,26 @@
        " p110a  0.000000  0.473684  \n",
        " p38a   0.473684  0.000000  ,\n",
        " 'ligand-profile':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       " EGFR   0.608962  0.556918  0.419355  0.116279  0.238462  0.225806  0.315789   \n",
-       " ErbB2  0.556918  0.605077  0.250000  0.153846  0.176471  0.035714  0.125000   \n",
-       " BRAF   0.419355  0.250000  0.811515  0.066667  0.511628  0.111111  0.644444   \n",
-       " CDK2   0.116279  0.153846  0.066667  0.544186  0.166667  0.086957  0.083333   \n",
-       " LCK    0.238462  0.176471  0.511628  0.166667  0.605075  0.287879  0.489362   \n",
-       " MET    0.225806  0.035714  0.111111  0.086957  0.287879  0.792272  0.047619   \n",
-       " p38a   0.315789  0.125000  0.644444  0.083333  0.489362  0.047619  0.764755   \n",
-       " KDR    0.348214  0.397790  0.748120  0.601695  0.432692  0.539359  0.500000   \n",
-       " p110a  0.071429       NaN  0.166667  0.266667  0.000000  0.000000  0.000000   \n",
+       " EGFR   0.000000  0.443082  0.580645  0.883721  0.761538  0.774194  0.684211   \n",
+       " ErbB2  0.443082  0.000000  0.750000  0.846154  0.823529  0.964286  0.875000   \n",
+       " BRAF   0.580645  0.750000  0.000000  0.933333  0.488372  0.888889  0.355556   \n",
+       " CDK2   0.883721  0.846154  0.933333  0.000000  0.833333  0.913043  0.916667   \n",
+       " LCK    0.761538  0.823529  0.488372  0.833333  0.000000  0.712121  0.510638   \n",
+       " MET    0.774194  0.964286  0.888889  0.913043  0.712121  0.000000  0.952381   \n",
+       " p38a   0.684211  0.875000  0.355556  0.916667  0.510638  0.952381  0.000000   \n",
+       " KDR    0.651786  0.602210  0.251880  0.398305  0.567308  0.460641  0.500000   \n",
+       " p110a  0.928571  1.000000  0.833333  0.733333  1.000000  1.000000  1.000000   \n",
        " \n",
        "             KDR     p110a  \n",
-       " EGFR   0.348214  0.071429  \n",
-       " ErbB2  0.397790       NaN  \n",
-       " BRAF   0.748120  0.166667  \n",
-       " CDK2   0.601695  0.266667  \n",
-       " LCK    0.432692  0.000000  \n",
-       " MET    0.539359  0.000000  \n",
-       " p38a   0.500000  0.000000  \n",
-       " KDR    0.696747  0.180791  \n",
-       " p110a  0.180791  0.639522  }"
+       " EGFR   0.651786  0.928571  \n",
+       " ErbB2  0.602210  1.000000  \n",
+       " BRAF   0.251880  0.833333  \n",
+       " CDK2   0.398305  0.733333  \n",
+       " LCK    0.567308  1.000000  \n",
+       " MET    0.460641  1.000000  \n",
+       " p38a   0.500000  1.000000  \n",
+       " KDR    0.000000  0.819209  \n",
+       " p110a  0.819209  0.000000  }"
       ]
      },
      "execution_count": 5,
@@ -697,51 +697,26 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO: lifand profile similarity matrix not considered symmetric due to nan value.\n",
-    "# kinase_distance_matrices[\"ligand-profile\"].values == kinase_distance_matrices[\"ligand-profile\"].values.transpose()\n",
-    "# kinase_distance_matrices[\"ligand-profile\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "34.435289999999995\n",
+      "46.564710000000005\n",
       "sequence\n",
       "1751.566954\n",
       "kissim\n",
       "22.258792\n",
       "ifp\n",
-      "24.793573\n",
+      "53.27453800000001\n",
       "ligand-profile\n"
      ]
     },
-    {
-     "ename": "ValueError",
-     "evalue": "Distance matrix 'X' must be symmetric.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_73855/77165837.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mperspective\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"sequence\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ligand-profile\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;31m# From similarity to distance matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mdendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperspective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mdendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mperspective\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_73855/3193762658.py\u001b[0m in \u001b[0;36mdendrogram\u001b[0;34m(distance_matrix, ax, title)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistance_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mD_condensed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquareform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mhclust\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhierarchy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinkage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_condensed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"average\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mtree\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhierarchy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhclust\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36msquareform\u001b[0;34m(X, force, checks)\u001b[0m\n\u001b[1;32m   2360\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The matrix argument must be square.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mchecks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2362\u001b[0;31m             \u001b[0mis_valid_dm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'X'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2364\u001b[0m         \u001b[0;31m# One-side of the dimensions is set here.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/scipy/spatial/distance.py\u001b[0m in \u001b[0;36mis_valid_dm\u001b[0;34m(D, tol, throw, name, warning)\u001b[0m\n\u001b[1;32m   2435\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2436\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2437\u001b[0;31m                     raise ValueError(('Distance matrix \\'%s\\' must be '\n\u001b[0m\u001b[1;32m   2438\u001b[0m                                      'symmetric.') % name)\n\u001b[1;32m   2439\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Distance matrix 'X' must be symmetric."
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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b1y0lrbb7HiolHYCXX1+7tLTmtg0oJZ1BA98oJR2Al14fXFpar7V19MqZ2g0a/HppafU2b6w8lCc/tWUpaa3+o5tKSQfgie+UkyeAtsHqeKMa9HutnHQA+r9S3rWxKPEy25wFg0pLq/9Lve/632ujhvLQsZuXkta6n7u1lHQA2rZ5Z2lpvWv7x0tJ57J/lhfDyz8bpaW1zPRlSkvr65/Yq7S0NKu82GsWr686lMePKKccrPnt8s4vq95SXhmY/sbypaSz3tCq7zzvkoOXvbu0tG5adqXS0vrq5I+WltYmA3tfvJSl9515zczMzMzMeiE33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQuoufEmqU3SnYXp2MVsd52kcVWWHyLpmbzvPZImSRqS131J0r2S7pb0d0lrdv0nmTWV/pLOk/RILuNXSFpP0lxJd0i6T9J/JB3cvkOOldPz56Uk/UHS7yUNlXS5pPtzDP2kcT/LrHySVna8mC3eY185hukn/RxgjOtiZn1TZ+68zY2IsYVpkROhpI7Gfp+Y990IeB3YNy+/AxgXEZsAk4ATOpEvs6YUEQDrANdFxNoRMQY4DlgJeCQiNouIDYH9gC9KOrS4vyQBvwYGAJ8GAjgxIjYANgPeJ2mXHvtBZvV3EY4Xs8XSgAGs9qUvA9zruphZ39TtbpOSpkr6lqR/AfvkxR+XdJOk/0l6T5V9+gNDgecBIuKfEfFqXn0LsHp382XWaNf/ex5ARMSv25dFxJ3AtOJ2EfEo8CXg6IokTgVWAA6KiAUR8WpE/DPv8zrwXxwr1nssA7zheDHrPNfFzPqOzjTeBld0m9y3sG5eRGwVEefl+aERsSXwWeD3he32lXQnMB1YHri0yvd8CriyWgYkHSZpsqTJr7e9Wm0Ts6Zx7/1vANRaUP8LbFCYPwB4F7BfRMyv3FjSCGAP4O/VEivGyvxXX+lMts0aZTBwe43b1i1e2l5+uVOZNutJ8cYbld0mG1oXa3vF5xezntadbpMTC+smVmx7LkBE3AAMzydOyLfqgZWBKcBXijtJ+jgwDvhZtQxExJkRMS4ixg3sN6QTWTdreqqY/y+wJrC4q6XnAr/IdyEWUYyV/kOGlp5ZswarW7z0Gzas9MyalaVKt8mG1sX6DfX5xaynlTXaZOWll1jSfKSHgS4F3t++TNL2wDeAD0XEayXly6xhNlx/AECtVxk2A+4rzN8PfAyYKGmjim3PBB6KiFO6m0ezJjKXdPesFo4Xs0W5LmbWB9TrVQH7AkjaCngxIl6sss1WwCN5u82A35AOFk/XKU9mPWqbrQZBGkfhM+3LJL2bdIeAwrLRwInAacXlEXETcARwuaRRedsfAMsCX6hj1s0a4SVgaceLWWlcFzPrhfp3YtvBuY90u6siouoQtcDzkm4ChgOfLCzfNx9ElgKeBA7Jy38GDAMuSAOG8UREfKgTeTNrOrksPwLskIdzngdMJVUk15Z0BzCIVGk9LSLGV6YREZdJWhG4Ko+U9w3SXYb/5vRPj4jf1v/XmPWIDwOnOF7Mqqt85g3Xxcz6nJobbxFRdejZiBhdMb/NYrabAExYzLrta82HWYt5IyI+VmX54MXtUBkruZLaXlGtfNbHrNeIiBmk7o+VHC9mwFo/OxGAx4758r0R8eZ73FwXM+s76tVt0szMzMzMzErkxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gI6M9pkU4lRC3jj9HmlpPXy62uXkg7A8F0eKS0tbh5eSjLLDi7n7wQwcljla2R6nxnze9erbRb0g9eHV77up2ue+M6WpaQDMOo7N5WW1oyvlpOvtgGlJAPAUvPLS2tBifl6acFix/7otDdGtJWWVtNYAEu9Ws51zbZt3llKOgD9rvtvaWldfNkWpaRTZgViwCvlHKMAntuovIBZfvjzpaUVU8p7ofVjpaXUPbF08Ppa5dQxVr1lmVLSAZjx3pdKS+t9U8up1+056YulpAOw8V5PlpbWd089qLS0Vppe3jnhtE3W7Hijmj1cYlqN5ztvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBXS58SapTdKdku6S9F9JW+bloyXNzevulfRHSQMK+/WX9KykH1ekd52kB/J+d0rau+s/y6x5FGKlfTo2L+8v6UeSHiqs+8YS9hstaRtJL0q6Q9L9kk5s3C8zK5/jxax2rouZ9T3dGSxqbkSMBZC0E/Bj4AN53SMRMVZSP+Ba4GPAn/O6HYEHgI9JOi4iikNMHRgRk7uRJ7Nm9GasVPgBsDKwcUTMk7QM8OUl7SdpNHBjROwuaTBwh6SLIuLf9cm6WY9zvJjVznUxsz6mrG6Tw4FFxtONiDbgP8BqhcX7A6cCTwDvLen7zVqKpCHAZ4D/i4h5ABHxUkR8p9Y0ImIucCcLx5dZr+N4MauJ62JmfUB37rwNlnQnMAhYBdi2cgNJg4DNgc/n+cHAdsDhwAjSwePmwi5/ljQ3f94uIp7rRv7MmkV7rLT7MXAf8ERELOllNMX9HouIDxdXSloOWBe4ocS8mjWa48Wsdq6LmfUx3bnzNjcixkbEBsDOwB8lKa9bOx9MniOdcO/Oy3cH/hkRrwJ/AT6cb+e3OzCnObbawULSYZImS5r8+otzK1ebNau5hXI9NiImVm4g6dD8fME0SWtU2a9YEd1a0t3ALOCyiJhVJb03Y2XBK73/xerWqzQ0XtocL9ZaGloXa5vjeDHraaV0m4yIm4GRwIp50SO5D/Y6wHslfSgv3x/YXtJU4HZgBeCDnfieMyNiXESMG7js4DKybtYoDwOj8nM7RMT4HDMvAv2WtCPpGZ5NgI2BIyWNrdygGCtLDR1abs7Nel6PxUs/x4u1qEbUxfoNd7yY9bRSGm+SNiCdQBe6QhMRM4Fjga9LGg5sBYyKiNERMRr4HOkgYtan5CuevwNOz11ayFc+B3YijQdJXcq+VpdMmjUJx4tZx1wXM+sbutN4G9w+lCwwETg4PxRb6WJgCKmv9T8i4rXCukuAD0lauhv5MGt2b8ZKnn6Sl38DmAn8T9IdwI3AH4AZnUj718D7Ja1VbpbNGsbxYlY718XM+pguD1gSEVW7qkTEVOAdhfkANl3MtrN56/b+Nl3Ni1kzW0KsvEG6GnrsYtYPq7LsOuC6wvxcPHqe9SKOF7PauS5m1veU9aoAMzMzMzMzqyM33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQvo8miTjbZ0v/m8fZlnS0lrbtuAUtIB4ObhpSX11BZzSklnwN+XLSUdgAFLVRuBuGsenLFSaWmNG/14aWmtNWCRQeta21LQNnRBKUm1DVYp6QDM+OqWpaW16gk3lZLOQ394ZynpAFDen4qY29F7qGv30WHlHFcAjh3+emlpNYuBQ95gjbGdefvA4r1r+/KOSxdftkVpaY3+5s2lpNPvn6uWkg7AsAGvdbxRjaa9NKK0tG7e9C+lpbX+7UeWllazGDX0OU7a8pxS0pr+xvKlpAPwvqmPlJbWl0aXE3ufv+fKUtIBGLHUq6Wl9Zsvn1paWp864/OlpXXj8+uUlhb8vcS0Gs933szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQvoUuNN0sqSzpP0iKR7JV0haT1JcyXdIek+Sf+RdHBhn0MknZ4/LyXpD5J+L2mopMsl3S/pHkk/KevHmTXKgNUe5p3bPwEwRtKdko6ttp2k6ySNq7L8EEnP5H3vkTRJ0pC87ks57u6W9HdJa9b315jVz6MHfJMnjz0dHCtmneK6mFnf1OnGmyQBFwHXRcTaETEGOA5YCXgkIjaLiA2B/YAvSjq0yv6/BgYAnwYCODEiNgA2A94naZfu/CizRhs8SPz3b6MA7o2IsRGxyIlQUkdDCE7M+24EvA7sm5ffAYyLiE2AScAJJWbdrEdp4ABW/8lR4Fgx6yzXxcz6oK7cefsg8EZE/Lp9QUTcCUwrbhQRjwJfAo6u2P9UYAXgoIhYEBGvRsQ/8z6vA/8FVu9CvsyanqSpkr4l6V/APnnxxyXdJOl/kt5TZZ/+wFDgeYCI+GdEtI8TfAuOF+uFHCtmS7QMrouZ9Uldaby9A7i9xm3/C2xQmD8AeBewX0TMr9xY0ghgD3rbCxmsz5k7Lyq7Te5bWD0vIraKiPPy/NCI2BL4LPD7wnb7SroTmA4sD1xa5as+BZT38hizHhavv1HZbdKxYtaxwbguZtYn1XvAksrX1P4XWBNY3BXTc4Ff5CtFiyYmHSZpsqTJ854v74WeZmWr0m1yYmH1xIrNzwWIiBuA4fnECbkrGLAyMAX4SnEnSR8HxgE/q/z+Yqy0vfxyCb/IrD6qdJvs0VjJ69+MlzdeKO/lt2ZNom51sRdnt5WeWTNbsq403u4hXbGpxWbAfYX5+4GPARMlbVSx7ZnAQxFxyuISi4gzI2JcRIwbtNzSnciyWVN5pWI+ljQfEUG6k/D+9mWStge+AXwoIha5klGMlX7DhpWTa7OeV/dYyfu9GS8DRgzpfq7N6m8uTVAXW3b5jh5HNbOydaXx9g9gaUmfaV8g6d2kqzgUlo0GTgROKy6PiJuAI4DLJY3K2/4AWBb4QhfyY9bq9gWQtBXwYkS8WGWbrYBH8nabAb8hVUaf7rFcmjWeY8UseQnXxcz6pP6d3SEiQtKHgVPykM7zgKmkYF9b0h3AINKB5bSIGF8ljcskrQhclUcz+gbpStB/0wBInB4Rv+3aTzJrvMpn3oCrIqLqEOjA85JuAoYDnyws3zdXUpcCngQOyct/BgwDLsjx8kREfKj0H2HWAyqfecOxYlYr18XM+qBON94AImIG6ZZ7pcFL2GcCMKEwPx5oP5hU9sc2a2lvTF8HgH6rPHxvRLz5bqqIGF3cLiK2qbZ/ZbxUrNu+nFyaNd7bz/k+AI/uf7xjxawTXBcz65vqPWCJmZmZmZmZlcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gK6NNpkM3jptUHc8Pg6paQ1aOAbpaQDsOzgeaWlNeDvy5aSzlLbTSslHYDnr1y7tLRWX/H50tL631OrlJbWzDVeLi2tprGgnEHE+r1W3mBkbQNKS4qH/vDOUtJZ9+D/lpIOwGM/3qK0tBYMrHw3dde1xYLS0ooZg0pLq1nMb1uKZ18eWkpal/1zy1LSgXJP1v3+uWop6bR9cEYp6QC8vM5apaX19LeHl5bWPa/PLS2tdX79eGlpPVxaSt3z/PyhXPxcre8KX7L1hs4qJR2APSd9sbS0Pn/PlaWkc9lGy5WSDsDV1+1eWlr33rVmxxvVKDZ8rbS0zn/730tLq7e9St533szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQuoS+NNUtURHyQdJOl/ku6RdK+kY/LyCZL2zp+Xl3SHpEPrkTezZlCMEUm7SnpI0ihJ35E0XdKdedmFksYUtr1O0gOS7pJ0m6SxDfkBZj3I8WLWea6LmfVOPXbnTdIuwBeAHSNiI+CdwIsV2ywLXA2cGRHjeypvZo0iaTvgNGDniHgiLz45IsZGxLrAROAfklYs7HZgRGwK/Ar4Wc/m2KxxHC9m3eO6mFnr68luk18HjomIGQARMS8iziqsHwZcCZwTEWf0YL7MGkLS1sBZwG4R8Ui1bSJiInANcECV1TcDq9Uvh2bNw/FiVgrXxcxaXE823t4B3L6E9ScB/4qIk3soP2aNtDRwCbBXRNzfwbb/BTaosnxn4OKS82XWjBwvZuVwXcysxTXTgCX/APaU9LbFbSDpMEmTJU1um/NKD2bNrHRvADcBn6ph28o3Y/9Z0pPA10hdyBbdoRgrLztWrOX1XLzMebV7OTVrbZ2qi817YV4PZs3MoGcbb/cA71rC+vOAM4ArJC1TbYOIODMixkXEuH7Dh9Yjj2Y9ZQHwMeDdko7rYNvNgPsK8wcCawHnAL+stsNCsTLMsWItr+fiZfiQMvJr1qxKrYsNGjGoHnk0syXoycbbj4ETJK0MIGlpSUcXN4iIU4C/AxdJGtiDeTPrcRHxKrA7cKCkqncUJH0U2BE4t2LfN4DjgfdK2rDeeTVrNMeLWSlcFzNrcfVqvA2R9GRh+lJEXEG66vk3SfeQ+lz3r9wxIr4GTAPOltRM3TrNShcRs0nP4hwvac+8+IvtQ58DHwe2jYhnquw7F/g5cEyPZdisgRwvZp3iuphZL7RIwJYhIqoGeh5ydpFhZyPikIp5v1fEerWIGFb4PI3UrQvSoAzfWcJ+21TM/7wO2TNrKo4Xs85zXcysd/LVFDMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLqMmBJT2mbX07b86XXB5eSDsDIYeW9EHnAUm2lpPP8lWuXkg7A8F0eKS2tfv9ctbS05s1drrS0Vuk/rOONWomAflFKUv1fKe96z1LzS0tq0dcyd9FjP96inISAtb5+c2lplZmvJ+aX9xLqtmXKOUY1kwXzl+Ll2eW86235Z8uJO4ABr5SX1rABr5WSzsvrrNXxRjVqe/ix0tIa+PCWpaV10IiDS0uL3VYoL63flJdUdyylYHC/10tJ6+Bl7y4lHYCN93qytLRGLFXOMfPq63YvJR2AN7aZWVpa1z1xQWlp7XD2V0pLyxbPd97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBZTaeJMUks4uzPeX9Iyky/L8IXn+zsK0aeHzbEmP5c9/KzNvZs1K0suLWX6QpP9JukfSvZKOycsnSNo7f15e0h2SDu3JPJs1gmPFrGOui5n1bmW/KuAV4B2SBkfEXGAHYHrFNhMj4qiKZWMhnWiByyJiUsn5MmspknYBvgDsGBEzJA0CPlGxzbLA1cCZETG+53Np1niOFbNFuC5m1ovVo9vklcBu+fP+wLl1+A6z3u7rwDERMQMgIuZFxFmF9cNIsXZORJzRiAyaNQnHitmiXBcz66Xq0Xg7D9gvX/3cBLi1Yv2+Fbfqy3tDtlnv8Q7g9iWsPwn4V0Sc3EP5MWtWjhWzRbkuZtZLld54i4i7gdGkKz1XVNlkYkSMLUxza01b0mGSJkua3DbnlZJybNaS/gHsKelt1VYuFCsvV31MyKyvWGKsQGW8+Nxira+n6mJzn59XUo7NrFb1Gm3yr8CJlHybPiLOjIhxETGu3/ChZSZt1mzuAd61hPXnAWcAV0hapnLlQrEybFi98mjWDLoVK1AZLz63WK9R97rY4OUGlZm0mdWgXo233wPfi4gpdUrfrLf7MXCCpJUBJC0t6ejiBhFxCvB34CJJA3s+i2ZNwbFiVp3rYma9UF0abxHxZEScupjVlf2st6xHHsxayBBJTxamL0XEFcAvgb9Juof0TM8io8NGxNeAacDZkvzeRuvtHCtmNXJdzKx3KvVVARGxSP+siLgOuC5/ngBMWML+h5SZH7NWEBFVK5J5SPNFhjWvjJOI8HurrE9wrJh1zHUxs97NVx/NzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLKHXAkp40oF8bq67wYilpvdbWr5R0yvbgjJVKSWf1FZ8vJR2Afv9ctbS02j44o7S0Vr+mvNG/Z87vZS+1XgBLzVMpSVUfLqJrFgwoL62YW04MLxgYpaQD8NiPtygtrbW+fnNpaQ04sLSkYEE55aqZ9BuwgBEjyzkGLDO96mvluuS5jcoLmGkvjSglnae/PbyUdAAGPlzeYIejvntTaWmtdHN5v/G2HZYuLS1+U15S3TF3/gDunr1aKWndtGw5dR6A7556UGlp/ebLixuws3PuvWvNUtIBuO6JC0pL6zOjtiotrfknl3cOtcXznTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAaU33iT9TtJdku6WNEnSsLx8WUmX5nX3SDq07O82ayWSQtLZhfn+kp6RdFmePyTP31mYNi18ni3psfz5b437JWb15Vgx6zzXx8x6p3q8KuCLETEHQNJJwFHAT4DPAfdGxB6SVgQekPTniHi9DnkwawWvAO+QNDgi5gI7ANMrtpkYEUdVLBsLIGkCcFlETKp3Rs0azLFi1nmuj5n1Ql2+8yZptKT7Jf2hcFVnSOFAIWAw0P7ShwCWycuHAbOB+XnbiyXdnq8AHdatX2TWWq4Edsuf9wfObWBezJqZY8WsCtfHzPqW7nabXB84MyI2AeYAnwWQNB6YBWwAnJa3PR3YEJgBTAE+HxEL8rpPRsS7gHHA0ZJWqPZlkg6TNFnS5DdefLWbWTdrCucB+0kaBGwC3Fqxft+KrmCDa0m0GCttr7xSdp7NGqEusQILx8v8Fx0v1pJ6rD7muphZY3W38TYtIv6dP/8J2AogIg4FVgXuA/bN63cC7szLxwKnSxqe1x0t6S7gFmANYN1qXxYRZ0bEuIgYN2DZId3MulnjRcTdwGjSnYQrqmwyMSLGFqa5Nab7Zqz0Gzq0xBybNUa9YiWn/Wa89F/W8WItqcfqY66LmTVWdxtvsbj5iGgDJgIfzYsOBS6M5GHgMWADSdsA2wNbRMSmwB3AoG7my6yV/BU4EXcDM+uIY8WsOtfHzPqI7jbeRknaIn/eH/iXpHXgzT7WewD35/VPANvldSuRbvE/CiwLPB8Rr0raAHhvN/Nk1mp+D3wvIqY0OiNmTc6xYlad62NmfUR3R5u8DzhY0m+Ah4AzgGvz7XcBdwFH5m2/D0yQNCWv+1pEPCvpKuAISXcDD5Bu1Zv1GRHxJHDqYlbvK2mrwvxnI+KmHsiWWdNxrJgtlutjZn1EdxtvCyLiiIpl76u2YUTMAHassvw1YJdu5sOs5UTEsCrLrgOuy58nABOWsP8hdcmYWZNxrJh1yPUxsz6i9Jd0m5mZmZmZWfm6fOctIqYC7ygvK2ZmZmbWGa6PmfUtvvNmZmZmZmbWAtx4MzMzMzMzawHdHbCkYV6f348nn1mulLQGDX69lHTKNm7046Wk87+nViklHYB5c8v5mwOsfs3A0tJaeseppaU1r5w/e9NYa7mn+f1HfllKWnMWlPfKn5cWDC4trY8Om1NKOm2xoJR0AJ6Y/2ppaQ04sLSk+NSorTreqEaXTV3cwI+dt0lpKXXPKoNe5PgNq70DvPO+/om9SkkHYPnhz5eW1s2b/qWUdO55veb3oHfooBEHl5bWSjcP73ijGj21RTnHFoDD77mxtLS+XFpK3dMWYs68pUtJ66uTP9rxRjVaaXpbaWl96ozPl5JObPhaKekA7HD2V0pLa/7Jla8I7Lp1vljeAKU/3X6R98N3w8MlptV4vvNmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLaDUxpuklwufd5X0kKRRkr4jabqkO/OyCyWNKWx7naQHJN0l6TZJY8vMl1mjSTpK0sOSQtLIwvINJN0s6TVJx1Tss3OOi4clHdvzuTZrDMeLWde5LmbWu9Xlzpuk7YDTgJ0j4om8+OSIGBsR6wITgX9IWrGw24ERsSnwK+Bn9ciXWQP9G9geqHwRwWzgaODE4kJJ/YBfArsAY4D9iydZs17O8WLWTa6LmfVOpTfeJG0NnAXsFhGPVNsmIiYC1wAHVFl9M7Ba2fky6wlPTpvPjh98hq988QV22/FZgLdLGhIRd0TE1MrtI+LpiLgNeKNi1XuAhyPi0Yh4HTgP2BNA0mfyVdG7JP1F0pD6/iqz+pg+bT4f2vYpvvGl2QBjJE1yvJh1n+tiZr1X2Y23pYFLgL0i4v4Otv0vsEGV5TsDF1fbQdJhkiZLmtz20ivdyqhZvTz6SBv7HTCEy68ZCbAA+GwXklkNmFaYf5K3TqQXRsS789XR+4BPVe5cjJUXnivvZaVmZZv6yHz23n8owL3AHBocL3Nmz+/C15s1lR6ri81/8dVuZdTMOq/sxtsbwE1UOTlWoYr5P0t6Evga6Tb/IiLizIgYFxHj+i0ztHs5NauTVVZdine9e2D77HPAVl1IpjI+ACL/+w5JN0qaAhwIbLTIhoVYGbFCvy58vVnPWHnVfmz27qXbZ/9Eg+Nl+PL9u/D1Zk2lx+pi/Zf1jWyznlZ2420B8DHg3ZKO62DbzUhXQdsdCKwFnEN6dsGsJWnRamRU2awjTwJrFOZXB2bkzxOAoyJiY+C7wKAupG/WFBwvZqVzXcysFyv9mbeIeBXYHThQUtWrPpI+CuwInFux7xvA8cB7JW1Ydt7MesKM6Qv47+2vt88uD/yrC8ncBqwraS1JA4H9gL/mdcsAMyUNIJ1ozVrWzOlt3HX7a+2z++N4Mes218XMeq+6jDYZEbNJ/aWPl7RnXvzF9uFpgY8D20bEM1X2nQv8HDimcp1ZK1h7nX5cNGlu+4Al/YEzJB2du6KsDtwt6bcAklbOy79EipcnJQ2PiPnAUcDVpKui50fEPfkrvgncClwLdPQ8g1lTe/s6/fnrX16FNErk8jhezErhuphZ71Rq5/6IGFb4PI106x3Sg7PfWcJ+21TM/7zMfJn1pKWWEt//8bIArDNq1iP5Cugv8rSQiJhFqqAuIiKuAK6osvwM4Iwy82zWKFoKvvmj5bjgz6/eGxEfzYsdL2Zd5LqYWe9WlztvZmZmZmZmVi433sxKtPoa/bnybyMbnQ2zlrDaGv256NqVGp0NMzOzluHGm5mZmZmZWQtw483MzMzMzKwFtOzbSDda5ln+vc2Zjc7GImbMf63jjWq01oBhHW9Ug5lrvFxKOgCr9C8nTwAz55eXr3mPl5YUR6zZlXcEL86kEtPqmsdefBsHXPnZUtLq/1J513veGNFWWlrHDn+9441qEDPKewVY2zLl/T4WVHsHdddcNvXU0tL60ugtSkurGWIF4MlXRvDVWz/a8YY10KzyylNMGVpaWuvffmQp6azz6xIPvLutUFpSt+2wdMcb1ejwe24sLa0rNxpRWlrNYvTg55iwyR9KSWuTgeXFy2mbrFlaWjc+v04p6Zz/9r+Xkk4z++n265aW1j82Lu+Y19v4zpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1gC433iQdJelhSSFpZGH5BpJulvSapGMq9tlZ0gN5v2O7k3Gz3kDS7yTdJeluSZMkDcvLl5V0aV53j6RDG51Xs0ZzvJgtzHUxs76nO3fe/g1sD1SOFTwbOBo4sbhQUj/gl8AuwBhgf0ljuvH9Zr3BFyNi04jYBHgCOCov/xxwb0RsCmwD/FzSwAbl0axZOF7MFua6mFkf02HjTdJoSfdL+kPhaueQiLgjIqZWbh8RT0fEbcAbFaveAzwcEY9GxOvAecCe+Ts+I+m2fNX0L5KGdP+nmTWPJcTRnLxewGAg8i4BLJOXDyOdiOfnbS+WdHu+w3BYA36OWV05XswWNjde4aa4mnviNm6JawHe7rqYWd9U65239YEz89XOOUBX3vi7GjCtMP9kXgZwYUS8O181vQ/4VLUEJB0mabKkyc8+V+ILcM16RtU4kjQemAVsAJyWtz0d2BCYAUwBPh8RC/K6T0bEu4BxwNGSFnm7bTFW2l4u72XoZj2oMfEy55V6/iazLnuVl1iNtXivdgBYQBPUxV6YvaDaJmZWR7U23qZFxL/z5z8BW3Xhu1RlWftV03dIulHSFOBAYKNqCUTEmRExLiLGjVyhXxeyYNZQVeMoIg4FViWdLPfN63cC7szLxwKnSxqe1x0t6S7gFmANYN3KLyrGSr9hw+rza8zqqzHxMnxofX6NWTctzWBGvPVY23M0QV1sxPIe986sp9UaddHBfC2eJJ04261OukoKMAE4KiI2Br4LDOpC+mbNbrFxFBFtwETgo3nRoaSroBERDwOPARtI2ob0fMMW+eroHTherHdyvJgVaNF2l+tiZn1QrY23UZK2yJ/3B/7Vhe+6DVhX0lr5QfL9gL/mdcsAMyUNIF3tMeuNFokjSevAm8/w7AHcn9c/AWyX161E6kL2KLAs8HxEvCppA+C9PZh/s57keDErmMervBDPtc8uj+tiZn1SrY23+4CDJd1NOmCcIeloSU+SrtrcLem3AJJWzsu/BBwv6UlJwyNiPmlksKtzeudHxD05/W8CtwLX8tbJ2Ky3WSSOgD/kLipTgFWA7+Vtvw9smdf9HfhaRDwLXAX0z2l8n9QVzKw3cryYFQxlGWbyePuAJf1xXcysT+pf43YLIuKIimW/yNNCImIW6SCyiIi4AriiyvIzSCdms96sWhy9r9qGETED2LHK8tdIQzyb9XaOF7OFiA31TgD+FpMeiYhXcV3MrM/xk6ZmZmZmZmYtoMM7b/n9Ie+of1bMei/HkVntHC9mCxusoWyx6M1lM+uDfOfNzMzMzMysBbjxZmZmZmZm1gIU0ZXXhDSepGeAxzvYbCTwbElf6bR6Pp3ekNaaEbFiSd/ZJTXGCrT+37on02rGPLV6Wg2PFWj5eGnGPDmt+qTVV+Ollf/PnFb5abVMXaxMLdt4q4WkyRExzmn1bFrNmKdmTqtZNOvfpxnTasY89ZW0mkUz/n2aMU9Oq7FpNQuXcadVj7R6Y6zUwt0mzczMzMzMWoAbb2ZmZmZmZi2gtzfeznRaDUmrGfPUzGk1i2b9+zRjWs2Yp76SVrNoxr9PM+bJaTU2rWbhMu606pFWb4yVDvXqZ97MzMzMzMx6i95+583MzMzMzKxXcOPN6krSxo3Og1lf5Ngzq53jxXo7l/Heo0813iSp0XmoRtI7JTXFUKeS3idph/y5W38vSTsB50naoJTMWY9qpnLZrlljuAyOvdbmeOlZjpfW1azlspliuMzyndNwGe9F+lTjDRhYZmKS1pa0kqQuv/hP0s7An4DXSsjPeyR9WtJ7O5unwsHh68DbAKIbD0TmA8VpwOci4n5JTVPWJA2SNKAO6TblCakrmqVcVtFUMVxGWWql2OtNZbxMZcVLybECjpda0nO89LymKpc5jaY455VdvnOaTVkfq1ddLKfdq2OvKf4De4KkHYGzJP1A0qYlpLczcCHwY+B8Set1MU8/AT4bEVMkjehGfnYD/gC8G/ga8KG8vNYCvHT+dx7dr4DslPPyMPCUpIERsaA7aeZ0lf9dVtKgLqbR/v92lqRDupunYt4iIiRtJemA/G+pJ6ie0mTlsjJfTRPDJZalpo+9nHa3y3gZMdxsyoqXMmOlkC/Hy5Lz5HjpYc1WLgt5apZzXmnlO39v6WW8metiOe1eUx9brIjo9ROwC3A78HHgYuAUYEA30tsSeBB4H7As8A3g+3mdakxjGHAD8Is8vwZwN/CeLuRnPeBeYPM8/zHgVmBYjftvDHw8fz4V+Gj+vDSwVP68VI1pjQWmAtsCXwFOBz4A9Ovm/2H7yKh75f/DS4DtgUGdSGMn4L+5HBwI3AG8v8RytkdO//icv+OAIT1Rxkv8DU1TLivSaqoYLqsstULsVXxHl8t4GTHcbFNZ8VJmrOT9HS8dp+V46eGp2cpl3q5pznlllu+8bellvIxyWdbxoIPvaPn62BJ/X6MzUPcfCG8H5pCuqAAsB/wT+GDFdrWcgES6W/lx4MDC8o8AF3Qhb1sB1wLHAjcD/9eFNNYBhgD7Af0Ly/8KbFRjGh/OhXsP0pWQA6ps02GwA1sAmwLvyPODgB+Sbte/vzsHjJzeDsBtwErAn4GHgP2BpWvYdy3gEeCQwrIvV5aDbuRtzfw3XDUfsP8HnAV8Bxhc73Je5tQs5bKwT1PFcJllqVViL6fZ7TLenRhu1qm78VJmrOT9HC8dp+N46eGp2cplRXpNcc4rq3zn7epWxrtTLss8HizhO3pNfWyxv7HRGajrj0tXYkaQWtznApvl5RcCFwC/BPYFRtaY3tL53/7AKoXlqwNXFeYXe5UFGFox/z7gOuDPhWW1XjFaJx/8hpOvXrUHJPA3YJv8eWNgxSr7bwUcmn/PR4HxpKs0DwIXka5GnQucA5zIEq74ADsC04Bxeb79KtFA4Af5gLFV8aBW4//fGoX5z+YD0p7Av4GjSF0BPg0sW0N6vwD+CKyf568AJpMOrl8GtuhE3tbMB4WNSAfs/sAGwHtIV5HWAT6ZDxo/WdLfrhmmZiqXVcrACJoohssoS80ee2WVcUqO4WaZyoqXMmPF8eJ4adapGcslTXTOK7N816OMl10uKbEulvfvVfWxmn5zozNQtx+WrlxcDgwlXR34AvAX0gOpN5Bu2/4iL/sXHVwxyMFwAXAMMLZi3VrA/fnzoblQLtIVgHRr+XLg8DzfP/+7ZQ78w4FlOvEbVwOuz7+xPa2B+d9zgfVzcF1decAgdV94MH9n+4F0N+Aa4OfA3nmbfYD/A96+hHzsRroK8748vzIworB+IPBd0gFpyxp/2xhSd4PrgXMLy5cjHcw2yvN/yXleaTHp9KdwACcduM4Hfgf8Hfgg6WrXNaQrM1XTqUhzA2B63v4GUl/79+d1uwM/KZSZM8hXvpp1aqZyWZFOU8VwWWWJJo+9sso4JcVws01lxktZseJ4cbw069Rs5bLsGO5uHJdZvutRxssql9ShLlZW7LXi1PAM1OVHpYPBTcBuhWWDSA+PzuKt/sjtVyOWWEiAnUn9tI/IB5yjKtYvB0witexvZTG3yElXBp4hXVE5jXQQWz6vey/ptv2X6KBfbj4QjCZdybqBKldPSLfHryF1Adi4Yt07Sbe5F+ljTLr69ft8wOiwDzOwCnAncEqeXxl4ANiv4m88kNT3eOUa0lwVuAX4WJ7/D7B2Yf1v8+/binTlbvMllIM/5LT+COyVl38FuA/YpLDtsI7+7oVtDweOK/wtj8oHjq2AdYE24IT8/1xaV4A6xktTlMsq/3dNE8NllSWaPPbKKuOUFMPNOJURL2XGiuPF8dKsU7OVy8J2TXHOK7N816OMl1UuqVNdrIzYa9Wp4Rko/QelPr4LyLddSbdLTyC1+kcCXwQmAlsV9lnsrfFcGF4A9sjz+5CuQBwE7FPY7nHSLdkxFfuLtx7wHA58k3QA+ijp6sfj+UCyArAh6bbxiCXkZ1ngR8CvSLf5J1K4slT4rt8AbwDrVEnjQ8CpFdv3K6zfE7gs/9b+i/v7kG+Pkw6UPycd7G4gX80qbDewk/+H65L6UQ/L81NI3SrOAkYB25GuGv2PwkmhIo2dgHtIV3K2zH/3PwBfyet/AJyd/387dQuddPXr74X5NfMB4+d5flw+oHyg0fGwhN/QdOWysG2zxXBpZYkmj72yyjglxHAzTWXGS5mx4nhxvDTr1ITlsunOeWWV73qV8TLKJXWsi5URe606NTwDdflR6db7ucCKpD7MXyysW5H0/oxzqO3hyg8AE4DPkPrT/pd0peZ40i3jz+WDwpnk/rsdpPcD4OL8eQzweg6Ee/KBo8ORl0j9eL+dg+ZB0kO2++cDyM45z2uwmNvrpIdNzyc98PvmCEb5d+yQDxK7AasuIQ87ka6AbZ3nP5Hnz6vYbk9gmyUddKqkvUoO7rNIV8/+QOqWcB5wZmG71Rez//uAGe15y8v6kx5m/iOwU152JunKVocHs8r85/2+UZjfmNQlYstq2zf71AzlsiKtpojhsstSM8demWWcbsZws0/djZcyY8Xx4nhp1qlZyuVi0mv4Oa+M8l3nMt50dbFqsdSd2GvVqeEZKO2HpH6vmxfmryZd9TkszxevZowk3yLvqHDkINqedLXhKd66WjAsH0R+WJl+IY0P5oPLwRT6ZpOuZPya9IDnbnnZu4ENlpCfVUjD0K6Zg3vdXOCnkR4Y/UVO93pS/+G1Kn9LYX6zHIgb5/mlCuu+Sb5F3sHf5/9I7yG5Ftg5L/sEqU/xJ/L8PqRb9uvWkN5a+e/0AdKwuBvng8zFwOjCdv8BNlxCOgOAI0lXwd5f8dv6kR5WHV9Y9rYa8rY2qf/8CoV0ts1l4uuF7X5KRTeOZpyapVxWSaupYriMstQKsVdWGaekGG62qax4KTNWHC+Ol2admq1c5mVNcc6rR/kuu4yXVS6pQ12srNjrDVN/eoH8sr/vAbdIei0i7oyInSRdRrotfWZEtEkaEBFvRMSzHaSnyP/7+d+/SXoeWCavHxYRL0taHlgnv/zvjYo0diIVzkuBXYG3S3qMNFTuP0i36feLiH/lfN22hPzsRuojviB/TxvpFvR3gadJo+v8OCKeaX9bfUQU89MPmF/4/XdI+itwlaRdSH2u50r6BOmq0R5L+vtk55KG/p0GHJ7TPju/u/E9+fevA3w4Ih5aUkKSNiD1X7+D9GDuuhHx27xuK2BDSU+SDpIDgJcXk84HSX/rH5EOOh8hXd37S/6btEm6FviSpP4RMT8inu4gb2vlfN0KDJF0fv4730L6u+4r6UrSFalDSF0wmlaTlctiWk0VwyWWpaaOvfxbu13Gy4rhZlNWvJQZKzk9x4vjpek0W7nM2zTTOa8e5RtKKuPNXBfL6faq+li3NLr12N2JVECmsJihRUnDtF7TifRW4q2hXo8gHYh+SLrqsxXpxYlfAg4jPXw5pkoam5CCu33Em/eQHlgdnedXAe4Hds/zS+pO0X47/IOkkYxWJgXXg8BgUhD9iHTlZ1SV/UeSHtRsfxh36cK640jdGq4kjfhzH0t4H0n+XZvkz0uRrmz8ljSKz6XArnndp4EbqWFUn/y3uIG3rg59PudrOOmh2r3y/+Ef8//zXlXSaL8y91HghPx5WVKf+pPJL7rMy48iXWmrtdvAtjk/e+b/+/8jjxZFuv0/iPTukOPa/z+bdWqmctmsMVxmWaLJY6+sMk4JMdyMU1nxUmasOF4cL806NVO5rCgHTXHOK7N816OMl1EuqWNdrIzY601TwzPQ5YynAB5GuiW7c8W6k4GTC/O3kfs2d5DeSqRb3R/OheM/hQJ7YS4Ym5OGqX1yccGV83UhC78v5CpSv+j/I731fv8c5IsdRYjU/aB44Cl2N/hT/g6R3rdxPIsfonUP0gFquTxfPGi8Pf+mbVhCxYH0EO8C4AnS6EfvzsHyS9JBbT/SA7575e2H1/j/OKL4/0e6ovIv0kO+E0gHyU2ArYF3t/9fVaTRfoD/NPDbwvLhhYPG+4EDgbsX9/+2mPwNJr/UkXRAaj9gVB2lqTJvzTQ1YblsuhguuyzRxLFXVhmnhBhuxqmMeCkrVhwvjpdGx0MrlctCWs12zut2+a5XGS+jXFLHulgZsdebpoZnoNs/IBWq9xTmP0m6ynAjCz9QuWYH6bRfMfgY6crMBNIt5vb1FwLn589bUHhhYWGbN/tvk65U/CkfXE7IgXAsqd/1A6Tb3IsdppXUF3pdUj/j8e0HC956OeU6OZ/tVx2WOJQs6Rb4I5UHDdJVpEWuWC0mjW3zAeN7pP7Y5wA/Az6e1x9KOoAPrTG9/hXz2wPfa///Ih1QD+ggjfarWcuSDowTK9Yvl4P7UuDRWn4rFS/2LB4ASAfKXwAH5LLynUbHQA2/p2nLZd5mAk0Qw/UoS3m/Zoy9Uso4JcRws01lxUs9YsXx4nhp1qlZymWhbDblOa+M8l12GS+jXFK/40Gvqo+VNTU8A13KNLyLNORo/1wQvlpYt0Xh83XAO2tIr/gg5aaklz/eSuonvWxePpjUX7fqaDikLgP/IY0c1P7g7LAcUC8V98uFe7GjRpFGF7qX9IDpWqSD18WF9cppXAOs1om/W+VB47OkB3U77KpTSGM70pC6K5CGX72e9HDuQFI/9M5exXxfPshsRb6iUlh3AnBkDWl8iHQb/wBSf/TVSVeR2h9oXTvntZZRDt8BPAccvIQyshXpqtYc8vtTmnVq1nLZjDFcdlmqSLdpYq/sMl5GDDfLVFa8lBkr4XhxvDTp1IzlsqwYzuubti5WZhkvq1yWfTwoO/Z609TwDHQ6w6nQ304eMYh0VeRO4JCK7T5CusU+shNpvx+4Nn/eDfgnacSetUkvTLyrWjCQhoT9F+n2/ljSOyvab+22X/U5h9qGnv1ADuLiaE3DSP2ML+atq1KHkEYWWrYLf7+7SX3F76Mw8lIn0mjv297+7o8ljopWZf/23/DenJcJ+ff9qf3/K/+/3k1hiNkO0tyRdBVqbj543ZHz+E/S1adhNaQxnDQE7iTSAfvAxeR7C2A+b41O1ZS35pu1XNKEMVx2WVrC72507JVSxusRw42eyoqXMmOlUG4cL46XppqasVyWFcN5+6avi3W3jNejXJZ1PCgr9nrr1PAMdCqzKTD/DeyY50eS3m6/R/7P/Ube5vAc3Eu8LUu6avTe/HnfXLgOLKz/KOmW/zWkW+yLPPAJLJ8L6ofz/HuAmaQXN56Zlw0kXRk4u4bf+CXg8/lz8YWPQ0kj6IwHPk66srRxF/+Ou+U8b9qN/4td8wFn+cKyzjx4+h7SMLqb5/nRpPeufJ90K/xROvnAKakv9pxcLvqRXrS5Uq0HM1I/+vYDwHbAY+3lgYWv9GwL7Nn+m5vxYNGs5bIZY7geZWkJ6TY09sos4/WI4UZNZcZLWbGS93G8OF6abmrGcllmDOdtW6IuVkIZb7q6WE6j19TH6jE1PAOd+I9sD8y98vw6OZi3yvObkK6o/DoHVUcj67RfNWofrWc06erC7yu2+zBwBbk/82LS2o10dWFT0hWY75JezHgr+SWJOeCX9CLR9qsIpwE/KC4rbLM+cAnwKp3oG72Y7xtSwv/JnqQXZS7V2YAhvYCyDTguz/fPB6DTgLeRX67YhTztCvyPJTz4v5j9qr0bZsd84GoffWl9ClcPm/1A0WzlspljuMyyVEO6DYm9sst4vWK4UVN346XMWMnbOl7C8dJsUzOXy+7GcPv/Yf63ZepiXS3j9SyX3TkelB17vXFqeAY6+R+6Wy6cm+TAPCYvb38zfXvQLfEt7Sx61ehtpPdzrA7cAxxfsX0tXe52zge0Y4v7ka5orNCJ37gtqYvBu9p/W+H3HZkPbKWeHLv5f9KlLjF53z1J3RL2z/PvzwfxEd3M0575AL5UJ/dr7/P9Qd562Hln0pXEE0gnlM0a/Tfv5G9qqnLZzDFcZlnqof/bTv+msst4vWK4gX/TbsdLmcdwx0up/7eOl/L+lk1bLsuI4bxPS9XFav379GS57M7xoOzY621TwzPQhf/QhQKTt0b+2QXYPn9e0rs6Kq8arU3qI71dnl+L1G/7J13I2w6k0YtG5PlDgZuAZTqRxlDSeypOaD9o5OX75iBY7MO1rTiRulnMIfWFHk9J3UZqPYgVTjLFPt8TSH21V87rjiuWmVabmq1cNnMMd6csNetU7zJerxhu4N+rW/FS9jHc8dLj//+Ol9p+R9OWy+7GcN7HdbFy0q35eFDv2OtNU8Mz0MXCsAPpXRkj8vwhpH7HtT7bVHnV6Mt5efvBZzSpG0DND9gW0t6FdKv4s6QXHtb88s9CGqsB3yKNHHQiqf/x/V1JqxUm0ghF/wW+lOd79PY3S+7zPRq4mvxyyZ7MV8m/sanKZTPHcG+c6l3GGx3Ddfh7dSteyj6GO156/P/f8VLb72jactndGM5puC7Ww+Wy3rHXW6aGZ6Ab/8G7kEawOTIHd2df9re4q0a7A2PoRrePnMbrnc1TRRqDSUOgfof00O96jf6b1/n/c0fSyzY/0oDvXlyf7zNJD82Oystb8gRb+J1NVS6bOYZ729QTZbyRMVynv1m34qXsY7jjpUf/7x0vtf+Opi2XzXbOa4Wp0eWyJ2KvN0wNz0A3/5O7e3Jd3FWjTr2TZjFpl/IQal+a8v9Ht//2XfzuPVm0z/dtdKKPfCtMzVYumzmGe9vUE2W8kTFcp7+Z46WPTo6XTv2Opi2XzRbDrTA1ulz2lfpYd6b2/qUtS9KQiHi1G/vvQurT/CvSiwWPiIh7ysqftQ5Je5D6Vl9JGkXqLxFxWWNz1fs5hnuOy3jrc7z0HMdL7VwurUyOvSVr+cZbGSTtDlxIGrnGB4s+TNKHSN0j/hQRJ0kSQDhQmppjuHYu4+Z4qZ3jpee4XFqRY2/x3HjLunvVyHoPSTsCvweOjogLG50fq41juHYu4+Z4qZ3jpee4XFqRY686N97MqpC0A/BIRDza6LyY1YPLuFntHC9mjeHYW5Qbb2ZmZmZmZi1gqUZnwMzMzMzMzDrmxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQtw483MzMzMzKwFuPFmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmfYakqZK2r7L8HknbdCPdKyUd3J28mbW69jhSMl7S85L+0+h8mVVqPxdIOk7Sbxvw/aMlhaT+PfR9K0m6QdJLkn5e/N09nRfrPv9HmVmfFxEbdXP/XcrKi1mrao8jSVsDOwCrR8Qrjc2V2eJFxI8anYcechjwLDA8IqLRmbHu8Z03MzMzK9OawFQ33MzqL9/p7qg+vyZwrxtuvYMbb01M0tckTc+3uR+QtJ2kpSQdK+kRSc9JOl/S8oV9PiHp8bzuG8VuYpImSPpBYdttJD1ZmF9V0l8kPSPpMUlHF9Z9J3/XH3N+7pE0rrB+DUkX5n2fk3R6Yd0nJd2Xu9BcLWnNev7dzGohaYNczveriJP3SJosaY6kpySdlJcPkvSnXL5fkHSbpJXyuuskfTp/PkTSvyWdnLd7VNKWefk0SU+7i6X1RjmODgd+C2wh6WVJ320/1+SuWs/m7Q5sdH7Nct3mT4X5gwp1qG9WOTfcnI/rMyWdLmlgYd+QdISkh3J955eSlNf1k3RiLv+PArt1kK/288hpkl6UdL+k7Qrrr5P0Q0n/Bl4F3p7PM7fl7W+TtGXedgJwMPDVHJPbV/7uiu9eVtLv8m+cLukHkvp19W9s5XPjrUlJWh84Cnh3RCwD7ARMBY4G9gI+AKwKPA/8Mu8zBjgD+ERetwKweo3ftxRwKXAXsBqwHfAFSTsVNvsQcB4wAvgrcHretx9wGfA4MDrvf15etxdwHPARYEXgRuDc2v8SZuWT9E7gGuD/IuK8itWnAqdGxHBgbeD8vPxgYFlgDVJsHQHMXcxXbA7cnbc7hxQP7wbWAT4OnC5pWGk/yKx5PEKKjZsjYlhEfDsvXxkYSTo/HAycmc9zZk0h16F+BRwIrEI63q9W2KQN+CKpHG9Bqid9tiKZ3UnH+k2Bj5HqbgCfyes2A8YBe9eQpc2BR/P3fRu4UIWL9aS63mHAMsBLwOXAL0jnnZOAyyWtEBGHAH8GTsgx+bcOvvcPwHzS+WozYEfg0zXk13qIG2/Nqw1YGhgjaUBETI2IR4DDgW9ExJMR8RrwHWBvpQdN9wYui4gb8rpvAgtq/L53AytGxPci4vWIeBQ4C9ivsM2/IuKKiGgDziYdnADeQ2osfiUiXomIeRHxr7zucODHEXFfRMwHfgSM9d03a6CtSRcfDo6Iy6qsfwNYR9LIiHg5Im4pLF8BWCci2iLi9oiYs5jveCwixudYmUhq8H0vIl6LiGuA10knRrO+5Js5Bq4nVTQ/1ugMmRXsDVwaEf+KiNeBbwFvdjPMx/xbImJ+REwFfkO6kF70k4h4ISKeAP4JjM3LPwacEhHTImI28OMa8vN03ueNiJgIPMDCd+wmRMQ9uW61I/BQRJyd83cucD+wR2f+ALk3yS7AF3J97mngZBauC1qDufHWpCLiYeALpMbZ05LOk7Qqqd/yRfm2/QvAfaSG3kqkBtS0QhqvAM/V+JVrAqu2p5vTPi6n225W4fOrwKDcaFwDeDwfQKqle2ohzdmAWPhqlllPOgK4KSL+uZj1nwLWA+7PXU92z8vPBq4GzpM0Q9IJkgYsJo2nCp/nAkRE5TLfebO+5PmKZ+AeJ52zzJpFZR3qVQp1KEnrSbpM0ixJc0gXo0dWpFFZT2o/zi+UNqn8t6e7de7O+LKkewrbTK94Rq0yZorprVpMs7B9Z+taawIDgJmFettvgLd1Mh2rIzfemlhEnBMRW5GCKYCfkoJ1l4gYUZgGRcR0YCapIQWApCGkOwXtXgGGFOZXLnyeRrpbUEx3mYjYtYasTgNGqfows9OAwyvSHRwRN9WQrlk9HEEqrydXWxkRD0XE/qST1U+BSZKG5quf342IMcCWpC4wB/VYrs1a23KShhbmRwEzGpUZsypmUnjURNJgFq5DnUG6m7Vu7lZ/HOlidK1pr1GYH9X+ISJuzN0Zh1WMfLxa+zNzhX2KMVNs2M0g1RWLRgHTa8xfu2nAa8DIQp1teHdHZLZyufHWpCStL2lbSUsD80hX6tuAXwM/bO92KGlFSXvm3SYBu0vaKj9E+z0W/j++E9hV0vKSVibd2Wv3H2CO0iApg/PDte+Q9O4asvsf0oHpJ5KGKg3s8L687tfA1yW1DyG9rKR9Ov0HMSvPS8DOwPsl/aRypaSPS1oxIhYAL+TFbZI+KGnj/IznHFI3yraeyrRZL/BdSQOVXiWwO3BBozNkVjAJ2CMP/DEQ+C4LN86WIR37X5a0AXBkJ9I+Hzha0uqSlgOOrWGft+V9BuR604bAFYvZ9gpgPUkHSOovaV9gDGk8gppFxEzS8+A/lzRcaZC8tSVVdg+1BnLjrXktDfyE9F6OWaQgPo40mMJfgWskvQTcQnqolYi4B/gcaYCEmaTBTJ4spHk2aUCSqaTgnNi+Ij+bswepf/Zj+Xt/S3pgd4kK+64DPJG/c9+87iLS3YvzcjeD/5H6U5s1TES8QHoP1S6Svl+xemfgHkkvk+Jtv4iYR7pTPYl08r4PuB6oOlqXmS1iFumcNIM0eMIREXF/Y7Nk9pZch/o/0gBTM0kX+p4m3YkCOAY4IC8/i0IdqgZnkbrd3wX8F7iwhn1uBdYl1cd+COwdEVUfhcnLdwe+TOrq+VVg94h4thN5bHcQMBC4lxSzk0gDuFiTkF/50LtJmgp8uobRhczMzEonaRvgTxFR0+jHZs0gjwj8Aqmb5GM9/N2HkOpuW/Xk91pr8J03MzMzM+vzJO0haUh+PvNEYAqpt5JZ03DjzczMzMwM9iR17Z1B6rK4X7iLmjUZd5s0MzMzMzNrAb7zZmZmZmZm1gLceDMzMzMzM2sB1V6q3BJGjhwZo0ePbnQ2zJbo9ttvfzYiVmxkHhwr1gqaIVbA8WKtwfFiVrtmiZeytGzjbfTo0UyePLnR2TBbIkmPNzoPjhVrBc0QK+B4sdbgeDGrXbPES1ncbdLMzMzMzKwFuPFmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNrIEkTZW0fWF+P0nPS/qApJD0cp6eknSZpB2q7D83bzNL0gRJw3r+l5g1hqQxkibnuHle0t8kjSmsX1rSr3MMzZZ0qaTVGplns3qrODe0T6fndatIOkvSjLz80Xzu2CCvH11x/nlZ0l153SGS2vKyOY38jWZlabW6mBtvZk1C0sHAL4HdgMfz4hERMQzYFLgWuEjSIRW77pG3GQtsBny9RzJs1hxmAHsDywMjgb8C5xXWfx7YAtgEWBV4ATitZ7No1hB7RMSwwnSUpBWAm4AhwNbAMsA7geuBHSr2H1HYd9PC8pvzOWdED/wGsx7VCnWx/vVK2Brjggcv4IpHr2h0NqyTJB0G/AjYKSImSxpdXB8Rs4BTJQ0AfirpjxGxoHIbSVeTDhxWT5PHw5RJjc5FnyJpKvAb4BPAKsDFwJER8QKpQYYkAW3AOoVd1wKujoin8jbnAScV0j0V+AiwLPDQu971rvr+EGt+vTu+vwjMAT5ROIe8AIzvbEIRsWDcuHElZs2aWV+oX7ZKXcx33nqZKx69ggdmP9DobFjnHAl8H9guIiZ3sO2FwNuA9StXSFod2AV4uPQc2sKmTIJZUxqdi77oQGAnYG1gPeD49hWSXgDmke6q/aiwz++A90laVdKQnMaVhfW3kU6yywPn1DHv1ip6d3xvD1xUWeHsCkn9SsiPtYg+UL9smbqY77z1Qusvvz7jd+70RTSrgwlMqGWzHYB/ArXUFmbkf5cvLLtYUgDDgH8A3+5EFq2rVt4YDr280bnoPT6pWrY6PSKmAUj6IamhdjxARIyQNBQ4mLe6ugA8CDwBTCfdlZsCHNW+MiL+VNj25+PGjTuxG7/Ceotmj+/a4uViSfML818hdS2e1b5A0oeAPwL9SN0hdyxs/2y6mQ3ADyKiPTbemy+WDO1a5q1VtWr9srfVxXznzazxjiDdRfitCmfKxWgfaGF2YdleEbEMsA2wAenkbNYbTSt8fpz0DNubIuIV4NfAHyW9LS8+AxgErECqbF5I4c6bpC9Luk/Si7lCatZb7BURIwrTWcBzpG7HAETEXyNiBKk75cCK/UcW9i1e1Lgl77NcnfNv1pNapi7mxptZ4z0NbEd6ePxXHWz74bz9In0XIuJ6YALgOwfWW61R+DyKt65+Fi1FGoyh/eS6KTAhImZHxGuku3XvkTRS0tbA14CPAcvlCqlZb/Z3YC9J3a7/RcTLJeTHrFm0TF3MjTezJhARM4BtgZ0lnVy5XtJKko4i3Yb/+hKeVzgF2EHS2Hrl1ayBPidpdUnLA8cBEyXtIGkzSf0kDScNRvI8cF/e5zbgIEnL5ofMPwvMiIhnSSPtzQeeAfpL+laP/yKznnUS6Y7Z2ZLWVrIMHujKrGXqYm68mTWJ/CzPtqRhz3+cF78g6RVSH+xdgX0i4vdLSOMZ0vML36xzds0a4RzgGuDRPP2ANFz5ucCLwCOkkSZ3joh5eZ9jSAOZPERqpO1KumoKcDWpC+WDpG6Y7fuY9QaXVryr7aJ80eK9pLL+L+Al4E7ShYwjG5dVs+bQCnUxD1hi1kARMbpi/jHe6hq2f2f3z8t8Arbe6raI+HHFsgvyVFVEPEcaYbLaujbgU3kCYNy4cT8tIZ9mDVXt3FBYN4NCma+yfipQ9ZmfiJgAtY3+YNYqWq0u5jtvZmZmZmZmLcCNNzMzMzMzsxbgbpNmZtb0ltQNzMzMrK/wnTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAX4mbde4oIHL+CKR6/g/tn3s8HyGzQ6O2a9y+TxMGXSW/Oz7oaVN2lcfsz6qspYrAfHtzWZ9jpeI7l+2Tx8562XuOLRK3hg9gONzoZZ7zRlEsya0uhcmJlj0fog1/GsyHfeepH1l1+/0Vkw671W3hgOvTx9Hr9bY/Ni1pcVY7EeHN/WhNZffn3G7zy+Yd9/6FWHNuy7bWG+82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQtw483MzMzMzKwFuPFm1kCSpkp6XdLIiuV3SgpJoyVNyNu8XJjukrR1Yf6VvH1xm1GN+l1mZcuxsn1hfj9Jz0v6QEXZf0rSZZJ2qLL/3LzNrBxXw3r+l5j1HEkHSJqcy/1MSVdK2krSdyS9IemlPD0o6XRJqxT23UbSk4X5gZIulPRvScMlHdyYX2VWrorzw/OSLpe0Rl5XrIPNlnStpA2qpHFd3nfpiuXV6nD7die/bryZNd5jwP7tM5I2BgZXbHNCRAwrTJtGxI3t88BGebsRhW2e6KH8m/WoXGn8JbAb8HhePCLHwqbAtcBFkg6p2HWPvM1YYDPg6z2SYbMGkPQl4BTgR8BKwCjgV8CeeZOJEbEMsDzwYWBl4PZiA66Q1tLAhcAIYMeImAMMqfNPMOtJ7eeHVYCngNMK607I61YDpgO/K+4oaTSwNRDAh6qkXVmHm9idjLrxZtZ4ZwMHFeYPBv7YoLyYNTVJhwE/B3aKiJsq10fErIg4FfgO8FNJi5znImIWcDWpEWfW60haFvge8LmIuDAiXomINyLi0oj4SnHbvPweYF/gGeDLFWkNAS4FBgC7RcQreb8zeuK3mPWkiJgHTALGVFk3FzifRc8dBwG3ABNIdbi66l/vL7Ce98DsBzj0qkMbnQ2r3S3AJyRtCDxIOoFuBfygobmyJZs1Bcbv1uhc9DVHkmJju4i4q4NtLwR+BqwP3FdcIWl1YBfgH/XIpPUCrR/fWwCDgItq3SEi2iRdAuxUWLw0cCXwIrBPRLxWai6tpfSF+mW+WLEvqW5WuW4oqafUwxWrDgJOAm4FbpG0UkQ8Va88uvHWy+z69l0bnQXrmva7b9cD95NuyxcdI+mowvwlEeHnDRpl470bnYO+agfgn8CUGradkf9dvrDsYkkBDCM13L5dbvasV+gd8b0C8GxEzO/kfjNYOGaWITUE93fDrW/rA/XLiyXNJ50fnmbhixjtdbDhpK767V2PkbQVsCZwfkQ8K+kR4ADg5Cr7A8yPiIXGOegsN956mX3W24d91tun0dmwbAITat30bOAGYC2qd5k8MSKOLylb1l3jDk2TleeTqmWrI4BvAr+V9KmIiCVsu1r+d3Zh2V4R8TdJHwDOAUYCL3Qht9abtUJ8dxwvzwEjJfXvZANuNRaOmWeBo4E/Sno5Iq7uXEatt2jl+mWNdbH280M/UuPsekntXSdPjIjj80BwV5F6dNyd1x0MXBMRz+b5c/KyYuOt1Dqcn3kzawIR8Thp4JJdSd29zGxRTwPbkR4M/1UH2344b/9A5YqIuJ70bMKJJefPrFncDMwD9qp1h/x86B7AjcXlEXEh8BlgkqQPlphHs6YTEW25zLeRuukX1z0BfB44VdJgSYOBjwEfyKMYzwK+CGwqadN65dGNN7Pm8Slg2/aHwc1sURExA9gW2FnSyZXrJa2Uu6d8G/h6RCxYTFKnADtIGluvvJo1SkS8CHwL+KWkvSQNkTRA0i6STihum5dvCJxLGnHypCrpnQscBVwi6X15v23r/kPMepiSPYHlqHheGiAiriV1Lz6MdHGkjTS4ydg8bUi6AHJQ5b5lcbdJsyYREY8sYfVXJX2hMD+vu32mzVpVREzLFccbSJVNgBckCXgFmEwaXOGqJaTxjKQ/krphfrTeeTbraRFxkqSngOOBPwMvAbcDPwR2BPaVtBcgUmX0WuBd+QJJtfT+IGkgcLmkHUmxY9ZbXCqpjTTc/+PAwRFxTzqtLOJnpIscDwLjK1/NJOl04BeSvlaPjLrxZtZAETF6Mcvnk06oAIfkaUnpTC1sb9brVMZKRDwGrJFn919khw72z8uOLCNvZs0qIv5MarhVuon0Oo0l7XsdsHrFsrOAs/LsB8eNG7ek507NWsLi6mJ53SFVlk0EFvuutog4n/RKAeig/tYVbryZmZlZeSaPhymT6pP2rLth5U3qk7ZZHVzw4AVc8egV3Urj/tn3s8HyG5SUI2t1fubNzMzMyjNlUnpPmplxxaNX8MDsRcZNMusy33kzMzOzcq28MRx6efnptvaLs62PWn/59Rm/8/gu79/bX4xtneM7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAV4wJIWs7ghZz2MrJmZmZlZ7+Y7by3GQ86amZmZmfVNvvPWgqoNOethZM3MzMzMejffeTMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WbWIiQdIulfjc6HmZmZmTWGG29mDSRpqqS5kl4uTKd3Ma3rJM3Labwo6QZJG5edZ7NGyLHyuqSRFcvvlBSSRkuakLcpxtNdkrYuzL+Sty9uM6pRv8usHhwvZt0naYykyZKez9PfJI0prF9a0q8lPSVptqRLJa1W73y58WbWeHtExLDCdFTlBpL615jWURExDFgBuA44u8R8mjXaY8D+7TP54sTgim1OqIinTSPixvZ5YKO83YjCNk/0UP7NepLjxax7ZgB7A8sDI4G/AucV1n8e2ALYBFgVeAE4rd6ZqrVCaNbrXfDgBVzx6BWNzgaQukgCnwH+AxwM/Ap4OK3SacBBwEzgcxHx98r9I2K+pPOAY3ss033Z5PEwZVKjc9EXnE0q++0nx4OBPwI/aFiOrOfNmgLjd2t0LlqB46UXeWD2Axx61aGNzkavJGkq8BvgE8AqwMXAkRHxAqlBhiQBbcA6hV3XAq6OiKfyNucBJxXSPRX4CLAs8BDwhYi4sbv59Z03s+yKR6/ggdkPNDobRZsDjwJvA35YsWwk8G3gQknLV+4oaSBwIHBLz2S1j5syKVUord5uAYZL2lBSP2Bf4E8NzpP1pI33hpXdG7xGjpdeYte378r6y6/f6Gz0dgcCOwFrA+sBx7evkPQCMI90IeRHhX1+B7xP0qqShuQ0riysvw0YS7pzdw5wgaRB3c2o77yZFay//PqM33l8aelNYEItm10saX5h/ivAG8CMiGi/Yjo/XfThaeCUiAhgoqQvA7vxVvfIX0g6ERgCzCVd8flbd3+H1WDljeHQyxudi9b1SdW6ZfvdhOuB+4HpFeuPkVTsenxJRBzc/QxaUxh3aJr6OsdLn7LPevuwz3r7NDobLavGutjpETENQNIPSQ214wEiYoSkoaS7148X9nkQeIIUV23AFODNeIqI4sWSn0s6HlgfuKurvwXceDNrBntFxEINrNxtclqVbafnhlu7x0n9rNsdHRG/lbQU8D5S/2yz3uRs4AZSd5U/Vll/YkQcX2W5WV/keDGrTbHOVVm3IiJekfRr4BlJG0bE08AZwCDSOAOvAF8l3XnbHCBfYP90TiuA4aSeU93ixptZ84oqy1aTpEIDbhRVGmgRsQC4UdLDwLg65tGsR0XE45IeA3YFPtXo/Jg1s74YL+fc+gSX3Fl5g7FxHpj9ATbb7PpGZ8M6tkbh8yjSYCWVliL1bFqN1BNqU+AbETEbII9J8L08yuuGwNeA7YB7ImKBpOeBmm+bL46feTNrLW8DjpY0QNI+pIND1VFWJG0BjKm2zqzFfQrYNiJeaXRGzFpAn4qXS+6czr0z5zQ6G9Z6Pidp9TyOwHGkR1N2kLSZpH6ShpMGI3keuC/vcxtwkKRlJQ0APkt65OVZYBlgPvAM0F/St0h33rrNd97MGu9SSW2F+WuBSxaz7a3AusCzwFPA3hHxXGH96ZJOyZ9nkfprn4RZLxIRjyxh9VclfaEwPy8iut1NxaxV9cV4GbPKcCYevkWjswHAoVed2egsWG3OAa4hdXG8hDQq626kZ99WJ40jcBuwc0TMy/scA/yCNJLkQOB/wIfzuqtJXSgfJHWpPJnqj8N0mhtvZg0UEaOXsHpCxbYTCssWeRdcRGxTLZFx48a58WYtb3GxEhHzeasbyiF5WlI6Uymh24pZM3O8mHXabRHx44plF+Spqnzx/MDFrGsj3fUudlc+obuZBHebNDMzMzMzawluvJmZmZmZmbUAd5s0MzMzM7M+qYNHWJqO77yZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3Az7z1Ig/MfoBDrzq00dloWffPvp8Nlt+g0dmwVjN5PMy6G1bepNE5MTMzs17OjbdeYte379roLJj1TVMmwWtzGp0LMzMz6wPceOsl9llvH/ZZb59GZ6Ol+a6lddnSwxudAzMzM+sD/MybmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAR6wpAmdc+sTXHLn9KrrHpj9ATbb7PoezpH1Zksqb5bN2pOJoy5pdC7MOq0h8e14MTOrG995a0KX3Dmde2d66HHrGS5vZr2X49vMrHfxnbcmNWaV4Uw8fItFlh961ZkNyI31dosrb5aN/0Gjc2DWZT0e344XM7O68Z03MzMzMzOzFuDGm1kDSZoqaa6klwvT6XndKpLOkjQjL39U0gRJG+T1oyVFxb535XWHSGqT9HIjf59ZWXKsbF9l+XBJp0h6IsfAw3l+ZLX9JO0n6XlJH+jJ/Js1A0ljJE3OMfC8pL9JGlNYv7SkX0t6StJsSZdKWq2ReTart1Y7v7jxZtZ4e0TEsMJ0lKQVgJuAIcDWwDLAO4HrgR0q9h9R2HfTwvKbI2JYj/wCswaQNBD4O7ARsDMwHNgSeA54T5XtDwZ+CewWER75yfqiGcDewPLASOCvwHmF9Z8HtgA2AVYFXgBO69ksmjVeM59f/MybWXP6IjAH+ERELMjLXgDGNyxHZs3nIGAU8MGIaL/L/DTw/coNJR0G/AjYKSIm91wWzXqepKnAb4BPAKsAFwNHRsQLpHMJkgS0AesUdl0LuDoinsrbnAecVEj3VOAjwLLAQ+9617vq+0PMGqdpzy9uvJkVPDD7AQ696tBGZwNge+CiQsPNmt2sKTB+t0bnoq/ZHriqcGJdnCOBrYDtIuKu+mfLrAOTx8OUSfX+lgOBnYBXgEuB4/OEpBeAYaQeWN8q7PM74FRJ7XfdDgSuLKy/Dfge8CLpLt076/kDeosmqltY7Zr2/OJuk2bZrm/flfWXX78RX32xpBcK02dI3VlmtW8g6UN53UuSrqnY/9nCvscUlr83n6Ct3oauCCtv3Ohc9EUrADNr2G4H4BZgSn2zY1ajKZPSBZ/6Oj0ipkXEbOCHwP7tKyJiBOnu2VHAHYV9HgSeAKaTen9sSGqste/3p4h4LiLmR8TP6/0DeoMG1i2se5r2/OI7b2bZPuvtwz7r7VNqmhOYUMtme0XE34oLJH2a1NUFgIj4KzAiL/94xf4jI2J+lXRviYitxo0bF53LtXXaMqvAoZc3Ohet7ZPqyl7PUYiTJTgC+CbwW0mfigjHhDXeyht3/bhRW7xMK3x+nPQM25si4hVJvwaekbRhRDwNnAEMIlVcXwG+SrrztjmApC8Dn85phbtNdqwedQvrnBrrYpWa9vziO29mzenvwF6SHKNmi/c3YCdJQzvY7mlgO9LgP7+qe67MmsMahc+jSIOVVFqKNDBW+4iSmwITImJ2RLxGGqzkPZJGStoa+BrwMWC5fPfOrLdq2vOLK4ZmzekkYDngbElrK1kGGNvYbJk11ABJg9on4GzS3YW/SNpA0lKSVpB0nKRdiztGxAxgW2BnSSc3IO9mPe1zklaXtDxwHDBR0g6SNpPUT9Jw0rnmeeC+vM9twEGSlpU0APgsMCMiniWNejwfeAboL+lbi3yjWetqmfOLG29mjXdpxbvaLsonyvcC84B/AS8Bd5JOnkc2LqtmDXUFMLcwfZP0UPn9wLWkZ3T+Q3pm9NbKnSNiGukEu7ekH/dQns0a5RzgGuDRPP0AGAGcSxpw5BHSSJM7R8S8vM8xpPPOQ6RG2q7Ah/O6q0ldKB8kdcNs38esN2iZ84ufeTNroIgYvYR1M4BPLWH9VKDqgw8RMQG61snbrBktKVaAL+Spw/0i4jEW7k5m1lvdFhGVlcgL8lRVRDxHGmGy2ro20jnpzfPSuHHjflpCPs0aqtXOL77zZmZmZmZm1gJ8583MzMzMmso5tz7BJXdO7/R+986cw5hVhtchR2bNwXfezMzMzHqRiBhd+QqaVnPJndO5d+acRmfDrOn4zpuZmZmZNZ0xqwxn4uFbdGqffX9zc51yY9YcfOfNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBfubNzKyzJo+HKZPS51l3w8qbNDY/ZtY1xVjuST5umFkX+c6bmVlnTZkEs6Y0Ohdm1l2OZTNrMb7zZmbWFStvDIdeDuN3a3ROzKw72mO5J/m4YWZd5DtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1AL/nrcHOufUJLrlz+kLL7p05hzGrDG9QjszMzMzMrJKkQ4BPR8RWjcqD77w12CV3TufemXManQ1rEElTJW1fZflwSadIekLSy5IezvMjq+0naT9Jz0v6QE/m36ynSTpA0uQcFzMlXSlpK0nfkfSGpJfy9KCk0yWtUth3G0lPFuYHSrpQ0r9zzB3cmF9lVq4lnSMkRY6flyU9JekySTtU2X9u3maWpAmShvX8LzGrv4ry3j6d3sW0rpM0L6fxoqQbJG1cWH+wpNslzZH0pKQTJHXqZpobb01gzCrDmXj4Fm9OvuvWt0kaCPwd2AjYGRgObAk8B7ynyvYHA78EdouI63swq2Y9StKXgFOAHwErAaOAXwF75k0mRsQywPLAh4GVgduLDbhCWksDFwIjgB0jYg4wpM4/wazHFc8RwON58YiIGAZsClwLXJTvKBTtkbcZC2wGfL1HMmzWGHtExLDCdFTlBp1oZB2VY2cF4Drg7MK6IcAXgJHA5sB2wDGdyai7TbagB2Y/wKFXHdrobFj9HESqlH4wIl7Oy54Gvl+5oaTDSBXZnSJics9l0Tpt8niYMqnRuWhZkpYFvgccGhEXFlZdClwq6TvtCyLiDeAeSfsC/wW+TOHkKGkIcDEQpIsec/N+Z4wbN+5Xdf4pZsmsKTB+t7p+ReU5QtLo4vqImAWcKmkA8FNJf4yIBZXbSLqa1IiznuJzRsPlCxqfAf4DHEy6WPhwWqXTSPW1mcDnIuLvlftHxHxJ5wHHFpadUdhkuqQ/Ax/sTL58563F7Pr2XVl/+fUbnQ2rr+2BqwoNt8U5ktSg284NtxYwZVKqrFlXbQEMAi6qdYeIaAMuAbYuLF4auBKYB3yoveFm1qM23htW3rjj7bqnM+eIC4G3AYtUMCStDuxCqrRaT/E5o1lsDjxKio8fViwbCXwbuFDS8pU75p5UBwK3LCH99wP3dCZDvvPWYvZZbx/2WW+fRmfDajSBCV3ZbQXg9hq22wH4J+Cje6tYeWM49PJG56I5fVIdbbEC8GxEzO9kyjNI3SjbLUNqCO4fEa91Mi2zcow7NE1d1XG8QOfOETPyv8VYuVhSAMOAf5AqqdaTfM4oR23xcrGk4vnlK8AbwIyIOC0vmy8JUm+oUyIigImSvkzqltzePfIXkk4kdZGcC3yk2hdKOhQYB3y6Mz/Hd97Mms9zwCLP6FRxBLAe8Fvlo4lZL/YcMLKzD3YDqwGzC/PPAvsBf5C0U1mZM2tCnTlHrJb/LcbKXvkZ0m2ADUh3Gcx6q70iYkRhOisvn1Zl2+m54dbucWDVwvzRETGC1Ftkd2CSpE2KCUjaC/gJsEtEPNuZjLrxZtZ8/gbsJGloB9s9TXrQdWtSP2yz3uxmUlfHvWrdQdJSwB7AjcXl+Zm5z5BOqJ161sCshXTmHPHhvP0DlSvyQFgTgBNLzp9ZK4gqy1aruCAyirfuXr+1Y8SCiLiR1OV4x/blknYGziINktLp3lNuvJk13gBJg9on0m33acBfJG0gaSlJK0g6TtKuxR0jYgawLbCzpJMbkHezHhERLwLfAn4paS9JQyQNkLSLpBOK2+blGwLnkkacPKlKeucCRwGXSHpf3m/buv8Qsx7U0TlC0kqSjiJ1ifx65WAlBacAO0gaW6+8mrWQtwFH53PNPsCGwBXVNpS0BTCG/FxbPs/8GfhoRPynK1/uZ97MGq8y4H9IGrTku6QhnJcDniINvHBr5c4RMS0fDG6QNC8iPJyz9UoRcZKkp4DjSSe/l0jPh/6QdFVz39wVRaSroNcC78oV2Grp/SE/UH65pB2Bb9b/V5j1rOI5gnQxA+CFfOfgFWAysE9EXLWENJ6R9EdSjHy03nk2a4BLJbUV5q8l1buquRVYl9QN/ylg74h4rrD+dEmn5M+zgOMj4so8/01gWeCKws27GyNil1oz6sabWQNFxOglrP5CnjrcLyIeA9YoKVtmTSsi/kxquFW6CfhOB/teB6xesewsUvcVgA+OGzeuWhcZ66weGAa/FLPuhpU36Xi7FtPBOWL/zu6flx1ZRt7Mmk0HdbEJFdtOKCxb5F1wEbFNB9/V7a76bryZmZlZeTbeu9E5MDPrtdx4MzMzs/J0dxj8ntQKdwfNzAo8YImZmZmZmVkL8J03MzOzJnfOrU9wyZ3TO73fvTPnMGaV4XXIkZmZNYLvvJmZmTW5S+6czr0z5zQ6G2Zm1mC+82ZmZtYCxqwynImHb9Gpffb9zc11yo2ZmTWCG29mZt1Vy7DovXRIcjOzZnPvzDmtf+Fi1p5MHLW414xZX+bGm5lZd3hYdDOzprHn2NUanQWzunLjzcysO2odFt1DkpuZ1d0Bm4/igM1HNTob3Tf+B43OgTUpD1hiZmZmZmbWAnznzczMzMxK1dXXW7Tzay7MqvOdNzMzMzMrlV9vYVYfvvNmZmZmZqXryust2rX8aJFmdeI7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WbWQJKmSpor6WVJz0u6XNIaed0ESa/ndbMlXStpgyppXJf3Xbpi+QRJr/fUbzGrp56IFUkv99TvMaunHC/bV1k+XNIpkp7I8fJwnh9ZbT9J++WY+UBP5t+sJ1WcX9qn0/O6VSSdJWlGXv5oPmdskNePlhQV+96V1x0iqS0vmyPpLkm7dze/bryZNd4eETEMWAV4CjitsO6EvG41YDrwu+KOkkYDWwMBfKhK2ifUI8NmDVLXWMn7m/VKkgYCfwc2AnYGhgNbAs8B76my/cHAL4HdIuL6HsyqWSPsERHDCtNRklYAbgKGkM4fywDvBK4HdqjYf0Rh300Ly2/O55YRwK+A8ySN6E5G3XgzaxIRMQ+YBIypsm4ucD4wtmLVQcAtwATg4Prm0Kw5OFbMuuQgYBTw4Yi4NyIWRMTTEfH9iLiiuKGkw4CfAztFxE2NyKxZE/giMAf4REQ8EskLETE+Ik7raOeiiFgAnA0MBdbtTqb8nrcmde/MOX7HSR8jaQiwL6mCWbluKLA/8HDFqoOAk4BbgVskrRQRT9U7r33SrCkwfrf8+W5YeZPupWFd5lixUvWduNweuCoiOuoefCSwFbBdRNxV/2zZYvWdstmstgcuyg2vbpHUDzgUeAN4vDtp+c5bE9pz7GqMWWV4o7NhPediSS+Qru7sAPyssO6YvO4l0sn0E+0rJG0FrAmcHxG3A48AB1SkfUz9st2HbLw3rLxx49OwusZK3t/6kr4VlysAM2vYbgfShZEp9c2OLVHfKpvN4GJJLxSmzwAjgVntG0j6UF73kqRrKvZ/trBvse713nxumQecCHw8Ip7uTkZ9560JHbD5KA7YfFSjs2ElOP+ImjbbKyL+lq/K7AlcL6m9O9iJEXG8pFHAVcD6wN153cHANRHxbJ4/Jy87uZD2icA3uvcrjHGHpqldV66EVqZhC/ukatmqrrESEcePGzcuuvtTrIW0alzWFi+VniM9L9qRI4BvAr+V9KmIcEw0QquWzWbUifNLcYGkT1OImYj4KzAiL/94xf4jI2J+lXRviYitJA0jPYu9Nalrf5f5zptZk4iItoi4EGgj3TkornsC+DxwqqTBkgYDHwM+IGmWpFmkvtmbStq0Mm2z3sSxYtYlfwN2yl2Ll+RpYDtSJfNXdc+VWfP6O7CXpG63l3J35c8Cn5C0WXfScuPNrEko2RNYDrivcn1EXAvMAA4D9iJVXMeQBmYYC2wI3Eh6tses13KsmNVkgKRB7RNpsIRpwF8kbSBpKUkrSDpO0q7FHSNiBrAtsLOkk6ukbdYXnEQ6z5wtae187lmGRQfEqklEPAf8FvhWdzLVK7tNnnPrE1xy5/RGZ6Mm986c4+fb7FJJbaQhzB8HDo6Ie6Sqt/l/RjqYPAiMz3cZ3pTfS/ILSV+rc57NGsGxYla7Kyrmf0gagOG7wLWkSulTwCWkgXwWEhHTJG0L3CBpXkR8vc75NWuk9vNLu2sj4sOS3gt8H/gX6VUBT+XPR3bxe04BHpG0SUTc3dHG1fTKxtsld053o8haQkSMXsK6Q6osmwhMXMI+5/NWX+pDAMaNG+dn3qzl9USsmPUWS4oX4At56nC/iHgMWKOkbJk1pQ7OLzOATy1h/VSg6hXEiJhAej1NcdmTwNKdz+VbemXjDWDMKsOZePgWjc5Gh/w6ADMzMzMzq4WfeTMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFpA/0ZnwMzMrBWcc+sTXHLn9IZ8970z5zBmleEN+W4zM2sevvNmZmZWg0vunM69M+c0OhtmZtaH+c6bmZlZjcasMpyJh2/R49+7729u7vHvNDOz5uM7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZn1KpIOkfSvRufDzMysbG68mTUhSWMkTZb0fJ7+JmlMYf3Skn4t6SlJsyVdKmm1RubZrJ4kTZU0V9LLhen0LqZ1naR5OY0XJd0gaeOy82zWKBXx8rykyyWtkddNkPR6Xjdb0rWSNqiSxnV536Urlr+5f0/9HrN6yvHyuqSRFcvvlBSSRlfETft0l6StC/Ov5O2L24wqO79uvJk1pxnA3sDywEjgr8B5hfWfB7YANgFWBV4ATuvZLJr1uD0iYlhhOqpyA0m1DsR1VEQMA1YArgPOLjGfZs1gj1zGVwGeYuFzxAl53WrAdOB3xR0ljQa2BgL4UJW02/c36y0eA/Zvn8kX9AZXbHNCxTlo04i4sX0e2ChvN6KwzRNlZ9SjTfY2k8fDlEmNzoXVSNJU4DfAJ0gn2IuBIyPiBVKDDEkC2oB1CruuBVwdEU/lbc4DTiqkeyrwEWDZd73rXXX+FdZpjtPSSDoE+AzwH+Bg4FfAw2mVTgMOAmYCn4uIv1fuHxHzc/wc22OZ7mH3zpzj0Sr7sIiYJ2kScEqVdXMlnQ9cULHqIOAW4FZSXFWu7xEuu9aDziaV+/aLHAcDfwR+0LAcLYbvvPU2UybBrCmNzoV1zoHATsDawHrA8e0rJL0AzCMdTH5U2Od3wPskrSppSE7jysL624CxpDt31mwcp2XbHHgUeBvww4plI4FvAxdKWiQeJA0kxc8tPZPVnrXn2NX8cu8+Lp8j9qVKGZc0lHS34eGKVQcBf87TTpJWqnc+K7nsWg+7BRguaUNJ/Ugx86cG56kq33nrjVbeGA69vNG5MIBPqpatTo+IaQCSfkhqqB0PEBEj8sn1YODxwj4PAk+Quru0AVOAN7uQRcSbB5xx48Z17zdYfThOF1ZbrFwsaX5h/ivAG8CMiGi/Wjo/3azmaeCUiAhgoqQvA7vxVvfIX0g6ERgCzCXdqe51Dth8FAdsXvojF9Zg5x9R02bt8TKMFA87FdYdI+koYDjp3LJn+wpJWwFrAudHxLOSHgEOAE6u3L+ePTtcdq0sNcYLvHX37XrgflIdq6g9btpdEhEHdzuDneQ7b2aNN63w+XHSM2xviohXgF8Df5T0trz4DGAQ6XmdocCFFO68SfqypPskvVjPjJv1sL0iYkRhOisvn1Zl2+m54dauMraOjogRpDjaHZgkaZO65NqsMfbKZXxp0sW96yWtnNedmNeNJl28WL+w38HANRHxbJ4/Jy8rat/frDc5m3Sh4hBSl8lKJ1acg3q84QZuvJk1gzUKn0eRBiuptBTpDkH7iJKbAhMiYnZEvEa6W/ceSSMlbQ18DfgYsFz9sm3WNKLKstXy86LtqsZWRCyIiBtJ3cZ2rFP+zBomItoi4kJSL42tKtY9QRoA61RJgyUNJp07PiBplqRZwBeBTSVt2tN5N+tJEfE4aeCSXUkXxZuSG29mjfc5Savn53GOI3Xx2kHSZpL6SRpOGozkeeC+vM9twEGSlpU0APgsqevYs8AywHzgGdw12vqutwFHSxogaR9gQ+CKahtK2gIYA9zTg/kz6xFK9iRdzLuvcn1EXEu6sHEYsBepkTeG9Nz0WFLs3EjqTmbW230K2Db3empKbryZNd45wDWkwRUeJY1sNAI4F3gReIQ00uTOETEv73MMaSCTh0iNtF2BD+d1V5O6UD7Iws/JmbW6Syven3PREra9FVgXeJY0iMneEfFcYf3p7emQusocHxFXVknHrFVdmsv3HFIMHBwRi7tA8TPgq6QG3PiIeCIiZrVPwOnAgZ14FYdZS4qIRyJi8mJWf7XiHPTsYrarKwehWePdFhE/rlh2AUsYmjlXQg9czLo20pWjTwGMGzeuWpcys5YSEaOXsHpCxbYTCssWeRdcRGxTTq7MmtOS4iUiDqmybCIwcQn7nA+cn2cX2d+slS0uXiJiPtDe/f4QOij7ETG1sH3d+M6bmZmZmZlZC3DjzczMzMzMrAW426RZA3XQFczMzMzM7E2+82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA/89YE7p05h31/c3M5ic3ak4mjLiknLTNbvFlTYPxuXdz3blh5k3LzY2ZmZr2eG28NtufY1RqdBTPrrI33bnQOzMzMrA9y463BDth8FAdsPqq8BMf/oLy0zKy6cYemqau6esfOzMzM+jQ/82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmdn/t3fvUVfVdR7H3x8FRAJExQuiRDo5aqWOYmSaYTmWmomTLgptyq62NDOz6Z7WZJnNOCyl2+jK26jZUrygstRK84YiJl4gNSNLlBJUwIfwht/5Y/+QzfHwsJ/nOefsvR8+r7X2Yp99ez77cL57n9++HTOrATfezMzMzMzMasCNNzMzMzMzsxpw483MzMzMzKwG3HgzMzMzMzOrATfezMzMzNZTkj4u6fayc5hZMW68mZVM0mRJsyV1SVooaYakfSWdKullSc+n7lFJUyWNys07QdKC3OtBkqZJukPScEkfK2etzFpL0uOSVqQ6WdVNTeNGSTpH0lNp+HxJ50vaKY0fKyka5r0/jfu4pJVp2LIy19GsVbqrl14s6xZJL6RlLJV0q6S3tTqzWdWk71SXp3oKSRMaxu8v6eZUF483mX9sGv8PSQ9LOqAVudx4MyuRpJOAKcD3ga2AMcBPgMPSJJdFxDBgM+BwYGvg3nwDLresjYBpwAjgwIhYBgxp8yqYddKhETE01x0vaXPgTrLP+ruAYcAewO+Af22Yf0Ru3t1yw2dGxFCy2jHrL15XL40TSBpQcFnHpxrZHLgFuKiFOc2q7HbgaOBvTcYtB34BfHkt814K3EdWN98ALpe0RV8DufFmVhJJmwDfBY6LiGkRsTwiXo6I6RGxxoYgDZ8LTAIWAV9qWNYQYDowEDgkIpan+X7aiXUxK9EXgWXARyPiT5FZEhHnRcTZPVlQRLzanohm1ZDONN8h6X8kPQucunqUzk5nEB6W9N5m80fEK8AvgV06FNms7dKZta9JmifpOUnnSRocES9FxJSIuB1Y2ThfRMyKiIuA+U2WuSPZgcRTImJFRFwBPAh8KI3fQdJvJT0jabGkiyWNKJK36BEXq5O/PQjnHVJ2Clu3vYHBwJVFZ4iIlZKuBt6XG7wRMANYChwZES+2NKW1h+u0VQ4ArmxFw0vShnvuuWcLIrXHvIXLmPTzmWXHsPobT9YA25LsgN+kNOxyYCTwb8A0SW+KiGfzM0oaBBwF3NXRxOtwyd1/5eo5T5Ydw+rtKLLvVsvJDoZ/M3W99RZgfkQ8nxt2fxoOIOAHwK3AcOAKsoMpJ65rwT7z1t+87QjY2pei18TmwOJ0JLMnniK7jHKVYWQNwQvccKsJ12lvXSVpSa77NNmXzdcuZ5H0wTTueUk3Nsy/ODfvybnh75C0BHih/avQO4ftPppdRg0vO4bVS7N6AXgqIs6OiFciYkUa9jQwJV3lcRnwCJA/unRWqpEu4HjgO51aiSKunvMk8xb6llXrk6kR8UQ6YHEa8JE+Lm8o2UH1vKVk39mIiMci4qaIeDEiFgFnAu8usmCfeetvxh2TdVYNn1B3Y58BRkoa0MMG3GggfzR0MXACcKGkroi4oedBraNcp6/Xfa2sMjEifp0fIOlTwGv3gEbENcCINPzohvlHrqXW7oqIfSUNBZ5vMr50k8ePYfL4MWXHsIr41bGFJmtWLx8Hnmgy7ZMREbnXfwG2yb0+ISLOlbQBsA9wjaR3V+lM9S6jhnPZZ/cuO4ZVUMF6yddF4+e/N7rIzqjlDSftYyRtCZzF6nu1NwCeK7Jgn3kzK89MsiP9E4vOkHachwK35YdHxDTg02Q3w+7fwoxmVfcbYGKqjT6JiK4W5DGrumgybLSk/BGUMWRXeaw5Y8SrEXEb8BhwYJvymZVhu1x/089/D80Ftpc0LDdstzQcsksmA9g1IoaTHWwsdBTTjTezkkTEUuDbwI8lTZQ0RNJASQdJOiM/bRq+M9mTi7YmO73euLxLyS5nuVrSPmm+97R9RczKdSawKXBRugFcaWe5e7mxzGplS+CEtK85EtgZuL7ZhJL2Jntgydxm481q6jhJ20raDPg6cBlkT/KWNDhNM0jS4FUHOiRtkMYNzF5qcLovlIh4FJgDnJKGHw7sSnZvG2Rn27qAJZJGs/YnVr6OG29mJYqIM4GTyG6KXUR22v544Ko0ySRJXcAS4BqySy33jIimR4Qi4gKyJ1FeJ+ntwLfamd+sw6Zrzd+tujIiFgPvIDuLfTvZJSlzyHaMnysvqlnpXlcv3Ux7N/BmssvwTwOOiIhncuOnrloO2c8EfDMiZrQvulnHXQLcSPbkyPnA99LwR4AVZLes3JD635jG7ZdeX092tm5FWsYqHwbGkV0OeTpZXS1K475D9jTKpcB1ZD/1VIjveTMrWURcDFzcZNSdrH6M89rmvQXYtmHYOcA56eX+48aNa3aJjFmtRMTYbsY9BXyym/GPs5bLUSLifOD8PoUzq5ju6oWGz3tDDbzut+AiYkJrUplV2j0R8YPGgevY99xCN5c6pn3PhLWMmws03jT63+uO6TNvZmZmZmZmteDGm5mZmZmZWQ34skkzMzMzM1svreMy48rxmTczMzMzM7MacOPNzMzMzMysBtx4MzMzMzMzqwHf82ZmZmZmLTdv4TIm/XxmR/7OLqOGt/3vmFWBG29mZmZm1lKH7T667Ahm/ZIbb2ZmZmbWUpPHj2Hy+DEd+VudOLtnVhW+583MzMzMzKwG3HgzMzMzMzOrATfezMzMzMzMasCNNzMzMzMzsxpw483MzMzMzKwG3HgzMzMzMzOrATfezMzMzMzMasCNNzMzMzMzsxpw482sgiQNknS5pMclhaQJDeP3l3SzpKWSHm8y/9g0/h8dimxWilbWiqSHOxTbrBSuF7O1kzRZ0mxJXZIWSpohaV9Jp0p6WdLzqXtU0lRJo3LzTpC0IPd6kKRpku6QNFzSxyTdK2mZpAWSzpA0oDc53Xgzq67bgaOBvzUZtxz4BfDltcx7KXAfsHl7oplVSqtq5RttSWdWLa4XswaSTgKmAN8HtgLGAD8BDkuTXBYRw4DNgMOBrYF78w243LI2AqYBI4ADI2IZMAQ4ERgJjAfeC5zcm6xuvJmVKB39/JqkeZKek3SepMER8VJETImI24GVjfNFxKyIuAiY32SZOwJ7AKdExIr2r4VZ+3WiViLiivaviVn7uV7MipO0CfBd4LiImBYRyyPi5YiYHhFrHMhIw+cCk4BFwJcaljUEmA4MBA6JiOVpvp9GxG2pBp8ELgb26U3eXp2uq4N5C5cx6eczy45hVsRRwPvIjnhOB76Zut56CzA/Ip4vOoPrxWrCtWJWXOn10kmuTeuDvYHBwJVFZ4iIlZKuJquxVTYCZgBLgSMj4sVuFrEfMLcXWfvnmbfDdh/NLqOGlx3DrKipEfFERDwLnAZ8pI/LG0q24SjE9WI14loxK67Ueukk16b10ebA4oh4pYfzPUV2GeUqw8gaghd013CTdAwwDvivngaFfnrmbfL4MUweP6bsGGb86thCkz2R6/8LsE0f/2wXUHgv5nqxKnCtmBVXh3rpJNemdadAvTwDjJQ0oIcNuNHAs7nXi4ETgAsldUXEDY0zSJoInA4cEBGLe/C3XtMvz7yZ1cx2uf4xZEdy+mIusL2kYX1cjlnVuFbMinO9mBUzE3gBmFh0BkkbAIcCt+WHR8Q04NPA5ZL2b5jn/cA5wKER8WBvw7rxZla+4yRtK2kz4OvAZZA9rUjS4DTNIEmDJSmN2yCNG5i91GBJgwAi4lFgDnBKbn6z/qCttSLp8E6vkFkbuV7MCoiIpcC3gR9LmihpiKSBkg6SdEZ+2jR8Z7Inr24NnNlkeZcCxwNXS9onzfcesoeUfCgiZvUlrxtvZuW7BLiR7Ole84HvpeGPACvITsvfkPrfmMbtl15fT3ZEdUVaxiofJrue+rk2ZzfrpHbXyuntjW/WUa4Xs4Ii4kzgJLKH+iwiu+z4eOCqNMkkSV3AEuAaskst94yIpme0I+ICsidRXifp7cC3gE2A69PvyHVJmtGbrIqI3sxXunHjxsXs2bPLjmHWLUn3RsS4bsY/DnwqIn7drgyuFauDKtQKuF6sHlwvZsWtq17qxmfezMzMzMzMasCNNzMzMzMzsxrolz8VYFYXETG27AxmdeBaMSvO9WLWf/nMm5mZmZmZWQ248WZmZmZmZlYDbryZmZmZmZnVgBtvZmZmZmZmNeDGm5mZmZmZWQ3U9ke6JS0C/tLixY4EFrd4mX3lTMVUMRPAP0fEsDIDNNRKVd+nRs7ZWnXIWXqtQNv2LWtTtf+XKuWpUhaoXp71oV6q9p5XLQ84U1GVqJdWqe1PBUTEFq1epqTZVfsFdmcqpoqZIMtVdoZ8rVT1fWrknK1Vh5xVqBVoz75lbar2/1KlPFXKAtXMU3YGaG+9VPE9r1IecKaiqlIvreLLJs3MzMzMzGrAjTczMzMzM7MacONtTf9bdoAmnKmYKmaC6uWqWp61cc7WqkPOOmRstaqtc5XyVCkLOE8ZqraOVcsDzlRUFTP1Wm0fWGJmZmZmZrY+8Zk3MzMzMzOzGlgvG2+S3i/pEUmPSfpqk/GbSJou6X5JcyUdU3amNM0ESXNSpt9VIVOabi9JKyUdUXYmSUdJeiB1d0rarQKZJOmsNP4BSXu0O1Pub28o6T5J16bXm0m6SdIf07+bdirLWvINljQrV2vfqWjO7STdLOkPKecXKprzF5KelvRQblilMjZTdFtTR1XbjhbYXh2WtlNzJM2WtG/JeTq6TS+QZydJMyW9KOnkdmYpmKe0/UsrFFg/fx8rmClN5+9j/bheXhMR61UHbAj8CdgeGATcD+zSMM3XgR+m/i2AZ4FBJWcaAcwDxqTXW5b9PuWm+y1wPXBE2ZmAdwKbpv6DgLsrkOlgYAYg4B3tztTwt08CLgGuTa/PAL6a+r+66nNeVpfek6GpfyBwd3qPqpZzFLBH6h8GPArsUsGc+wF7AA/lhlUqY5PMhbY1deyqth0tuL0ayupbKnYFHi45T8e26QXzbAnsBZwGnFz256fM/UuH1s/fxyq2HenB++TvY23q1sczb28HHouI+RHxEvBL4LCGaQIYJklkO7JngVdKzjQZmBYRfwWIiKfbmKdoJoDPA1cA7c5TKFNE3BkRz6WXdwHblp0pvb4wMncBIySNanMuJG0LHAKc25DlgtR/ATCx3Tm6k96TrvRyYOqC6uVcGBG/T/3PA38ARlO9nLeSba/yKpWxiaLbmjqq2na0yDa0K9K3HOANZPVYZp5ObtOL5Hk6Iu4BXm5jjsJ5KGn/0iL+Pta6TODvY/29Xl6zPjbeRgNP5F4vSMPypgI7A08BDwJfiIhXS860I7CppFsk3Svp39uYp1AmSaOBw4GftTlL4UwNPkl2hKWdimTqae5WmQL8B5D/7G4VEQsha5CQHUUulbJLO+eQ7XBuioi7qWDOVSSNBf6F7CxhZXPmVD1jWfXRCVXbjhZ6ryUdLulh4DrgE2XnyWn3Nr1qn8Uq719awd/HWpTJ38eA/l8vrxlQdoASqMmwxiOL7wPmAO8BdgBuknRbRCwrMdMAYE/gvcDGwExJd0XEoyVmmgJ8JSJWZgfF2q5IpmxCaX+yjUVb79egWKbCuVtF0geApyPiXkkT2vm3+ioiVgK7SxoBXCnprSVHWitJQ8mObJ4YEcs69Lnv7zpeHx1Ute1oofc6Iq4kq8X9gP8EDigzD3Rsm161z2Il9y8t5O9jrcs0BX8f6+/18pr1sfG2ANgu93pbsiM6eccAp6dLRx6T9GdgJ2BWiZkWAIsjYjmwXNKtwG5k996UlWkc8Mu0oRgJHCzplYi4qsRMSNqV7FLBgyLimTZl6UmmQrlbbB/gg5IOBgYDwyX9H/B3SaMiYmG6VKATl1cUEhFLJN0CvJ8K5pQ0kKzhdnFETEuDK5eziapnLKM+OqVq29EevdcRcaukHSSNjIjFZeXp4Da9ap/Fqu5fWsXfx1qXyd/H+n+9rNabG+Xq3JE1WOcDb2L1DY1vaZjmp8CpqX8r4ElgZMmZdgZ+k6YdAjwEvLXMTA3Tn0/7b5At8j6NAR4D3lmhz9MhrHmD7KxOZMv9/QmsfmDJj1jz4RVndDJLk2xbACNS/8bAbcAHKphTwIXAlIbhlcqZcoxlzQeWVC5jQ94ebWvq1FVtO1pwe/VPrH5gyR5p/6cS83Rsm96T/y/gVNr/wJLK7186sH7+Plax7UgP3id/H2vXupYdoJSVzp428yjZU2m+kYYdCxyb+rcBbiS7vvoh4OiyM6XXXyZ7wtFDZJdtlZ4pN23bNxYF/+/OBZ4ju8xiDjC7ApkE/DiNfxAY1+5MDfkmsLrxtnna6fwx/btZJ7M0ybYrcB/wQPpcf7uiOfclu7Tigdxn6+AK5rwUWEj2MIUFZJeqVCrjWnK/rob6S1e17WiB7dVXgLnpMz4T2LfkPB3dphfIs3WqrWXAktQ/vMQ8pe5fOrB+/j5WMFNu2rZvRwr+3/n7WJu6VUfXzMzMzMzMrMLWx6dNmpmZmZmZ1Y4bb2ZmZmZmZjXgxpuZmZmZmVkNuPFmZmZmZmZWA268mZmZmZmZ1YAbbzUlaaWkOZLmSrpf0kmSNkjjxkk6q5t5x0qa3Lm0ZuVxrZgV53oxK871YmXwTwXUlKSuiBia+rcELgHuiIhTCsw7gezHRT/Q1pBmFeBaMSvO9WJWnOvFyuAzb/1ARDwNfAY4XpkJkq4FkPTudFRojqT7JA0DTgfelYZ9MR39uU3S71P3zjTvBEm3SLpc0sOSLpakNG4vSXemI02zJA2TtKGkH0m6R9IDkj5b1nti1oxrxaw414tZca4X65iyfyXcXe86oKvJsOeArYAJwLVp2HRgn9Q/FBiQH5+GDwEGp/43A7NT/wRgKbAtWUN/JrAvMAiYD+yVphuelvsZ4Jtp2EbAbOBNZb9X7tbvzrXizl3xzvXizl3xzvXiroxuANafqMmwO4AzJV0MTIuIBemATd5AYKqk3YGVwI65cbMiYgGApDnAWLKNyMKIuAcgIpal8QcCu0o6Is27CdkG6M99XjOz1nKtmBXnejErzvVibeXGWz8haXuyYn8a2HnV8Ig4XdJ1wMHAXZIOaDL7F4G/A7uRHdV5ITfuxVz/SrLPjIBmN0sK+HxE3NCHVTFrK9eKWXGuF7PiXC/WCb7nrR+QtAXwM2BqRETDuB0i4sGI+CHZqfOdgOeBYbnJNiE7evMq8FFgw3X8yYeBbSTtlf7GMEkDgBuAz0kamIbvKOkNfV9Ds9ZwrZgV53oxK871Yp3iM2/1tXE6dT4QeAW4CDizyXQnStqf7EjNPGAG8CrwiqT7gfOBnwBXSDoSuBlY3t0fjoiXJE0Czpa0MbACOAA4l+xU/u/TzbSLgIl9WkuzvnOtmBXnejErzvViHeefCjAzMzMzM6sBXzZpZmZmZmZWA268mZmZmZmZ1YAbb2ZmZmZmZjXgxpuZmZmZmVkNuPFmZmZmZmZWA268mZmZmZmZ1YAbb2ZmZmZmZjXgxpuZmZmZmVkN/D9U5ZCUChNNMgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 1152x720 with 8 Axes>"
+       "<Figure size 1008x720 with 8 Axes>"
       ]
      },
      "metadata": {
@@ -752,15 +727,11 @@
    ],
    "source": [
     "n_perspectives = len(kinase_distance_matrices)\n",
-    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*4, 10))\n",
+    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*3.5, 10))\n",
     "for i, (perspective, matrix) in enumerate(kinase_distance_matrices.items()):\n",
     "    print(matrix.sum().sum())\n",
     "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
-    "    if perspective in [\"sequence\", \"ligand-profile\"]:\n",
-    "        # From similarity to distance matrix\n",
-    "        dendrogram(np.ones(matrix.shape) - matrix, ax=axes[1][i], title=perspective)\n",
-    "    else:\n",
-    "        dendrogram(matrix, ax=axes[1][i], title=perspective)"
+    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
    ]
   },
   {

From f294ec68f24efa1b36b5acb1aedd9a93490ca472 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 18:23:33 +0200
Subject: [PATCH 056/158] add more details in theory.

---
 .../talktorial.ipynb                          | 682 +++++++++++-------
 1 file changed, 403 insertions(+), 279 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 74b48783..72ed7a04 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -131,6 +131,46 @@
     "The identity score is computed by sum the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's consider the identity matrix $I$ below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "|       | A     | C     | D     | E     | ... |\n",
+    "|-------|-------|-----  |-----  |-----  |-----|\n",
+    "| **A** | **1** | 0     | 0     | 0     | ... |\n",
+    "| **C** | 0     | **1** | 0     | 0     | ... |\n",
+    "| **D** | 0     | 0     | **1** | 0     | ... |\n",
+    "| **E** | 0     | 0     | 0     | **1** | ... |\n",
+    "| ...   | ...   | ...   | ...   | ...   | ... |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and let $M = len(K_i) = len(K_j)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
+    "\n",
+    "$$\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n}^{M} I (K_i[n], K_j[n]),\n",
+    "$$\n",
+    "\n",
+    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -143,12 +183,36 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The BLOSUM substitution matrix $SM$ is defined as below (the full matrix will be displayed in the practical part):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# TODO: add aggregation and normalization"
+    "|       | A     | C     | D     | E     | ... |\n",
+    "|-------|-------|-----  |-----  |-----  |-----|\n",
+    "| **A** | 4     | 0     | -2    | -1    | ... |\n",
+    "| **C** | 0     | 9     | -3    | -4    | ... |\n",
+    "| **D** | -2    | -3    | 6     | 2     | ... |\n",
+    "| **E** | -1    | -4    | 2     | 5     | ... |\n",
+    "| ...   | ...   | ...   | ...   | ...   | ... |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
+    "\n",
+    "$$\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{1}{\\sum_i SM[i,i]} \\sum_{n}^{M} SM\\big((K_i[n], K_j[n])\\big),\n",
+    "$$\n",
+    "\n",
+    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$."
    ]
   },
   {
@@ -160,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -170,12 +234,13 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "import requests\n",
-    "import biotite.sequence.align as align"
+    "import biotite.sequence.align as align\n",
+    "from sklearn import preprocessing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -262,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -279,7 +344,7 @@
        " 'p110a': 'CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF'}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -315,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -352,6 +417,55 @@
     "We now define the function which is more specific to amino acids grouping and use the `biotite` library for retrieving the BLOSUM substitution matrix."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The substituion matrix can be retrieve from `biotite` using the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    A   C   D   E   F   G   H   I   K   L   M   N   P   Q   R   S   T   V   W   Y   B   Z   X   *\n",
+      "A   4   0  -2  -1  -2   0  -2  -1  -1  -1  -1  -2  -1  -1  -1   1   0   0  -3  -2  -2  -1   0  -4\n",
+      "C   0   9  -3  -4  -2  -3  -3  -1  -3  -1  -1  -3  -3  -3  -3  -1  -1  -1  -2  -2  -3  -3  -2  -4\n",
+      "D  -2  -3   6   2  -3  -1  -1  -3  -1  -4  -3   1  -1   0  -2   0  -1  -3  -4  -3   4   1  -1  -4\n",
+      "E  -1  -4   2   5  -3  -2   0  -3   1  -3  -2   0  -1   2   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
+      "F  -2  -2  -3  -3   6  -3  -1   0  -3   0   0  -3  -4  -3  -3  -2  -2  -1   1   3  -3  -3  -1  -4\n",
+      "G   0  -3  -1  -2  -3   6  -2  -4  -2  -4  -3   0  -2  -2  -2   0  -2  -3  -2  -3  -1  -2  -1  -4\n",
+      "H  -2  -3  -1   0  -1  -2   8  -3  -1  -3  -2   1  -2   0   0  -1  -2  -3  -2   2   0   0  -1  -4\n",
+      "I  -1  -1  -3  -3   0  -4  -3   4  -3   2   1  -3  -3  -3  -3  -2  -1   3  -3  -1  -3  -3  -1  -4\n",
+      "K  -1  -3  -1   1  -3  -2  -1  -3   5  -2  -1   0  -1   1   2   0  -1  -2  -3  -2   0   1  -1  -4\n",
+      "L  -1  -1  -4  -3   0  -4  -3   2  -2   4   2  -3  -3  -2  -2  -2  -1   1  -2  -1  -4  -3  -1  -4\n",
+      "M  -1  -1  -3  -2   0  -3  -2   1  -1   2   5  -2  -2   0  -1  -1  -1   1  -1  -1  -3  -1  -1  -4\n",
+      "N  -2  -3   1   0  -3   0   1  -3   0  -3  -2   6  -2   0   0   1   0  -3  -4  -2   3   0  -1  -4\n",
+      "P  -1  -3  -1  -1  -4  -2  -2  -3  -1  -3  -2  -2   7  -1  -2  -1  -1  -2  -4  -3  -2  -1  -2  -4\n",
+      "Q  -1  -3   0   2  -3  -2   0  -3   1  -2   0   0  -1   5   1   0  -1  -2  -2  -1   0   3  -1  -4\n",
+      "R  -1  -3  -2   0  -3  -2   0  -3   2  -2  -1   0  -2   1   5  -1  -1  -3  -3  -2  -1   0  -1  -4\n",
+      "S   1  -1   0   0  -2   0  -1  -2   0  -2  -1   1  -1   0  -1   4   1  -2  -3  -2   0   0   0  -4\n",
+      "T   0  -1  -1  -1  -2  -2  -2  -1  -1  -1  -1   0  -1  -1  -1   1   5   0  -2  -2  -1  -1   0  -4\n",
+      "V   0  -1  -3  -2  -1  -3  -3   3  -2   1   1  -3  -2  -2  -3  -2   0   4  -3  -1  -3  -2  -1  -4\n",
+      "W  -3  -2  -4  -3   1  -2  -2  -3  -3  -2  -1  -4  -4  -2  -3  -3  -2  -3  11   2  -4  -3  -2  -4\n",
+      "Y  -2  -2  -3  -2   3  -3   2  -1  -2  -1  -1  -2  -3  -1  -2  -2  -2  -1   2   7  -3  -2  -1  -4\n",
+      "B  -2  -3   4   1  -3  -1   0  -3   0  -4  -3   3  -2   0  -1   0  -1  -3  -4  -3   4   1  -1  -4\n",
+      "Z  -1  -3   1   4  -3  -2   0  -3   1  -3  -1   0  -1   3   0   0  -1  -2  -3  -2   1   4  -1  -4\n",
+      "X   0  -2  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -2  -1  -1   0   0  -1  -2  -1  -1  -1  -1  -4\n",
+      "*  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4  -4   1\n"
+     ]
+    }
+   ],
+   "source": [
+    "substitution_matrix = align.SubstitutionMatrix.std_protein_matrix()\n",
+    "print(substitution_matrix)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
@@ -378,12 +492,20 @@
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    np.array :\n",
-    "        The vector of match score given the substitution matrix.\n",
+    "    tuple (np.array, np.float) :\n",
+    "        The vector of match score given the substitution matrix,\n",
+    "        and the normalizing value.\n",
     "    \"\"\"\n",
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
-    "\n",
+    "    \n",
+    "    # Normalize values to be in (0,1)\n",
+    "    score_matrix = preprocessing.minmax_scale(score_matrix,\n",
+    "                                              feature_range=(0, 1))\n",
+    "    \n",
+    "    # Sum diagonal values for normalization, called the trace\n",
+    "    normalizing_value = np.trace(score_matrix)\n",
+    "    \n",
     "    # Retireve the letter\n",
     "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
     "\n",
@@ -398,7 +520,7 @@
     "        ind1 = dict_letters[character_seq1]\n",
     "        ind2 = dict_letters[character_seq2]\n",
     "        match_score[i] = score_matrix[ind1, ind2]\n",
-    "    return match_score"
+    "    return match_score, normalizing_value"
    ]
   },
   {
@@ -452,15 +574,17 @@
     "    else:\n",
     "        seq_array1 = np.array(list(sequence_1))\n",
     "        seq_array2 = np.array(list(sequence_2))\n",
+    "\n",
     "        if type_ == \"identity\":\n",
     "            is_match = identity_score(seq_array1, seq_array2)\n",
     "            similarity_normed = sum(is_match)/len(sequence_1)\n",
     "            return similarity_normed\n",
+    "\n",
     "        elif type_ == \"substitution\":\n",
-    "            match_score = substitution_score(seq_array1,\n",
-    "                                             seq_array2)\n",
-    "            similarity = sum(match_score)\n",
-    "            return similarity\n",
+    "            match_score, normalizing_value = substitution_score(seq_array1, seq_array2)\n",
+    "            similarity_normed = sum(match_score)/normalizing_value\n",
+    "            return similarity_normed\n",
+    "\n",
     "        else:\n",
     "            print(\"Type not defined.\")\n",
     "            return None"
@@ -482,7 +606,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR and MET kinases: 205.0 using substitution.\n"
+      "Pocket sequence similarity between EGFR and MET kinases: 2.54 using substitution.\n"
      ]
     }
    ],
@@ -491,7 +615,7 @@
     "                                              \"MET\",\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{EGFR_MET_seq_similarity} using substitution.\")"
+    "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
    ]
   },
   {
@@ -550,7 +674,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR itself: 429.00 using substitution.\n"
+      "Pocket sequence similarity between EGFR itself: 3.58 using substitution.\n"
      ]
     }
    ],
@@ -776,144 +900,144 @@
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        "}\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_3e429_row5_col1, #T_3e429_row7_col0 {\n",
+       "#T_03cd7_row5_col1, #T_03cd7_row7_col0 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e429_row5_col6 {\n",
+       "#T_03cd7_row5_col6 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e429_row6_col0 {\n",
+       "#T_03cd7_row6_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e429_row7_col1 {\n",
+       "#T_03cd7_row7_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e429_row7_col4 {\n",
+       "#T_03cd7_row7_col4 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_3e429_\">\n",
+       "<table id=\"T_03cd7_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -930,118 +1054,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_3e429_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_3e429_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
-       "      <td id=\"T_3e429_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
-       "      <td id=\"T_3e429_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
-       "      <td id=\"T_3e429_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
+       "      <th id=\"T_03cd7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_03cd7_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_03cd7_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
+       "      <td id=\"T_03cd7_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
+       "      <td id=\"T_03cd7_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
+       "      <td id=\"T_03cd7_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_3e429_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_3e429_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
-       "      <td id=\"T_3e429_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
-       "      <td id=\"T_3e429_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
-       "      <td id=\"T_3e429_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
+       "      <th id=\"T_03cd7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_03cd7_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_03cd7_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
+       "      <td id=\"T_03cd7_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
+       "      <td id=\"T_03cd7_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
+       "      <td id=\"T_03cd7_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_3e429_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
-       "      <td id=\"T_3e429_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
+       "      <th id=\"T_03cd7_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_03cd7_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
+       "      <td id=\"T_03cd7_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_3e429_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
-       "      <td id=\"T_3e429_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
-       "      <td id=\"T_3e429_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
-       "      <td id=\"T_3e429_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
-       "      <td id=\"T_3e429_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
-       "      <td id=\"T_3e429_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
+       "      <th id=\"T_03cd7_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_03cd7_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
+       "      <td id=\"T_03cd7_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
+       "      <td id=\"T_03cd7_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
+       "      <td id=\"T_03cd7_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
+       "      <td id=\"T_03cd7_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
+       "      <td id=\"T_03cd7_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_3e429_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
-       "      <td id=\"T_3e429_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
-       "      <td id=\"T_3e429_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
-       "      <td id=\"T_3e429_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
+       "      <th id=\"T_03cd7_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_03cd7_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
+       "      <td id=\"T_03cd7_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
+       "      <td id=\"T_03cd7_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
+       "      <td id=\"T_03cd7_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_3e429_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
-       "      <td id=\"T_3e429_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
-       "      <td id=\"T_3e429_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
-       "      <td id=\"T_3e429_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
+       "      <th id=\"T_03cd7_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_03cd7_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
+       "      <td id=\"T_03cd7_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
+       "      <td id=\"T_03cd7_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
+       "      <td id=\"T_03cd7_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_3e429_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
-       "      <td id=\"T_3e429_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
-       "      <td id=\"T_3e429_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
+       "      <th id=\"T_03cd7_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_03cd7_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
+       "      <td id=\"T_03cd7_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
+       "      <td id=\"T_03cd7_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_3e429_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
-       "      <td id=\"T_3e429_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
-       "      <td id=\"T_3e429_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
-       "      <td id=\"T_3e429_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
-       "      <td id=\"T_3e429_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
-       "      <td id=\"T_3e429_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
-       "      <td id=\"T_3e429_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_3e429_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
+       "      <th id=\"T_03cd7_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_03cd7_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
+       "      <td id=\"T_03cd7_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
+       "      <td id=\"T_03cd7_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
+       "      <td id=\"T_03cd7_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
+       "      <td id=\"T_03cd7_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
+       "      <td id=\"T_03cd7_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
+       "      <td id=\"T_03cd7_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_03cd7_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e429_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_3e429_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
-       "      <td id=\"T_3e429_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
-       "      <td id=\"T_3e429_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
-       "      <td id=\"T_3e429_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
-       "      <td id=\"T_3e429_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
-       "      <td id=\"T_3e429_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
-       "      <td id=\"T_3e429_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
-       "      <td id=\"T_3e429_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
-       "      <td id=\"T_3e429_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_03cd7_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_03cd7_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
+       "      <td id=\"T_03cd7_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
+       "      <td id=\"T_03cd7_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
+       "      <td id=\"T_03cd7_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
+       "      <td id=\"T_03cd7_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
+       "      <td id=\"T_03cd7_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
+       "      <td id=\"T_03cd7_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
+       "      <td id=\"T_03cd7_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
+       "      <td id=\"T_03cd7_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7feae981fd30>"
+       "<pandas.io.formats.style.Styler at 0x7fa7344b8220>"
       ]
      },
      "execution_count": 16,
@@ -1091,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1119,147 +1243,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_8ac29_row0_col0, #T_8ac29_row1_col1, #T_8ac29_row2_col2, #T_8ac29_row3_col3, #T_8ac29_row4_col4, #T_8ac29_row5_col5, #T_8ac29_row6_col6, #T_8ac29_row7_col7, #T_8ac29_row8_col8 {\n",
+       "#T_99d04_row0_col0, #T_99d04_row1_col1, #T_99d04_row2_col2, #T_99d04_row3_col3, #T_99d04_row4_col4, #T_99d04_row5_col5, #T_99d04_row6_col6, #T_99d04_row7_col7, #T_99d04_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8ac29_row0_col1, #T_8ac29_row1_col0 {\n",
+       "#T_99d04_row0_col1, #T_99d04_row1_col0 {\n",
        "  background-color: #cfe5cf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8ac29_row0_col2, #T_8ac29_row5_col2, #T_8ac29_row6_col2, #T_8ac29_row7_col3 {\n",
+       "#T_99d04_row0_col2, #T_99d04_row5_col2, #T_99d04_row6_col2, #T_99d04_row7_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row0_col3 {\n",
+       "#T_99d04_row0_col3 {\n",
        "  background-color: #379b37;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row0_col4 {\n",
+       "#T_99d04_row0_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row0_col5 {\n",
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        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
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-       "#T_8ac29_row0_col6, #T_8ac29_row2_col4, #T_8ac29_row4_col6, #T_8ac29_row6_col4, #T_8ac29_row7_col6 {\n",
+       "#T_99d04_row0_col6, #T_99d04_row2_col4, #T_99d04_row4_col6, #T_99d04_row6_col4, #T_99d04_row7_col6 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row0_col7, #T_8ac29_row4_col0, #T_8ac29_row5_col7 {\n",
+       "#T_99d04_row0_col7, #T_99d04_row4_col0, #T_99d04_row5_col7 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row0_col8, #T_8ac29_row1_col8 {\n",
+       "#T_99d04_row0_col8, #T_99d04_row1_col8 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col2, #T_8ac29_row2_col7, #T_8ac29_row3_col5, #T_8ac29_row5_col3, #T_8ac29_row6_col5, #T_8ac29_row7_col2 {\n",
+       "#T_99d04_row1_col2, #T_99d04_row2_col7, #T_99d04_row3_col5, #T_99d04_row5_col3, #T_99d04_row6_col5, #T_99d04_row7_col2 {\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col3, #T_8ac29_row2_col3 {\n",
+       "#T_99d04_row1_col3, #T_99d04_row2_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col4, #T_8ac29_row4_col5 {\n",
+       "#T_99d04_row1_col4, #T_99d04_row4_col5 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col5, #T_8ac29_row6_col3, #T_8ac29_row7_col5 {\n",
+       "#T_99d04_row1_col5, #T_99d04_row6_col3, #T_99d04_row7_col5 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col6, #T_8ac29_row2_col5, #T_8ac29_row4_col3, #T_8ac29_row5_col4 {\n",
+       "#T_99d04_row1_col6, #T_99d04_row2_col5, #T_99d04_row4_col3, #T_99d04_row5_col4 {\n",
        "  background-color: #47a347;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row1_col7, #T_8ac29_row4_col7 {\n",
+       "#T_99d04_row1_col7, #T_99d04_row4_col7 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row2_col0 {\n",
+       "#T_99d04_row2_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row2_col1, #T_8ac29_row6_col1 {\n",
+       "#T_99d04_row2_col1, #T_99d04_row6_col1 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row2_col6, #T_8ac29_row3_col4 {\n",
+       "#T_99d04_row2_col6, #T_99d04_row3_col4 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row2_col8, #T_8ac29_row7_col8 {\n",
+       "#T_99d04_row2_col8, #T_99d04_row7_col8 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col0 {\n",
+       "#T_99d04_row3_col0 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col1 {\n",
+       "#T_99d04_row3_col1 {\n",
        "  background-color: #389b38;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col2 {\n",
+       "#T_99d04_row3_col2 {\n",
        "  background-color: #319831;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col6, #T_8ac29_row5_col0 {\n",
+       "#T_99d04_row3_col6, #T_99d04_row5_col0 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col7 {\n",
+       "#T_99d04_row3_col7 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row3_col8, #T_8ac29_row5_col8, #T_8ac29_row8_col0, #T_8ac29_row8_col1, #T_8ac29_row8_col2, #T_8ac29_row8_col3, #T_8ac29_row8_col4, #T_8ac29_row8_col5, #T_8ac29_row8_col6, #T_8ac29_row8_col7 {\n",
+       "#T_99d04_row3_col8, #T_99d04_row5_col8, #T_99d04_row8_col0, #T_99d04_row8_col1, #T_99d04_row8_col2, #T_99d04_row8_col3, #T_99d04_row8_col4, #T_99d04_row8_col5, #T_99d04_row8_col6, #T_99d04_row8_col7 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row4_col1 {\n",
+       "#T_99d04_row4_col1 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row4_col2, #T_8ac29_row6_col7 {\n",
+       "#T_99d04_row4_col2, #T_99d04_row6_col7 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row4_col8, #T_8ac29_row6_col8 {\n",
+       "#T_99d04_row4_col8, #T_99d04_row6_col8 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row5_col1, #T_8ac29_row7_col0 {\n",
+       "#T_99d04_row5_col1, #T_99d04_row7_col0 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row5_col6 {\n",
+       "#T_99d04_row5_col6 {\n",
        "  background-color: #3d9e3d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row6_col0 {\n",
+       "#T_99d04_row6_col0 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row7_col1 {\n",
+       "#T_99d04_row7_col1 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8ac29_row7_col4 {\n",
+       "#T_99d04_row7_col4 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_8ac29_\">\n",
+       "<table id=\"T_99d04_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1276,121 +1400,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_8ac29_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
-       "      <td id=\"T_8ac29_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
-       "      <td id=\"T_8ac29_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
-       "      <td id=\"T_8ac29_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
-       "      <td id=\"T_8ac29_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
+       "      <th id=\"T_99d04_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_99d04_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
+       "      <td id=\"T_99d04_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
+       "      <td id=\"T_99d04_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
+       "      <td id=\"T_99d04_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
+       "      <td id=\"T_99d04_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_8ac29_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
-       "      <td id=\"T_8ac29_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
-       "      <td id=\"T_8ac29_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
-       "      <td id=\"T_8ac29_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
-       "      <td id=\"T_8ac29_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
+       "      <th id=\"T_99d04_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_99d04_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
+       "      <td id=\"T_99d04_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
+       "      <td id=\"T_99d04_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
+       "      <td id=\"T_99d04_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
+       "      <td id=\"T_99d04_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_8ac29_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
-       "      <td id=\"T_8ac29_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
+       "      <th id=\"T_99d04_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_99d04_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
+       "      <td id=\"T_99d04_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_8ac29_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
-       "      <td id=\"T_8ac29_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
-       "      <td id=\"T_8ac29_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
-       "      <td id=\"T_8ac29_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
-       "      <td id=\"T_8ac29_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
-       "      <td id=\"T_8ac29_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
+       "      <th id=\"T_99d04_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_99d04_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
+       "      <td id=\"T_99d04_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
+       "      <td id=\"T_99d04_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
+       "      <td id=\"T_99d04_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
+       "      <td id=\"T_99d04_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
+       "      <td id=\"T_99d04_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_8ac29_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
-       "      <td id=\"T_8ac29_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
-       "      <td id=\"T_8ac29_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
-       "      <td id=\"T_8ac29_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
+       "      <th id=\"T_99d04_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_99d04_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
+       "      <td id=\"T_99d04_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
+       "      <td id=\"T_99d04_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
+       "      <td id=\"T_99d04_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_8ac29_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
-       "      <td id=\"T_8ac29_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
-       "      <td id=\"T_8ac29_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
-       "      <td id=\"T_8ac29_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
+       "      <th id=\"T_99d04_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_99d04_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
+       "      <td id=\"T_99d04_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
+       "      <td id=\"T_99d04_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
+       "      <td id=\"T_99d04_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_8ac29_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
-       "      <td id=\"T_8ac29_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
-       "      <td id=\"T_8ac29_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
+       "      <th id=\"T_99d04_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_99d04_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
+       "      <td id=\"T_99d04_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
+       "      <td id=\"T_99d04_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_8ac29_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
-       "      <td id=\"T_8ac29_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
-       "      <td id=\"T_8ac29_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
-       "      <td id=\"T_8ac29_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
-       "      <td id=\"T_8ac29_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
-       "      <td id=\"T_8ac29_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
-       "      <td id=\"T_8ac29_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_8ac29_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
+       "      <th id=\"T_99d04_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_99d04_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
+       "      <td id=\"T_99d04_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
+       "      <td id=\"T_99d04_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
+       "      <td id=\"T_99d04_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
+       "      <td id=\"T_99d04_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
+       "      <td id=\"T_99d04_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
+       "      <td id=\"T_99d04_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_99d04_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8ac29_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_8ac29_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
-       "      <td id=\"T_8ac29_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
-       "      <td id=\"T_8ac29_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
-       "      <td id=\"T_8ac29_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
-       "      <td id=\"T_8ac29_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
-       "      <td id=\"T_8ac29_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
-       "      <td id=\"T_8ac29_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
-       "      <td id=\"T_8ac29_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
-       "      <td id=\"T_8ac29_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_99d04_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_99d04_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
+       "      <td id=\"T_99d04_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
+       "      <td id=\"T_99d04_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
+       "      <td id=\"T_99d04_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
+       "      <td id=\"T_99d04_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
+       "      <td id=\"T_99d04_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
+       "      <td id=\"T_99d04_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
+       "      <td id=\"T_99d04_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
+       "      <td id=\"T_99d04_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7feae5fad580>"
+       "<pandas.io.formats.style.Styler at 0x7fa73485c3a0>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1410,7 +1534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [

From 4c1af9a00cedcb133e89bc8121068d002ccf19cf Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 7 Sep 2021 19:09:40 +0200
Subject: [PATCH 057/158] Final fixes.

---
 .../talktorial.ipynb                          | 628 +++++++++---------
 1 file changed, 330 insertions(+), 298 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 72ed7a04..3c813fa1 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -128,7 +128,7 @@
     "A simple way of assessing the similarity between two sequences is to use the so-called identity score.\n",
     "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If there are, the entry is set to $1$, and $0$ otherwise.\n",
     "\n",
-    "The identity score is computed by sum the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
+    "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
    ]
   },
   {
@@ -162,7 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
+    "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
     "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n}^{M} I (K_i[n], K_j[n]),\n",
@@ -179,7 +179,7 @@
     "Although the identity score is an easy measure of similarity, it does not take into account the rate at which an amino acid may change into another and treats all residues uniformly.\n",
     "\n",
     "The substitution score takes the changes of the amino acids over evolutionary time into account. It makes use of a substitution matrix, where each entry gives a score between two amino acids.\n",
-    "In this talktorial, we use the BLOSUM substitution matrix [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915), implemented in biotite."
+    "In this talktorial, we use the BLOSUM substitution matrix [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915), implemented in `biotite`."
    ]
   },
   {
@@ -206,13 +206,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
+    "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
     "\\text{similarity}(K_i, K_j) = \\frac{1}{\\sum_i SM[i,i]} \\sum_{n}^{M} SM\\big((K_i[n], K_j[n])\\big),\n",
     "$$\n",
     "\n",
-    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$."
+    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$.\n",
+    "\n",
+    "For computational reasons, we will use the normalized version of this matrix, i.e. all entries are between $0$ and $1$."
    ]
   },
   {
@@ -234,6 +236,7 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "import requests\n",
+    "import matplotlib.pyplot as plt\n",
     "import biotite.sequence.align as align\n",
     "from sklearn import preprocessing"
    ]
@@ -414,14 +417,14 @@
    "metadata": {},
    "source": [
     "#### Substitution score\n",
-    "We now define the function which is more specific to amino acids grouping and use the `biotite` library for retrieving the BLOSUM substitution matrix."
+    "We now define the function which is more specific to amino acids grouping and use the `biotite` library to retrieve the BLOSUM substitution matrix."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The substituion matrix can be retrieve from `biotite` using the following command:"
+    "The substitution matrix can be retrieve from `biotite` using the following command:"
    ]
   },
   {
@@ -466,10 +469,37 @@
     "print(substitution_matrix)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check for symmetry:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "substitution_matrix.is_symmetric()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def substitution_score(sequence1,\n",
@@ -492,21 +522,19 @@
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    tuple (np.array, np.float) :\n",
-    "        The vector of match score given the substitution matrix,\n",
-    "        and the normalizing value.\n",
+    "    np.array :\n",
+    "        The vector of match score\n",
+    "        using the normalized substitution matrix.\n",
     "    \"\"\"\n",
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
     "    \n",
     "    # Normalize values to be in (0,1)\n",
-    "    score_matrix = preprocessing.minmax_scale(score_matrix,\n",
-    "                                              feature_range=(0, 1))\n",
-    "    \n",
-    "    # Sum diagonal values for normalization, called the trace\n",
-    "    normalizing_value = np.trace(score_matrix)\n",
-    "    \n",
-    "    # Retireve the letter\n",
+    "    normalized_score_matrix = preprocessing.minmax_scale(score_matrix,\n",
+    "                                              feature_range=(0, 1),\n",
+    "                                              axis=1)\n",
+    "\n",
+    "    # Retrieve the letter (amino acid)\n",
     "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
     "\n",
     "    # Map letter to index\n",
@@ -514,13 +542,13 @@
     "    for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
     "        dict_letters[letter] = i\n",
     "\n",
-    "    match_score = match_score = np.zeros(len(sequence1))\n",
+    "    match_score  = np.zeros(len(sequence1))\n",
     "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1,\n",
     "                                                             sequence2)):\n",
     "        ind1 = dict_letters[character_seq1]\n",
     "        ind2 = dict_letters[character_seq2]\n",
-    "        match_score[i] = score_matrix[ind1, ind2]\n",
-    "    return match_score, normalizing_value"
+    "        match_score[i] = normalized_score_matrix[ind1, ind2]\n",
+    "    return match_score"
    ]
   },
   {
@@ -539,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -578,16 +606,14 @@
     "        if type_ == \"identity\":\n",
     "            is_match = identity_score(seq_array1, seq_array2)\n",
     "            similarity_normed = sum(is_match)/len(sequence_1)\n",
-    "            return similarity_normed\n",
-    "\n",
     "        elif type_ == \"substitution\":\n",
-    "            match_score, normalizing_value = substitution_score(seq_array1, seq_array2)\n",
-    "            similarity_normed = sum(match_score)/normalizing_value\n",
-    "            return similarity_normed\n",
-    "\n",
+    "            match_score = substitution_score(seq_array1, seq_array2)\n",
+    "            similarity_normed = sum(match_score)/len(sequence_1)\n",
     "        else:\n",
     "            print(\"Type not defined.\")\n",
-    "            return None"
+    "            return None\n",
+    "\n",
+    "        return similarity_normed"
    ]
   },
   {
@@ -599,14 +625,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR and MET kinases: 2.54 using substitution.\n"
+      "Pocket sequence similarity between EGFR and MET kinases: 0.72 using substitution.\n"
      ]
     }
    ],
@@ -620,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -648,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -667,22 +693,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR itself: 3.58 using substitution.\n"
+      "Pocket sequence similarity between EGFR itself: 1.00 using substitution.\n"
      ]
     }
    ],
    "source": [
     "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \"EGFR\", type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")\n",
-    "# TODO: normalize"
+    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
    ]
   },
   {
@@ -692,9 +717,16 @@
     "### Visualize similarity as kinase matrix"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We visualize the similarity matrix using substitution:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -707,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -879,7 +911,7 @@
        "p110a  0.152941  1.000000  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -893,151 +925,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
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        "}\n",
-       "#T_03cd7_row3_col8, #T_03cd7_row5_col8, #T_03cd7_row8_col0, #T_03cd7_row8_col1, #T_03cd7_row8_col2, #T_03cd7_row8_col3, #T_03cd7_row8_col4, #T_03cd7_row8_col5, #T_03cd7_row8_col6, #T_03cd7_row8_col7 {\n",
+       "#T_7fe3e_row3_col8, #T_7fe3e_row5_col8, #T_7fe3e_row8_col0, #T_7fe3e_row8_col1, #T_7fe3e_row8_col2, #T_7fe3e_row8_col3, #T_7fe3e_row8_col4, #T_7fe3e_row8_col5, #T_7fe3e_row8_col6, #T_7fe3e_row8_col7 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row4_col0 {\n",
+       "#T_7fe3e_row4_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row4_col1 {\n",
+       "#T_7fe3e_row4_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row4_col2, #T_03cd7_row6_col7 {\n",
+       "#T_7fe3e_row4_col2, #T_7fe3e_row6_col7 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row4_col8, #T_03cd7_row6_col8 {\n",
+       "#T_7fe3e_row4_col8, #T_7fe3e_row6_col8 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row5_col1, #T_03cd7_row7_col0 {\n",
+       "#T_7fe3e_row5_col1, #T_7fe3e_row7_col0 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row5_col6 {\n",
+       "#T_7fe3e_row5_col6 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row6_col0 {\n",
+       "#T_7fe3e_row6_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row7_col1 {\n",
+       "#T_7fe3e_row7_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_03cd7_row7_col4 {\n",
+       "#T_7fe3e_row7_col4 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_03cd7_\">\n",
+       "<table id=\"T_7fe3e_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1054,121 +1086,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_03cd7_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_03cd7_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
-       "      <td id=\"T_03cd7_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
-       "      <td id=\"T_03cd7_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
-       "      <td id=\"T_03cd7_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
+       "      <th id=\"T_7fe3e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_7fe3e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_7fe3e_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
+       "      <td id=\"T_7fe3e_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
+       "      <td id=\"T_7fe3e_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
+       "      <td id=\"T_7fe3e_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_03cd7_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_03cd7_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
-       "      <td id=\"T_03cd7_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
-       "      <td id=\"T_03cd7_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
-       "      <td id=\"T_03cd7_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
+       "      <th id=\"T_7fe3e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_7fe3e_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_7fe3e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
+       "      <td id=\"T_7fe3e_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
+       "      <td id=\"T_7fe3e_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
+       "      <td id=\"T_7fe3e_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_03cd7_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
-       "      <td id=\"T_03cd7_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
+       "      <th id=\"T_7fe3e_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_7fe3e_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
+       "      <td id=\"T_7fe3e_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_03cd7_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
-       "      <td id=\"T_03cd7_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
-       "      <td id=\"T_03cd7_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
-       "      <td id=\"T_03cd7_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
-       "      <td id=\"T_03cd7_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
-       "      <td id=\"T_03cd7_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
+       "      <th id=\"T_7fe3e_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_7fe3e_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
+       "      <td id=\"T_7fe3e_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
+       "      <td id=\"T_7fe3e_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
+       "      <td id=\"T_7fe3e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
+       "      <td id=\"T_7fe3e_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
+       "      <td id=\"T_7fe3e_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_03cd7_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
-       "      <td id=\"T_03cd7_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
-       "      <td id=\"T_03cd7_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
-       "      <td id=\"T_03cd7_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
+       "      <th id=\"T_7fe3e_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_7fe3e_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
+       "      <td id=\"T_7fe3e_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
+       "      <td id=\"T_7fe3e_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
+       "      <td id=\"T_7fe3e_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_03cd7_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
-       "      <td id=\"T_03cd7_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
-       "      <td id=\"T_03cd7_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
-       "      <td id=\"T_03cd7_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
+       "      <th id=\"T_7fe3e_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_7fe3e_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
+       "      <td id=\"T_7fe3e_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
+       "      <td id=\"T_7fe3e_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
+       "      <td id=\"T_7fe3e_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_03cd7_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
-       "      <td id=\"T_03cd7_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
-       "      <td id=\"T_03cd7_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
+       "      <th id=\"T_7fe3e_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_7fe3e_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
+       "      <td id=\"T_7fe3e_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
+       "      <td id=\"T_7fe3e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_03cd7_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
-       "      <td id=\"T_03cd7_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
-       "      <td id=\"T_03cd7_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
-       "      <td id=\"T_03cd7_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
-       "      <td id=\"T_03cd7_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
-       "      <td id=\"T_03cd7_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
-       "      <td id=\"T_03cd7_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_03cd7_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
+       "      <th id=\"T_7fe3e_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_7fe3e_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
+       "      <td id=\"T_7fe3e_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
+       "      <td id=\"T_7fe3e_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
+       "      <td id=\"T_7fe3e_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
+       "      <td id=\"T_7fe3e_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
+       "      <td id=\"T_7fe3e_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
+       "      <td id=\"T_7fe3e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_7fe3e_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_03cd7_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_03cd7_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
-       "      <td id=\"T_03cd7_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
-       "      <td id=\"T_03cd7_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
-       "      <td id=\"T_03cd7_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
-       "      <td id=\"T_03cd7_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
-       "      <td id=\"T_03cd7_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
-       "      <td id=\"T_03cd7_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
-       "      <td id=\"T_03cd7_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
-       "      <td id=\"T_03cd7_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_7fe3e_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_7fe3e_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
+       "      <td id=\"T_7fe3e_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
+       "      <td id=\"T_7fe3e_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
+       "      <td id=\"T_7fe3e_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
+       "      <td id=\"T_7fe3e_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
+       "      <td id=\"T_7fe3e_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
+       "      <td id=\"T_7fe3e_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
+       "      <td id=\"T_7fe3e_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
+       "      <td id=\"T_7fe3e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa7344b8220>"
+       "<pandas.io.formats.style.Styler at 0x7fc7a4b84760>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1190,7 +1222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1215,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1234,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1243,147 +1275,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_99d04_row0_col0, #T_99d04_row1_col1, #T_99d04_row2_col2, #T_99d04_row3_col3, #T_99d04_row4_col4, #T_99d04_row5_col5, #T_99d04_row6_col6, #T_99d04_row7_col7, #T_99d04_row8_col8 {\n",
+       "#T_8a0c0_row0_col0, #T_8a0c0_row1_col1, #T_8a0c0_row2_col2, #T_8a0c0_row3_col3, #T_8a0c0_row4_col4, #T_8a0c0_row5_col5, #T_8a0c0_row6_col6, #T_8a0c0_row7_col7, #T_8a0c0_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99d04_row0_col1, #T_99d04_row1_col0 {\n",
+       "#T_8a0c0_row0_col1, #T_8a0c0_row1_col0 {\n",
        "  background-color: #cfe5cf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99d04_row0_col2, #T_99d04_row5_col2, #T_99d04_row6_col2, #T_99d04_row7_col3 {\n",
+       "#T_8a0c0_row0_col2, #T_8a0c0_row5_col2, #T_8a0c0_row6_col2, #T_8a0c0_row7_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col3 {\n",
+       "#T_8a0c0_row0_col3 {\n",
        "  background-color: #379b37;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col4 {\n",
+       "#T_8a0c0_row0_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col5 {\n",
+       "#T_8a0c0_row0_col5 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col6, #T_99d04_row2_col4, #T_99d04_row4_col6, #T_99d04_row6_col4, #T_99d04_row7_col6 {\n",
+       "#T_8a0c0_row0_col6, #T_8a0c0_row2_col4, #T_8a0c0_row4_col6, #T_8a0c0_row6_col4, #T_8a0c0_row7_col6 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col7, #T_99d04_row4_col0, #T_99d04_row5_col7 {\n",
+       "#T_8a0c0_row0_col7, #T_8a0c0_row4_col0, #T_8a0c0_row5_col7 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row0_col8, #T_99d04_row1_col8 {\n",
+       "#T_8a0c0_row0_col8, #T_8a0c0_row1_col8 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col2, #T_99d04_row2_col7, #T_99d04_row3_col5, #T_99d04_row5_col3, #T_99d04_row6_col5, #T_99d04_row7_col2 {\n",
+       "#T_8a0c0_row1_col2, #T_8a0c0_row2_col7, #T_8a0c0_row3_col5, #T_8a0c0_row5_col3, #T_8a0c0_row6_col5, #T_8a0c0_row7_col2 {\n",
        "  background-color: #44a144;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col3, #T_99d04_row2_col3 {\n",
+       "#T_8a0c0_row1_col3, #T_8a0c0_row2_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col4, #T_99d04_row4_col5 {\n",
+       "#T_8a0c0_row1_col4, #T_8a0c0_row4_col5 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col5, #T_99d04_row6_col3, #T_99d04_row7_col5 {\n",
+       "#T_8a0c0_row1_col5, #T_8a0c0_row6_col3, #T_8a0c0_row7_col5 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col6, #T_99d04_row2_col5, #T_99d04_row4_col3, #T_99d04_row5_col4 {\n",
+       "#T_8a0c0_row1_col6, #T_8a0c0_row2_col5, #T_8a0c0_row4_col3, #T_8a0c0_row5_col4 {\n",
        "  background-color: #47a347;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row1_col7, #T_99d04_row4_col7 {\n",
+       "#T_8a0c0_row1_col7, #T_8a0c0_row4_col7 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row2_col0 {\n",
+       "#T_8a0c0_row2_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row2_col1, #T_99d04_row6_col1 {\n",
+       "#T_8a0c0_row2_col1, #T_8a0c0_row6_col1 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row2_col6, #T_99d04_row3_col4 {\n",
+       "#T_8a0c0_row2_col6, #T_8a0c0_row3_col4 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row2_col8, #T_99d04_row7_col8 {\n",
+       "#T_8a0c0_row2_col8, #T_8a0c0_row7_col8 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col0 {\n",
+       "#T_8a0c0_row3_col0 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col1 {\n",
+       "#T_8a0c0_row3_col1 {\n",
        "  background-color: #389b38;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col2 {\n",
+       "#T_8a0c0_row3_col2 {\n",
        "  background-color: #319831;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col6, #T_99d04_row5_col0 {\n",
+       "#T_8a0c0_row3_col6, #T_8a0c0_row5_col0 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col7 {\n",
+       "#T_8a0c0_row3_col7 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row3_col8, #T_99d04_row5_col8, #T_99d04_row8_col0, #T_99d04_row8_col1, #T_99d04_row8_col2, #T_99d04_row8_col3, #T_99d04_row8_col4, #T_99d04_row8_col5, #T_99d04_row8_col6, #T_99d04_row8_col7 {\n",
+       "#T_8a0c0_row3_col8, #T_8a0c0_row5_col8, #T_8a0c0_row8_col0, #T_8a0c0_row8_col1, #T_8a0c0_row8_col2, #T_8a0c0_row8_col3, #T_8a0c0_row8_col4, #T_8a0c0_row8_col5, #T_8a0c0_row8_col6, #T_8a0c0_row8_col7 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row4_col1 {\n",
+       "#T_8a0c0_row4_col1 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row4_col2, #T_99d04_row6_col7 {\n",
+       "#T_8a0c0_row4_col2, #T_8a0c0_row6_col7 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row4_col8, #T_99d04_row6_col8 {\n",
+       "#T_8a0c0_row4_col8, #T_8a0c0_row6_col8 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row5_col1, #T_99d04_row7_col0 {\n",
+       "#T_8a0c0_row5_col1, #T_8a0c0_row7_col0 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row5_col6 {\n",
+       "#T_8a0c0_row5_col6 {\n",
        "  background-color: #3d9e3d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row6_col0 {\n",
+       "#T_8a0c0_row6_col0 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row7_col1 {\n",
+       "#T_8a0c0_row7_col1 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99d04_row7_col4 {\n",
+       "#T_8a0c0_row7_col4 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_99d04_\">\n",
+       "<table id=\"T_8a0c0_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1400,121 +1432,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_99d04_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
-       "      <td id=\"T_99d04_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
-       "      <td id=\"T_99d04_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
-       "      <td id=\"T_99d04_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
-       "      <td id=\"T_99d04_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
+       "      <th id=\"T_8a0c0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_8a0c0_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
+       "      <td id=\"T_8a0c0_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
+       "      <td id=\"T_8a0c0_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
+       "      <td id=\"T_8a0c0_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
+       "      <td id=\"T_8a0c0_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_99d04_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
-       "      <td id=\"T_99d04_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
-       "      <td id=\"T_99d04_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
-       "      <td id=\"T_99d04_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
-       "      <td id=\"T_99d04_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
+       "      <th id=\"T_8a0c0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_8a0c0_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
+       "      <td id=\"T_8a0c0_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
+       "      <td id=\"T_8a0c0_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
+       "      <td id=\"T_8a0c0_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
+       "      <td id=\"T_8a0c0_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_99d04_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
-       "      <td id=\"T_99d04_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
+       "      <th id=\"T_8a0c0_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_8a0c0_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
+       "      <td id=\"T_8a0c0_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_99d04_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
-       "      <td id=\"T_99d04_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
-       "      <td id=\"T_99d04_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
-       "      <td id=\"T_99d04_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
-       "      <td id=\"T_99d04_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
-       "      <td id=\"T_99d04_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
+       "      <th id=\"T_8a0c0_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_8a0c0_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
+       "      <td id=\"T_8a0c0_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
+       "      <td id=\"T_8a0c0_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
+       "      <td id=\"T_8a0c0_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
+       "      <td id=\"T_8a0c0_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
+       "      <td id=\"T_8a0c0_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_99d04_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
-       "      <td id=\"T_99d04_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
-       "      <td id=\"T_99d04_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
-       "      <td id=\"T_99d04_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
+       "      <th id=\"T_8a0c0_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_8a0c0_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
+       "      <td id=\"T_8a0c0_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
+       "      <td id=\"T_8a0c0_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
+       "      <td id=\"T_8a0c0_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_99d04_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
-       "      <td id=\"T_99d04_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
-       "      <td id=\"T_99d04_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
-       "      <td id=\"T_99d04_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
+       "      <th id=\"T_8a0c0_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_8a0c0_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
+       "      <td id=\"T_8a0c0_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
+       "      <td id=\"T_8a0c0_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
+       "      <td id=\"T_8a0c0_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_99d04_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
-       "      <td id=\"T_99d04_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
-       "      <td id=\"T_99d04_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
+       "      <th id=\"T_8a0c0_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_8a0c0_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
+       "      <td id=\"T_8a0c0_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
+       "      <td id=\"T_8a0c0_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_99d04_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
-       "      <td id=\"T_99d04_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
-       "      <td id=\"T_99d04_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
-       "      <td id=\"T_99d04_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
-       "      <td id=\"T_99d04_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
-       "      <td id=\"T_99d04_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
-       "      <td id=\"T_99d04_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_99d04_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
+       "      <th id=\"T_8a0c0_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_8a0c0_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
+       "      <td id=\"T_8a0c0_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
+       "      <td id=\"T_8a0c0_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
+       "      <td id=\"T_8a0c0_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
+       "      <td id=\"T_8a0c0_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
+       "      <td id=\"T_8a0c0_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
+       "      <td id=\"T_8a0c0_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_8a0c0_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_99d04_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_99d04_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
-       "      <td id=\"T_99d04_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
-       "      <td id=\"T_99d04_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
-       "      <td id=\"T_99d04_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
-       "      <td id=\"T_99d04_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
-       "      <td id=\"T_99d04_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
-       "      <td id=\"T_99d04_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
-       "      <td id=\"T_99d04_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
-       "      <td id=\"T_99d04_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_8a0c0_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_8a0c0_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
+       "      <td id=\"T_8a0c0_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
+       "      <td id=\"T_8a0c0_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
+       "      <td id=\"T_8a0c0_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
+       "      <td id=\"T_8a0c0_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
+       "      <td id=\"T_8a0c0_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
+       "      <td id=\"T_8a0c0_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
+       "      <td id=\"T_8a0c0_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
+       "      <td id=\"T_8a0c0_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa73485c3a0>"
+       "<pandas.io.formats.style.Styler at 0x7fc7a4fcc100>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1534,7 +1566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [

From 3fc081f8f5728d9d44a03b26f345918478db73d2 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Tue, 21 Sep 2021 14:03:02 +0200
Subject: [PATCH 058/158] small adaptions to T024

---
 .../talktorial.ipynb                          | 1285 ++++++++---------
 1 file changed, 631 insertions(+), 654 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 3c813fa1..c4c24ffc 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -19,7 +19,11 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the 85 residues pocket sequence for each kinase. Two similarity measures are implemented: 1. the identity similarity which is based on character-wise discrepancy and 2. the substitution similarity which is amino acid specific."
+    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the 85 residues pocket sequence for each kinase. \n",
+    "\n",
+    "Two similarity measures are implemented:\n",
+    "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy\n",
+    "   2. Sequence similarity, i.e., the similarity which is based on a substitution matrix, thus, reflecting similarities between amino acids."
    ]
   },
   {
@@ -40,12 +44,13 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
+    "* Define the kinases of interest\n",
     "* Retrieve sequences from KLIFS\n",
     "* Sequence similarity\n",
     "    * Identity score\n",
     "    * Substitution score\n",
     "* Kinase comparison\n",
-    "* Visualize similarity as kinase matrix\n",
+    "* Visualize kinase similarity matrix\n",
     "* Save kinase distance matrix"
    ]
   },
@@ -80,8 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
-    "\n",
+    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
     " \n",
     "\n",
     "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
@@ -255,7 +259,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve sequences from KLIFS"
+    "### Define the kinases of interest"
    ]
   },
   {
@@ -282,6 +286,13 @@
     "                 'p110a']"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve sequences from KLIFS"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -610,7 +621,7 @@
     "            match_score = substitution_score(seq_array1, seq_array2)\n",
     "            similarity_normed = sum(match_score)/len(sequence_1)\n",
     "        else:\n",
-    "            print(\"Type not defined.\")\n",
+    "            print(f\"Type {type_} not defined.\")\n",
     "            return None\n",
     "\n",
     "        return similarity_normed"
@@ -632,16 +643,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR and MET kinases: 0.72 using substitution.\n"
+      "EGFR :KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
+      "MET  :EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n",
+      "Pocket sequence similarity between EGFR and MET kinases: 0.46 using identity.\n"
      ]
     }
    ],
    "source": [
     "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                              \"MET\",\n",
-    "                                              \"substitution\")\n",
+    "                                              \"identity\")\n",
+    "for key in ('EGFR','MET'):\n",
+    "    print(f\"{key:5s}:{kinase_sequences[key]}\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
+    "      f\"{EGFR_MET_seq_similarity:.2f} using identity.\")"
    ]
   },
   {
@@ -653,23 +668,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR and MET kinases: 0.46 using identity.\n"
+      "Pocket sequence similarity between EGFR and MET kinases: 0.72 using substitution.\n"
      ]
     }
    ],
    "source": [
     "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                              \"MET\",\n",
-    "                                              \"identity\")\n",
+    "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{EGFR_MET_seq_similarity:.2f} using identity.\")"
+    "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As expected, the similarity between a kinase and itself leads to the highest possible score:"
+    "We can also look at self-similarity:"
    ]
   },
   {
@@ -710,6 +725,13 @@
     "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As expected, the similarity between a kinase and itself leads to the highest possible score:"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -729,17 +751,27 @@
    "execution_count": 15,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "similarity_measure=\"substitution\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_similarity_matrix = np.zeros((len(query_kinases), len(query_kinases)))\n",
     "for i, kinase_name1 in enumerate(query_kinases):\n",
     "    for j, kinase_name2 in enumerate(query_kinases):\n",
     "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
-    "                                                             kinase_name2)"
+    "                                                             kinase_name2,\n",
+    "                                                             similarity_measure)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -778,109 +810,109 @@
        "    <tr>\n",
        "      <th>EGFR</th>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.894118</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.317647</td>\n",
-       "      <td>0.447059</td>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.117647</td>\n",
+       "      <td>0.940995</td>\n",
+       "      <td>0.658852</td>\n",
+       "      <td>0.650192</td>\n",
+       "      <td>0.710442</td>\n",
+       "      <td>0.715203</td>\n",
+       "      <td>0.644984</td>\n",
+       "      <td>0.715307</td>\n",
+       "      <td>0.429135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ErbB2</th>\n",
-       "      <td>0.894118</td>\n",
+       "      <td>0.941257</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.329412</td>\n",
-       "      <td>0.423529</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.435294</td>\n",
-       "      <td>0.117647</td>\n",
+       "      <td>0.659334</td>\n",
+       "      <td>0.632521</td>\n",
+       "      <td>0.685368</td>\n",
+       "      <td>0.702937</td>\n",
+       "      <td>0.636820</td>\n",
+       "      <td>0.701761</td>\n",
+       "      <td>0.416966</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BRAF</th>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.400000</td>\n",
+       "      <td>0.653021</td>\n",
+       "      <td>0.651452</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.329412</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.152941</td>\n",
+       "      <td>0.646301</td>\n",
+       "      <td>0.670291</td>\n",
+       "      <td>0.639468</td>\n",
+       "      <td>0.637391</td>\n",
+       "      <td>0.667424</td>\n",
+       "      <td>0.437786</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDK2</th>\n",
-       "      <td>0.317647</td>\n",
-       "      <td>0.329412</td>\n",
-       "      <td>0.329412</td>\n",
+       "      <td>0.648024</td>\n",
+       "      <td>0.629135</td>\n",
+       "      <td>0.649093</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.364706</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.341176</td>\n",
-       "      <td>0.105882</td>\n",
+       "      <td>0.679931</td>\n",
+       "      <td>0.658547</td>\n",
+       "      <td>0.724206</td>\n",
+       "      <td>0.652563</td>\n",
+       "      <td>0.425303</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LCK</th>\n",
-       "      <td>0.447059</td>\n",
-       "      <td>0.423529</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.376471</td>\n",
+       "      <td>0.713619</td>\n",
+       "      <td>0.685906</td>\n",
+       "      <td>0.677195</td>\n",
+       "      <td>0.684067</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.435294</td>\n",
-       "      <td>0.141176</td>\n",
+       "      <td>0.696126</td>\n",
+       "      <td>0.665214</td>\n",
+       "      <td>0.689147</td>\n",
+       "      <td>0.454323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MET</th>\n",
-       "      <td>0.458824</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.364706</td>\n",
-       "      <td>0.400000</td>\n",
+       "      <td>0.709202</td>\n",
+       "      <td>0.694562</td>\n",
+       "      <td>0.638929</td>\n",
+       "      <td>0.655510</td>\n",
+       "      <td>0.687189</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.364706</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.105882</td>\n",
+       "      <td>0.628056</td>\n",
+       "      <td>0.711115</td>\n",
+       "      <td>0.392443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p38a</th>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.376471</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.364706</td>\n",
+       "      <td>0.645767</td>\n",
+       "      <td>0.634558</td>\n",
+       "      <td>0.640101</td>\n",
+       "      <td>0.723386</td>\n",
+       "      <td>0.661388</td>\n",
+       "      <td>0.632620</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.388235</td>\n",
-       "      <td>0.141176</td>\n",
+       "      <td>0.652120</td>\n",
+       "      <td>0.432959</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KDR</th>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.435294</td>\n",
-       "      <td>0.400000</td>\n",
-       "      <td>0.341176</td>\n",
-       "      <td>0.435294</td>\n",
-       "      <td>0.470588</td>\n",
-       "      <td>0.388235</td>\n",
+       "      <td>0.718258</td>\n",
+       "      <td>0.703629</td>\n",
+       "      <td>0.676719</td>\n",
+       "      <td>0.656019</td>\n",
+       "      <td>0.687444</td>\n",
+       "      <td>0.718457</td>\n",
+       "      <td>0.656647</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.152941</td>\n",
+       "      <td>0.424930</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p110a</th>\n",
-       "      <td>0.117647</td>\n",
-       "      <td>0.117647</td>\n",
-       "      <td>0.152941</td>\n",
-       "      <td>0.105882</td>\n",
-       "      <td>0.141176</td>\n",
-       "      <td>0.105882</td>\n",
-       "      <td>0.141176</td>\n",
-       "      <td>0.152941</td>\n",
+       "      <td>0.427023</td>\n",
+       "      <td>0.412055</td>\n",
+       "      <td>0.437192</td>\n",
+       "      <td>0.424418</td>\n",
+       "      <td>0.450303</td>\n",
+       "      <td>0.397434</td>\n",
+       "      <td>0.431120</td>\n",
+       "      <td>0.422578</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -889,318 +921,288 @@
       ],
       "text/plain": [
        "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       "EGFR   1.000000  0.894118  0.376471  0.317647  0.447059  0.458824  0.388235   \n",
-       "ErbB2  0.894118  1.000000  0.400000  0.329412  0.423529  0.470588  0.400000   \n",
-       "BRAF   0.376471  0.400000  1.000000  0.329412  0.388235  0.376471  0.376471   \n",
-       "CDK2   0.317647  0.329412  0.329412  1.000000  0.376471  0.364706  0.470588   \n",
-       "LCK    0.447059  0.423529  0.388235  0.376471  1.000000  0.400000  0.388235   \n",
-       "MET    0.458824  0.470588  0.376471  0.364706  0.400000  1.000000  0.364706   \n",
-       "p38a   0.388235  0.400000  0.376471  0.470588  0.388235  0.364706  1.000000   \n",
-       "KDR    0.470588  0.435294  0.400000  0.341176  0.435294  0.470588  0.388235   \n",
-       "p110a  0.117647  0.117647  0.152941  0.105882  0.141176  0.105882  0.141176   \n",
+       "EGFR   1.000000  0.940995  0.658852  0.650192  0.710442  0.715203  0.644984   \n",
+       "ErbB2  0.941257  1.000000  0.659334  0.632521  0.685368  0.702937  0.636820   \n",
+       "BRAF   0.653021  0.651452  1.000000  0.646301  0.670291  0.639468  0.637391   \n",
+       "CDK2   0.648024  0.629135  0.649093  1.000000  0.679931  0.658547  0.724206   \n",
+       "LCK    0.713619  0.685906  0.677195  0.684067  1.000000  0.696126  0.665214   \n",
+       "MET    0.709202  0.694562  0.638929  0.655510  0.687189  1.000000  0.628056   \n",
+       "p38a   0.645767  0.634558  0.640101  0.723386  0.661388  0.632620  1.000000   \n",
+       "KDR    0.718258  0.703629  0.676719  0.656019  0.687444  0.718457  0.656647   \n",
+       "p110a  0.427023  0.412055  0.437192  0.424418  0.450303  0.397434  0.431120   \n",
        "\n",
        "            KDR     p110a  \n",
-       "EGFR   0.470588  0.117647  \n",
-       "ErbB2  0.435294  0.117647  \n",
-       "BRAF   0.400000  0.152941  \n",
-       "CDK2   0.341176  0.105882  \n",
-       "LCK    0.435294  0.141176  \n",
-       "MET    0.470588  0.105882  \n",
-       "p38a   0.388235  0.141176  \n",
-       "KDR    1.000000  0.152941  \n",
-       "p110a  0.152941  1.000000  "
+       "EGFR   0.715307  0.429135  \n",
+       "ErbB2  0.701761  0.416966  \n",
+       "BRAF   0.667424  0.437786  \n",
+       "CDK2   0.652563  0.425303  \n",
+       "LCK    0.689147  0.454323  \n",
+       "MET    0.711115  0.392443  \n",
+       "p38a   0.652120  0.432959  \n",
+       "KDR    1.000000  0.424930  \n",
+       "p110a  0.422578  1.000000  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
+    "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
     "                                        index=query_kinases,\n",
     "                                        columns=query_kinases)\n",
-    "kinase_similarity_matrix"
+    "kinase_similarity_matrix_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
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-       "<table id=\"T_7fe3e_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_7fe3e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_7fe3e_row0_col2\" class=\"data row0 col2\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row0_col3\" class=\"data row0 col3\" >0.318</td>\n",
-       "      <td id=\"T_7fe3e_row0_col4\" class=\"data row0 col4\" >0.447</td>\n",
-       "      <td id=\"T_7fe3e_row0_col5\" class=\"data row0 col5\" >0.459</td>\n",
-       "      <td id=\"T_7fe3e_row0_col6\" class=\"data row0 col6\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row0_col7\" class=\"data row0 col7\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row0_col8\" class=\"data row0 col8\" >0.118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_7fe3e_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_7fe3e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row1_col2\" class=\"data row1 col2\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row1_col3\" class=\"data row1 col3\" >0.329</td>\n",
-       "      <td id=\"T_7fe3e_row1_col4\" class=\"data row1 col4\" >0.424</td>\n",
-       "      <td id=\"T_7fe3e_row1_col5\" class=\"data row1 col5\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row1_col6\" class=\"data row1 col6\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row1_col7\" class=\"data row1 col7\" >0.435</td>\n",
-       "      <td id=\"T_7fe3e_row1_col8\" class=\"data row1 col8\" >0.118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_7fe3e_row2_col0\" class=\"data row2 col0\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row2_col1\" class=\"data row2 col1\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row2_col3\" class=\"data row2 col3\" >0.329</td>\n",
-       "      <td id=\"T_7fe3e_row2_col4\" class=\"data row2 col4\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row2_col5\" class=\"data row2 col5\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row2_col6\" class=\"data row2 col6\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row2_col7\" class=\"data row2 col7\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row2_col8\" class=\"data row2 col8\" >0.153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_7fe3e_row3_col0\" class=\"data row3 col0\" >0.318</td>\n",
-       "      <td id=\"T_7fe3e_row3_col1\" class=\"data row3 col1\" >0.329</td>\n",
-       "      <td id=\"T_7fe3e_row3_col2\" class=\"data row3 col2\" >0.329</td>\n",
-       "      <td id=\"T_7fe3e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row3_col4\" class=\"data row3 col4\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row3_col5\" class=\"data row3 col5\" >0.365</td>\n",
-       "      <td id=\"T_7fe3e_row3_col6\" class=\"data row3 col6\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row3_col7\" class=\"data row3 col7\" >0.341</td>\n",
-       "      <td id=\"T_7fe3e_row3_col8\" class=\"data row3 col8\" >0.106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_7fe3e_row4_col0\" class=\"data row4 col0\" >0.447</td>\n",
-       "      <td id=\"T_7fe3e_row4_col1\" class=\"data row4 col1\" >0.424</td>\n",
-       "      <td id=\"T_7fe3e_row4_col2\" class=\"data row4 col2\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row4_col3\" class=\"data row4 col3\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row4_col5\" class=\"data row4 col5\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row4_col7\" class=\"data row4 col7\" >0.435</td>\n",
-       "      <td id=\"T_7fe3e_row4_col8\" class=\"data row4 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_7fe3e_row5_col0\" class=\"data row5 col0\" >0.459</td>\n",
-       "      <td id=\"T_7fe3e_row5_col1\" class=\"data row5 col1\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row5_col2\" class=\"data row5 col2\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row5_col3\" class=\"data row5 col3\" >0.365</td>\n",
-       "      <td id=\"T_7fe3e_row5_col4\" class=\"data row5 col4\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row5_col6\" class=\"data row5 col6\" >0.365</td>\n",
-       "      <td id=\"T_7fe3e_row5_col7\" class=\"data row5 col7\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row5_col8\" class=\"data row5 col8\" >0.106</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_7fe3e_row6_col0\" class=\"data row6 col0\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row6_col1\" class=\"data row6 col1\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row6_col2\" class=\"data row6 col2\" >0.376</td>\n",
-       "      <td id=\"T_7fe3e_row6_col3\" class=\"data row6 col3\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row6_col5\" class=\"data row6 col5\" >0.365</td>\n",
-       "      <td id=\"T_7fe3e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row6_col7\" class=\"data row6 col7\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row6_col8\" class=\"data row6 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_7fe3e_row7_col0\" class=\"data row7 col0\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row7_col1\" class=\"data row7 col1\" >0.435</td>\n",
-       "      <td id=\"T_7fe3e_row7_col2\" class=\"data row7 col2\" >0.400</td>\n",
-       "      <td id=\"T_7fe3e_row7_col3\" class=\"data row7 col3\" >0.341</td>\n",
-       "      <td id=\"T_7fe3e_row7_col4\" class=\"data row7 col4\" >0.435</td>\n",
-       "      <td id=\"T_7fe3e_row7_col5\" class=\"data row7 col5\" >0.471</td>\n",
-       "      <td id=\"T_7fe3e_row7_col6\" class=\"data row7 col6\" >0.388</td>\n",
-       "      <td id=\"T_7fe3e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_7fe3e_row7_col8\" class=\"data row7 col8\" >0.153</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_7fe3e_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_7fe3e_row8_col0\" class=\"data row8 col0\" >0.118</td>\n",
-       "      <td id=\"T_7fe3e_row8_col1\" class=\"data row8 col1\" >0.118</td>\n",
-       "      <td id=\"T_7fe3e_row8_col2\" class=\"data row8 col2\" >0.153</td>\n",
-       "      <td id=\"T_7fe3e_row8_col3\" class=\"data row8 col3\" >0.106</td>\n",
-       "      <td id=\"T_7fe3e_row8_col4\" class=\"data row8 col4\" >0.141</td>\n",
-       "      <td id=\"T_7fe3e_row8_col5\" class=\"data row8 col5\" >0.106</td>\n",
-       "      <td id=\"T_7fe3e_row8_col6\" class=\"data row8 col6\" >0.141</td>\n",
-       "      <td id=\"T_7fe3e_row8_col7\" class=\"data row8 col7\" >0.153</td>\n",
-       "      <td id=\"T_7fe3e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
+       "#T_33654_row0_col0,#T_33654_row1_col1,#T_33654_row2_col2,#T_33654_row3_col3,#T_33654_row4_col4,#T_33654_row5_col5,#T_33654_row6_col6,#T_33654_row7_col7,#T_33654_row8_col8{\n",
+       "            background-color:  #008000;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_33654_row0_col1{\n",
+       "            background-color:  #178b17;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #8fc68f;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row0_col4{\n",
+       "            background-color:  #7bbc7b;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row0_col5{\n",
+       "            background-color:  #6eb66e;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row0_col6,#T_33654_row3_col2,#T_33654_row6_col1{\n",
+       "            background-color:  #92c892;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #74b974;\n",
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+       "            background-color:  #ddecdd;\n",
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+       "            background-color:  #188c18;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #8ec58e;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row1_col3,#T_33654_row1_col6,#T_33654_row2_col6,#T_33654_row6_col2{\n",
+       "            background-color:  #96c996;\n",
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+       "            background-color:  #8bc48b;\n",
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+       "            background-color:  #90c790;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row2_col4,#T_33654_row2_col5,#T_33654_row5_col3{\n",
+       "            background-color:  #8dc58d;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row2_col7,#T_33654_row7_col2{\n",
+       "            background-color:  #87c287;\n",
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+       "            background-color:  #d9ead9;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row3_col1{\n",
+       "            background-color:  #94c994;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row3_col4{\n",
+       "            background-color:  #89c389;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row3_col5,#T_33654_row5_col4,#T_33654_row7_col4{\n",
+       "            background-color:  #85c185;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row3_col6{\n",
+       "            background-color:  #72b872;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #dfeddf;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row4_col0{\n",
+       "            background-color:  #75b975;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row4_col1{\n",
+       "            background-color:  #7dbd7d;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row4_col3{\n",
+       "            background-color:  #81bf81;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row4_col5,#T_33654_row5_col0,#T_33654_row7_col1{\n",
+       "            background-color:  #77ba77;\n",
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+       "            background-color:  #8ac48a;\n",
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+       "        }#T_33654_row4_col7{\n",
+       "            background-color:  #7ebe7e;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row4_col8{\n",
+       "            background-color:  #d3e7d3;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row5_col2{\n",
+       "            background-color:  #97ca97;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #9acb9a;\n",
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+       "        }#T_33654_row5_col7{\n",
+       "            background-color:  #76ba76;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row5_col8,#T_33654_row8_col0,#T_33654_row8_col1,#T_33654_row8_col2,#T_33654_row8_col3,#T_33654_row8_col4,#T_33654_row8_col5,#T_33654_row8_col6,#T_33654_row8_col7{\n",
+       "            background-color:  #ebf3eb;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row6_col0{\n",
+       "            background-color:  #91c791;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row6_col3{\n",
+       "            background-color:  #71b771;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row6_col8{\n",
+       "            background-color:  #dbebdb;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row7_col0{\n",
+       "            background-color:  #73b873;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row7_col3{\n",
+       "            background-color:  #8cc58c;\n",
+       "            color:  #000000;\n",
+       "        }#T_33654_row7_col5{\n",
+       "            background-color:  #6db66d;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_33654_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_33654_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_33654_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "                        <td id=\"T_33654_row0_col2\" class=\"data row0 col2\" >0.659</td>\n",
+       "                        <td id=\"T_33654_row0_col3\" class=\"data row0 col3\" >0.650</td>\n",
+       "                        <td id=\"T_33654_row0_col4\" class=\"data row0 col4\" >0.710</td>\n",
+       "                        <td id=\"T_33654_row0_col5\" class=\"data row0 col5\" >0.715</td>\n",
+       "                        <td id=\"T_33654_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
+       "                        <td id=\"T_33654_row0_col7\" class=\"data row0 col7\" >0.715</td>\n",
+       "                        <td id=\"T_33654_row0_col8\" class=\"data row0 col8\" >0.429</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_33654_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "                        <td id=\"T_33654_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row1_col2\" class=\"data row1 col2\" >0.659</td>\n",
+       "                        <td id=\"T_33654_row1_col3\" class=\"data row1 col3\" >0.633</td>\n",
+       "                        <td id=\"T_33654_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
+       "                        <td id=\"T_33654_row1_col5\" class=\"data row1 col5\" >0.703</td>\n",
+       "                        <td id=\"T_33654_row1_col6\" class=\"data row1 col6\" >0.637</td>\n",
+       "                        <td id=\"T_33654_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
+       "                        <td id=\"T_33654_row1_col8\" class=\"data row1 col8\" >0.417</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_33654_row2_col0\" class=\"data row2 col0\" >0.653</td>\n",
+       "                        <td id=\"T_33654_row2_col1\" class=\"data row2 col1\" >0.651</td>\n",
+       "                        <td id=\"T_33654_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row2_col3\" class=\"data row2 col3\" >0.646</td>\n",
+       "                        <td id=\"T_33654_row2_col4\" class=\"data row2 col4\" >0.670</td>\n",
+       "                        <td id=\"T_33654_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
+       "                        <td id=\"T_33654_row2_col6\" class=\"data row2 col6\" >0.637</td>\n",
+       "                        <td id=\"T_33654_row2_col7\" class=\"data row2 col7\" >0.667</td>\n",
+       "                        <td id=\"T_33654_row2_col8\" class=\"data row2 col8\" >0.438</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_33654_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
+       "                        <td id=\"T_33654_row3_col1\" class=\"data row3 col1\" >0.629</td>\n",
+       "                        <td id=\"T_33654_row3_col2\" class=\"data row3 col2\" >0.649</td>\n",
+       "                        <td id=\"T_33654_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row3_col4\" class=\"data row3 col4\" >0.680</td>\n",
+       "                        <td id=\"T_33654_row3_col5\" class=\"data row3 col5\" >0.659</td>\n",
+       "                        <td id=\"T_33654_row3_col6\" class=\"data row3 col6\" >0.724</td>\n",
+       "                        <td id=\"T_33654_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
+       "                        <td id=\"T_33654_row3_col8\" class=\"data row3 col8\" >0.425</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_33654_row4_col0\" class=\"data row4 col0\" >0.714</td>\n",
+       "                        <td id=\"T_33654_row4_col1\" class=\"data row4 col1\" >0.686</td>\n",
+       "                        <td id=\"T_33654_row4_col2\" class=\"data row4 col2\" >0.677</td>\n",
+       "                        <td id=\"T_33654_row4_col3\" class=\"data row4 col3\" >0.684</td>\n",
+       "                        <td id=\"T_33654_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row4_col5\" class=\"data row4 col5\" >0.696</td>\n",
+       "                        <td id=\"T_33654_row4_col6\" class=\"data row4 col6\" >0.665</td>\n",
+       "                        <td id=\"T_33654_row4_col7\" class=\"data row4 col7\" >0.689</td>\n",
+       "                        <td id=\"T_33654_row4_col8\" class=\"data row4 col8\" >0.454</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_33654_row5_col0\" class=\"data row5 col0\" >0.709</td>\n",
+       "                        <td id=\"T_33654_row5_col1\" class=\"data row5 col1\" >0.695</td>\n",
+       "                        <td id=\"T_33654_row5_col2\" class=\"data row5 col2\" >0.639</td>\n",
+       "                        <td id=\"T_33654_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
+       "                        <td id=\"T_33654_row5_col4\" class=\"data row5 col4\" >0.687</td>\n",
+       "                        <td id=\"T_33654_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row5_col6\" class=\"data row5 col6\" >0.628</td>\n",
+       "                        <td id=\"T_33654_row5_col7\" class=\"data row5 col7\" >0.711</td>\n",
+       "                        <td id=\"T_33654_row5_col8\" class=\"data row5 col8\" >0.392</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_33654_row6_col0\" class=\"data row6 col0\" >0.646</td>\n",
+       "                        <td id=\"T_33654_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
+       "                        <td id=\"T_33654_row6_col2\" class=\"data row6 col2\" >0.640</td>\n",
+       "                        <td id=\"T_33654_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
+       "                        <td id=\"T_33654_row6_col4\" class=\"data row6 col4\" >0.661</td>\n",
+       "                        <td id=\"T_33654_row6_col5\" class=\"data row6 col5\" >0.633</td>\n",
+       "                        <td id=\"T_33654_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row6_col7\" class=\"data row6 col7\" >0.652</td>\n",
+       "                        <td id=\"T_33654_row6_col8\" class=\"data row6 col8\" >0.433</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_33654_row7_col0\" class=\"data row7 col0\" >0.718</td>\n",
+       "                        <td id=\"T_33654_row7_col1\" class=\"data row7 col1\" >0.704</td>\n",
+       "                        <td id=\"T_33654_row7_col2\" class=\"data row7 col2\" >0.677</td>\n",
+       "                        <td id=\"T_33654_row7_col3\" class=\"data row7 col3\" >0.656</td>\n",
+       "                        <td id=\"T_33654_row7_col4\" class=\"data row7 col4\" >0.687</td>\n",
+       "                        <td id=\"T_33654_row7_col5\" class=\"data row7 col5\" >0.718</td>\n",
+       "                        <td id=\"T_33654_row7_col6\" class=\"data row7 col6\" >0.657</td>\n",
+       "                        <td id=\"T_33654_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_33654_row7_col8\" class=\"data row7 col8\" >0.425</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_33654_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_33654_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
+       "                        <td id=\"T_33654_row8_col1\" class=\"data row8 col1\" >0.412</td>\n",
+       "                        <td id=\"T_33654_row8_col2\" class=\"data row8 col2\" >0.437</td>\n",
+       "                        <td id=\"T_33654_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
+       "                        <td id=\"T_33654_row8_col4\" class=\"data row8 col4\" >0.450</td>\n",
+       "                        <td id=\"T_33654_row8_col5\" class=\"data row8 col5\" >0.397</td>\n",
+       "                        <td id=\"T_33654_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
+       "                        <td id=\"T_33654_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
+       "                        <td id=\"T_33654_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc7a4b84760>"
+       "<pandas.io.formats.style.Styler at 0x7f6365e71ca0>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1208,7 +1210,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix.style.\\\n",
+    "kinase_similarity_matrix_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
@@ -1222,11 +1224,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_similarity_matrix.to_csv(DATA / \"kinase_similarity_matrix_sequence.csv\")"
+    "f_name = f\"kinase_similarity_{similarity_measure}_matrix_sequence.csv\"\n",
+    "kinase_similarity_matrix_df.to_csv(DATA / f_name)"
    ]
   },
   {
@@ -1247,312 +1250,286 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The values of the similarity matrix lie between: 0.11 and 1.00\n"
+      "The values of the similarity matrix lie between: 0.39 and 1.00\n"
      ]
     }
    ],
    "source": [
     "print(f\"The values of the similarity matrix lie between: \"\n",
-    "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
-    "      f\" and {kinase_similarity_matrix.max().max():.2f}\")"
+    "      f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
+    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix = 1 - kinase_similarity_matrix"
+    "kinase_distance_matrix_df = 1 - kinase_similarity_matrix_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_8a0c0_row5_col6 {\n",
-       "  background-color: #3d9e3d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_8a0c0_row6_col0 {\n",
-       "  background-color: #48a348;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_8a0c0_row7_col1 {\n",
-       "  background-color: #55a955;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_8a0c0_row7_col4 {\n",
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-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_8a0c0_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_8a0c0_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row0_col1\" class=\"data row0 col1\" >0.106</td>\n",
-       "      <td id=\"T_8a0c0_row0_col2\" class=\"data row0 col2\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row0_col3\" class=\"data row0 col3\" >0.682</td>\n",
-       "      <td id=\"T_8a0c0_row0_col4\" class=\"data row0 col4\" >0.553</td>\n",
-       "      <td id=\"T_8a0c0_row0_col5\" class=\"data row0 col5\" >0.541</td>\n",
-       "      <td id=\"T_8a0c0_row0_col6\" class=\"data row0 col6\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row0_col7\" class=\"data row0 col7\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row0_col8\" class=\"data row0 col8\" >0.882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_8a0c0_row1_col0\" class=\"data row1 col0\" >0.106</td>\n",
-       "      <td id=\"T_8a0c0_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row1_col2\" class=\"data row1 col2\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row1_col3\" class=\"data row1 col3\" >0.671</td>\n",
-       "      <td id=\"T_8a0c0_row1_col4\" class=\"data row1 col4\" >0.576</td>\n",
-       "      <td id=\"T_8a0c0_row1_col5\" class=\"data row1 col5\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row1_col6\" class=\"data row1 col6\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row1_col7\" class=\"data row1 col7\" >0.565</td>\n",
-       "      <td id=\"T_8a0c0_row1_col8\" class=\"data row1 col8\" >0.882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_8a0c0_row2_col0\" class=\"data row2 col0\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row2_col1\" class=\"data row2 col1\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row2_col3\" class=\"data row2 col3\" >0.671</td>\n",
-       "      <td id=\"T_8a0c0_row2_col4\" class=\"data row2 col4\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row2_col5\" class=\"data row2 col5\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row2_col6\" class=\"data row2 col6\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row2_col7\" class=\"data row2 col7\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row2_col8\" class=\"data row2 col8\" >0.847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_8a0c0_row3_col0\" class=\"data row3 col0\" >0.682</td>\n",
-       "      <td id=\"T_8a0c0_row3_col1\" class=\"data row3 col1\" >0.671</td>\n",
-       "      <td id=\"T_8a0c0_row3_col2\" class=\"data row3 col2\" >0.671</td>\n",
-       "      <td id=\"T_8a0c0_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row3_col4\" class=\"data row3 col4\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row3_col5\" class=\"data row3 col5\" >0.635</td>\n",
-       "      <td id=\"T_8a0c0_row3_col6\" class=\"data row3 col6\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row3_col7\" class=\"data row3 col7\" >0.659</td>\n",
-       "      <td id=\"T_8a0c0_row3_col8\" class=\"data row3 col8\" >0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_8a0c0_row4_col0\" class=\"data row4 col0\" >0.553</td>\n",
-       "      <td id=\"T_8a0c0_row4_col1\" class=\"data row4 col1\" >0.576</td>\n",
-       "      <td id=\"T_8a0c0_row4_col2\" class=\"data row4 col2\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row4_col3\" class=\"data row4 col3\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row4_col5\" class=\"data row4 col5\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row4_col6\" class=\"data row4 col6\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row4_col7\" class=\"data row4 col7\" >0.565</td>\n",
-       "      <td id=\"T_8a0c0_row4_col8\" class=\"data row4 col8\" >0.859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_8a0c0_row5_col0\" class=\"data row5 col0\" >0.541</td>\n",
-       "      <td id=\"T_8a0c0_row5_col1\" class=\"data row5 col1\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row5_col2\" class=\"data row5 col2\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row5_col3\" class=\"data row5 col3\" >0.635</td>\n",
-       "      <td id=\"T_8a0c0_row5_col4\" class=\"data row5 col4\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row5_col6\" class=\"data row5 col6\" >0.635</td>\n",
-       "      <td id=\"T_8a0c0_row5_col7\" class=\"data row5 col7\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row5_col8\" class=\"data row5 col8\" >0.894</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_8a0c0_row6_col0\" class=\"data row6 col0\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row6_col1\" class=\"data row6 col1\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row6_col2\" class=\"data row6 col2\" >0.624</td>\n",
-       "      <td id=\"T_8a0c0_row6_col3\" class=\"data row6 col3\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row6_col4\" class=\"data row6 col4\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row6_col5\" class=\"data row6 col5\" >0.635</td>\n",
-       "      <td id=\"T_8a0c0_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row6_col7\" class=\"data row6 col7\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row6_col8\" class=\"data row6 col8\" >0.859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_8a0c0_row7_col0\" class=\"data row7 col0\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row7_col1\" class=\"data row7 col1\" >0.565</td>\n",
-       "      <td id=\"T_8a0c0_row7_col2\" class=\"data row7 col2\" >0.600</td>\n",
-       "      <td id=\"T_8a0c0_row7_col3\" class=\"data row7 col3\" >0.659</td>\n",
-       "      <td id=\"T_8a0c0_row7_col4\" class=\"data row7 col4\" >0.565</td>\n",
-       "      <td id=\"T_8a0c0_row7_col5\" class=\"data row7 col5\" >0.529</td>\n",
-       "      <td id=\"T_8a0c0_row7_col6\" class=\"data row7 col6\" >0.612</td>\n",
-       "      <td id=\"T_8a0c0_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_8a0c0_row7_col8\" class=\"data row7 col8\" >0.847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8a0c0_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_8a0c0_row8_col0\" class=\"data row8 col0\" >0.882</td>\n",
-       "      <td id=\"T_8a0c0_row8_col1\" class=\"data row8 col1\" >0.882</td>\n",
-       "      <td id=\"T_8a0c0_row8_col2\" class=\"data row8 col2\" >0.847</td>\n",
-       "      <td id=\"T_8a0c0_row8_col3\" class=\"data row8 col3\" >0.894</td>\n",
-       "      <td id=\"T_8a0c0_row8_col4\" class=\"data row8 col4\" >0.859</td>\n",
-       "      <td id=\"T_8a0c0_row8_col5\" class=\"data row8 col5\" >0.894</td>\n",
-       "      <td id=\"T_8a0c0_row8_col6\" class=\"data row8 col6\" >0.859</td>\n",
-       "      <td id=\"T_8a0c0_row8_col7\" class=\"data row8 col7\" >0.847</td>\n",
-       "      <td id=\"T_8a0c0_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
+       "#T_b338a_row0_col0,#T_b338a_row1_col1,#T_b338a_row2_col2,#T_b338a_row3_col3,#T_b338a_row4_col4,#T_b338a_row5_col5,#T_b338a_row6_col6,#T_b338a_row7_col7,#T_b338a_row8_col8{\n",
+       "            background-color:  #ebf3eb;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col1{\n",
+       "            background-color:  #d4e8d4;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col2,#T_b338a_row0_col3,#T_b338a_row2_col0{\n",
+       "            background-color:  #5cad5c;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col4{\n",
+       "            background-color:  #6fb76f;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col5{\n",
+       "            background-color:  #7cbd7c;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col6,#T_b338a_row3_col2,#T_b338a_row6_col1{\n",
+       "            background-color:  #58ab58;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col7,#T_b338a_row1_col5{\n",
+       "            background-color:  #77ba77;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row0_col8{\n",
+       "            background-color:  #0e870e;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row1_col0{\n",
+       "            background-color:  #d3e7d3;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row1_col2,#T_b338a_row3_col7,#T_b338a_row6_col7,#T_b338a_row7_col6{\n",
+       "            background-color:  #5dae5d;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #55a955;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row1_col4,#T_b338a_row4_col2{\n",
+       "            background-color:  #64b164;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row1_col7,#T_b338a_row5_col1{\n",
+       "            background-color:  #71b771;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row1_col8{\n",
+       "            background-color:  #098509;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row2_col1{\n",
+       "            background-color:  #60af60;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row2_col3,#T_b338a_row3_col0,#T_b338a_row6_col4{\n",
+       "            background-color:  #5aac5a;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row2_col4,#T_b338a_row2_col5,#T_b338a_row5_col3{\n",
+       "            background-color:  #5eae5e;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row2_col7,#T_b338a_row7_col2{\n",
+       "            background-color:  #63b163;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row2_col8{\n",
+       "            background-color:  #118911;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row3_col1{\n",
+       "            background-color:  #56aa56;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row3_col4{\n",
+       "            background-color:  #61b061;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row3_col5,#T_b338a_row5_col4,#T_b338a_row7_col4{\n",
+       "            background-color:  #65b265;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row3_col6,#T_b338a_row7_col0{\n",
+       "            background-color:  #78bb78;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row3_col8,#T_b338a_row7_col8{\n",
+       "            background-color:  #0c860c;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row4_col0{\n",
+       "            background-color:  #76ba76;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col1{\n",
+       "            background-color:  #6db66d;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col3{\n",
+       "            background-color:  #6ab46a;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col5,#T_b338a_row5_col0,#T_b338a_row7_col1{\n",
+       "            background-color:  #74b974;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col6{\n",
+       "            background-color:  #61af61;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col7{\n",
+       "            background-color:  #6db56d;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row4_col8{\n",
+       "            background-color:  #188c18;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row5_col2{\n",
+       "            background-color:  #54a954;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row5_col6{\n",
+       "            background-color:  #51a851;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row5_col7{\n",
+       "            background-color:  #75b975;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row5_col8,#T_b338a_row8_col0,#T_b338a_row8_col1,#T_b338a_row8_col2,#T_b338a_row8_col3,#T_b338a_row8_col4,#T_b338a_row8_col5,#T_b338a_row8_col6,#T_b338a_row8_col7{\n",
+       "            background-color:  #008000;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row6_col0{\n",
+       "            background-color:  #59ac59;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row6_col3{\n",
+       "            background-color:  #79bc79;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row6_col5{\n",
+       "            background-color:  #5bad5b;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row6_col8{\n",
+       "            background-color:  #108810;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b338a_row7_col3{\n",
+       "            background-color:  #5fae5f;\n",
+       "            color:  #000000;\n",
+       "        }#T_b338a_row7_col5{\n",
+       "            background-color:  #7dbd7d;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_b338a_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_b338a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_b338a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "                        <td id=\"T_b338a_row0_col2\" class=\"data row0 col2\" >0.341</td>\n",
+       "                        <td id=\"T_b338a_row0_col3\" class=\"data row0 col3\" >0.350</td>\n",
+       "                        <td id=\"T_b338a_row0_col4\" class=\"data row0 col4\" >0.290</td>\n",
+       "                        <td id=\"T_b338a_row0_col5\" class=\"data row0 col5\" >0.285</td>\n",
+       "                        <td id=\"T_b338a_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
+       "                        <td id=\"T_b338a_row0_col7\" class=\"data row0 col7\" >0.285</td>\n",
+       "                        <td id=\"T_b338a_row0_col8\" class=\"data row0 col8\" >0.571</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_b338a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "                        <td id=\"T_b338a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row1_col2\" class=\"data row1 col2\" >0.341</td>\n",
+       "                        <td id=\"T_b338a_row1_col3\" class=\"data row1 col3\" >0.367</td>\n",
+       "                        <td id=\"T_b338a_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
+       "                        <td id=\"T_b338a_row1_col5\" class=\"data row1 col5\" >0.297</td>\n",
+       "                        <td id=\"T_b338a_row1_col6\" class=\"data row1 col6\" >0.363</td>\n",
+       "                        <td id=\"T_b338a_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
+       "                        <td id=\"T_b338a_row1_col8\" class=\"data row1 col8\" >0.583</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_b338a_row2_col0\" class=\"data row2 col0\" >0.347</td>\n",
+       "                        <td id=\"T_b338a_row2_col1\" class=\"data row2 col1\" >0.349</td>\n",
+       "                        <td id=\"T_b338a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row2_col3\" class=\"data row2 col3\" >0.354</td>\n",
+       "                        <td id=\"T_b338a_row2_col4\" class=\"data row2 col4\" >0.330</td>\n",
+       "                        <td id=\"T_b338a_row2_col5\" class=\"data row2 col5\" >0.361</td>\n",
+       "                        <td id=\"T_b338a_row2_col6\" class=\"data row2 col6\" >0.363</td>\n",
+       "                        <td id=\"T_b338a_row2_col7\" class=\"data row2 col7\" >0.333</td>\n",
+       "                        <td id=\"T_b338a_row2_col8\" class=\"data row2 col8\" >0.562</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_b338a_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
+       "                        <td id=\"T_b338a_row3_col1\" class=\"data row3 col1\" >0.371</td>\n",
+       "                        <td id=\"T_b338a_row3_col2\" class=\"data row3 col2\" >0.351</td>\n",
+       "                        <td id=\"T_b338a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row3_col4\" class=\"data row3 col4\" >0.320</td>\n",
+       "                        <td id=\"T_b338a_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "                        <td id=\"T_b338a_row3_col6\" class=\"data row3 col6\" >0.276</td>\n",
+       "                        <td id=\"T_b338a_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
+       "                        <td id=\"T_b338a_row3_col8\" class=\"data row3 col8\" >0.575</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_b338a_row4_col0\" class=\"data row4 col0\" >0.286</td>\n",
+       "                        <td id=\"T_b338a_row4_col1\" class=\"data row4 col1\" >0.314</td>\n",
+       "                        <td id=\"T_b338a_row4_col2\" class=\"data row4 col2\" >0.323</td>\n",
+       "                        <td id=\"T_b338a_row4_col3\" class=\"data row4 col3\" >0.316</td>\n",
+       "                        <td id=\"T_b338a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row4_col5\" class=\"data row4 col5\" >0.304</td>\n",
+       "                        <td id=\"T_b338a_row4_col6\" class=\"data row4 col6\" >0.335</td>\n",
+       "                        <td id=\"T_b338a_row4_col7\" class=\"data row4 col7\" >0.311</td>\n",
+       "                        <td id=\"T_b338a_row4_col8\" class=\"data row4 col8\" >0.546</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_b338a_row5_col0\" class=\"data row5 col0\" >0.291</td>\n",
+       "                        <td id=\"T_b338a_row5_col1\" class=\"data row5 col1\" >0.305</td>\n",
+       "                        <td id=\"T_b338a_row5_col2\" class=\"data row5 col2\" >0.361</td>\n",
+       "                        <td id=\"T_b338a_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
+       "                        <td id=\"T_b338a_row5_col4\" class=\"data row5 col4\" >0.313</td>\n",
+       "                        <td id=\"T_b338a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row5_col6\" class=\"data row5 col6\" >0.372</td>\n",
+       "                        <td id=\"T_b338a_row5_col7\" class=\"data row5 col7\" >0.289</td>\n",
+       "                        <td id=\"T_b338a_row5_col8\" class=\"data row5 col8\" >0.608</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_b338a_row6_col0\" class=\"data row6 col0\" >0.354</td>\n",
+       "                        <td id=\"T_b338a_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
+       "                        <td id=\"T_b338a_row6_col2\" class=\"data row6 col2\" >0.360</td>\n",
+       "                        <td id=\"T_b338a_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
+       "                        <td id=\"T_b338a_row6_col4\" class=\"data row6 col4\" >0.339</td>\n",
+       "                        <td id=\"T_b338a_row6_col5\" class=\"data row6 col5\" >0.367</td>\n",
+       "                        <td id=\"T_b338a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row6_col7\" class=\"data row6 col7\" >0.348</td>\n",
+       "                        <td id=\"T_b338a_row6_col8\" class=\"data row6 col8\" >0.567</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_b338a_row7_col0\" class=\"data row7 col0\" >0.282</td>\n",
+       "                        <td id=\"T_b338a_row7_col1\" class=\"data row7 col1\" >0.296</td>\n",
+       "                        <td id=\"T_b338a_row7_col2\" class=\"data row7 col2\" >0.323</td>\n",
+       "                        <td id=\"T_b338a_row7_col3\" class=\"data row7 col3\" >0.344</td>\n",
+       "                        <td id=\"T_b338a_row7_col4\" class=\"data row7 col4\" >0.313</td>\n",
+       "                        <td id=\"T_b338a_row7_col5\" class=\"data row7 col5\" >0.282</td>\n",
+       "                        <td id=\"T_b338a_row7_col6\" class=\"data row7 col6\" >0.343</td>\n",
+       "                        <td id=\"T_b338a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "                        <td id=\"T_b338a_row7_col8\" class=\"data row7 col8\" >0.575</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b338a_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_b338a_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
+       "                        <td id=\"T_b338a_row8_col1\" class=\"data row8 col1\" >0.588</td>\n",
+       "                        <td id=\"T_b338a_row8_col2\" class=\"data row8 col2\" >0.563</td>\n",
+       "                        <td id=\"T_b338a_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
+       "                        <td id=\"T_b338a_row8_col4\" class=\"data row8 col4\" >0.550</td>\n",
+       "                        <td id=\"T_b338a_row8_col5\" class=\"data row8 col5\" >0.603</td>\n",
+       "                        <td id=\"T_b338a_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
+       "                        <td id=\"T_b338a_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
+       "                        <td id=\"T_b338a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc7a4fcc100>"
+       "<pandas.io.formats.style.Styler at 0x7f63654bf100>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_distance_matrix.style.\\\n",
+    "kinase_distance_matrix_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
@@ -1566,12 +1543,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix.to_csv(DATA /\n",
-    "                              \"kinase_distance_matrix_sequence.csv\")"
+    "f_name = f\"kinase_distance_{similarity_measure}_matrix_sequence.csv\"\n",
+    "kinase_distance_matrix_df.to_csv(DATA / f_name)"
    ]
   },
   {
@@ -1580,7 +1557,7 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we investigate how sequences can be used to measure similarity between kinases. The focus is made of the pocket sequence, which is retrieve from KLIFS. Sequence similarity can be assessed using two scores: 1. the identity, which treats all amino acids uniformly, and 2. the substitution, which takes into account the rate of change of residues over evolutionary time.\n",
+    "In this talktorial, we investigate how sequences can be used to measure similarity between kinases. The focus is set on the pocket sequence, which is retrieved from KLIFS. Sequence similarity can be assessed using two scores: 1. the identity, which treats all amino acids uniformly, and 2. the substitution, which takes into account the rate of change of residues over evolutionary time.\n",
     "\n",
     "The kinase similarity matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket sequence perspective we have talked about in this talktorial."
    ]
@@ -1599,7 +1576,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1613,7 +1590,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From 3271321d5aacf95568f1881bd2a10fd53e9bb10f Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Tue, 21 Sep 2021 14:07:58 +0200
Subject: [PATCH 059/158] small adaptions to T025

---
 .../talktorial.ipynb                          | 1183 ++++++++++++-----
 1 file changed, 835 insertions(+), 348 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index f85a137d..f2068fb8 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -23,7 +23,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "We will assess the similarity between a set of kinases based on the KiSSim fingerprint, which describes the physicochemical and spatial properties in structurally resolved kinases."
+    "We will assess the similarity between a set of kinases from a structural point of view using the [KiSSim](https://kissim.readthedocs.io/en/latest/) fingerprint. KiSSim describes the physicochemical and spatial properties in structurally resolved kinases."
    ]
   },
   {
@@ -43,6 +43,7 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
+    "* Define the kinases of interest\n",
     "* Retrieve and preprocess data\n",
     "* Show kinase coverage\n",
     "* Load KiSSim fingerprints\n",
@@ -94,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
     "\n",
     "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
     "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
@@ -123,7 +124,7 @@
     "\n",
     "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
     "\n",
-    "We here use the novel KiSSim (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
+    "We here use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
    ]
   },
   {
@@ -178,7 +179,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -204,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve and preprocess data"
+    "### Define the kinases of interest"
    ]
   },
   {
@@ -238,6 +247,13 @@
     "]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -265,7 +281,16 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    }
+   ],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -286,7 +311,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 2466\n",
+      "Number of structures: 2484\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
@@ -297,6 +322,284 @@
     "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's have a look at what is stored in the structures dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['structure.klifs_id', 'structure.pdb_id', 'structure.alternate_model',\n",
+       "       'structure.chain', 'species.klifs', 'kinase.klifs_id',\n",
+       "       'kinase.klifs_name', 'kinase.family', 'kinase.group',\n",
+       "       'structure.pocket', 'ligand.expo_id', 'ligand_allosteric.expo_id',\n",
+       "       'ligand.name', 'ligand_allosteric.name', 'structure.dfg',\n",
+       "       'structure.ac_helix', 'structure.resolution', 'structure.qualityscore',\n",
+       "       'structure.missing_residues', 'structure.missing_atoms',\n",
+       "       'structure.rmsd1', 'structure.rmsd2', 'structure.front',\n",
+       "       'structure.gate', 'structure.back', 'structure.fp_i', 'structure.fp_ii',\n",
+       "       'structure.bp_i_a', 'structure.bp_i_b', 'structure.bp_ii_in',\n",
+       "       'structure.bp_ii_a_in', 'structure.bp_ii_b_in', 'structure.bp_ii_out',\n",
+       "       'structure.bp_ii_b', 'structure.bp_iii', 'structure.bp_iv',\n",
+       "       'structure.bp_v', 'structure.grich_distance', 'structure.grich_angle',\n",
+       "       'structure.grich_rotation', 'structure.filepath'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "structures.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>structure.pdb_id</th>\n",
+       "      <th>structure.alternate_model</th>\n",
+       "      <th>structure.chain</th>\n",
+       "      <th>species.klifs</th>\n",
+       "      <th>kinase.klifs_id</th>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>kinase.family</th>\n",
+       "      <th>kinase.group</th>\n",
+       "      <th>structure.pocket</th>\n",
+       "      <th>...</th>\n",
+       "      <th>structure.bp_ii_b_in</th>\n",
+       "      <th>structure.bp_ii_out</th>\n",
+       "      <th>structure.bp_ii_b</th>\n",
+       "      <th>structure.bp_iii</th>\n",
+       "      <th>structure.bp_iv</th>\n",
+       "      <th>structure.bp_v</th>\n",
+       "      <th>structure.grich_distance</th>\n",
+       "      <th>structure.grich_angle</th>\n",
+       "      <th>structure.grich_rotation</th>\n",
+       "      <th>structure.filepath</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4221</td>\n",
+       "      <td>4eor</td>\n",
+       "      <td>A</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>198</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>14.9071</td>\n",
+       "      <td>50.806301</td>\n",
+       "      <td>71.737297</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>11794</td>\n",
+       "      <td>6inl</td>\n",
+       "      <td>-</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>198</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>13.7758</td>\n",
+       "      <td>47.825100</td>\n",
+       "      <td>70.074600</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12008</td>\n",
+       "      <td>6jgm</td>\n",
+       "      <td>-</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>198</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>13.3718</td>\n",
+       "      <td>46.579498</td>\n",
+       "      <td>67.647102</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4116</td>\n",
+       "      <td>1h27</td>\n",
+       "      <td>-</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>198</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>17.3950</td>\n",
+       "      <td>59.113201</td>\n",
+       "      <td>78.272400</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11219</td>\n",
+       "      <td>6q4e</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>198</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "      <td>EKIGEGTYGVVYKVA_KKITAIR_ISLLKE_NPNIVKLLDVYLVFE...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>13.9936</td>\n",
+       "      <td>49.519501</td>\n",
+       "      <td>71.110497</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 41 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   structure.klifs_id structure.pdb_id structure.alternate_model  \\\n",
+       "0                4221             4eor                         A   \n",
+       "1               11794             6inl                         -   \n",
+       "2               12008             6jgm                         -   \n",
+       "3                4116             1h27                         -   \n",
+       "4               11219             6q4e                         C   \n",
+       "\n",
+       "  structure.chain species.klifs  kinase.klifs_id kinase.klifs_name  \\\n",
+       "0               C         Human              198              CDK2   \n",
+       "1               A         Human              198              CDK2   \n",
+       "2               A         Human              198              CDK2   \n",
+       "3               A         Human              198              CDK2   \n",
+       "4               A         Human              198              CDK2   \n",
+       "\n",
+       "  kinase.family kinase.group  \\\n",
+       "0          <NA>         <NA>   \n",
+       "1          <NA>         <NA>   \n",
+       "2          <NA>         <NA>   \n",
+       "3          <NA>         <NA>   \n",
+       "4          <NA>         <NA>   \n",
+       "\n",
+       "                                    structure.pocket  ...  \\\n",
+       "0  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
+       "1  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
+       "2  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
+       "3  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
+       "4  EKIGEGTYGVVYKVA_KKITAIR_ISLLKE_NPNIVKLLDVYLVFE...  ...   \n",
+       "\n",
+       "  structure.bp_ii_b_in structure.bp_ii_out structure.bp_ii_b structure.bp_iii  \\\n",
+       "0                False               False             False            False   \n",
+       "1                False               False             False            False   \n",
+       "2                False               False             False            False   \n",
+       "3                False               False             False            False   \n",
+       "4                False               False             False            False   \n",
+       "\n",
+       "  structure.bp_iv structure.bp_v  structure.grich_distance  \\\n",
+       "0           False          False                   14.9071   \n",
+       "1           False          False                   13.7758   \n",
+       "2           False          False                   13.3718   \n",
+       "3           False          False                   17.3950   \n",
+       "4           False          False                   13.9936   \n",
+       "\n",
+       "   structure.grich_angle  structure.grich_rotation  structure.filepath  \n",
+       "0              50.806301                 71.737297                <NA>  \n",
+       "1              47.825100                 70.074600                <NA>  \n",
+       "2              46.579498                 67.647102                <NA>  \n",
+       "3              59.113201                 78.272400                <NA>  \n",
+       "4              49.519501                 71.110497                <NA>  \n",
+       "\n",
+       "[5 rows x 41 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "structures.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -315,14 +618,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 1620\n",
+      "Number of structures: 1632\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
@@ -347,14 +657,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 1620\n"
+      "Number of structures: 1632\n"
      ]
     }
    ],
@@ -372,7 +682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -405,7 +715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -419,12 +729,12 @@
        "BRAF      72\n",
        "MET       99\n",
        "p38a     151\n",
-       "EGFR     357\n",
+       "EGFR     369\n",
        "CDK2     850\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -445,12 +755,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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R+SSU8ZKuAR4CPqgrjIjnCxDPZGBNRCyUNLQAxy9ZX/va11i1ahW77bYbEydO5GMf+xjjx49n4sSJAJx66qmMHDmyyFGamTUun4TSHzgHOJa/d3lFup6piKgBxjey+QxJR+esfysinso6hmJ54oknPlJ2ySWXcMkllxQhGjOzHZdPQjkFOCgiNhUqiIjo1EDZPGBeunwHcEcT+59XkMDMzCxv+SSUBUAXYGVhQ2ke/Xt0pspjZ5mZZS6fhNINeFXSH9n2HspXChaVmZm1OPkklGsKHoWZmbV4200oEfH75gjEzMxatnzelB8i6Y+S/iZpk6RaSR721szMtpHP4JATSN5efx3YAxidlpmZmW2Vzz0UIuINSWURUQtMkdRq3v8wM7Ns5JNQ1ktqD1RLuhFYAXh+eTMz20Y+XV7npPUuJpm3ZD/ga4UMyszMWp58nvL6U7q4Efh+YcMxM7OWarsJRdLngWuBA3LrR8RBhQvLzMxamnzuofwCuAx4DqgtbDhmZtZS5ZNQ1kTEgwWPpJksXL6GiquaZ6KqpR4zzMzakHwSymOSbgLuo/DzoZiZWQuVT0I5Mv06OKesIPOhtEaLFy/mjDPO2Lr+5ptv8oMf/IDly5fzm9/8hvbt23PwwQczZcoUunTpUrxAzcx2kSKad4p1SbXAQkAk92Qujoin0vnkXwEWA+2BKmBURGxO9ysH/gzcHhFX5xxvHtAd2JAWXRcRv26s/Q7dD43u596S8Vk1rH6XV21tLT169ODZZ59l8eLFHHvssZSXl3PllVcCcMMNNzRLXGZmO0rScxExuKk6+byHkrUNEVEZEQOBq4GxOduWREQlySyRPYHTc7YdR5JsTpekesc8Kz1mZVPJpNgeeeQRDj74YA444ACOO+44ysuTC8QhQ4ZQU1NT5OjMzHZNMRJKrr2B1fUL0yFe5gM9copHkEwP/DYwpFmiy9j06dMZMWLER8onT57MCSecUISIzMyyk9dYXhnbQ1I1sDtJV9VH7sVI2p3k3s0l6foewJeAb5LMHjkCeDpnl2mS6rq8vhQRq+od70LgQoCyvffN8FTyt2nTJmbOnMnYsWO3Kb/++uspLy/nrLPOKkpcZmZZyWf4+o6S/q+k29P1QyUN34U267q8egHHA/+d04V1cJpsVgFvR8SLaflw4LGIWA/cC5wiqSznmLldXtskE4CImBQRgyNicFnHzrsQ+s578MEHOeyww+jWrdvWsjvvvJNZs2Yxbdo0PtqLZ2bWsuTT5TWF5HHho9L1GuC6LBqPiKeBfYC6y4a6eyiHAEMk1U0zPAL4sqSlJC9YfgL4YhYxNJe77rprm+6uuXPncsMNNzBz5kw6duxYxMjMzLKRT0I5OCJuBDYDRMQGkie0dpmkXkAZyRXJVhGxArgKuFrS3sDRwP4RURERFcC/kCSZFmH9+vX87ne/49RTT91advHFF7Nu3TqGDRtGZWUlF110UREjNDPbdfncQ9mU3sMIAEkHk/OC406ou4cCSWI6NyJqG+jyuZ9kDLFLgEcjIrfNB4AbJXXYhTiaTceOHVm1atueuDfeeKNI0ZiZFUY+CeUaYC6wn6RpwOeB83a2wYgoa6R8KdAvZz2AgY3U/St/7yYburOxmJlZdvJ6sVHSJ0ge1RXwTET8pdCBFcrgwYOjqqqq2GGYmbUombzYmA5fvzEiZpM8svs9SQdkE6KZmbUW+dyU/ynJNMADge8CfwL+u6BRmZlZi5NPQvkwvZ9xMnBrRIwH9ipsWGZm1tLkc1N+naSrgbOBY9IXCncrbFhmZtbS5HOFcgbJY8KjIuLPJONr3VTQqMzMrMXZ7hVKmkR+lLP+Nr6HYmZm9eTzlNcQSX+U9DdJmyTVSlrTHMGZmVnLkU+X1wSSYU5eB/YARgMTCxmUmZm1PHkNXx8Rb0gqS+cpmSLpqQLHZWZmLUw+CWW9pPZAtaQbgRXAnoUNy8zMWpp8urzOSetdDLwP7Ad8rZBBmZlZy5PXWF6tSYfuh0b3c2/Z5eMsHfdPux6MmVkLkc9YXtvt8krH8roWOCC3fkQctKsBmplZ65FPl9cvSN5DORr4bM7HUrW1tQwaNIjhw5OZkRcsWMBRRx1F//79Oemkk1i7dm2RIzQzK7x8EsqaiHgwIlZGxKq6z842KOliSW9ICkn75JT3kvS0pA8kXVFvn+MlLU73u2pn2y6U8ePH07t3763ro0ePZty4cSxcuJBTTjmFm27ywAJm1vrlk1Aek3STpKMkHVb32YU2nwS+TDJqca6/At8Gbs4tTMcOmwicAPQBRkjqswvtZ6qmpobZs2czevTorWWLFy/mmGOOAWDYsGHce++9xQrPzKzZ5PPY8JHp19ybMQEc29ROkipIZnp8FhgEvAZ8IyJeSLdvUz8iVgIrJdW/230E8EZEvJnuN51k5OOXJV0AXAi0B94AzomI9XmcU2YuvfRSbrzxRtatW7e1rF+/fsycOZOTTz6Ze+65h2XLljVnSGZmRbHdK5SI+GIDnyaTSY7PAJMiYgCwFvjWTsTYA8j9jVyTlgHcFxGfjYiBwCvAqIYOIOlCSVWSqmrXZzdqzKxZs+jatSuHH374NuWTJ09m4sSJHH744axbt4727dtn1qaZWalq9ApF0tkRMVXSdxraHhE/aqi8nmUR8WS6PJUGurTyoAbK6p517ifpOpKZJDsBv23oABExCZgEyWPDO9h+o5588klmzpzJnDlz2LhxI2vXruXss89m6tSpPPTQQwC89tprzJ49O6smzcxKVlNXKHVvw+/VwKdTnsev/8t7Z36Z15C8TFmnJ/BOunwHcHFE9Ae+D+y+E8ffaWPHjqWmpoalS5cyffp0jj32WKZOncrKlSsB2LJlC9dddx0XXXRRc4ZlZlYUTd1DmQ0QEd+vv0HSSXkef39JR0XE0yQDTP5hx0Pkj8Chkg4ElgNnAv+cbtsLWCFpN+CsdHvR3XXXXUycmIyfeeqppzJy5MgiR2RmVniNvikvaTHwjxGxtF75SOD/RMTBTR44uSk/B3gc+BzJaMXnkIxW/G/AJ4GVwJyIGC3pk0AVsDewBfgb0Cci1ko6EbgFKAMmR8T1aRtj0mP9CVgI7BUR5zUVl9+UNzPbcbv6pvxlwO8knRgRr6cHvJrk6uALecawJSLq9/fcmn62kU7k1bOhg0TEHJLkVL/8p8BP84zFzMwKqNGEEhFzJH0APCjpqyRXFp8FjomI1c0UX+b69+hMla8uzMwy1+RjwxHxCHAeMA84CPhSvskkIpZGRL9dDdDMzFqGph4bXkfyVJaADsCXSF48FBARsXfzhGhmZi1BU11eezVnIGZm1rLlM5aXmZnZdjmhmJlZJpxQzMwsE04oZmaWCScUMzPLhBOKmZllwgnFzMwy4YRiZmaZyGcK4FZl4fI1VFyV/4RXHlXYzCw/vkIxM7NMOKFsx8aNGzniiCMYOHAgffv25ZprrgHgu9/9Lr169WLAgAGccsopvPfee8UN1MysyEoqoUj6WyPl35C0SNJLkl6WdEVafoek09Llj0t6IZ0ALDMdOnTg0UcfZcGCBVRXVzN37lyeeeYZhg0bxqJFi3jxxRf59Kc/zdixY7Ns1sysxSmphNIQSScAlwLHRURf4DBgTb06nYHfApMiYkrG7dOpUycANm/ezObNm5HEcccdR3l5cgtqyJAh1NTUZNmsmVmLU/IJBbgauCIi3gGIiI0RcXvO9k7Ag8Cv0hkcM1dbW0tlZSVdu3Zl2LBhHHnkkdtsnzx5MieccEIhmjYzazFaQkLpBzzXxPYfAX+IiB83VkHShZKqJFXVrl/TWLVGlZWVUV1dTU1NDfPnz2fRokVbt11//fWUl5dz1lln7fBxzcxak5aQULbnUeBkSV0bqxARkyJicEQMLuvYeacb6tKlC0OHDmXu3LkA3HnnncyaNYtp06aRzDtmZtZ2tYSE8hJweBPbpwM/BeZIynxSsHfffXfrE1wbNmzg4YcfplevXsydO5cbbriBmTNn0rFjx6ybNTNrcVrCi41jgRslDY+IP0vqAHwzIm6tqxARt0jqDsyQdGJEbMqq8RUrVnDuuedSW1vLli1bOP300xk+fDiHHHIIH3zwAcOGDQOSG/M/+9nPsmrWzKzFKbWE0lFS7uNSP4qIH0nqBjxcN589MLn+jhFxpaQpwC8ljYiILVkENGDAAF544YWPlL/xxhtZHN7MrNUoqYQSEQ12waWPAn/kceCIOK/eeqbvoJiZWf5KKqE0h/49OlPl8bnMzDLXEm7Km5lZC+CEYmZmmXBCMTOzTDihmJlZJpxQzMwsE04oZmaWCScUMzPLhBOKmZllwgnFzMwy4YRiZmaZcEIxM7NMOKGYmVkm2lxCWbh8DRVXzS52GGZmrU6bSyj1vffee5x22mn06tWL3r178/TTTxc7JDOzFqkkEoqkv+UsnyjpdUn7S7pW0nJJ1WnZfZL65NSdJ2mxpAWS/iipckfbvuSSSzj++ON59dVXWbBgAb17987orMzM2paSSCh1JH0J+AlwfES8nRb/OCIqI+JQ4G7gUUn75ux2VkQMBP4LuGlH2lu7di2PP/44o0aNAqB9+/Z06dJlV0/DzKxNKpmEIukfgNuBf4qIJQ3ViYi7gYeAf25g89NAjx1p880332Tfffdl5MiRDBo0iNGjR/P+++/vaOhmZkbpJJQOwAPAVyPi1e3UfR7o1UD58cD9O9Lohx9+yPPPP8+YMWN44YUX2HPPPRk3btyOHMLMzFKlklA2A08Bo/Koq3rr0yTVAFeSdJd9dAfpQklVkqpq16/ZWt6zZ0969uzJkUceCcBpp53G888/vzPxm5m1eaWSULYApwOflfS97dQdBLySs34WcCDwK2BiQztExKSIGBwRg8s6dt5a/slPfpL99tuPxYsXA/DII4/Qp0+fhg5hZmbbUV7sAOpExHpJw4EnJP1vRPyifh1JXwOOAy6vt+9mSf8HWCKpd0S8Un/fxvzkJz/hrLPOYtOmTRx00EFMmTJlV0/FzKxNKpmEAhARf5V0PPC4pL+kxZdJOhvYE1gEHBsR7zaw7wZJPwSuIL+uMwAqKyupqqrKIHozs7atJBJKRHTKWV5G0oUFyY36a5vYb2i99R8WIDwzM8tDqdxDMTOzFq7NJZT+PTqzdNw/FTsMM7NWp80lFDMzKwwnFDMzy4QTipmZZcIJxczMMuGEYmZmmXBCMTOzTDihmJlZJpxQzMwsE04oZmaWCScUMzPLhBOKmZllos0llIXL12y/kpmZ7bA2l1DMzKww2nxCqa2tZdCgQQwfPrzYoZiZtWgFTyiSaiVV53yuaqTePEmDGyg/T9K76b4vSfq1pI7ptu9IelnSi5IekXTAjsY3fvx4evfuveMnZmZm22iOK5QNEVGZ8xlXv4Kksu0c4+50377AJuCMtPwFYHBEDAB+Ddy4I4HV1NQwe/ZsRo8evSO7mZlZA4rW5SVpqaT/kPQH4Otp8dmSnpK0SNIRDexTTjK3/GqAiHgsItanm58Beu5IDJdeeik33ngj7dq1+Z4/M7Nd1hy/Sfeo1+V1Rs62jRFxdERMT9f3jIjPAd8CJufUO0NSNbAc+DjwmwbaGQU82FAAki6UVCWpqnZ98pTXrFmz6Nq1K4cffviunZ2ZmQHF6fK6O2fb3fXq3gUQEY8De0vqUlcvIiqBTwILge/m7iTpbGAwcFNDAUTEpIgYHBGDyzp2BuDJJ59k5syZVFRUcOaZZ/Loo49y9tln79qZmpm1YcXu63m/3no0tR4RQXJ1ckxdmaQvA/8OfCUiPsi34bFjx1JTU8PSpUuZPn06xx57LFOnTt2x6M3MbKtiJ5T6zgCQdDSwJiIaegvxaGBJWm8QcBtJMlnZbFGamdlHlDdDG3uk9z/qzI2IBh8dBlZLegrYGzg/p/yMNMm0A2qA89Lym4BOwD2SAN6OiK/saIBDhw5l6NChO7qbmZnlUNKL1HZ06H5ofLDi9WKHYWbWokh6LiI+8q5grlLr8iq4/j06FzsEM7NWqc0lFDMzKwwnFDMzy4QTipmZZcIJxczMMuGEYmZmmWhzjw1LWgcsLnYc9ewD/KXYQTSgFOMqxZigNOMqxZigNOMqxZigtOI6ICL2bapCc7zYWGoWb+9Z6uYmqarUYoLSjKsUY4LSjKsUY4LSjKsUY4LSjasx7vIyM7NMOKGYmVkm2mJCmVTsABpQijFBacZVijFBacZVijFBacZVijFB6cbVoDZ3U97MzAqjLV6hmJlZATihmJlZJtpMQpF0vKTFkt6Q1Nh8LIVqe7KklZIW5ZR9XNLvJL2efv1Yzrar0zgXS/rHAsW0n6THJL0i6SVJlxQ7Lkm7S5ovaUEa0/eLHVO9+MokvSBpVqnEJWmppIWSqiVVlUJckrpI+rWkV9Ofr6NKIKbPpN+jus9aSZeWQFyXpT/riyTdlf4fKPrP1U6LiFb/AcpIZnk8CGgPLAD6NGP7xwCHAYtyym4ErkqXrwJuSJf7pPF1AA5M4y4rQEzdgcPS5b2A19K2ixYXIKBTurwb8CwwpNjfq5z4vgP8CphVCv+GaVtLgX3qlRX7Z+tOYHS63B7oUuyY6sVXBvwZOKDIP+89gLeAPdL1/yGZPLBkvlc7fE7FDqBZThKOAn6bs341cHUzx1DBtgllMdA9Xe5O8sLlR2IDfgsc1QzxPQAMK5W4gI7A88CRpRAT0BN4BDiWvyeUUohrKR9NKEWLi2S21bdIH/gphZgaiPE44Mlix0WSUJYBHyd5yXxWGlvJfK929NNWurzq/uHq1KRlxdQtIlYApF+7puXNHqukCmAQyRVBUeNKu5WqgZXA7yKi6DGlbgH+DdiSU1YKcQXwkKTnJF1YAnEdBLwLTEm7B38uac8ix1TfmcBd6XLR4oqI5cDNwNvACmBNRDxUzJh2VVtJKGqgrFSfl27WWCV1Au4FLo2ItU1VbaAs87giojYiKkmuCI6Q1K/YMUkaDqyMiOfy3aWBskL9G34+Ig4DTgD+RdIxTdRtjrjKSbp3fxoRg4D3SbptihnT3xuT2gNfAe7ZXtUGyjKNK703cjJJ99WngD0lnV3MmHZVW0koNcB+Oes9gXeKFEud/5XUHSD9ujItb7ZYJe1GkkymRcR9pRIXQES8B8wDji+BmD4PfEXSUmA6cKykqSUQFxHxTvp1JTADOKLIcdUANemVJcCvSRJM0b9XqROA5yPif9P1Ysb1ZeCtiHg3IjYD9wGfK3JMu6StJJQ/AodKOjD9C+VMYGaRY5oJnJsun0tyD6Ou/ExJHSQdCBwKzM+6cUkCfgG8EhE/KoW4JO0rqUu6vAfJf7hXixkTQERcHRE9I6KC5Gfn0Yg4u9hxSdpT0l51yyT974uKGVdE/BlYJukzadGXgJeLGVM9I/h7d1dd+8WK621giKSO6f/HLwGvFDmmXVPsmzjN9QFOJHmSaQnw783c9l0kfaSbSf7KGAV8guQm7+vp14/n1P/3NM7FwAkFiuloksvlF4Hq9HNiMeMCBgAvpDEtAv4jLS/q96pejEP5+035Yv8bHkTy1M8C4KW6n+sSiKsSqEr/He8HPlbsmNJ2OgKrgM45ZcX+Xn2f5I+mRcAvSZ7gKvr3amc/HnrFzMwy0Va6vMzMrMCcUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxVosSSHphznrV0i6NqNj3yHptCyOtZ12vp6OyPtYnvW/l2HbXSR9K6vjmTmhWEv2AXCqpH2KHUguSWU7UH0U8K2I+GKe9RtMKErs6P/nLsAOJ5QdPD9rQ5xQrCX7kGTO7cvqb6h/hSHpb+nXoZJ+L+l/JL0maZyks5TMw7JQ0sE5h/mypCfSesPT/csk3STpj5JelPTNnOM+JulXwMIG4hmRHn+RpBvSsv8gecH0Z5Juqle/u6THlczdsUjSP0gaB+yRlk2TVJFe3fwXycjM+9WdZ3qM0yTdkS53kzRDyVwzCyR9DhgHHJwe76b0HGbl7D9B0nnp8lJJ/yHpD8DXJR0n6WlJz0u6R8mYcKTfz5fT783N+f0zWmtRXuwAzHbRROBFSTfuwD4Dgd7AX4E3gZ9HxBFKJhn7V+DStF4F8AXgYOAxSYcA3yAZFfazkjoAT0p6KK1/BNAvIt7KbUzSp4AbgMOB1SSjA381In4g6VjgioioqhfjP5NMuXB9ekXQMSKekHRxJINn1o0S/RlgZER8Ky1r7JxvBX4fEaekx+tEMmhjv5zjDd3O921jRBydXhHeB3w5It6XdCXwHUkTgFOAXhERdcPoWNvhhGItWkSslfTfwLeBDXnu9sdIhweXtASoSwgLgdyup/+JiC3A65LeBHqRjJc1IOfqpzPJmEqbgPn1k0nqs8C8iHg3bXMayaRr9zcVIzBZyQCe90dEdSP1/hQRzzR1sqljSZIhEVELrFHOTIB5ujv9OoRksqcn0wTWHngaWAtsBH4uaTbJ/B7WhrjLy1qDW0juReyZU/Yh6c93OvBe+5xtH+Qsb8lZ38K2f2TVH5coSIYQ/9eIqEw/B0YyhwUkQ7U3pNHLhsZExOMkSWc58EtJ32ikav02c2PefQeb3fo9a2T/urZEMldN3fegT0SMiogPSa7S7gW+CszdwfathXNCsRYvIv5KMn3qqJzipSRdTJDMObHbThz665LapfdVDiIZkO+3wJj0ygFJn1Yy0m9TngW+IGmftLtpBPD7pnaQdADJHCy3k4wKfVi6aXNd2434X0m90xv0p+SUPwKMSY9dJmlvYB3J9M91/gT0UTKabWeS0W8b8gzw+bQLECWj5X46vY/SOSLmkHQbVjZ1jtb6OKFYa/FDIPdpr9tJfonPJ5lGuLGrh6YsJvnF/yBwUURsBH5OMhz785IWAbexna7jtHvtauAxkpGBn4+IB5rah2RU42pJLwBfA8an5ZNI7hlNa2S/q0i6mh4lGeG6ziXAFyUtBJ4D+kbEKpJuq0WSboqIZSSJ+UVgGsnIzw2dz7skc5/fJelFkgTTiyQ5zUrLfk8DD0tY6+bRhs3MLBO+QjEzs0w4oZiZWSacUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMvH/AQUvaKqLjkRhAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -517,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -542,7 +852,225 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You might notice that we have fewer fingerprints than structures. This can happen during the `kissim` fingerprinting when some structures cannot be encoded."
+    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprinting when some structures cannot be encoded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's have a look at the fingerprint data frame. \n",
+    "* Kinase structures are stored by KLIFS Id (index)\n",
+    "* Each kinase is represented by 1032 features (85 residues * 8 physicohem. features + 85 residues * 4 distances + 12 moments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>1022</th>\n",
+       "      <th>1023</th>\n",
+       "      <th>1024</th>\n",
+       "      <th>1025</th>\n",
+       "      <th>1026</th>\n",
+       "      <th>1027</th>\n",
+       "      <th>1028</th>\n",
+       "      <th>1029</th>\n",
+       "      <th>1030</th>\n",
+       "      <th>1031</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6285</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.150351</td>\n",
+       "      <td>11.958837</td>\n",
+       "      <td>4.717011</td>\n",
+       "      <td>4.843444</td>\n",
+       "      <td>4.655707</td>\n",
+       "      <td>3.577213</td>\n",
+       "      <td>2.771821</td>\n",
+       "      <td>4.302192</td>\n",
+       "      <td>3.583341</td>\n",
+       "      <td>2.066700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10568</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.069152</td>\n",
+       "      <td>11.883944</td>\n",
+       "      <td>4.691527</td>\n",
+       "      <td>5.006221</td>\n",
+       "      <td>4.679352</td>\n",
+       "      <td>3.531177</td>\n",
+       "      <td>2.714736</td>\n",
+       "      <td>4.165350</td>\n",
+       "      <td>3.549843</td>\n",
+       "      <td>2.138838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11187</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.297023</td>\n",
+       "      <td>11.991511</td>\n",
+       "      <td>4.590040</td>\n",
+       "      <td>5.141397</td>\n",
+       "      <td>4.699467</td>\n",
+       "      <td>3.625989</td>\n",
+       "      <td>2.549692</td>\n",
+       "      <td>4.442117</td>\n",
+       "      <td>3.699695</td>\n",
+       "      <td>2.261646</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4060</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>12.910837</td>\n",
+       "      <td>11.775556</td>\n",
+       "      <td>4.359330</td>\n",
+       "      <td>4.844833</td>\n",
+       "      <td>4.214195</td>\n",
+       "      <td>3.383812</td>\n",
+       "      <td>2.699580</td>\n",
+       "      <td>3.860920</td>\n",
+       "      <td>3.161863</td>\n",
+       "      <td>2.185979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10566</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13.196581</td>\n",
+       "      <td>12.115342</td>\n",
+       "      <td>4.701176</td>\n",
+       "      <td>4.690081</td>\n",
+       "      <td>4.683674</td>\n",
+       "      <td>3.633418</td>\n",
+       "      <td>2.489983</td>\n",
+       "      <td>3.972552</td>\n",
+       "      <td>3.692501</td>\n",
+       "      <td>0.759234</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1032 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         0    1    2    3    4    5    6    7    8    9  ...       1022  \\\n",
+       "6285   2.0  0.0  2.0 -1.0  0.0  0.0  2.0  3.0  2.0  1.0  ...  13.150351   \n",
+       "10568  2.0  0.0  2.0 -1.0  0.0  0.0  2.0  3.0  2.0  1.0  ...  13.069152   \n",
+       "11187  2.0  0.0  2.0 -1.0  0.0  0.0  2.0  3.0  2.0  1.0  ...  13.297023   \n",
+       "4060   2.0  0.0  2.0 -1.0  0.0  0.0  2.0  3.0  2.0  1.0  ...  12.910837   \n",
+       "10566  2.0  0.0  2.0 -1.0  0.0  0.0  2.0  3.0  2.0  1.0  ...  13.196581   \n",
+       "\n",
+       "            1023      1024      1025      1026      1027      1028      1029  \\\n",
+       "6285   11.958837  4.717011  4.843444  4.655707  3.577213  2.771821  4.302192   \n",
+       "10568  11.883944  4.691527  5.006221  4.679352  3.531177  2.714736  4.165350   \n",
+       "11187  11.991511  4.590040  5.141397  4.699467  3.625989  2.549692  4.442117   \n",
+       "4060   11.775556  4.359330  4.844833  4.214195  3.383812  2.699580  3.860920   \n",
+       "10566  12.115342  4.701176  4.690081  4.683674  3.633418  2.489983  3.972552   \n",
+       "\n",
+       "           1030      1031  \n",
+       "6285   3.583341  2.066700  \n",
+       "10568  3.549843  2.138838  \n",
+       "11187  3.699695  2.261646  \n",
+       "4060   3.161863  2.185979  \n",
+       "10566  3.692501  0.759234  \n",
+       "\n",
+       "[5 rows x 1032 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kissim_fingerprints.head()"
    ]
   },
   {
@@ -564,7 +1092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -573,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -666,7 +1194,7 @@
        "10566  14.307291  16.833932  18.338492  28.905189   0.000000"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -695,7 +1223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +1324,7 @@
        "CDK2               14.307291  16.833932  18.338492  28.905189   0.000000"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -823,341 +1351,300 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We unstack the matrix (each pairwise comparison in a single row)\n",
+    "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
+    "# We take the minimum value in each kinase pair group\n",
+    "# We unstack the remaining data points\n",
+    "kinase_distance_matrix = (\n",
+    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    ")\n",
+    "kinase_distance_matrix.index.name = None\n",
+    "kinase_distance_matrix.columns.name = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure matrix of shape (1611, 1611) reduced to kinase matrix of shape (9, 9).\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Structure matrix of shape {structure_distance_matrix.shape} reduced to kinase matrix of shape {kinase_distance_matrix.shape}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
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-       "<table id=\"T_c9147_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_c9147_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_c9147_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_c9147_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_c9147_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_c9147_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_c9147_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_c9147_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_c9147_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_c9147_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_c9147_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_c9147_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_c9147_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_c9147_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_c9147_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_c9147_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_c9147_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_c9147_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_c9147_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_c9147_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_c9147_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_c9147_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_c9147_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_c9147_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_c9147_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_c9147_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_c9147_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_c9147_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_c9147_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_c9147_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_c9147_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_c9147_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_c9147_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_c9147_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_c9147_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_c9147_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_c9147_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_c9147_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_c9147_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_c9147_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_c9147_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_c9147_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_c9147_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_c9147_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_c9147_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_c9147_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_c9147_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_c9147_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_c9147_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_c9147_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_c9147_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_c9147_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_c9147_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_c9147_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_c9147_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_c9147_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_c9147_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_c9147_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_c9147_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_c9147_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_c9147_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_c9147_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_c9147_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_c9147_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_c9147_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_c9147_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c9147_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c9147_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_c9147_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_c9147_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_c9147_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_c9147_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_c9147_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_c9147_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_c9147_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_c9147_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
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+       "            background-color:  #ebf3eb;\n",
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+       "        }#T_71b38_row5_col1,#T_71b38_row5_col6,#T_71b38_row6_col2{\n",
+       "            background-color:  #78bb78;\n",
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+       "            color:  #f1f1f1;\n",
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+       "            background-color:  #76ba76;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row6_col3{\n",
+       "            background-color:  #6bb46b;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row6_col7{\n",
+       "            background-color:  #0a850a;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_71b38_row8_col1{\n",
+       "            background-color:  #6eb66e;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row8_col2{\n",
+       "            background-color:  #5aac5a;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row8_col3{\n",
+       "            background-color:  #5eae5e;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row8_col6{\n",
+       "            background-color:  #68b368;\n",
+       "            color:  #000000;\n",
+       "        }#T_71b38_row8_col7{\n",
+       "            background-color:  #108810;\n",
+       "            color:  #f1f1f1;\n",
+       "        }</style><table id=\"T_71b38_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >BRAF</th>        <th class=\"col_heading level0 col1\" >CDK2</th>        <th class=\"col_heading level0 col2\" >EGFR</th>        <th class=\"col_heading level0 col3\" >ErbB2</th>        <th class=\"col_heading level0 col4\" >KDR</th>        <th class=\"col_heading level0 col5\" >LCK</th>        <th class=\"col_heading level0 col6\" >MET</th>        <th class=\"col_heading level0 col7\" >p110a</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_71b38_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "                        <td id=\"T_71b38_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "                        <td id=\"T_71b38_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "                        <td id=\"T_71b38_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "                        <td id=\"T_71b38_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "                        <td id=\"T_71b38_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "                        <td id=\"T_71b38_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "                        <td id=\"T_71b38_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "                        <td id=\"T_71b38_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "                        <td id=\"T_71b38_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "                        <td id=\"T_71b38_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "                        <td id=\"T_71b38_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "                        <td id=\"T_71b38_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "                        <td id=\"T_71b38_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "                        <td id=\"T_71b38_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "                        <td id=\"T_71b38_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "                        <td id=\"T_71b38_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "                        <td id=\"T_71b38_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "                        <td id=\"T_71b38_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "                        <td id=\"T_71b38_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "                        <td id=\"T_71b38_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "                        <td id=\"T_71b38_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "                        <td id=\"T_71b38_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "                        <td id=\"T_71b38_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "                        <td id=\"T_71b38_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "                        <td id=\"T_71b38_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "                        <td id=\"T_71b38_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "                        <td id=\"T_71b38_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "                        <td id=\"T_71b38_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "                        <td id=\"T_71b38_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "                        <td id=\"T_71b38_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "                        <td id=\"T_71b38_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "                        <td id=\"T_71b38_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "                        <td id=\"T_71b38_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "                        <td id=\"T_71b38_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "                        <td id=\"T_71b38_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "                        <td id=\"T_71b38_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "                        <td id=\"T_71b38_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "                        <td id=\"T_71b38_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "                        <td id=\"T_71b38_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "                        <td id=\"T_71b38_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "                        <td id=\"T_71b38_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "                        <td id=\"T_71b38_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "                        <td id=\"T_71b38_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "                        <td id=\"T_71b38_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "                        <td id=\"T_71b38_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "                        <td id=\"T_71b38_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "                        <td id=\"T_71b38_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "                        <td id=\"T_71b38_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "                        <td id=\"T_71b38_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "                        <td id=\"T_71b38_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "                        <td id=\"T_71b38_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "                        <td id=\"T_71b38_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "                        <td id=\"T_71b38_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "                        <td id=\"T_71b38_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "                        <td id=\"T_71b38_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "                        <td id=\"T_71b38_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "                        <td id=\"T_71b38_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "                        <td id=\"T_71b38_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "                        <td id=\"T_71b38_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "                        <td id=\"T_71b38_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "                        <td id=\"T_71b38_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "                        <td id=\"T_71b38_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "                        <td id=\"T_71b38_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "                        <td id=\"T_71b38_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "                        <td id=\"T_71b38_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_71b38_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "                        <td id=\"T_71b38_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "                        <td id=\"T_71b38_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "                        <td id=\"T_71b38_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "                        <td id=\"T_71b38_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "                        <td id=\"T_71b38_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "                        <td id=\"T_71b38_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "                        <td id=\"T_71b38_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "                        <td id=\"T_71b38_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "                        <td id=\"T_71b38_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff6700192e0>"
+       "<pandas.io.formats.style.Styler at 0x7f13fc2f1dc0>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# We unstack the matrix (each pairwise comparison in a single row)\n",
-    "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
-    "# We take the minimum value in each kinase pair group\n",
-    "# We unstack the remaining data points\n",
-    "kinase_distance_matrix = (\n",
-    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
-    ")\n",
-    "kinase_distance_matrix.index.name = None\n",
-    "kinase_distance_matrix.columns.name = None\n",
-    "\n",
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
     "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
@@ -1175,7 +1662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1217,7 +1704,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1231,7 +1718,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From 2de81017ba6a39a7e8e5407225f866916be0e255 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Tue, 21 Sep 2021 21:13:25 +0200
Subject: [PATCH 060/158] small adaptions to T026

---
 .../talktorial.ipynb                          | 548 +++++-------------
 1 file changed, 143 insertions(+), 405 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 2578f585..0cc1a99a 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "We will assess the similarity between a set of kinases based on the KLIFS interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex.  "
+    "We will assess the similarity between a set of kinases based on detected protein-ligand interactions in available complex structures. The [KLIFS](https://klifs.net/) interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex, will be used for this exercise.  "
    ]
   },
   {
@@ -41,6 +41,7 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
+    "* Define the kinases of interest\n",
     "* Retrieve and preprocess data\n",
     "* Show kinase coverage\n",
     "* Compare structures\n",
@@ -86,7 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
     "\n",
     "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
     "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
@@ -113,7 +114,7 @@
    "source": [
     "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
-    "Interaction fingerprints encode the binding mode of a ligand in a binding site that is seen in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand intentionally targets multiple proteins at once.\n",
+    "Interaction fingerprints encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once.\n",
     "\n",
     "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. We will make use of pre-calculated interaction fingerprints provided by the kinase-focused resource KLIFS."
    ]
@@ -167,7 +168,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
+     ]
+    }
+   ],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -194,7 +203,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve and preprocess data"
+    "### Define the kinases of interest"
    ]
   },
   {
@@ -228,6 +237,13 @@
     "]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Retrieve and preprocess data"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -255,7 +271,16 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    }
+   ],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -276,7 +301,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 2466\n",
+      "Number of structures: 2484\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
@@ -308,11 +333,18 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 1620\n",
+      "Number of structures: 1632\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
@@ -344,7 +376,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 1620\n"
+      "Number of structures: 1632\n"
      ]
     }
    ],
@@ -387,16 +419,92 @@
     "tags": []
    },
    "source": [
-    "#### Fetch the structures' IFP (if available)\n",
+    "#### Fetch the structures' IFPs (if available)\n",
     "\n",
     "We fetch the IFPs for the set of structures. Not all structures will have an IFP because not all structures have a co-crystallized ligand."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of structures with IFPs: 1466\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>interaction.fingerprint</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>775</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>777</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>778</td>\n",
+       "      <td>0000000000000010000000000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>779</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>782</td>\n",
+       "      <td>0000000000000010000001000000000000000000000000...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   structure.klifs_id                            interaction.fingerprint\n",
+       "0                 775  0000000000000010000000000000000000000000000000...\n",
+       "1                 777  0000000000000010000001000000000000000000000000...\n",
+       "2                 778  0000000000000010000000000000000000000000000000...\n",
+       "3                 779  0000000000000010000001000000000000000000000000...\n",
+       "4                 782  0000000000000010000001000000000000000000000000..."
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ifps = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
     "print(f\"Number of structures with IFPs: {len(ifps)}\")\n",
@@ -644,9 +752,9 @@
     }
    ],
    "source": [
-    "structures = ifps.merge(structures, on=\"structure.klifs_id\", how=\"inner\")\n",
-    "print(f\"Number of structures with IFPs: {len(structures)}\")\n",
-    "structures.head()"
+    "structures_with_ifps = ifps.merge(structures, on=\"structure.klifs_id\", how=\"inner\")\n",
+    "print(f\"Number of structures with IFPs: {len(structures_with_ifps)}\")\n",
+    "structures_with_ifps.head()"
    ]
   },
   {
@@ -688,7 +796,7 @@
    ],
    "source": [
     "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
-    "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
+    "n_structures_per_kinase = structures_with_ifps.groupby(\"kinase.klifs_name\").size().sort_values()\n",
     "n_structures_per_kinase\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -707,7 +815,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -776,36 +884,19 @@
    ],
    "source": [
     "# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\n",
-    "ifps = structures.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
-    "ifps.head()"
+    "ifp_series = structures_with_ifps.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
+    "ifp_series.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "structure.klifs_id  kinase.klifs_name\n",
-       "775                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
-       "777                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
-       "778                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
-       "779                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
-       "782                 EGFR                 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...\n",
-       "Name: interaction.fingerprint, dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Cast string to list of \"0\" and \"1\"\n",
-    "ifps = ifps.apply(list)\n",
-    "ifps.head()"
+    "# ifps_series = ifp_series.apply(list)\n",
+    "# ifp_series.head()"
    ]
   },
   {
@@ -832,8 +923,8 @@
    ],
    "source": [
     "# Cast \"0\" and \"1\" to boolean False and True\n",
-    "ifps = ifps.apply(lambda x: [True if i == \"1\" else False for i in x])\n",
-    "ifps.head()"
+    "ifp_series = ifp_series.apply(lambda x: [True if i == \"1\" else False for i in x])\n",
+    "ifp_series.head()"
    ]
   },
   {
@@ -860,7 +951,7 @@
    ],
    "source": [
     "# Convert to numpy array\n",
-    "ifps_array = np.array(ifps.to_list())\n",
+    "ifps_array = np.array(ifp_series.to_list())\n",
     "ifps_array"
    ]
   },
@@ -995,7 +1086,7 @@
    ],
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
-    "structure_klifs_ids = ifps.index.get_level_values(0)\n",
+    "structure_klifs_ids = ifp_series.index.get_level_values(0)\n",
     "structure_distance_matrix = pd.DataFrame(\n",
     "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
     ")\n",
@@ -1127,7 +1218,7 @@
     "# Copy distance matrix to kinase matrix\n",
     "kinase_distance_matrix = structure_distance_matrix\n",
     "# Replace structure KLIFS IDs with the structures' kinase names\n",
-    "kinase_names = ifps.index.get_level_values(1)\n",
+    "kinase_names = ifp_series.index.get_level_values(1)\n",
     "kinase_distance_matrix.index = kinase_names\n",
     "kinase_distance_matrix.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
@@ -1148,7 +1239,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1173,367 +1264,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, we will consider per kinase pair the two structures that show the most similar binding mode for their co-crystallized ligands. Hence, we select the structure pair with the minimum distance as representative for a kinase pair."
+    "In this talktorial, we will consider per kinase pair the two structures that show the most similar binding mode for their co-crystallized ligands. Hence, we select the structure pair with the minimum IFP distance as representative for a kinase pair."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-       "  background-color: #6bb46b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row4_col1 {\n",
-       "  background-color: #6fb76f;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row4_col2, #T_e3880_row6_col4 {\n",
-       "  background-color: #57ab57;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row4_col3 {\n",
-       "  background-color: #43a143;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row4_col5 {\n",
-       "  background-color: #cae3ca;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e3880_row4_col6 {\n",
-       "  background-color: #349934;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row4_col7 {\n",
-       "  background-color: #108810;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
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-       "  color: #000000;\n",
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-       "#T_e3880_row5_col0 {\n",
-       "  background-color: #55a955;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row5_col1 {\n",
-       "  background-color: #88c388;\n",
-       "  color: #000000;\n",
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-       "#T_e3880_row5_col2 {\n",
-       "  background-color: #84c084;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e3880_row5_col4 {\n",
-       "  background-color: #d1e6d1;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e3880_row5_col6 {\n",
-       "  background-color: #61b061;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_e3880_row5_col7 {\n",
-       "  background-color: #50a750;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_e3880_row5_col8 {\n",
-       "  background-color: #8dc58d;\n",
-       "  color: #000000;\n",
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-       "#T_e3880_row6_col0 {\n",
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-       "  color: #f1f1f1;\n",
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-       "#T_e3880_row6_col2, #T_e3880_row6_col5 {\n",
-       "  background-color: #5fae5f;\n",
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-       "  background-color: #4da64d;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #3a9c3a;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_e3880_row6_col8 {\n",
-       "  background-color: #3c9d3c;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row7_col1 {\n",
-       "  background-color: #359a35;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row8_col1 {\n",
-       "  background-color: #5aac5a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row8_col4 {\n",
-       "  background-color: #d8ead8;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e3880_row8_col6 {\n",
-       "  background-color: #299429;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e3880_row8_col7 {\n",
-       "  background-color: #289328;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_e3880_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_e3880_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_e3880_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_e3880_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_e3880_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_e3880_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_e3880_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_e3880_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_e3880_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_e3880_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_e3880_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_e3880_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_e3880_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_e3880_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_e3880_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_e3880_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_e3880_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_e3880_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_e3880_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_e3880_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_e3880_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_e3880_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_e3880_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_e3880_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_e3880_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_e3880_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_e3880_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_e3880_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_e3880_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_e3880_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_e3880_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_e3880_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_e3880_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_e3880_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_e3880_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_e3880_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_e3880_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_e3880_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_e3880_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_e3880_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_e3880_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_e3880_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_e3880_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_e3880_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_e3880_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_e3880_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_e3880_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_e3880_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_e3880_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_e3880_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_e3880_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_e3880_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_e3880_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_e3880_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_e3880_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_e3880_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_e3880_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_e3880_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_e3880_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_e3880_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_e3880_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_e3880_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_e3880_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_e3880_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_e3880_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_e3880_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e3880_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_e3880_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_e3880_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_e3880_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_e3880_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_e3880_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_e3880_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_e3880_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_e3880_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_e3880_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f2651232340>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# We unstack the matrix (each pairwise comparison in a single row)\n",
     "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
@@ -1562,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1580,7 +1318,7 @@
     "\n",
     "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity.\n",
     "\n",
-    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have &mdash at least for kinases such as EGFR and CDK2 &mdash much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
+    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have - at least for kinases such as EGFR and CDK2 - much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
     "\n",
     "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
    ]
@@ -1606,7 +1344,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1620,7 +1358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From b400e8d9283921cfc33dc8d0fc40932ba5253ddf Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Tue, 21 Sep 2021 22:28:11 +0200
Subject: [PATCH 061/158] small adaptions to T027

---
 .../talktorial.ipynb                          | 2274 +++++++----------
 1 file changed, 951 insertions(+), 1323 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index cf8e8192..14854ae3 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "The aim of this talktorial is to investigate kinase similarity through ligand profiling. In the context of drug design, the following assumption can be made: if a compound was tested as active on two different kinases, it is suspected that these two kinases may have some degree of similarity. The concept of kinase promiscuity is also covered."
+    "The aim of this talktorial is to investigate kinase similarity through ligand profiling data. In the context of drug design, the following assumption can be made: if a compound was tested as active on two different kinases, it is suspected that these two kinases may have some degree of similarity. The concept of kinase promiscuity is also covered."
    ]
   },
   {
@@ -81,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
     "\n",
     " \n",
     "\n",
@@ -119,7 +119,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to measure kinase similarity through ligand profiling, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome.\n",
+    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
     "For more details on querying the ChEMBL database, please refer to [Talktorial T001](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb)."
    ]
   },
@@ -127,7 +127,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In drug design, it is common to determine the activity of a compound against a kinase of interest as a \"hit\" or \"non hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active, and inactive otherwise."
+    "In drug design, it is common to binarize the activity of a compound against a target of interest as a \"hit\" or \"non-hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active (hit), and inactive (non-hit) otherwise."
    ]
   },
   {
@@ -141,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a measure of similarity, we use ligand profiling in this talktorial."
+    "As a measure of similarity, we use ligand profiling data in this talktorial."
    ]
   },
   {
@@ -160,7 +160,7 @@
     "{\\#\\text{ of compounds that were tested on both } K_i \\text{ and } K_j}.\n",
     "$$\n",
     "\n",
-    "Assuming that only one compound was tested on two kinases, and that the compound was tested as active for one and inactive for the other, then the similarity between these two kinases would be null."
+    "Assuming that only one compound was tested on two kinases, and that the compound was tested as active for one and inactive for the other, then the similarity between these two kinases would be zero."
    ]
   },
   {
@@ -212,7 +212,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We retrieve a pre-curated version of ChEMBL v.28 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.2."
+    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.28 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.2."
    ]
   },
   {
@@ -492,16 +492,45 @@
     "data = data[data[\"activities.standard_type\"] == \"pIC50\"]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'activities.activity_id', 'assays.chembl_id',\n",
+       "       'target_dictionary.chembl_id', 'molecule_dictionary.chembl_id',\n",
+       "       'molecule_dictionary.max_phase', 'activities.standard_type',\n",
+       "       'activities.standard_value', 'activities.standard_units',\n",
+       "       'compound_structures.canonical_smiles',\n",
+       "       'compound_structures.standard_inchi', 'component_sequences.sequence',\n",
+       "       'assays.confidence_score', 'docs.chembl_id', 'docs.year',\n",
+       "       'docs.authors', 'UniprotID'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataframe contains many columns that won't be necessary for the rest of the notebook and are therefore removed. Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the UniProt ID of the kinase. These columns are renamed for readability."
+    "The dataframe contains many columns that won't be necessary for the rest of the notebook which are therefore removed. \n",
+    "Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the UniProt ID of the kinase. These columns are renamed for readability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -514,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +623,7 @@
        "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.920819    O75116"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -613,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -635,12 +664,12 @@
    "source": [
     "#### Kinases of interest\n",
     "\n",
-    "We focus on the kinases of interest and map them to their UniProt IDs, using Table 1 from the theory."
+    "We focus on the kinases of interest and map them to their [UniProt](https://www.uniprot.org/) IDs, using Table 1 from the theory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -664,7 +693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -682,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -742,84 +771,26 @@
        "      <td>11.096910</td>\n",
        "      <td>P00533</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186925</th>\n",
-       "      <td>Nc1nc2cc(-c3cncc(S(N)(=O)=O)c3)ccn2n1</td>\n",
-       "      <td>1.800000</td>\n",
-       "      <td>P42336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186927</th>\n",
-       "      <td>O=C(/C=C/c1ccc(O)c(O)c1)c1ccc(O[C@@H]2O[C@H](C...</td>\n",
-       "      <td>1.721246</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186929</th>\n",
-       "      <td>CS(=O)(=O)c1cncc(-c2ccn3nc(N)nc3c2)c1</td>\n",
-       "      <td>1.700000</td>\n",
-       "      <td>P42336</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186936</th>\n",
-       "      <td>O=C(CCc1ccc(O)cc1)c1c(O)cc(O)cc1O</td>\n",
-       "      <td>1.602060</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186957</th>\n",
-       "      <td>Nc1nc2cc(-c3cncc(S(=O)(=O)Nc4ccccc4)c3)ccn2n1</td>\n",
-       "      <td>1.400000</td>\n",
-       "      <td>P42336</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>33169 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                                   smiles  activity_value  \\\n",
-       "57                      Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1       11.522879   \n",
-       "98                    CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC       11.221849   \n",
-       "101                    CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.221849   \n",
-       "139                   Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1       11.096910   \n",
-       "140                       CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.096910   \n",
-       "...                                                   ...             ...   \n",
-       "186925              Nc1nc2cc(-c3cncc(S(N)(=O)=O)c3)ccn2n1        1.800000   \n",
-       "186927  O=C(/C=C/c1ccc(O)c(O)c1)c1ccc(O[C@@H]2O[C@H](C...        1.721246   \n",
-       "186929              CS(=O)(=O)c1cncc(-c2ccn3nc(N)nc3c2)c1        1.700000   \n",
-       "186936                  O=C(CCc1ccc(O)cc1)c1c(O)cc(O)cc1O        1.602060   \n",
-       "186957      Nc1nc2cc(-c3cncc(S(=O)(=O)Nc4ccccc4)c3)ccn2n1        1.400000   \n",
-       "\n",
-       "       UniprotID  \n",
-       "57        P00533  \n",
-       "98        P00533  \n",
-       "101       P00533  \n",
-       "139       P00533  \n",
-       "140       P00533  \n",
-       "...          ...  \n",
-       "186925    P42336  \n",
-       "186927    P00533  \n",
-       "186929    P42336  \n",
-       "186936    P00533  \n",
-       "186957    P42336  \n",
-       "\n",
-       "[33169 rows x 3 columns]"
+       "                                  smiles  activity_value UniprotID\n",
+       "57     Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1       11.522879    P00533\n",
+       "98   CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC       11.221849    P00533\n",
+       "101   CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.221849    P00533\n",
+       "139  Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1       11.096910    P00533\n",
+       "140      CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.096910    P00533"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data"
+    "data.head()"
    ]
   },
   {
@@ -831,7 +802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -847,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -861,7 +832,7 @@
        "  7)]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,12 +846,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a simple workaround, we keep the value for which the compound has the highest activity value."
+    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., highest pIC50 value here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -940,78 +911,20 @@
        "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
        "      <td>8.420216</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32683</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1</td>\n",
-       "      <td>5.102373</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32684</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1</td>\n",
-       "      <td>6.292430</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32685</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1</td>\n",
-       "      <td>7.522879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32686</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1</td>\n",
-       "      <td>5.903090</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32687</th>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1</td>\n",
-       "      <td>6.397940</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>32688 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      UniprotID                                             smiles  \\\n",
-       "0        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
-       "1        P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
-       "2        P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
-       "3        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "4        P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "...         ...                                                ...   \n",
-       "32683    Q16539           c1ccc(-c2nn3c(c2-c2ccnc4ccccc24)CCC3)nc1   \n",
-       "32684    Q16539     c1ccc(Cn2cc(-c3ccnc4ccccc34)c(-c3ccccn3)n2)cc1   \n",
-       "32685    Q16539             c1ccc(Nc2ccc3c(-c4ccccc4)n[nH]c3c2)cc1   \n",
-       "32686    Q16539        c1ccc(Sc2ncnc3c2N=C(c2cccnc2)c2ccccc2S3)cc1   \n",
-       "32687    Q16539                c1csc(-c2n[nH]cc2-c2ccnc3ccccc23)c1   \n",
-       "\n",
-       "       activity_value  \n",
-       "0            5.336488  \n",
-       "1            5.996539  \n",
-       "2            8.397940  \n",
-       "3            7.207608  \n",
-       "4            8.420216  \n",
-       "...               ...  \n",
-       "32683        5.102373  \n",
-       "32684        6.292430  \n",
-       "32685        7.522879  \n",
-       "32686        5.903090  \n",
-       "32687        6.397940  \n",
-       "\n",
-       "[32688 rows x 3 columns]"
+       "  UniprotID                                             smiles  activity_value\n",
+       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...        5.336488\n",
+       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...        5.996539\n",
+       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...        8.397940\n",
+       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...        7.207608\n",
+       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...        8.420216"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,7 +932,7 @@
    "source": [
     "data = data.groupby([\"UniprotID\",\n",
     "                     \"smiles\"])['activity_value'].max().reset_index()\n",
-    "data"
+    "data.head()"
    ]
   },
   {
@@ -1033,12 +946,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a cut-off of $6.3$, similarly to the [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a pIC50 cut-off of $6.3$, similarly to the [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1047,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1075,7 +988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1178,7 +1091,7 @@
        "4        8.420216                1  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1210,18 +1123,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def kinase_to_activity_numbers(kinase_name, activity_dataframe):\n",
+    "def kinase_to_activity_numbers(uniprot_id, activity_dataframe):\n",
     "    \"\"\"\n",
     "    Retrieve the three values for a given kinase.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    kinase_name : str\n",
-    "        The name of the kinase of interest, e.g. \"EGFR\".\n",
+    "    uniprot_id : str\n",
+    "        The uniprot id of the kinase of interest, e.g. \"P00533\" for \"EGFR\".\n",
     "\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
@@ -1234,7 +1147,6 @@
     "        2. The number of active compounds against the kinase.\n",
     "        3. The fraction of active compounds against the kinase.\n",
     "    \"\"\"\n",
-    "    uniprot_id = name_to_uniprot[kinase_name]\n",
     "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"]\n",
     "                                     == uniprot_id]\n",
     "    total_measured_compounds = len(kinase_data)\n",
@@ -1256,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1271,7 +1183,8 @@
     }
    ],
    "source": [
-    "EGFR_metrics = kinase_to_activity_numbers(\"EGFR\", data)\n",
+    "uniprot_id = name_to_uniprot[\"EGFR\"]\n",
+    "EGFR_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
     "print(f\"Total number of measured compounds: \\t\"\n",
     "      f\"{EGFR_metrics[0]} \\n\"\n",
     "      f\"Number of active compounds: \\t\\t\"\n",
@@ -1289,7 +1202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1309,15 +1222,15 @@
     "    \"\"\"\n",
     "    promiscuity_table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
     "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
-    "    for name in name_to_uniprot:\n",
-    "        values = kinase_to_activity_numbers(name, activity_dataframe)\n",
+    "    for name, uniprot_id in name_to_uniprot.items():\n",
+    "        values = kinase_to_activity_numbers(uniprot_id, activity_dataframe)\n",
     "        promiscuity_table.loc[name] = values\n",
     "    return promiscuity_table"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1418,7 +1331,7 @@
        "p110a  4347    2780  0.639522"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,120 +1350,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "zero-size array to reduction operation fmin which has no identity",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mHooks\u001b[0m \u001b[0minto\u001b[0m \u001b[0mJupyter\u001b[0m \u001b[0mnotebook\u001b[0m \u001b[0mrich\u001b[0m \u001b[0mdisplay\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \"\"\"\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m     @doc(\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    617\u001b[0m         \u001b[0;34m*\u001b[0m \u001b[0mtable_attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m         \"\"\"\n\u001b[0;32m--> 619\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m         \u001b[0;31m# TODO: namespace all the pandas keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_translate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_compute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_todo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, func, axis, subset, **kwargs)\u001b[0m\n\u001b[1;32m    720\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m                 raise TypeError(\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_highlight_extrema\u001b[0;34m(data, color, max_)\u001b[0m\n\u001b[1;32m   1547\u001b[0m             \u001b[0mextrema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1548\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1549\u001b[0;31m             \u001b[0mextrema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Series from .apply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/numpy/lib/nanfunctions.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# Fast, but not safe for subclasses of ndarray, or object arrays,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;31m# which do not implement isnan (gh-9009), or fmin correctly (gh-8975)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m             warnings.warn(\"All-NaN slice encountered\", RuntimeWarning,\n",
+      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation fmin which has no identity"
+     ]
+    },
     {
      "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >total</th>\n",
-       "      <th class=\"col_heading level0 col1\" >actives</th>\n",
-       "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_2731f_row0_col0\" class=\"data row0 col0\" >5869</td>\n",
-       "      <td id=\"T_2731f_row0_col1\" class=\"data row0 col1\" >3574</td>\n",
-       "      <td id=\"T_2731f_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_2731f_row1_col0\" class=\"data row1 col0\" >1694</td>\n",
-       "      <td id=\"T_2731f_row1_col1\" class=\"data row1 col1\" >1025</td>\n",
-       "      <td id=\"T_2731f_row1_col2\" class=\"data row1 col2\" >0.605</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_2731f_row2_col0\" class=\"data row2 col0\" >3682</td>\n",
-       "      <td id=\"T_2731f_row2_col1\" class=\"data row2 col1\" >2988</td>\n",
-       "      <td id=\"T_2731f_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_2731f_row3_col0\" class=\"data row3 col0\" >1505</td>\n",
-       "      <td id=\"T_2731f_row3_col1\" class=\"data row3 col1\" >819</td>\n",
-       "      <td id=\"T_2731f_row3_col2\" class=\"data row3 col2\" >0.544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_2731f_row4_col0\" class=\"data row4 col0\" >1537</td>\n",
-       "      <td id=\"T_2731f_row4_col1\" class=\"data row4 col1\" >930</td>\n",
-       "      <td id=\"T_2731f_row4_col2\" class=\"data row4 col2\" >0.605</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_2731f_row5_col0\" class=\"data row5 col0\" >2821</td>\n",
-       "      <td id=\"T_2731f_row5_col1\" class=\"data row5 col1\" >2235</td>\n",
-       "      <td id=\"T_2731f_row5_col2\" class=\"data row5 col2\" >0.792</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_2731f_row6_col0\" class=\"data row6 col0\" >3609</td>\n",
-       "      <td id=\"T_2731f_row6_col1\" class=\"data row6 col1\" >2760</td>\n",
-       "      <td id=\"T_2731f_row6_col2\" class=\"data row6 col2\" >0.765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_2731f_row7_col0\" class=\"data row7 col0\" >7624</td>\n",
-       "      <td id=\"T_2731f_row7_col1\" class=\"data row7 col1\" >5312</td>\n",
-       "      <td id=\"T_2731f_row7_col2\" class=\"data row7 col2\" >0.697</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2731f_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_2731f_row8_col0\" class=\"data row8 col0\" >4347</td>\n",
-       "      <td id=\"T_2731f_row8_col1\" class=\"data row8 col1\" >2780</td>\n",
-       "      <td id=\"T_2731f_row8_col2\" class=\"data row8 col2\" >0.640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9a161977f0>"
+       "<pandas.io.formats.style.Styler at 0x7fd9e5ed4be0>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1567,7 +1394,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the table, we notice that CDK2, BRAF are the least, most promiscuous kinases, respectively."
+    "From the table, we notice that CDK2 and BRAF are the least and most promiscuous kinases, respectively."
    ]
   },
   {
@@ -1586,20 +1413,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def similarity_ligand_profile(kinase_name1, kinase_name2, activity_dataframe):\n",
+    "def similarity_ligand_profile(uniprot_id1, uniprot_id2, activity_dataframe):\n",
     "    \"\"\"\n",
     "    Compute the similarity between two kinases using ligand profiling.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    kinase_name1 : str\n",
-    "        First kinase of interest.\n",
-    "    kinase_name2 : str\n",
-    "        Second kinase of interest.\n",
+    "    uniprot_id1 : str\n",
+    "        Uniprot id of first kinase of interest.\n",
+    "    uniprot_id2 : str\n",
+    "        Uniprot id of second kinase of interest.\n",
     "\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
@@ -1611,20 +1438,17 @@
     "        i.e. number of active compounds on both kinases\n",
     "        over number of measured compounds on both kinases.\n",
     "    \"\"\"\n",
-    "    if kinase_name1 == kinase_name2:\n",
+    "    if uniprot_id1 == uniprot_id2:\n",
     "        (total_compounds,\n",
     "         active_compounds,\n",
-    "         fraction) = kinase_to_activity_numbers(kinase_name1,\n",
+    "         fraction) = kinase_to_activity_numbers(uniprot_id1,\n",
     "                                                activity_dataframe)\n",
     "        return fraction\n",
     "    else:\n",
-    "        uniprot1 = name_to_uniprot[kinase_name1]\n",
-    "        uniprot2 = name_to_uniprot[kinase_name2]\n",
-    "\n",
     "        # Data for the two kinases only\n",
     "        reduced_data = activity_dataframe[activity_dataframe\n",
-    "                                          [\"UniprotID\"].isin([uniprot1,\n",
-    "                                                              uniprot2])]\n",
+    "                                          [\"UniprotID\"].isin([uniprot_id1,\n",
+    "                                                              uniprot_id2])]\n",
     "\n",
     "        # Look at active compounds only\n",
     "        active_entries = reduced_data[reduced_data[\"activity_binary\"] == 1]\n",
@@ -1643,7 +1467,7 @@
     "                        measured_compounds_on_both[0])\n",
     "        else:\n",
     "            print(f\"No compounds were measured on both kinases, \"\n",
-    "                  f\"namely {kinase_name1} and {kinase_name2}.\")\n",
+    "                  f\"namely {uniprot_id1} and {uniprot_id2}.\")\n",
     "            fraction = np.nan\n",
     "        return fraction"
    ]
@@ -1657,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1669,7 +1493,9 @@
     }
    ],
    "source": [
-    "similarity_EGFR_MET = similarity_ligand_profile(\"EGFR\", \"MET\", data)\n",
+    "uniprot_id1=name_to_uniprot[\"EGFR\"]\n",
+    "uniprot_id2=name_to_uniprot[\"MET\"]\n",
+    "similarity_EGFR_MET = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)\n",
     "print(f\"Ligand profile similarity between EGFR and MET: \"\n",
     "      f\"{similarity_EGFR_MET:.2f}.\")"
    ]
@@ -1683,15 +1509,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "No compounds were measured on both kinases, namely ErbB2 and p110a.\n",
-      "No compounds were measured on both kinases, namely p110a and ErbB2.\n"
+      "No compounds were measured on both kinases, namely P04626 and P42336.\n",
+      "No compounds were measured on both kinases, namely P42336 and P04626.\n"
      ]
     }
    ],
@@ -1700,15 +1526,17 @@
     "                                     len(name_to_uniprot)))\n",
     "for i, kinase_name1 in enumerate(name_to_uniprot):\n",
     "    for j, kinase_name2 in enumerate(name_to_uniprot):\n",
+    "        uniprot_id1=name_to_uniprot[kinase_name1]\n",
+    "        uniprot_id2=name_to_uniprot[kinase_name2]\n",
     "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(\n",
-    "            kinase_name1,\n",
-    "            kinase_name2,\n",
+    "            uniprot_id1,\n",
+    "            uniprot_id2,\n",
     "            data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1880,7 +1708,7 @@
        "p110a  0.180791  0.639522  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1894,358 +1722,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
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-       "</style>\n",
-       "<table id=\"T_ad0a6_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_ad0a6_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_ad0a6_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "      <td id=\"T_ad0a6_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "      <td id=\"T_ad0a6_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "      <td id=\"T_ad0a6_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "      <td id=\"T_ad0a6_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "      <td id=\"T_ad0a6_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "      <td id=\"T_ad0a6_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "      <td id=\"T_ad0a6_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_ad0a6_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "      <td id=\"T_ad0a6_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "      <td id=\"T_ad0a6_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "      <td id=\"T_ad0a6_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "      <td id=\"T_ad0a6_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "      <td id=\"T_ad0a6_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "      <td id=\"T_ad0a6_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "      <td id=\"T_ad0a6_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "      <td id=\"T_ad0a6_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_ad0a6_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "      <td id=\"T_ad0a6_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "      <td id=\"T_ad0a6_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "      <td id=\"T_ad0a6_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "      <td id=\"T_ad0a6_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "      <td id=\"T_ad0a6_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "      <td id=\"T_ad0a6_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "      <td id=\"T_ad0a6_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "      <td id=\"T_ad0a6_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_ad0a6_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "      <td id=\"T_ad0a6_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "      <td id=\"T_ad0a6_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "      <td id=\"T_ad0a6_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "      <td id=\"T_ad0a6_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "      <td id=\"T_ad0a6_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "      <td id=\"T_ad0a6_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "      <td id=\"T_ad0a6_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "      <td id=\"T_ad0a6_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_ad0a6_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "      <td id=\"T_ad0a6_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "      <td id=\"T_ad0a6_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "      <td id=\"T_ad0a6_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "      <td id=\"T_ad0a6_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "      <td id=\"T_ad0a6_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "      <td id=\"T_ad0a6_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "      <td id=\"T_ad0a6_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "      <td id=\"T_ad0a6_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_ad0a6_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "      <td id=\"T_ad0a6_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "      <td id=\"T_ad0a6_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "      <td id=\"T_ad0a6_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "      <td id=\"T_ad0a6_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "      <td id=\"T_ad0a6_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "      <td id=\"T_ad0a6_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "      <td id=\"T_ad0a6_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "      <td id=\"T_ad0a6_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_ad0a6_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "      <td id=\"T_ad0a6_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "      <td id=\"T_ad0a6_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "      <td id=\"T_ad0a6_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "      <td id=\"T_ad0a6_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "      <td id=\"T_ad0a6_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "      <td id=\"T_ad0a6_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "      <td id=\"T_ad0a6_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "      <td id=\"T_ad0a6_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_ad0a6_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "      <td id=\"T_ad0a6_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "      <td id=\"T_ad0a6_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "      <td id=\"T_ad0a6_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "      <td id=\"T_ad0a6_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "      <td id=\"T_ad0a6_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "      <td id=\"T_ad0a6_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "      <td id=\"T_ad0a6_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "      <td id=\"T_ad0a6_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ad0a6_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_ad0a6_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "      <td id=\"T_ad0a6_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
-       "      <td id=\"T_ad0a6_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "      <td id=\"T_ad0a6_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "      <td id=\"T_ad0a6_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "      <td id=\"T_ad0a6_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "      <td id=\"T_ad0a6_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "      <td id=\"T_ad0a6_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "      <td id=\"T_ad0a6_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
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+       "        }#T_9e788_row8_col2{\n",
+       "            background-color:  #cbe4cb;\n",
+       "            color:  #000000;\n",
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+       "            background-color:  #93c893;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_9e788_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_9e788_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_9e788_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "                        <td id=\"T_9e788_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "                        <td id=\"T_9e788_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "                        <td id=\"T_9e788_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "                        <td id=\"T_9e788_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "                        <td id=\"T_9e788_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "                        <td id=\"T_9e788_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "                        <td id=\"T_9e788_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "                        <td id=\"T_9e788_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_9e788_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "                        <td id=\"T_9e788_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "                        <td id=\"T_9e788_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "                        <td id=\"T_9e788_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "                        <td id=\"T_9e788_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "                        <td id=\"T_9e788_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "                        <td id=\"T_9e788_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "                        <td id=\"T_9e788_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "                        <td id=\"T_9e788_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_9e788_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "                        <td id=\"T_9e788_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "                        <td id=\"T_9e788_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "                        <td id=\"T_9e788_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "                        <td id=\"T_9e788_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "                        <td id=\"T_9e788_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "                        <td id=\"T_9e788_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "                        <td id=\"T_9e788_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "                        <td id=\"T_9e788_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_9e788_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "                        <td id=\"T_9e788_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "                        <td id=\"T_9e788_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "                        <td id=\"T_9e788_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "                        <td id=\"T_9e788_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "                        <td id=\"T_9e788_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "                        <td id=\"T_9e788_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "                        <td id=\"T_9e788_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "                        <td id=\"T_9e788_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_9e788_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "                        <td id=\"T_9e788_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "                        <td id=\"T_9e788_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "                        <td id=\"T_9e788_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "                        <td id=\"T_9e788_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "                        <td id=\"T_9e788_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "                        <td id=\"T_9e788_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "                        <td id=\"T_9e788_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "                        <td id=\"T_9e788_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_9e788_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "                        <td id=\"T_9e788_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "                        <td id=\"T_9e788_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "                        <td id=\"T_9e788_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "                        <td id=\"T_9e788_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "                        <td id=\"T_9e788_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "                        <td id=\"T_9e788_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "                        <td id=\"T_9e788_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "                        <td id=\"T_9e788_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_9e788_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "                        <td id=\"T_9e788_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "                        <td id=\"T_9e788_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "                        <td id=\"T_9e788_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "                        <td id=\"T_9e788_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "                        <td id=\"T_9e788_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "                        <td id=\"T_9e788_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "                        <td id=\"T_9e788_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "                        <td id=\"T_9e788_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_9e788_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "                        <td id=\"T_9e788_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "                        <td id=\"T_9e788_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "                        <td id=\"T_9e788_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "                        <td id=\"T_9e788_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "                        <td id=\"T_9e788_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "                        <td id=\"T_9e788_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "                        <td id=\"T_9e788_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "                        <td id=\"T_9e788_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_9e788_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_9e788_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "                        <td id=\"T_9e788_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
+       "                        <td id=\"T_9e788_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "                        <td id=\"T_9e788_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "                        <td id=\"T_9e788_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "                        <td id=\"T_9e788_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "                        <td id=\"T_9e788_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "                        <td id=\"T_9e788_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "                        <td id=\"T_9e788_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9a15a26e80>"
+       "<pandas.io.formats.style.Styler at 0x7fd9e5b8bbb0>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2262,6 +2020,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Note that the diagonal contains the previously discussed promiscuity values.\n",
+    "\n",
     "As mentioned above, no compounds were measured on both ErbB2 and p110a and therefore create a `np.nan` entry which can be problematic for algorithmic reason.\n",
     "\n",
     "As a simple workaround, we will fill the NA values with zero."
@@ -2269,358 +2029,288 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
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-       "#T_f829b_row7_col6 {\n",
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-       "#T_f829b_row7_col7 {\n",
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-       "  color: #f1f1f1;\n",
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-       "#T_f829b_row8_col2 {\n",
-       "  background-color: #cbe4cb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_f829b_row8_col3 {\n",
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-       "</style>\n",
-       "<table id=\"T_f829b_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_f829b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_f829b_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "      <td id=\"T_f829b_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "      <td id=\"T_f829b_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "      <td id=\"T_f829b_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "      <td id=\"T_f829b_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "      <td id=\"T_f829b_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "      <td id=\"T_f829b_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "      <td id=\"T_f829b_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_f829b_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "      <td id=\"T_f829b_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "      <td id=\"T_f829b_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "      <td id=\"T_f829b_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "      <td id=\"T_f829b_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "      <td id=\"T_f829b_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "      <td id=\"T_f829b_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "      <td id=\"T_f829b_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "      <td id=\"T_f829b_row1_col8\" class=\"data row1 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_f829b_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "      <td id=\"T_f829b_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "      <td id=\"T_f829b_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "      <td id=\"T_f829b_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "      <td id=\"T_f829b_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "      <td id=\"T_f829b_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "      <td id=\"T_f829b_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "      <td id=\"T_f829b_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "      <td id=\"T_f829b_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_f829b_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "      <td id=\"T_f829b_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "      <td id=\"T_f829b_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "      <td id=\"T_f829b_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "      <td id=\"T_f829b_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "      <td id=\"T_f829b_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "      <td id=\"T_f829b_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "      <td id=\"T_f829b_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "      <td id=\"T_f829b_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_f829b_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "      <td id=\"T_f829b_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "      <td id=\"T_f829b_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "      <td id=\"T_f829b_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "      <td id=\"T_f829b_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "      <td id=\"T_f829b_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "      <td id=\"T_f829b_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "      <td id=\"T_f829b_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "      <td id=\"T_f829b_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_f829b_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "      <td id=\"T_f829b_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "      <td id=\"T_f829b_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "      <td id=\"T_f829b_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "      <td id=\"T_f829b_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "      <td id=\"T_f829b_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "      <td id=\"T_f829b_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "      <td id=\"T_f829b_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "      <td id=\"T_f829b_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_f829b_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "      <td id=\"T_f829b_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "      <td id=\"T_f829b_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "      <td id=\"T_f829b_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "      <td id=\"T_f829b_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "      <td id=\"T_f829b_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "      <td id=\"T_f829b_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "      <td id=\"T_f829b_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "      <td id=\"T_f829b_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_f829b_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "      <td id=\"T_f829b_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "      <td id=\"T_f829b_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "      <td id=\"T_f829b_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "      <td id=\"T_f829b_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "      <td id=\"T_f829b_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "      <td id=\"T_f829b_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "      <td id=\"T_f829b_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "      <td id=\"T_f829b_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f829b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_f829b_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "      <td id=\"T_f829b_row8_col1\" class=\"data row8 col1\" >0.000</td>\n",
-       "      <td id=\"T_f829b_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "      <td id=\"T_f829b_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "      <td id=\"T_f829b_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "      <td id=\"T_f829b_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "      <td id=\"T_f829b_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "      <td id=\"T_f829b_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "      <td id=\"T_f829b_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
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+       "            background-color:  #50a750;\n",
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+       "        }#T_07da8_row7_col2{\n",
+       "            background-color:  #138913;\n",
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+       "        }#T_07da8_row7_col4{\n",
+       "            background-color:  #42a042;\n",
+       "            color:  #000000;\n",
+       "        }#T_07da8_row7_col5{\n",
+       "            background-color:  #4ba54a;\n",
+       "            color:  #000000;\n",
+       "        }#T_07da8_row7_col6{\n",
+       "            background-color:  #51a851;\n",
+       "            color:  #000000;\n",
+       "        }#T_07da8_row7_col7{\n",
+       "            background-color:  #158a15;\n",
+       "            color:  #000000;\n",
+       "        }#T_07da8_row8_col2{\n",
+       "            background-color:  #cbe4cb;\n",
+       "            color:  #000000;\n",
+       "        }#T_07da8_row8_col3{\n",
+       "            background-color:  #93c893;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_07da8_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_07da8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_07da8_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "                        <td id=\"T_07da8_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
+       "                        <td id=\"T_07da8_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
+       "                        <td id=\"T_07da8_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
+       "                        <td id=\"T_07da8_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
+       "                        <td id=\"T_07da8_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
+       "                        <td id=\"T_07da8_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
+       "                        <td id=\"T_07da8_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
+       "                        <td id=\"T_07da8_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_07da8_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
+       "                        <td id=\"T_07da8_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
+       "                        <td id=\"T_07da8_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
+       "                        <td id=\"T_07da8_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
+       "                        <td id=\"T_07da8_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
+       "                        <td id=\"T_07da8_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
+       "                        <td id=\"T_07da8_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
+       "                        <td id=\"T_07da8_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
+       "                        <td id=\"T_07da8_row1_col8\" class=\"data row1 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_07da8_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
+       "                        <td id=\"T_07da8_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
+       "                        <td id=\"T_07da8_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
+       "                        <td id=\"T_07da8_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
+       "                        <td id=\"T_07da8_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
+       "                        <td id=\"T_07da8_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
+       "                        <td id=\"T_07da8_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
+       "                        <td id=\"T_07da8_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
+       "                        <td id=\"T_07da8_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_07da8_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
+       "                        <td id=\"T_07da8_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
+       "                        <td id=\"T_07da8_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
+       "                        <td id=\"T_07da8_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
+       "                        <td id=\"T_07da8_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
+       "                        <td id=\"T_07da8_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
+       "                        <td id=\"T_07da8_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
+       "                        <td id=\"T_07da8_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
+       "                        <td id=\"T_07da8_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_07da8_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
+       "                        <td id=\"T_07da8_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
+       "                        <td id=\"T_07da8_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
+       "                        <td id=\"T_07da8_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
+       "                        <td id=\"T_07da8_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
+       "                        <td id=\"T_07da8_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
+       "                        <td id=\"T_07da8_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
+       "                        <td id=\"T_07da8_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
+       "                        <td id=\"T_07da8_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_07da8_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
+       "                        <td id=\"T_07da8_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
+       "                        <td id=\"T_07da8_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
+       "                        <td id=\"T_07da8_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
+       "                        <td id=\"T_07da8_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
+       "                        <td id=\"T_07da8_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
+       "                        <td id=\"T_07da8_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
+       "                        <td id=\"T_07da8_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
+       "                        <td id=\"T_07da8_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_07da8_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
+       "                        <td id=\"T_07da8_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
+       "                        <td id=\"T_07da8_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
+       "                        <td id=\"T_07da8_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
+       "                        <td id=\"T_07da8_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
+       "                        <td id=\"T_07da8_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
+       "                        <td id=\"T_07da8_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
+       "                        <td id=\"T_07da8_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "                        <td id=\"T_07da8_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_07da8_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
+       "                        <td id=\"T_07da8_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
+       "                        <td id=\"T_07da8_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
+       "                        <td id=\"T_07da8_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
+       "                        <td id=\"T_07da8_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
+       "                        <td id=\"T_07da8_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
+       "                        <td id=\"T_07da8_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "                        <td id=\"T_07da8_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
+       "                        <td id=\"T_07da8_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_07da8_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_07da8_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
+       "                        <td id=\"T_07da8_row8_col1\" class=\"data row8 col1\" >0.000</td>\n",
+       "                        <td id=\"T_07da8_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
+       "                        <td id=\"T_07da8_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
+       "                        <td id=\"T_07da8_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
+       "                        <td id=\"T_07da8_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
+       "                        <td id=\"T_07da8_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
+       "                        <td id=\"T_07da8_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
+       "                        <td id=\"T_07da8_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9a16197d90>"
+       "<pandas.io.formats.style.Styler at 0x7fd9e5b8bd90>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2642,7 +2332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2668,7 +2358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2687,7 +2377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2703,7 +2393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2712,342 +2402,273 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "#T_1a2d4_row2_col7 {\n",
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-       "  background-color: #0b850b;\n",
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-       "#T_1a2d4_row6_col7 {\n",
-       "  background-color: #5bad5b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_1a2d4_row7_col0 {\n",
-       "  background-color: #46a246;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_1a2d4_row7_col1 {\n",
-       "  background-color: #5dae5d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_1a2d4_row7_col2 {\n",
-       "  background-color: #abd4ab;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_1a2d4_row7_col3 {\n",
-       "  background-color: #86c286;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_1a2d4_row7_col4 {\n",
-       "  background-color: #65b265;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_1a2d4_row7_col5 {\n",
-       "  background-color: #7fbe7f;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_1a2d4_row7_col6 {\n",
-       "  background-color: #75b975;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_1a2d4_row8_col3 {\n",
-       "  background-color: #329832;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_1a2d4_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_1a2d4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row0_col1\" class=\"data row0 col1\" >0.443</td>\n",
-       "      <td id=\"T_1a2d4_row0_col2\" class=\"data row0 col2\" >0.581</td>\n",
-       "      <td id=\"T_1a2d4_row0_col3\" class=\"data row0 col3\" >0.884</td>\n",
-       "      <td id=\"T_1a2d4_row0_col4\" class=\"data row0 col4\" >0.762</td>\n",
-       "      <td id=\"T_1a2d4_row0_col5\" class=\"data row0 col5\" >0.774</td>\n",
-       "      <td id=\"T_1a2d4_row0_col6\" class=\"data row0 col6\" >0.684</td>\n",
-       "      <td id=\"T_1a2d4_row0_col7\" class=\"data row0 col7\" >0.652</td>\n",
-       "      <td id=\"T_1a2d4_row0_col8\" class=\"data row0 col8\" >0.929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_1a2d4_row1_col0\" class=\"data row1 col0\" >0.443</td>\n",
-       "      <td id=\"T_1a2d4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row1_col2\" class=\"data row1 col2\" >0.750</td>\n",
-       "      <td id=\"T_1a2d4_row1_col3\" class=\"data row1 col3\" >0.846</td>\n",
-       "      <td id=\"T_1a2d4_row1_col4\" class=\"data row1 col4\" >0.824</td>\n",
-       "      <td id=\"T_1a2d4_row1_col5\" class=\"data row1 col5\" >0.964</td>\n",
-       "      <td id=\"T_1a2d4_row1_col6\" class=\"data row1 col6\" >0.875</td>\n",
-       "      <td id=\"T_1a2d4_row1_col7\" class=\"data row1 col7\" >0.602</td>\n",
-       "      <td id=\"T_1a2d4_row1_col8\" class=\"data row1 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_1a2d4_row2_col0\" class=\"data row2 col0\" >0.581</td>\n",
-       "      <td id=\"T_1a2d4_row2_col1\" class=\"data row2 col1\" >0.750</td>\n",
-       "      <td id=\"T_1a2d4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row2_col3\" class=\"data row2 col3\" >0.933</td>\n",
-       "      <td id=\"T_1a2d4_row2_col4\" class=\"data row2 col4\" >0.488</td>\n",
-       "      <td id=\"T_1a2d4_row2_col5\" class=\"data row2 col5\" >0.889</td>\n",
-       "      <td id=\"T_1a2d4_row2_col6\" class=\"data row2 col6\" >0.356</td>\n",
-       "      <td id=\"T_1a2d4_row2_col7\" class=\"data row2 col7\" >0.252</td>\n",
-       "      <td id=\"T_1a2d4_row2_col8\" class=\"data row2 col8\" >0.833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_1a2d4_row3_col0\" class=\"data row3 col0\" >0.884</td>\n",
-       "      <td id=\"T_1a2d4_row3_col1\" class=\"data row3 col1\" >0.846</td>\n",
-       "      <td id=\"T_1a2d4_row3_col2\" class=\"data row3 col2\" >0.933</td>\n",
-       "      <td id=\"T_1a2d4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row3_col4\" class=\"data row3 col4\" >0.833</td>\n",
-       "      <td id=\"T_1a2d4_row3_col5\" class=\"data row3 col5\" >0.913</td>\n",
-       "      <td id=\"T_1a2d4_row3_col6\" class=\"data row3 col6\" >0.917</td>\n",
-       "      <td id=\"T_1a2d4_row3_col7\" class=\"data row3 col7\" >0.398</td>\n",
-       "      <td id=\"T_1a2d4_row3_col8\" class=\"data row3 col8\" >0.733</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_1a2d4_row4_col0\" class=\"data row4 col0\" >0.762</td>\n",
-       "      <td id=\"T_1a2d4_row4_col1\" class=\"data row4 col1\" >0.824</td>\n",
-       "      <td id=\"T_1a2d4_row4_col2\" class=\"data row4 col2\" >0.488</td>\n",
-       "      <td id=\"T_1a2d4_row4_col3\" class=\"data row4 col3\" >0.833</td>\n",
-       "      <td id=\"T_1a2d4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row4_col5\" class=\"data row4 col5\" >0.712</td>\n",
-       "      <td id=\"T_1a2d4_row4_col6\" class=\"data row4 col6\" >0.511</td>\n",
-       "      <td id=\"T_1a2d4_row4_col7\" class=\"data row4 col7\" >0.567</td>\n",
-       "      <td id=\"T_1a2d4_row4_col8\" class=\"data row4 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_1a2d4_row5_col0\" class=\"data row5 col0\" >0.774</td>\n",
-       "      <td id=\"T_1a2d4_row5_col1\" class=\"data row5 col1\" >0.964</td>\n",
-       "      <td id=\"T_1a2d4_row5_col2\" class=\"data row5 col2\" >0.889</td>\n",
-       "      <td id=\"T_1a2d4_row5_col3\" class=\"data row5 col3\" >0.913</td>\n",
-       "      <td id=\"T_1a2d4_row5_col4\" class=\"data row5 col4\" >0.712</td>\n",
-       "      <td id=\"T_1a2d4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row5_col6\" class=\"data row5 col6\" >0.952</td>\n",
-       "      <td id=\"T_1a2d4_row5_col7\" class=\"data row5 col7\" >0.461</td>\n",
-       "      <td id=\"T_1a2d4_row5_col8\" class=\"data row5 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_1a2d4_row6_col0\" class=\"data row6 col0\" >0.684</td>\n",
-       "      <td id=\"T_1a2d4_row6_col1\" class=\"data row6 col1\" >0.875</td>\n",
-       "      <td id=\"T_1a2d4_row6_col2\" class=\"data row6 col2\" >0.356</td>\n",
-       "      <td id=\"T_1a2d4_row6_col3\" class=\"data row6 col3\" >0.917</td>\n",
-       "      <td id=\"T_1a2d4_row6_col4\" class=\"data row6 col4\" >0.511</td>\n",
-       "      <td id=\"T_1a2d4_row6_col5\" class=\"data row6 col5\" >0.952</td>\n",
-       "      <td id=\"T_1a2d4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "      <td id=\"T_1a2d4_row6_col8\" class=\"data row6 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_1a2d4_row7_col0\" class=\"data row7 col0\" >0.652</td>\n",
-       "      <td id=\"T_1a2d4_row7_col1\" class=\"data row7 col1\" >0.602</td>\n",
-       "      <td id=\"T_1a2d4_row7_col2\" class=\"data row7 col2\" >0.252</td>\n",
-       "      <td id=\"T_1a2d4_row7_col3\" class=\"data row7 col3\" >0.398</td>\n",
-       "      <td id=\"T_1a2d4_row7_col4\" class=\"data row7 col4\" >0.567</td>\n",
-       "      <td id=\"T_1a2d4_row7_col5\" class=\"data row7 col5\" >0.461</td>\n",
-       "      <td id=\"T_1a2d4_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "      <td id=\"T_1a2d4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_1a2d4_row7_col8\" class=\"data row7 col8\" >0.819</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1a2d4_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_1a2d4_row8_col0\" class=\"data row8 col0\" >0.929</td>\n",
-       "      <td id=\"T_1a2d4_row8_col1\" class=\"data row8 col1\" >1.000</td>\n",
-       "      <td id=\"T_1a2d4_row8_col2\" class=\"data row8 col2\" >0.833</td>\n",
-       "      <td id=\"T_1a2d4_row8_col3\" class=\"data row8 col3\" >0.733</td>\n",
-       "      <td id=\"T_1a2d4_row8_col4\" class=\"data row8 col4\" >1.000</td>\n",
-       "      <td id=\"T_1a2d4_row8_col5\" class=\"data row8 col5\" >1.000</td>\n",
-       "      <td id=\"T_1a2d4_row8_col6\" class=\"data row8 col6\" >1.000</td>\n",
-       "      <td id=\"T_1a2d4_row8_col7\" class=\"data row8 col7\" >0.819</td>\n",
-       "      <td id=\"T_1a2d4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
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+       "            background-color:  #ebf3eb;\n",
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+       "            background-color:  #008000;\n",
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+       "            background-color:  #48a348;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row5_col3{\n",
+       "            background-color:  #058205;\n",
+       "            color:  #f1f1f1;\n",
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+       "            color:  #000000;\n",
+       "        }#T_1426b_row6_col2{\n",
+       "            background-color:  #91c791;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row6_col3{\n",
+       "            background-color:  #048204;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_1426b_row6_col7{\n",
+       "            background-color:  #5bad5b;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col0{\n",
+       "            background-color:  #46a246;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col1{\n",
+       "            background-color:  #5dae5d;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col2{\n",
+       "            background-color:  #abd4ab;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col3{\n",
+       "            background-color:  #86c286;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col4{\n",
+       "            background-color:  #65b265;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col5{\n",
+       "            background-color:  #7fbe7f;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row7_col6{\n",
+       "            background-color:  #75b975;\n",
+       "            color:  #000000;\n",
+       "        }#T_1426b_row8_col3{\n",
+       "            background-color:  #329832;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_1426b_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_1426b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_1426b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row0_col1\" class=\"data row0 col1\" >0.443</td>\n",
+       "                        <td id=\"T_1426b_row0_col2\" class=\"data row0 col2\" >0.581</td>\n",
+       "                        <td id=\"T_1426b_row0_col3\" class=\"data row0 col3\" >0.884</td>\n",
+       "                        <td id=\"T_1426b_row0_col4\" class=\"data row0 col4\" >0.762</td>\n",
+       "                        <td id=\"T_1426b_row0_col5\" class=\"data row0 col5\" >0.774</td>\n",
+       "                        <td id=\"T_1426b_row0_col6\" class=\"data row0 col6\" >0.684</td>\n",
+       "                        <td id=\"T_1426b_row0_col7\" class=\"data row0 col7\" >0.652</td>\n",
+       "                        <td id=\"T_1426b_row0_col8\" class=\"data row0 col8\" >0.929</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_1426b_row1_col0\" class=\"data row1 col0\" >0.443</td>\n",
+       "                        <td id=\"T_1426b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row1_col2\" class=\"data row1 col2\" >0.750</td>\n",
+       "                        <td id=\"T_1426b_row1_col3\" class=\"data row1 col3\" >0.846</td>\n",
+       "                        <td id=\"T_1426b_row1_col4\" class=\"data row1 col4\" >0.824</td>\n",
+       "                        <td id=\"T_1426b_row1_col5\" class=\"data row1 col5\" >0.964</td>\n",
+       "                        <td id=\"T_1426b_row1_col6\" class=\"data row1 col6\" >0.875</td>\n",
+       "                        <td id=\"T_1426b_row1_col7\" class=\"data row1 col7\" >0.602</td>\n",
+       "                        <td id=\"T_1426b_row1_col8\" class=\"data row1 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_1426b_row2_col0\" class=\"data row2 col0\" >0.581</td>\n",
+       "                        <td id=\"T_1426b_row2_col1\" class=\"data row2 col1\" >0.750</td>\n",
+       "                        <td id=\"T_1426b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row2_col3\" class=\"data row2 col3\" >0.933</td>\n",
+       "                        <td id=\"T_1426b_row2_col4\" class=\"data row2 col4\" >0.488</td>\n",
+       "                        <td id=\"T_1426b_row2_col5\" class=\"data row2 col5\" >0.889</td>\n",
+       "                        <td id=\"T_1426b_row2_col6\" class=\"data row2 col6\" >0.356</td>\n",
+       "                        <td id=\"T_1426b_row2_col7\" class=\"data row2 col7\" >0.252</td>\n",
+       "                        <td id=\"T_1426b_row2_col8\" class=\"data row2 col8\" >0.833</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_1426b_row3_col0\" class=\"data row3 col0\" >0.884</td>\n",
+       "                        <td id=\"T_1426b_row3_col1\" class=\"data row3 col1\" >0.846</td>\n",
+       "                        <td id=\"T_1426b_row3_col2\" class=\"data row3 col2\" >0.933</td>\n",
+       "                        <td id=\"T_1426b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row3_col4\" class=\"data row3 col4\" >0.833</td>\n",
+       "                        <td id=\"T_1426b_row3_col5\" class=\"data row3 col5\" >0.913</td>\n",
+       "                        <td id=\"T_1426b_row3_col6\" class=\"data row3 col6\" >0.917</td>\n",
+       "                        <td id=\"T_1426b_row3_col7\" class=\"data row3 col7\" >0.398</td>\n",
+       "                        <td id=\"T_1426b_row3_col8\" class=\"data row3 col8\" >0.733</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_1426b_row4_col0\" class=\"data row4 col0\" >0.762</td>\n",
+       "                        <td id=\"T_1426b_row4_col1\" class=\"data row4 col1\" >0.824</td>\n",
+       "                        <td id=\"T_1426b_row4_col2\" class=\"data row4 col2\" >0.488</td>\n",
+       "                        <td id=\"T_1426b_row4_col3\" class=\"data row4 col3\" >0.833</td>\n",
+       "                        <td id=\"T_1426b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row4_col5\" class=\"data row4 col5\" >0.712</td>\n",
+       "                        <td id=\"T_1426b_row4_col6\" class=\"data row4 col6\" >0.511</td>\n",
+       "                        <td id=\"T_1426b_row4_col7\" class=\"data row4 col7\" >0.567</td>\n",
+       "                        <td id=\"T_1426b_row4_col8\" class=\"data row4 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_1426b_row5_col0\" class=\"data row5 col0\" >0.774</td>\n",
+       "                        <td id=\"T_1426b_row5_col1\" class=\"data row5 col1\" >0.964</td>\n",
+       "                        <td id=\"T_1426b_row5_col2\" class=\"data row5 col2\" >0.889</td>\n",
+       "                        <td id=\"T_1426b_row5_col3\" class=\"data row5 col3\" >0.913</td>\n",
+       "                        <td id=\"T_1426b_row5_col4\" class=\"data row5 col4\" >0.712</td>\n",
+       "                        <td id=\"T_1426b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row5_col6\" class=\"data row5 col6\" >0.952</td>\n",
+       "                        <td id=\"T_1426b_row5_col7\" class=\"data row5 col7\" >0.461</td>\n",
+       "                        <td id=\"T_1426b_row5_col8\" class=\"data row5 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_1426b_row6_col0\" class=\"data row6 col0\" >0.684</td>\n",
+       "                        <td id=\"T_1426b_row6_col1\" class=\"data row6 col1\" >0.875</td>\n",
+       "                        <td id=\"T_1426b_row6_col2\" class=\"data row6 col2\" >0.356</td>\n",
+       "                        <td id=\"T_1426b_row6_col3\" class=\"data row6 col3\" >0.917</td>\n",
+       "                        <td id=\"T_1426b_row6_col4\" class=\"data row6 col4\" >0.511</td>\n",
+       "                        <td id=\"T_1426b_row6_col5\" class=\"data row6 col5\" >0.952</td>\n",
+       "                        <td id=\"T_1426b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
+       "                        <td id=\"T_1426b_row6_col8\" class=\"data row6 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_1426b_row7_col0\" class=\"data row7 col0\" >0.652</td>\n",
+       "                        <td id=\"T_1426b_row7_col1\" class=\"data row7 col1\" >0.602</td>\n",
+       "                        <td id=\"T_1426b_row7_col2\" class=\"data row7 col2\" >0.252</td>\n",
+       "                        <td id=\"T_1426b_row7_col3\" class=\"data row7 col3\" >0.398</td>\n",
+       "                        <td id=\"T_1426b_row7_col4\" class=\"data row7 col4\" >0.567</td>\n",
+       "                        <td id=\"T_1426b_row7_col5\" class=\"data row7 col5\" >0.461</td>\n",
+       "                        <td id=\"T_1426b_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
+       "                        <td id=\"T_1426b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "                        <td id=\"T_1426b_row7_col8\" class=\"data row7 col8\" >0.819</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_1426b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_1426b_row8_col0\" class=\"data row8 col0\" >0.929</td>\n",
+       "                        <td id=\"T_1426b_row8_col1\" class=\"data row8 col1\" >1.000</td>\n",
+       "                        <td id=\"T_1426b_row8_col2\" class=\"data row8 col2\" >0.833</td>\n",
+       "                        <td id=\"T_1426b_row8_col3\" class=\"data row8 col3\" >0.733</td>\n",
+       "                        <td id=\"T_1426b_row8_col4\" class=\"data row8 col4\" >1.000</td>\n",
+       "                        <td id=\"T_1426b_row8_col5\" class=\"data row8 col5\" >1.000</td>\n",
+       "                        <td id=\"T_1426b_row8_col6\" class=\"data row8 col6\" >1.000</td>\n",
+       "                        <td id=\"T_1426b_row8_col7\" class=\"data row8 col7\" >0.819</td>\n",
+       "                        <td id=\"T_1426b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9a15b05130>"
+       "<pandas.io.formats.style.Styler at 0x7fd9e9fd2700>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3067,7 +2688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3081,7 +2702,7 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we investigate how activity can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
+    "In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
     "The kinase distance matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
@@ -3096,11 +2717,18 @@
     "2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few? \n",
     "3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of $0$, as is the case for p110a and LCK?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -3114,7 +2742,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From c31d2645eb1458841aa8223348b3d2a3d5edb0f1 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Wed, 22 Sep 2021 09:42:16 +0200
Subject: [PATCH 062/158] added fix for table to T027

---
 .../talktorial.ipynb                          | 1928 +----------------
 1 file changed, 75 insertions(+), 1853 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 14854ae3..c41ef2d7 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -217,224 +217,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current shape of data: (186972, 17)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>activities.activity_id</th>\n",
-       "      <th>assays.chembl_id</th>\n",
-       "      <th>target_dictionary.chembl_id</th>\n",
-       "      <th>molecule_dictionary.chembl_id</th>\n",
-       "      <th>molecule_dictionary.max_phase</th>\n",
-       "      <th>activities.standard_type</th>\n",
-       "      <th>activities.standard_value</th>\n",
-       "      <th>activities.standard_units</th>\n",
-       "      <th>compound_structures.canonical_smiles</th>\n",
-       "      <th>compound_structures.standard_inchi</th>\n",
-       "      <th>component_sequences.sequence</th>\n",
-       "      <th>assays.confidence_score</th>\n",
-       "      <th>docs.chembl_id</th>\n",
-       "      <th>docs.year</th>\n",
-       "      <th>docs.authors</th>\n",
-       "      <th>UniprotID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>96251</td>\n",
-       "      <td>16291323</td>\n",
-       "      <td>CHEMBL3705523</td>\n",
-       "      <td>CHEMBL2973</td>\n",
-       "      <td>CHEMBL3666724</td>\n",
-       "      <td>0</td>\n",
-       "      <td>pIC50</td>\n",
-       "      <td>14.096910</td>\n",
-       "      <td>nM</td>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...</td>\n",
-       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>CHEMBL3639077</td>\n",
-       "      <td>2014.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>97672</td>\n",
-       "      <td>16306943</td>\n",
-       "      <td>CHEMBL3705523</td>\n",
-       "      <td>CHEMBL2973</td>\n",
-       "      <td>CHEMBL1968705</td>\n",
-       "      <td>0</td>\n",
-       "      <td>pIC50</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>nM</td>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...</td>\n",
-       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>CHEMBL3639077</td>\n",
-       "      <td>2014.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>93879</td>\n",
-       "      <td>16264754</td>\n",
-       "      <td>CHEMBL3705523</td>\n",
-       "      <td>CHEMBL2973</td>\n",
-       "      <td>CHEMBL3666728</td>\n",
-       "      <td>0</td>\n",
-       "      <td>pIC50</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>nM</td>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...</td>\n",
-       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>CHEMBL3639077</td>\n",
-       "      <td>2014.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100714</td>\n",
-       "      <td>16340050</td>\n",
-       "      <td>CHEMBL3705523</td>\n",
-       "      <td>CHEMBL2973</td>\n",
-       "      <td>CHEMBL1997433</td>\n",
-       "      <td>0</td>\n",
-       "      <td>pIC50</td>\n",
-       "      <td>13.958607</td>\n",
-       "      <td>nM</td>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...</td>\n",
-       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>CHEMBL3639077</td>\n",
-       "      <td>2014.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>95877</td>\n",
-       "      <td>16287186</td>\n",
-       "      <td>CHEMBL3705523</td>\n",
-       "      <td>CHEMBL2973</td>\n",
-       "      <td>CHEMBL3666721</td>\n",
-       "      <td>0</td>\n",
-       "      <td>pIC50</td>\n",
-       "      <td>13.920819</td>\n",
-       "      <td>nM</td>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...</td>\n",
-       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
-       "      <td>9</td>\n",
-       "      <td>CHEMBL3639077</td>\n",
-       "      <td>2014.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0  activities.activity_id assays.chembl_id  \\\n",
-       "0       96251                16291323    CHEMBL3705523   \n",
-       "1       97672                16306943    CHEMBL3705523   \n",
-       "2       93879                16264754    CHEMBL3705523   \n",
-       "3      100714                16340050    CHEMBL3705523   \n",
-       "4       95877                16287186    CHEMBL3705523   \n",
-       "\n",
-       "  target_dictionary.chembl_id molecule_dictionary.chembl_id  \\\n",
-       "0                  CHEMBL2973                 CHEMBL3666724   \n",
-       "1                  CHEMBL2973                 CHEMBL1968705   \n",
-       "2                  CHEMBL2973                 CHEMBL3666728   \n",
-       "3                  CHEMBL2973                 CHEMBL1997433   \n",
-       "4                  CHEMBL2973                 CHEMBL3666721   \n",
-       "\n",
-       "   molecule_dictionary.max_phase activities.standard_type  \\\n",
-       "0                              0                    pIC50   \n",
-       "1                              0                    pIC50   \n",
-       "2                              0                    pIC50   \n",
-       "3                              0                    pIC50   \n",
-       "4                              0                    pIC50   \n",
-       "\n",
-       "   activities.standard_value activities.standard_units  \\\n",
-       "0                  14.096910                        nM   \n",
-       "1                  14.000000                        nM   \n",
-       "2                  14.000000                        nM   \n",
-       "3                  13.958607                        nM   \n",
-       "4                  13.920819                        nM   \n",
-       "\n",
-       "                compound_structures.canonical_smiles  \\\n",
-       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
-       "\n",
-       "                  compound_structures.standard_inchi  \\\n",
-       "0  InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...   \n",
-       "1  InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...   \n",
-       "2  InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...   \n",
-       "3  InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...   \n",
-       "4  InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...   \n",
-       "\n",
-       "                        component_sequences.sequence  assays.confidence_score  \\\n",
-       "0  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
-       "1  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
-       "2  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
-       "3  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
-       "4  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
-       "\n",
-       "  docs.chembl_id  docs.year docs.authors UniprotID  \n",
-       "0  CHEMBL3639077     2014.0          NaN    O75116  \n",
-       "1  CHEMBL3639077     2014.0          NaN    O75116  \n",
-       "2  CHEMBL3639077     2014.0          NaN    O75116  \n",
-       "3  CHEMBL3639077     2014.0          NaN    O75116  \n",
-       "4  CHEMBL3639077     2014.0          NaN    O75116  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "path = \"https://github.com/openkinome/kinodata/releases/download/\\\n",
     "v0.2/activities-chembl28_v0.2.zip\"\n",
@@ -459,18 +244,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
-      "Units: {'nM'}\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
     "      f\"Units: {set(data['activities.standard_units'])}\")"
@@ -485,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,28 +270,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Unnamed: 0', 'activities.activity_id', 'assays.chembl_id',\n",
-       "       'target_dictionary.chembl_id', 'molecule_dictionary.chembl_id',\n",
-       "       'molecule_dictionary.max_phase', 'activities.standard_type',\n",
-       "       'activities.standard_value', 'activities.standard_units',\n",
-       "       'compound_structures.canonical_smiles',\n",
-       "       'compound_structures.standard_inchi', 'component_sequences.sequence',\n",
-       "       'assays.confidence_score', 'docs.chembl_id', 'docs.year',\n",
-       "       'docs.authors', 'UniprotID'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data.columns"
    ]
@@ -530,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,91 +300,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current shape of data: (159978, 3)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>activity_value</th>\n",
-       "      <th>UniprotID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.096910</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>14.000000</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>13.958607</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
-       "      <td>13.920819</td>\n",
-       "      <td>O75116</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                              smiles  activity_value UniprotID\n",
-       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.096910    O75116\n",
-       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.000000    O75116\n",
-       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       14.000000    O75116\n",
-       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.958607    O75116\n",
-       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...       13.920819    O75116"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
@@ -642,17 +317,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current shape of data: (159823, 3)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = data.dropna()\n",
     "print(f\"Current shape of data: {data.shape}\")"
@@ -669,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -693,17 +360,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current shape of data: (33169, 3)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = data[data[\"UniprotID\"].isin(name_to_uniprot.values())]\n",
     "print(f\"Current shape of data: {data.shape}\")"
@@ -711,84 +370,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>activity_value</th>\n",
-       "      <th>UniprotID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1</td>\n",
-       "      <td>11.522879</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC</td>\n",
-       "      <td>11.221849</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101</th>\n",
-       "      <td>CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
-       "      <td>11.221849</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>139</th>\n",
-       "      <td>Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1</td>\n",
-       "      <td>11.096910</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>140</th>\n",
-       "      <td>CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
-       "      <td>11.096910</td>\n",
-       "      <td>P00533</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  smiles  activity_value UniprotID\n",
-       "57     Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1       11.522879    P00533\n",
-       "98   CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC       11.221849    P00533\n",
-       "101   CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.221849    P00533\n",
-       "139  Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1       11.096910    P00533\n",
-       "140      CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1       11.096910    P00533"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data.head()"
    ]
@@ -802,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -818,25 +402,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 40),\n",
-       " ('C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 24),\n",
-       " ('C=CC(=O)Nc1cc(Nc2nccc(-c3cn(C)c4ccccc34)n2)c(OC)cc1N(C)CCN(C)C', 12),\n",
-       " ('C=CC(=O)Nc1cccc(Oc2nc(Nc3ccc(N4CCN(C)CC4)cc3OC)ncc2Cl)c1', 10),\n",
-       " ('CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4',\n",
-       "  7)]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "measured_compounds = Counter(EGFR_data[\"smiles\"])\n",
     "measured_compounds.most_common()[0:5]"
@@ -851,84 +419,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UniprotID</th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>activity_value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
-       "      <td>5.336488</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
-       "      <td>5.996539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
-       "      <td>8.397940</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>7.207608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>8.420216</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  UniprotID                                             smiles  activity_value\n",
-       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...        5.336488\n",
-       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...        5.996539\n",
-       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...        8.397940\n",
-       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...        7.207608\n",
-       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...        8.420216"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = data.groupby([\"UniprotID\",\n",
     "                     \"smiles\"])['activity_value'].max().reset_index()\n",
@@ -951,7 +444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -960,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -988,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -998,104 +491,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Current shape of data: (32688, 4)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UniprotID</th>\n",
-       "      <th>smiles</th>\n",
-       "      <th>activity_value</th>\n",
-       "      <th>activity_binary</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
-       "      <td>5.336488</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
-       "      <td>5.996539</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
-       "      <td>8.397940</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>7.207608</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>P00533</td>\n",
-       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
-       "      <td>8.420216</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  UniprotID                                             smiles  \\\n",
-       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...   \n",
-       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...   \n",
-       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...   \n",
-       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...   \n",
-       "\n",
-       "   activity_value  activity_binary  \n",
-       "0        5.336488                0  \n",
-       "1        5.996539                0  \n",
-       "2        8.397940                1  \n",
-       "3        7.207608                1  \n",
-       "4        8.420216                1  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
@@ -1123,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1168,20 +566,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total number of measured compounds: \t5869 \n",
-      "Number of active compounds: \t\t3574 \n",
-      "Fraction of active compounds: \t\t0.61 \n",
-      "\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "uniprot_id = name_to_uniprot[\"EGFR\"]\n",
     "EGFR_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
@@ -1202,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1230,112 +617,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>total</th>\n",
-       "      <th>actives</th>\n",
-       "      <th>fraction</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>5869</td>\n",
-       "      <td>3574</td>\n",
-       "      <td>0.608962</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>1694</td>\n",
-       "      <td>1025</td>\n",
-       "      <td>0.605077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>3682</td>\n",
-       "      <td>2988</td>\n",
-       "      <td>0.811515</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>1505</td>\n",
-       "      <td>819</td>\n",
-       "      <td>0.544186</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>1537</td>\n",
-       "      <td>930</td>\n",
-       "      <td>0.605075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>2821</td>\n",
-       "      <td>2235</td>\n",
-       "      <td>0.792272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>3609</td>\n",
-       "      <td>2760</td>\n",
-       "      <td>0.764755</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>7624</td>\n",
-       "      <td>5312</td>\n",
-       "      <td>0.696747</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>4347</td>\n",
-       "      <td>2780</td>\n",
-       "      <td>0.639522</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      total actives  fraction\n",
-       "EGFR   5869    3574  0.608962\n",
-       "ErbB2  1694    1025  0.605077\n",
-       "BRAF   3682    2988  0.811515\n",
-       "CDK2   1505     819  0.544186\n",
-       "LCK    1537     930  0.605075\n",
-       "MET    2821    2235  0.792272\n",
-       "p38a   3609    2760  0.764755\n",
-       "KDR    7624    5312  0.696747\n",
-       "p110a  4347    2780  0.639522"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_promiscuity_table = from_numbers_to_table(data)\n",
     "kinase_promiscuity_table"
@@ -1350,43 +634,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "zero-size array to reduction operation fmin which has no identity",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mHooks\u001b[0m \u001b[0minto\u001b[0m \u001b[0mJupyter\u001b[0m \u001b[0mnotebook\u001b[0m \u001b[0mrich\u001b[0m \u001b[0mdisplay\u001b[0m \u001b[0msystem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \"\"\"\n\u001b[0;32m--> 205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m     @doc(\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36mrender\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m    617\u001b[0m         \u001b[0;34m*\u001b[0m \u001b[0mtable_attributes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m         \"\"\"\n\u001b[0;32m--> 619\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m         \u001b[0;31m# TODO: namespace all the pandas keys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_translate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_compute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_todo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, func, axis, subset, **kwargs)\u001b[0m\n\u001b[1;32m    720\u001b[0m             \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m                 raise TypeError(\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_highlight_extrema\u001b[0;34m(data, color, max_)\u001b[0m\n\u001b[1;32m   1547\u001b[0m             \u001b[0mextrema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1548\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1549\u001b[0;31m             \u001b[0mextrema\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Series from .apply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "\u001b[0;32m~/.local/miniconda/envs/teachopencadd_review/lib/python3.8/site-packages/numpy/lib/nanfunctions.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(a, axis, out, keepdims)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# Fast, but not safe for subclasses of ndarray, or object arrays,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;31m# which do not implement isnan (gh-9009), or fmin correctly (gh-8975)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m             warnings.warn(\"All-NaN slice encountered\", RuntimeWarning,\n",
-      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation fmin which has no identity"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd9e5ed4be0>"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_promiscuity_table.style.\\\n",
     "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
     "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
-    "    highlight_min(color=\"yellow\", axis=None).\\\n",
+    "    highlight_min(color=\"yellow\", axis=None, subset=[\"fraction\"]).\\\n",
     "    highlight_max(color=\"red\", subset=[\"fraction\"])"
    ]
   },
@@ -1413,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1481,17 +736,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ligand profile similarity between EGFR and MET: 0.23.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "uniprot_id1=name_to_uniprot[\"EGFR\"]\n",
     "uniprot_id2=name_to_uniprot[\"MET\"]\n",
@@ -1509,18 +756,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No compounds were measured on both kinases, namely P04626 and P42336.\n",
-      "No compounds were measured on both kinases, namely P42336 and P04626.\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_similarity_matrix = np.zeros((len(name_to_uniprot),\n",
     "                                     len(name_to_uniprot)))\n",
@@ -1536,183 +774,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>ErbB2</th>\n",
-       "      <th>BRAF</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>LCK</th>\n",
-       "      <th>MET</th>\n",
-       "      <th>p38a</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>p110a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.608962</td>\n",
-       "      <td>0.556918</td>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.116279</td>\n",
-       "      <td>0.238462</td>\n",
-       "      <td>0.225806</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.348214</td>\n",
-       "      <td>0.071429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>0.556918</td>\n",
-       "      <td>0.605077</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.153846</td>\n",
-       "      <td>0.176471</td>\n",
-       "      <td>0.035714</td>\n",
-       "      <td>0.125000</td>\n",
-       "      <td>0.397790</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>0.419355</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.811515</td>\n",
-       "      <td>0.066667</td>\n",
-       "      <td>0.511628</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.644444</td>\n",
-       "      <td>0.748120</td>\n",
-       "      <td>0.166667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>0.116279</td>\n",
-       "      <td>0.153846</td>\n",
-       "      <td>0.066667</td>\n",
-       "      <td>0.544186</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.086957</td>\n",
-       "      <td>0.083333</td>\n",
-       "      <td>0.601695</td>\n",
-       "      <td>0.266667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>0.238462</td>\n",
-       "      <td>0.176471</td>\n",
-       "      <td>0.511628</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.605075</td>\n",
-       "      <td>0.287879</td>\n",
-       "      <td>0.489362</td>\n",
-       "      <td>0.432692</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>0.225806</td>\n",
-       "      <td>0.035714</td>\n",
-       "      <td>0.111111</td>\n",
-       "      <td>0.086957</td>\n",
-       "      <td>0.287879</td>\n",
-       "      <td>0.792272</td>\n",
-       "      <td>0.047619</td>\n",
-       "      <td>0.539359</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>0.125000</td>\n",
-       "      <td>0.644444</td>\n",
-       "      <td>0.083333</td>\n",
-       "      <td>0.489362</td>\n",
-       "      <td>0.047619</td>\n",
-       "      <td>0.764755</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>0.348214</td>\n",
-       "      <td>0.397790</td>\n",
-       "      <td>0.748120</td>\n",
-       "      <td>0.601695</td>\n",
-       "      <td>0.432692</td>\n",
-       "      <td>0.539359</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.696747</td>\n",
-       "      <td>0.180791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.071429</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.166667</td>\n",
-       "      <td>0.266667</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.180791</td>\n",
-       "      <td>0.639522</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       "EGFR   0.608962  0.556918  0.419355  0.116279  0.238462  0.225806  0.315789   \n",
-       "ErbB2  0.556918  0.605077  0.250000  0.153846  0.176471  0.035714  0.125000   \n",
-       "BRAF   0.419355  0.250000  0.811515  0.066667  0.511628  0.111111  0.644444   \n",
-       "CDK2   0.116279  0.153846  0.066667  0.544186  0.166667  0.086957  0.083333   \n",
-       "LCK    0.238462  0.176471  0.511628  0.166667  0.605075  0.287879  0.489362   \n",
-       "MET    0.225806  0.035714  0.111111  0.086957  0.287879  0.792272  0.047619   \n",
-       "p38a   0.315789  0.125000  0.644444  0.083333  0.489362  0.047619  0.764755   \n",
-       "KDR    0.348214  0.397790  0.748120  0.601695  0.432692  0.539359  0.500000   \n",
-       "p110a  0.071429       NaN  0.166667  0.266667  0.000000  0.000000  0.000000   \n",
-       "\n",
-       "            KDR     p110a  \n",
-       "EGFR   0.348214  0.071429  \n",
-       "ErbB2  0.397790       NaN  \n",
-       "BRAF   0.748120  0.166667  \n",
-       "CDK2   0.601695  0.266667  \n",
-       "LCK    0.432692  0.000000  \n",
-       "MET    0.539359  0.000000  \n",
-       "p38a   0.500000  0.000000  \n",
-       "KDR    0.696747  0.180791  \n",
-       "p110a  0.180791  0.639522  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
     "                                        index=name_to_uniprot.keys(),\n",
@@ -1722,292 +786,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
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-       "        }</style><table id=\"T_9e788_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_9e788_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_9e788_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "                        <td id=\"T_9e788_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "                        <td id=\"T_9e788_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "                        <td id=\"T_9e788_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "                        <td id=\"T_9e788_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "                        <td id=\"T_9e788_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "                        <td id=\"T_9e788_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "                        <td id=\"T_9e788_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "                        <td id=\"T_9e788_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_9e788_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "                        <td id=\"T_9e788_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "                        <td id=\"T_9e788_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "                        <td id=\"T_9e788_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "                        <td id=\"T_9e788_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "                        <td id=\"T_9e788_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "                        <td id=\"T_9e788_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "                        <td id=\"T_9e788_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "                        <td id=\"T_9e788_row1_col8\" class=\"data row1 col8\" >nan</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_9e788_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "                        <td id=\"T_9e788_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "                        <td id=\"T_9e788_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "                        <td id=\"T_9e788_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "                        <td id=\"T_9e788_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "                        <td id=\"T_9e788_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "                        <td id=\"T_9e788_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "                        <td id=\"T_9e788_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "                        <td id=\"T_9e788_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_9e788_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "                        <td id=\"T_9e788_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "                        <td id=\"T_9e788_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "                        <td id=\"T_9e788_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "                        <td id=\"T_9e788_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "                        <td id=\"T_9e788_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "                        <td id=\"T_9e788_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "                        <td id=\"T_9e788_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "                        <td id=\"T_9e788_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_9e788_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "                        <td id=\"T_9e788_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "                        <td id=\"T_9e788_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "                        <td id=\"T_9e788_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "                        <td id=\"T_9e788_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "                        <td id=\"T_9e788_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "                        <td id=\"T_9e788_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "                        <td id=\"T_9e788_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "                        <td id=\"T_9e788_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_9e788_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "                        <td id=\"T_9e788_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "                        <td id=\"T_9e788_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "                        <td id=\"T_9e788_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "                        <td id=\"T_9e788_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "                        <td id=\"T_9e788_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "                        <td id=\"T_9e788_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "                        <td id=\"T_9e788_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "                        <td id=\"T_9e788_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_9e788_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "                        <td id=\"T_9e788_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "                        <td id=\"T_9e788_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "                        <td id=\"T_9e788_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "                        <td id=\"T_9e788_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "                        <td id=\"T_9e788_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "                        <td id=\"T_9e788_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "                        <td id=\"T_9e788_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "                        <td id=\"T_9e788_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_9e788_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "                        <td id=\"T_9e788_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "                        <td id=\"T_9e788_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "                        <td id=\"T_9e788_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "                        <td id=\"T_9e788_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "                        <td id=\"T_9e788_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "                        <td id=\"T_9e788_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "                        <td id=\"T_9e788_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "                        <td id=\"T_9e788_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_9e788_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_9e788_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "                        <td id=\"T_9e788_row8_col1\" class=\"data row8 col1\" >nan</td>\n",
-       "                        <td id=\"T_9e788_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "                        <td id=\"T_9e788_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "                        <td id=\"T_9e788_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "                        <td id=\"T_9e788_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "                        <td id=\"T_9e788_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "                        <td id=\"T_9e788_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "                        <td id=\"T_9e788_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd9e5b8bbb0>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
@@ -2029,292 +810,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "            background-color:  #198c19;\n",
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-       "            background-color:  #aad3aa;\n",
-       "            color:  #000000;\n",
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-       "            background-color:  #d2e7d2;\n",
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-       "            color:  #000000;\n",
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-       "            color:  #000000;\n",
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-       "            background-color:  #a2cfa2;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row4_col2{\n",
-       "            background-color:  #5fae5f;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row4_col3{\n",
-       "            background-color:  #bfdebf;\n",
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-       "        }#T_07da8_row4_col5{\n",
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-       "        }#T_07da8_row4_col6{\n",
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-       "            background-color:  #a7d2a7;\n",
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-       "        }#T_07da8_row6_col2{\n",
-       "            background-color:  #349a34;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row6_col3{\n",
-       "            background-color:  #e4f0e4;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row6_col4{\n",
-       "            background-color:  #2c962c;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row6_col7{\n",
-       "            background-color:  #66b266;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col0{\n",
-       "            background-color:  #72b872;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col1{\n",
-       "            background-color:  #50a750;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col2{\n",
-       "            background-color:  #138913;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_07da8_row7_col4{\n",
-       "            background-color:  #42a042;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col5{\n",
-       "            background-color:  #4ba54a;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col6{\n",
-       "            background-color:  #51a851;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row7_col7{\n",
-       "            background-color:  #158a15;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row8_col2{\n",
-       "            background-color:  #cbe4cb;\n",
-       "            color:  #000000;\n",
-       "        }#T_07da8_row8_col3{\n",
-       "            background-color:  #93c893;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_07da8_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_07da8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_07da8_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "                        <td id=\"T_07da8_row0_col1\" class=\"data row0 col1\" >0.557</td>\n",
-       "                        <td id=\"T_07da8_row0_col2\" class=\"data row0 col2\" >0.419</td>\n",
-       "                        <td id=\"T_07da8_row0_col3\" class=\"data row0 col3\" >0.116</td>\n",
-       "                        <td id=\"T_07da8_row0_col4\" class=\"data row0 col4\" >0.238</td>\n",
-       "                        <td id=\"T_07da8_row0_col5\" class=\"data row0 col5\" >0.226</td>\n",
-       "                        <td id=\"T_07da8_row0_col6\" class=\"data row0 col6\" >0.316</td>\n",
-       "                        <td id=\"T_07da8_row0_col7\" class=\"data row0 col7\" >0.348</td>\n",
-       "                        <td id=\"T_07da8_row0_col8\" class=\"data row0 col8\" >0.071</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_07da8_row1_col0\" class=\"data row1 col0\" >0.557</td>\n",
-       "                        <td id=\"T_07da8_row1_col1\" class=\"data row1 col1\" >0.605</td>\n",
-       "                        <td id=\"T_07da8_row1_col2\" class=\"data row1 col2\" >0.250</td>\n",
-       "                        <td id=\"T_07da8_row1_col3\" class=\"data row1 col3\" >0.154</td>\n",
-       "                        <td id=\"T_07da8_row1_col4\" class=\"data row1 col4\" >0.176</td>\n",
-       "                        <td id=\"T_07da8_row1_col5\" class=\"data row1 col5\" >0.036</td>\n",
-       "                        <td id=\"T_07da8_row1_col6\" class=\"data row1 col6\" >0.125</td>\n",
-       "                        <td id=\"T_07da8_row1_col7\" class=\"data row1 col7\" >0.398</td>\n",
-       "                        <td id=\"T_07da8_row1_col8\" class=\"data row1 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_07da8_row2_col0\" class=\"data row2 col0\" >0.419</td>\n",
-       "                        <td id=\"T_07da8_row2_col1\" class=\"data row2 col1\" >0.250</td>\n",
-       "                        <td id=\"T_07da8_row2_col2\" class=\"data row2 col2\" >0.812</td>\n",
-       "                        <td id=\"T_07da8_row2_col3\" class=\"data row2 col3\" >0.067</td>\n",
-       "                        <td id=\"T_07da8_row2_col4\" class=\"data row2 col4\" >0.512</td>\n",
-       "                        <td id=\"T_07da8_row2_col5\" class=\"data row2 col5\" >0.111</td>\n",
-       "                        <td id=\"T_07da8_row2_col6\" class=\"data row2 col6\" >0.644</td>\n",
-       "                        <td id=\"T_07da8_row2_col7\" class=\"data row2 col7\" >0.748</td>\n",
-       "                        <td id=\"T_07da8_row2_col8\" class=\"data row2 col8\" >0.167</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_07da8_row3_col0\" class=\"data row3 col0\" >0.116</td>\n",
-       "                        <td id=\"T_07da8_row3_col1\" class=\"data row3 col1\" >0.154</td>\n",
-       "                        <td id=\"T_07da8_row3_col2\" class=\"data row3 col2\" >0.067</td>\n",
-       "                        <td id=\"T_07da8_row3_col3\" class=\"data row3 col3\" >0.544</td>\n",
-       "                        <td id=\"T_07da8_row3_col4\" class=\"data row3 col4\" >0.167</td>\n",
-       "                        <td id=\"T_07da8_row3_col5\" class=\"data row3 col5\" >0.087</td>\n",
-       "                        <td id=\"T_07da8_row3_col6\" class=\"data row3 col6\" >0.083</td>\n",
-       "                        <td id=\"T_07da8_row3_col7\" class=\"data row3 col7\" >0.602</td>\n",
-       "                        <td id=\"T_07da8_row3_col8\" class=\"data row3 col8\" >0.267</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_07da8_row4_col0\" class=\"data row4 col0\" >0.238</td>\n",
-       "                        <td id=\"T_07da8_row4_col1\" class=\"data row4 col1\" >0.176</td>\n",
-       "                        <td id=\"T_07da8_row4_col2\" class=\"data row4 col2\" >0.512</td>\n",
-       "                        <td id=\"T_07da8_row4_col3\" class=\"data row4 col3\" >0.167</td>\n",
-       "                        <td id=\"T_07da8_row4_col4\" class=\"data row4 col4\" >0.605</td>\n",
-       "                        <td id=\"T_07da8_row4_col5\" class=\"data row4 col5\" >0.288</td>\n",
-       "                        <td id=\"T_07da8_row4_col6\" class=\"data row4 col6\" >0.489</td>\n",
-       "                        <td id=\"T_07da8_row4_col7\" class=\"data row4 col7\" >0.433</td>\n",
-       "                        <td id=\"T_07da8_row4_col8\" class=\"data row4 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_07da8_row5_col0\" class=\"data row5 col0\" >0.226</td>\n",
-       "                        <td id=\"T_07da8_row5_col1\" class=\"data row5 col1\" >0.036</td>\n",
-       "                        <td id=\"T_07da8_row5_col2\" class=\"data row5 col2\" >0.111</td>\n",
-       "                        <td id=\"T_07da8_row5_col3\" class=\"data row5 col3\" >0.087</td>\n",
-       "                        <td id=\"T_07da8_row5_col4\" class=\"data row5 col4\" >0.288</td>\n",
-       "                        <td id=\"T_07da8_row5_col5\" class=\"data row5 col5\" >0.792</td>\n",
-       "                        <td id=\"T_07da8_row5_col6\" class=\"data row5 col6\" >0.048</td>\n",
-       "                        <td id=\"T_07da8_row5_col7\" class=\"data row5 col7\" >0.539</td>\n",
-       "                        <td id=\"T_07da8_row5_col8\" class=\"data row5 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_07da8_row6_col0\" class=\"data row6 col0\" >0.316</td>\n",
-       "                        <td id=\"T_07da8_row6_col1\" class=\"data row6 col1\" >0.125</td>\n",
-       "                        <td id=\"T_07da8_row6_col2\" class=\"data row6 col2\" >0.644</td>\n",
-       "                        <td id=\"T_07da8_row6_col3\" class=\"data row6 col3\" >0.083</td>\n",
-       "                        <td id=\"T_07da8_row6_col4\" class=\"data row6 col4\" >0.489</td>\n",
-       "                        <td id=\"T_07da8_row6_col5\" class=\"data row6 col5\" >0.048</td>\n",
-       "                        <td id=\"T_07da8_row6_col6\" class=\"data row6 col6\" >0.765</td>\n",
-       "                        <td id=\"T_07da8_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "                        <td id=\"T_07da8_row6_col8\" class=\"data row6 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_07da8_row7_col0\" class=\"data row7 col0\" >0.348</td>\n",
-       "                        <td id=\"T_07da8_row7_col1\" class=\"data row7 col1\" >0.398</td>\n",
-       "                        <td id=\"T_07da8_row7_col2\" class=\"data row7 col2\" >0.748</td>\n",
-       "                        <td id=\"T_07da8_row7_col3\" class=\"data row7 col3\" >0.602</td>\n",
-       "                        <td id=\"T_07da8_row7_col4\" class=\"data row7 col4\" >0.433</td>\n",
-       "                        <td id=\"T_07da8_row7_col5\" class=\"data row7 col5\" >0.539</td>\n",
-       "                        <td id=\"T_07da8_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "                        <td id=\"T_07da8_row7_col7\" class=\"data row7 col7\" >0.697</td>\n",
-       "                        <td id=\"T_07da8_row7_col8\" class=\"data row7 col8\" >0.181</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_07da8_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_07da8_row8_col0\" class=\"data row8 col0\" >0.071</td>\n",
-       "                        <td id=\"T_07da8_row8_col1\" class=\"data row8 col1\" >0.000</td>\n",
-       "                        <td id=\"T_07da8_row8_col2\" class=\"data row8 col2\" >0.167</td>\n",
-       "                        <td id=\"T_07da8_row8_col3\" class=\"data row8 col3\" >0.267</td>\n",
-       "                        <td id=\"T_07da8_row8_col4\" class=\"data row8 col4\" >0.000</td>\n",
-       "                        <td id=\"T_07da8_row8_col5\" class=\"data row8 col5\" >0.000</td>\n",
-       "                        <td id=\"T_07da8_row8_col6\" class=\"data row8 col6\" >0.000</td>\n",
-       "                        <td id=\"T_07da8_row8_col7\" class=\"data row8 col7\" >0.181</td>\n",
-       "                        <td id=\"T_07da8_row8_col8\" class=\"data row8 col8\" >0.640</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd9e5b8bd90>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_similarity_matrix = kinase_similarity_matrix.fillna(0)\n",
     "\n",
@@ -2332,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2358,17 +856,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The values of the similarity matrix lie between: 0.00 and 0.81\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print(f\"The values of the similarity matrix lie between: \"\n",
     "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
@@ -2377,7 +867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2393,7 +883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2402,277 +892,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
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-       "            background-color:  #46a246;\n",
-       "            color:  #000000;\n",
-       "        }#T_1426b_row7_col1{\n",
-       "            background-color:  #5dae5d;\n",
-       "            color:  #000000;\n",
-       "        }#T_1426b_row7_col2{\n",
-       "            background-color:  #abd4ab;\n",
-       "            color:  #000000;\n",
-       "        }#T_1426b_row7_col3{\n",
-       "            background-color:  #86c286;\n",
-       "            color:  #000000;\n",
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-       "            background-color:  #65b265;\n",
-       "            color:  #000000;\n",
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-       "            background-color:  #7fbe7f;\n",
-       "            color:  #000000;\n",
-       "        }#T_1426b_row7_col6{\n",
-       "            background-color:  #75b975;\n",
-       "            color:  #000000;\n",
-       "        }#T_1426b_row8_col3{\n",
-       "            background-color:  #329832;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_1426b_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_1426b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_1426b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row0_col1\" class=\"data row0 col1\" >0.443</td>\n",
-       "                        <td id=\"T_1426b_row0_col2\" class=\"data row0 col2\" >0.581</td>\n",
-       "                        <td id=\"T_1426b_row0_col3\" class=\"data row0 col3\" >0.884</td>\n",
-       "                        <td id=\"T_1426b_row0_col4\" class=\"data row0 col4\" >0.762</td>\n",
-       "                        <td id=\"T_1426b_row0_col5\" class=\"data row0 col5\" >0.774</td>\n",
-       "                        <td id=\"T_1426b_row0_col6\" class=\"data row0 col6\" >0.684</td>\n",
-       "                        <td id=\"T_1426b_row0_col7\" class=\"data row0 col7\" >0.652</td>\n",
-       "                        <td id=\"T_1426b_row0_col8\" class=\"data row0 col8\" >0.929</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_1426b_row1_col0\" class=\"data row1 col0\" >0.443</td>\n",
-       "                        <td id=\"T_1426b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row1_col2\" class=\"data row1 col2\" >0.750</td>\n",
-       "                        <td id=\"T_1426b_row1_col3\" class=\"data row1 col3\" >0.846</td>\n",
-       "                        <td id=\"T_1426b_row1_col4\" class=\"data row1 col4\" >0.824</td>\n",
-       "                        <td id=\"T_1426b_row1_col5\" class=\"data row1 col5\" >0.964</td>\n",
-       "                        <td id=\"T_1426b_row1_col6\" class=\"data row1 col6\" >0.875</td>\n",
-       "                        <td id=\"T_1426b_row1_col7\" class=\"data row1 col7\" >0.602</td>\n",
-       "                        <td id=\"T_1426b_row1_col8\" class=\"data row1 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_1426b_row2_col0\" class=\"data row2 col0\" >0.581</td>\n",
-       "                        <td id=\"T_1426b_row2_col1\" class=\"data row2 col1\" >0.750</td>\n",
-       "                        <td id=\"T_1426b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row2_col3\" class=\"data row2 col3\" >0.933</td>\n",
-       "                        <td id=\"T_1426b_row2_col4\" class=\"data row2 col4\" >0.488</td>\n",
-       "                        <td id=\"T_1426b_row2_col5\" class=\"data row2 col5\" >0.889</td>\n",
-       "                        <td id=\"T_1426b_row2_col6\" class=\"data row2 col6\" >0.356</td>\n",
-       "                        <td id=\"T_1426b_row2_col7\" class=\"data row2 col7\" >0.252</td>\n",
-       "                        <td id=\"T_1426b_row2_col8\" class=\"data row2 col8\" >0.833</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_1426b_row3_col0\" class=\"data row3 col0\" >0.884</td>\n",
-       "                        <td id=\"T_1426b_row3_col1\" class=\"data row3 col1\" >0.846</td>\n",
-       "                        <td id=\"T_1426b_row3_col2\" class=\"data row3 col2\" >0.933</td>\n",
-       "                        <td id=\"T_1426b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row3_col4\" class=\"data row3 col4\" >0.833</td>\n",
-       "                        <td id=\"T_1426b_row3_col5\" class=\"data row3 col5\" >0.913</td>\n",
-       "                        <td id=\"T_1426b_row3_col6\" class=\"data row3 col6\" >0.917</td>\n",
-       "                        <td id=\"T_1426b_row3_col7\" class=\"data row3 col7\" >0.398</td>\n",
-       "                        <td id=\"T_1426b_row3_col8\" class=\"data row3 col8\" >0.733</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_1426b_row4_col0\" class=\"data row4 col0\" >0.762</td>\n",
-       "                        <td id=\"T_1426b_row4_col1\" class=\"data row4 col1\" >0.824</td>\n",
-       "                        <td id=\"T_1426b_row4_col2\" class=\"data row4 col2\" >0.488</td>\n",
-       "                        <td id=\"T_1426b_row4_col3\" class=\"data row4 col3\" >0.833</td>\n",
-       "                        <td id=\"T_1426b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row4_col5\" class=\"data row4 col5\" >0.712</td>\n",
-       "                        <td id=\"T_1426b_row4_col6\" class=\"data row4 col6\" >0.511</td>\n",
-       "                        <td id=\"T_1426b_row4_col7\" class=\"data row4 col7\" >0.567</td>\n",
-       "                        <td id=\"T_1426b_row4_col8\" class=\"data row4 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_1426b_row5_col0\" class=\"data row5 col0\" >0.774</td>\n",
-       "                        <td id=\"T_1426b_row5_col1\" class=\"data row5 col1\" >0.964</td>\n",
-       "                        <td id=\"T_1426b_row5_col2\" class=\"data row5 col2\" >0.889</td>\n",
-       "                        <td id=\"T_1426b_row5_col3\" class=\"data row5 col3\" >0.913</td>\n",
-       "                        <td id=\"T_1426b_row5_col4\" class=\"data row5 col4\" >0.712</td>\n",
-       "                        <td id=\"T_1426b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row5_col6\" class=\"data row5 col6\" >0.952</td>\n",
-       "                        <td id=\"T_1426b_row5_col7\" class=\"data row5 col7\" >0.461</td>\n",
-       "                        <td id=\"T_1426b_row5_col8\" class=\"data row5 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_1426b_row6_col0\" class=\"data row6 col0\" >0.684</td>\n",
-       "                        <td id=\"T_1426b_row6_col1\" class=\"data row6 col1\" >0.875</td>\n",
-       "                        <td id=\"T_1426b_row6_col2\" class=\"data row6 col2\" >0.356</td>\n",
-       "                        <td id=\"T_1426b_row6_col3\" class=\"data row6 col3\" >0.917</td>\n",
-       "                        <td id=\"T_1426b_row6_col4\" class=\"data row6 col4\" >0.511</td>\n",
-       "                        <td id=\"T_1426b_row6_col5\" class=\"data row6 col5\" >0.952</td>\n",
-       "                        <td id=\"T_1426b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row6_col7\" class=\"data row6 col7\" >0.500</td>\n",
-       "                        <td id=\"T_1426b_row6_col8\" class=\"data row6 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_1426b_row7_col0\" class=\"data row7 col0\" >0.652</td>\n",
-       "                        <td id=\"T_1426b_row7_col1\" class=\"data row7 col1\" >0.602</td>\n",
-       "                        <td id=\"T_1426b_row7_col2\" class=\"data row7 col2\" >0.252</td>\n",
-       "                        <td id=\"T_1426b_row7_col3\" class=\"data row7 col3\" >0.398</td>\n",
-       "                        <td id=\"T_1426b_row7_col4\" class=\"data row7 col4\" >0.567</td>\n",
-       "                        <td id=\"T_1426b_row7_col5\" class=\"data row7 col5\" >0.461</td>\n",
-       "                        <td id=\"T_1426b_row7_col6\" class=\"data row7 col6\" >0.500</td>\n",
-       "                        <td id=\"T_1426b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "                        <td id=\"T_1426b_row7_col8\" class=\"data row7 col8\" >0.819</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_1426b_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_1426b_row8_col0\" class=\"data row8 col0\" >0.929</td>\n",
-       "                        <td id=\"T_1426b_row8_col1\" class=\"data row8 col1\" >1.000</td>\n",
-       "                        <td id=\"T_1426b_row8_col2\" class=\"data row8 col2\" >0.833</td>\n",
-       "                        <td id=\"T_1426b_row8_col3\" class=\"data row8 col3\" >0.733</td>\n",
-       "                        <td id=\"T_1426b_row8_col4\" class=\"data row8 col4\" >1.000</td>\n",
-       "                        <td id=\"T_1426b_row8_col5\" class=\"data row8 col5\" >1.000</td>\n",
-       "                        <td id=\"T_1426b_row8_col6\" class=\"data row8 col6\" >1.000</td>\n",
-       "                        <td id=\"T_1426b_row8_col7\" class=\"data row8 col7\" >0.819</td>\n",
-       "                        <td id=\"T_1426b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd9e9fd2700>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "kinase_distance_matrix.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
@@ -2688,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [

From 952da33d078f558d60f244ff29d790cae8e34584 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Wed, 22 Sep 2021 10:39:02 +0200
Subject: [PATCH 063/158] fix long runtime issue for T024

---
 .../talktorial.ipynb                          | 559 +++++++++---------
 1 file changed, 281 insertions(+), 278 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index c4c24ffc..4611ecb4 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -582,7 +582,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sequence_similarity(kinase_name1, kinase_name2, type_=\"identity\"):\n",
+    "def sequence_similarity(sequence_1, sequence_2, type_=\"identity\"):\n",
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
@@ -598,8 +598,8 @@
     "    float :\n",
     "        The similarity between the pocket sequences of the two kinases.\n",
     "    \"\"\"\n",
-    "    sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
-    "    sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
+    "    #sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
+    "    #sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
     "\n",
     "    # Replace possible unavailable residue\n",
     "    # noted in KLIFS with \"-\"\n",
@@ -650,8 +650,8 @@
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                              \"MET\",\n",
+    "EGFR_MET_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                              kinase_sequences[\"MET\"],\n",
     "                                              \"identity\")\n",
     "for key in ('EGFR','MET'):\n",
     "    print(f\"{key:5s}:{kinase_sequences[key]}\")\n",
@@ -673,8 +673,8 @@
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                              \"MET\",\n",
+    "EGFR_MET_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                              kinase_sequences[\"MET\"],\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
     "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
@@ -701,7 +701,8 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \"EGFR\")\n",
+    "EGFR_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"], \n",
+    "                                          kinase_sequences[\"EGFR\"])\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using identity.\")"
    ]
@@ -720,7 +721,9 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \"EGFR\", type_=\"substitution\")\n",
+    "EGFR_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"], \n",
+    "                                          kinase_sequences[\"EGFR\"], \n",
+    "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
    ]
@@ -764,9 +767,9 @@
     "kinase_similarity_matrix = np.zeros((len(query_kinases), len(query_kinases)))\n",
     "for i, kinase_name1 in enumerate(query_kinases):\n",
     "    for j, kinase_name2 in enumerate(query_kinases):\n",
-    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
-    "                                                             kinase_name2,\n",
-    "                                                             similarity_measure)"
+    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
+    "                                                             kinase_sequences[kinase_name2],\n",
+    "                                                             type_=similarity_measure)"
    ]
   },
   {
@@ -964,242 +967,242 @@
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        "            color:  #000000;\n",
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+       "        }#T_efaac_row7_col0{\n",
        "            background-color:  #73b873;\n",
        "            color:  #000000;\n",
-       "        }#T_33654_row7_col3{\n",
+       "        }#T_efaac_row7_col3{\n",
        "            background-color:  #8cc58c;\n",
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+       "        }#T_efaac_row7_col5{\n",
        "            background-color:  #6db66d;\n",
        "            color:  #000000;\n",
-       "        }</style><table id=\"T_33654_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "        }</style><table id=\"T_efaac_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                        <th id=\"T_33654_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_33654_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "                        <td id=\"T_33654_row0_col2\" class=\"data row0 col2\" >0.659</td>\n",
-       "                        <td id=\"T_33654_row0_col3\" class=\"data row0 col3\" >0.650</td>\n",
-       "                        <td id=\"T_33654_row0_col4\" class=\"data row0 col4\" >0.710</td>\n",
-       "                        <td id=\"T_33654_row0_col5\" class=\"data row0 col5\" >0.715</td>\n",
-       "                        <td id=\"T_33654_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
-       "                        <td id=\"T_33654_row0_col7\" class=\"data row0 col7\" >0.715</td>\n",
-       "                        <td id=\"T_33654_row0_col8\" class=\"data row0 col8\" >0.429</td>\n",
+       "                        <th id=\"T_efaac_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_efaac_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "                        <td id=\"T_efaac_row0_col2\" class=\"data row0 col2\" >0.659</td>\n",
+       "                        <td id=\"T_efaac_row0_col3\" class=\"data row0 col3\" >0.650</td>\n",
+       "                        <td id=\"T_efaac_row0_col4\" class=\"data row0 col4\" >0.710</td>\n",
+       "                        <td id=\"T_efaac_row0_col5\" class=\"data row0 col5\" >0.715</td>\n",
+       "                        <td id=\"T_efaac_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
+       "                        <td id=\"T_efaac_row0_col7\" class=\"data row0 col7\" >0.715</td>\n",
+       "                        <td id=\"T_efaac_row0_col8\" class=\"data row0 col8\" >0.429</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_33654_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "                        <td id=\"T_33654_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row1_col2\" class=\"data row1 col2\" >0.659</td>\n",
-       "                        <td id=\"T_33654_row1_col3\" class=\"data row1 col3\" >0.633</td>\n",
-       "                        <td id=\"T_33654_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
-       "                        <td id=\"T_33654_row1_col5\" class=\"data row1 col5\" >0.703</td>\n",
-       "                        <td id=\"T_33654_row1_col6\" class=\"data row1 col6\" >0.637</td>\n",
-       "                        <td id=\"T_33654_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
-       "                        <td id=\"T_33654_row1_col8\" class=\"data row1 col8\" >0.417</td>\n",
+       "                        <th id=\"T_efaac_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_efaac_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "                        <td id=\"T_efaac_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row1_col2\" class=\"data row1 col2\" >0.659</td>\n",
+       "                        <td id=\"T_efaac_row1_col3\" class=\"data row1 col3\" >0.633</td>\n",
+       "                        <td id=\"T_efaac_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
+       "                        <td id=\"T_efaac_row1_col5\" class=\"data row1 col5\" >0.703</td>\n",
+       "                        <td id=\"T_efaac_row1_col6\" class=\"data row1 col6\" >0.637</td>\n",
+       "                        <td id=\"T_efaac_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
+       "                        <td id=\"T_efaac_row1_col8\" class=\"data row1 col8\" >0.417</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_33654_row2_col0\" class=\"data row2 col0\" >0.653</td>\n",
-       "                        <td id=\"T_33654_row2_col1\" class=\"data row2 col1\" >0.651</td>\n",
-       "                        <td id=\"T_33654_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row2_col3\" class=\"data row2 col3\" >0.646</td>\n",
-       "                        <td id=\"T_33654_row2_col4\" class=\"data row2 col4\" >0.670</td>\n",
-       "                        <td id=\"T_33654_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
-       "                        <td id=\"T_33654_row2_col6\" class=\"data row2 col6\" >0.637</td>\n",
-       "                        <td id=\"T_33654_row2_col7\" class=\"data row2 col7\" >0.667</td>\n",
-       "                        <td id=\"T_33654_row2_col8\" class=\"data row2 col8\" >0.438</td>\n",
+       "                        <th id=\"T_efaac_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_efaac_row2_col0\" class=\"data row2 col0\" >0.653</td>\n",
+       "                        <td id=\"T_efaac_row2_col1\" class=\"data row2 col1\" >0.651</td>\n",
+       "                        <td id=\"T_efaac_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row2_col3\" class=\"data row2 col3\" >0.646</td>\n",
+       "                        <td id=\"T_efaac_row2_col4\" class=\"data row2 col4\" >0.670</td>\n",
+       "                        <td id=\"T_efaac_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
+       "                        <td id=\"T_efaac_row2_col6\" class=\"data row2 col6\" >0.637</td>\n",
+       "                        <td id=\"T_efaac_row2_col7\" class=\"data row2 col7\" >0.667</td>\n",
+       "                        <td id=\"T_efaac_row2_col8\" class=\"data row2 col8\" >0.438</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_33654_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
-       "                        <td id=\"T_33654_row3_col1\" class=\"data row3 col1\" >0.629</td>\n",
-       "                        <td id=\"T_33654_row3_col2\" class=\"data row3 col2\" >0.649</td>\n",
-       "                        <td id=\"T_33654_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row3_col4\" class=\"data row3 col4\" >0.680</td>\n",
-       "                        <td id=\"T_33654_row3_col5\" class=\"data row3 col5\" >0.659</td>\n",
-       "                        <td id=\"T_33654_row3_col6\" class=\"data row3 col6\" >0.724</td>\n",
-       "                        <td id=\"T_33654_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
-       "                        <td id=\"T_33654_row3_col8\" class=\"data row3 col8\" >0.425</td>\n",
+       "                        <th id=\"T_efaac_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_efaac_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
+       "                        <td id=\"T_efaac_row3_col1\" class=\"data row3 col1\" >0.629</td>\n",
+       "                        <td id=\"T_efaac_row3_col2\" class=\"data row3 col2\" >0.649</td>\n",
+       "                        <td id=\"T_efaac_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row3_col4\" class=\"data row3 col4\" >0.680</td>\n",
+       "                        <td id=\"T_efaac_row3_col5\" class=\"data row3 col5\" >0.659</td>\n",
+       "                        <td id=\"T_efaac_row3_col6\" class=\"data row3 col6\" >0.724</td>\n",
+       "                        <td id=\"T_efaac_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
+       "                        <td id=\"T_efaac_row3_col8\" class=\"data row3 col8\" >0.425</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_33654_row4_col0\" class=\"data row4 col0\" >0.714</td>\n",
-       "                        <td id=\"T_33654_row4_col1\" class=\"data row4 col1\" >0.686</td>\n",
-       "                        <td id=\"T_33654_row4_col2\" class=\"data row4 col2\" >0.677</td>\n",
-       "                        <td id=\"T_33654_row4_col3\" class=\"data row4 col3\" >0.684</td>\n",
-       "                        <td id=\"T_33654_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row4_col5\" class=\"data row4 col5\" >0.696</td>\n",
-       "                        <td id=\"T_33654_row4_col6\" class=\"data row4 col6\" >0.665</td>\n",
-       "                        <td id=\"T_33654_row4_col7\" class=\"data row4 col7\" >0.689</td>\n",
-       "                        <td id=\"T_33654_row4_col8\" class=\"data row4 col8\" >0.454</td>\n",
+       "                        <th id=\"T_efaac_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_efaac_row4_col0\" class=\"data row4 col0\" >0.714</td>\n",
+       "                        <td id=\"T_efaac_row4_col1\" class=\"data row4 col1\" >0.686</td>\n",
+       "                        <td id=\"T_efaac_row4_col2\" class=\"data row4 col2\" >0.677</td>\n",
+       "                        <td id=\"T_efaac_row4_col3\" class=\"data row4 col3\" >0.684</td>\n",
+       "                        <td id=\"T_efaac_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row4_col5\" class=\"data row4 col5\" >0.696</td>\n",
+       "                        <td id=\"T_efaac_row4_col6\" class=\"data row4 col6\" >0.665</td>\n",
+       "                        <td id=\"T_efaac_row4_col7\" class=\"data row4 col7\" >0.689</td>\n",
+       "                        <td id=\"T_efaac_row4_col8\" class=\"data row4 col8\" >0.454</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_33654_row5_col0\" class=\"data row5 col0\" >0.709</td>\n",
-       "                        <td id=\"T_33654_row5_col1\" class=\"data row5 col1\" >0.695</td>\n",
-       "                        <td id=\"T_33654_row5_col2\" class=\"data row5 col2\" >0.639</td>\n",
-       "                        <td id=\"T_33654_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
-       "                        <td id=\"T_33654_row5_col4\" class=\"data row5 col4\" >0.687</td>\n",
-       "                        <td id=\"T_33654_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row5_col6\" class=\"data row5 col6\" >0.628</td>\n",
-       "                        <td id=\"T_33654_row5_col7\" class=\"data row5 col7\" >0.711</td>\n",
-       "                        <td id=\"T_33654_row5_col8\" class=\"data row5 col8\" >0.392</td>\n",
+       "                        <th id=\"T_efaac_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_efaac_row5_col0\" class=\"data row5 col0\" >0.709</td>\n",
+       "                        <td id=\"T_efaac_row5_col1\" class=\"data row5 col1\" >0.695</td>\n",
+       "                        <td id=\"T_efaac_row5_col2\" class=\"data row5 col2\" >0.639</td>\n",
+       "                        <td id=\"T_efaac_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
+       "                        <td id=\"T_efaac_row5_col4\" class=\"data row5 col4\" >0.687</td>\n",
+       "                        <td id=\"T_efaac_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row5_col6\" class=\"data row5 col6\" >0.628</td>\n",
+       "                        <td id=\"T_efaac_row5_col7\" class=\"data row5 col7\" >0.711</td>\n",
+       "                        <td id=\"T_efaac_row5_col8\" class=\"data row5 col8\" >0.392</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_33654_row6_col0\" class=\"data row6 col0\" >0.646</td>\n",
-       "                        <td id=\"T_33654_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
-       "                        <td id=\"T_33654_row6_col2\" class=\"data row6 col2\" >0.640</td>\n",
-       "                        <td id=\"T_33654_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
-       "                        <td id=\"T_33654_row6_col4\" class=\"data row6 col4\" >0.661</td>\n",
-       "                        <td id=\"T_33654_row6_col5\" class=\"data row6 col5\" >0.633</td>\n",
-       "                        <td id=\"T_33654_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row6_col7\" class=\"data row6 col7\" >0.652</td>\n",
-       "                        <td id=\"T_33654_row6_col8\" class=\"data row6 col8\" >0.433</td>\n",
+       "                        <th id=\"T_efaac_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_efaac_row6_col0\" class=\"data row6 col0\" >0.646</td>\n",
+       "                        <td id=\"T_efaac_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
+       "                        <td id=\"T_efaac_row6_col2\" class=\"data row6 col2\" >0.640</td>\n",
+       "                        <td id=\"T_efaac_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
+       "                        <td id=\"T_efaac_row6_col4\" class=\"data row6 col4\" >0.661</td>\n",
+       "                        <td id=\"T_efaac_row6_col5\" class=\"data row6 col5\" >0.633</td>\n",
+       "                        <td id=\"T_efaac_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row6_col7\" class=\"data row6 col7\" >0.652</td>\n",
+       "                        <td id=\"T_efaac_row6_col8\" class=\"data row6 col8\" >0.433</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_33654_row7_col0\" class=\"data row7 col0\" >0.718</td>\n",
-       "                        <td id=\"T_33654_row7_col1\" class=\"data row7 col1\" >0.704</td>\n",
-       "                        <td id=\"T_33654_row7_col2\" class=\"data row7 col2\" >0.677</td>\n",
-       "                        <td id=\"T_33654_row7_col3\" class=\"data row7 col3\" >0.656</td>\n",
-       "                        <td id=\"T_33654_row7_col4\" class=\"data row7 col4\" >0.687</td>\n",
-       "                        <td id=\"T_33654_row7_col5\" class=\"data row7 col5\" >0.718</td>\n",
-       "                        <td id=\"T_33654_row7_col6\" class=\"data row7 col6\" >0.657</td>\n",
-       "                        <td id=\"T_33654_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "                        <td id=\"T_33654_row7_col8\" class=\"data row7 col8\" >0.425</td>\n",
+       "                        <th id=\"T_efaac_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_efaac_row7_col0\" class=\"data row7 col0\" >0.718</td>\n",
+       "                        <td id=\"T_efaac_row7_col1\" class=\"data row7 col1\" >0.704</td>\n",
+       "                        <td id=\"T_efaac_row7_col2\" class=\"data row7 col2\" >0.677</td>\n",
+       "                        <td id=\"T_efaac_row7_col3\" class=\"data row7 col3\" >0.656</td>\n",
+       "                        <td id=\"T_efaac_row7_col4\" class=\"data row7 col4\" >0.687</td>\n",
+       "                        <td id=\"T_efaac_row7_col5\" class=\"data row7 col5\" >0.718</td>\n",
+       "                        <td id=\"T_efaac_row7_col6\" class=\"data row7 col6\" >0.657</td>\n",
+       "                        <td id=\"T_efaac_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_efaac_row7_col8\" class=\"data row7 col8\" >0.425</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_33654_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_33654_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
-       "                        <td id=\"T_33654_row8_col1\" class=\"data row8 col1\" >0.412</td>\n",
-       "                        <td id=\"T_33654_row8_col2\" class=\"data row8 col2\" >0.437</td>\n",
-       "                        <td id=\"T_33654_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
-       "                        <td id=\"T_33654_row8_col4\" class=\"data row8 col4\" >0.450</td>\n",
-       "                        <td id=\"T_33654_row8_col5\" class=\"data row8 col5\" >0.397</td>\n",
-       "                        <td id=\"T_33654_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
-       "                        <td id=\"T_33654_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
-       "                        <td id=\"T_33654_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "                        <th id=\"T_efaac_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_efaac_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
+       "                        <td id=\"T_efaac_row8_col1\" class=\"data row8 col1\" >0.412</td>\n",
+       "                        <td id=\"T_efaac_row8_col2\" class=\"data row8 col2\" >0.437</td>\n",
+       "                        <td id=\"T_efaac_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
+       "                        <td id=\"T_efaac_row8_col4\" class=\"data row8 col4\" >0.450</td>\n",
+       "                        <td id=\"T_efaac_row8_col5\" class=\"data row8 col5\" >0.397</td>\n",
+       "                        <td id=\"T_efaac_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
+       "                        <td id=\"T_efaac_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
+       "                        <td id=\"T_efaac_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "            </tr>\n",
        "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f6365e71ca0>"
+       "<pandas.io.formats.style.Styler at 0x7f35feba3f10>"
       ]
      },
      "execution_count": 18,
@@ -1285,242 +1288,242 @@
      "data": {
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        "            background-color:  #6ab46a;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row4_col5,#T_b338a_row5_col0,#T_b338a_row7_col1{\n",
+       "        }#T_f9fbf_row4_col5,#T_f9fbf_row5_col0,#T_f9fbf_row7_col1{\n",
        "            background-color:  #74b974;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row4_col6{\n",
+       "        }#T_f9fbf_row4_col6{\n",
        "            background-color:  #61af61;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row4_col7{\n",
+       "        }#T_f9fbf_row4_col7{\n",
        "            background-color:  #6db56d;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row4_col8{\n",
+       "        }#T_f9fbf_row4_col8{\n",
        "            background-color:  #188c18;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row5_col2{\n",
+       "        }#T_f9fbf_row5_col2{\n",
        "            background-color:  #54a954;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row5_col6{\n",
+       "        }#T_f9fbf_row5_col6{\n",
        "            background-color:  #51a851;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row5_col7{\n",
+       "        }#T_f9fbf_row5_col7{\n",
        "            background-color:  #75b975;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row5_col8,#T_b338a_row8_col0,#T_b338a_row8_col1,#T_b338a_row8_col2,#T_b338a_row8_col3,#T_b338a_row8_col4,#T_b338a_row8_col5,#T_b338a_row8_col6,#T_b338a_row8_col7{\n",
+       "        }#T_f9fbf_row5_col8,#T_f9fbf_row8_col0,#T_f9fbf_row8_col1,#T_f9fbf_row8_col2,#T_f9fbf_row8_col3,#T_f9fbf_row8_col4,#T_f9fbf_row8_col5,#T_f9fbf_row8_col6,#T_f9fbf_row8_col7{\n",
        "            background-color:  #008000;\n",
        "            color:  #f1f1f1;\n",
-       "        }#T_b338a_row6_col0{\n",
+       "        }#T_f9fbf_row6_col0{\n",
        "            background-color:  #59ac59;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row6_col3{\n",
+       "        }#T_f9fbf_row6_col3{\n",
        "            background-color:  #79bc79;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row6_col5{\n",
+       "        }#T_f9fbf_row6_col5{\n",
        "            background-color:  #5bad5b;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row6_col8{\n",
+       "        }#T_f9fbf_row6_col8{\n",
        "            background-color:  #108810;\n",
        "            color:  #f1f1f1;\n",
-       "        }#T_b338a_row7_col3{\n",
+       "        }#T_f9fbf_row7_col3{\n",
        "            background-color:  #5fae5f;\n",
        "            color:  #000000;\n",
-       "        }#T_b338a_row7_col5{\n",
+       "        }#T_f9fbf_row7_col5{\n",
        "            background-color:  #7dbd7d;\n",
        "            color:  #000000;\n",
-       "        }</style><table id=\"T_b338a_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
+       "        }</style><table id=\"T_f9fbf_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
        "                <tr>\n",
-       "                        <th id=\"T_b338a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_b338a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "                        <td id=\"T_b338a_row0_col2\" class=\"data row0 col2\" >0.341</td>\n",
-       "                        <td id=\"T_b338a_row0_col3\" class=\"data row0 col3\" >0.350</td>\n",
-       "                        <td id=\"T_b338a_row0_col4\" class=\"data row0 col4\" >0.290</td>\n",
-       "                        <td id=\"T_b338a_row0_col5\" class=\"data row0 col5\" >0.285</td>\n",
-       "                        <td id=\"T_b338a_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
-       "                        <td id=\"T_b338a_row0_col7\" class=\"data row0 col7\" >0.285</td>\n",
-       "                        <td id=\"T_b338a_row0_col8\" class=\"data row0 col8\" >0.571</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_f9fbf_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col2\" class=\"data row0 col2\" >0.341</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col3\" class=\"data row0 col3\" >0.350</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col4\" class=\"data row0 col4\" >0.290</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col5\" class=\"data row0 col5\" >0.285</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col7\" class=\"data row0 col7\" >0.285</td>\n",
+       "                        <td id=\"T_f9fbf_row0_col8\" class=\"data row0 col8\" >0.571</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_b338a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "                        <td id=\"T_b338a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row1_col2\" class=\"data row1 col2\" >0.341</td>\n",
-       "                        <td id=\"T_b338a_row1_col3\" class=\"data row1 col3\" >0.367</td>\n",
-       "                        <td id=\"T_b338a_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
-       "                        <td id=\"T_b338a_row1_col5\" class=\"data row1 col5\" >0.297</td>\n",
-       "                        <td id=\"T_b338a_row1_col6\" class=\"data row1 col6\" >0.363</td>\n",
-       "                        <td id=\"T_b338a_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
-       "                        <td id=\"T_b338a_row1_col8\" class=\"data row1 col8\" >0.583</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_f9fbf_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col2\" class=\"data row1 col2\" >0.341</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col3\" class=\"data row1 col3\" >0.367</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col5\" class=\"data row1 col5\" >0.297</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col6\" class=\"data row1 col6\" >0.363</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
+       "                        <td id=\"T_f9fbf_row1_col8\" class=\"data row1 col8\" >0.583</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_b338a_row2_col0\" class=\"data row2 col0\" >0.347</td>\n",
-       "                        <td id=\"T_b338a_row2_col1\" class=\"data row2 col1\" >0.349</td>\n",
-       "                        <td id=\"T_b338a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row2_col3\" class=\"data row2 col3\" >0.354</td>\n",
-       "                        <td id=\"T_b338a_row2_col4\" class=\"data row2 col4\" >0.330</td>\n",
-       "                        <td id=\"T_b338a_row2_col5\" class=\"data row2 col5\" >0.361</td>\n",
-       "                        <td id=\"T_b338a_row2_col6\" class=\"data row2 col6\" >0.363</td>\n",
-       "                        <td id=\"T_b338a_row2_col7\" class=\"data row2 col7\" >0.333</td>\n",
-       "                        <td id=\"T_b338a_row2_col8\" class=\"data row2 col8\" >0.562</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "                        <td id=\"T_f9fbf_row2_col0\" class=\"data row2 col0\" >0.347</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col1\" class=\"data row2 col1\" >0.349</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col3\" class=\"data row2 col3\" >0.354</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col4\" class=\"data row2 col4\" >0.330</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col5\" class=\"data row2 col5\" >0.361</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col6\" class=\"data row2 col6\" >0.363</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col7\" class=\"data row2 col7\" >0.333</td>\n",
+       "                        <td id=\"T_f9fbf_row2_col8\" class=\"data row2 col8\" >0.562</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_b338a_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
-       "                        <td id=\"T_b338a_row3_col1\" class=\"data row3 col1\" >0.371</td>\n",
-       "                        <td id=\"T_b338a_row3_col2\" class=\"data row3 col2\" >0.351</td>\n",
-       "                        <td id=\"T_b338a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row3_col4\" class=\"data row3 col4\" >0.320</td>\n",
-       "                        <td id=\"T_b338a_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "                        <td id=\"T_b338a_row3_col6\" class=\"data row3 col6\" >0.276</td>\n",
-       "                        <td id=\"T_b338a_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
-       "                        <td id=\"T_b338a_row3_col8\" class=\"data row3 col8\" >0.575</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "                        <td id=\"T_f9fbf_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col1\" class=\"data row3 col1\" >0.371</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col2\" class=\"data row3 col2\" >0.351</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col4\" class=\"data row3 col4\" >0.320</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col6\" class=\"data row3 col6\" >0.276</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
+       "                        <td id=\"T_f9fbf_row3_col8\" class=\"data row3 col8\" >0.575</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_b338a_row4_col0\" class=\"data row4 col0\" >0.286</td>\n",
-       "                        <td id=\"T_b338a_row4_col1\" class=\"data row4 col1\" >0.314</td>\n",
-       "                        <td id=\"T_b338a_row4_col2\" class=\"data row4 col2\" >0.323</td>\n",
-       "                        <td id=\"T_b338a_row4_col3\" class=\"data row4 col3\" >0.316</td>\n",
-       "                        <td id=\"T_b338a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row4_col5\" class=\"data row4 col5\" >0.304</td>\n",
-       "                        <td id=\"T_b338a_row4_col6\" class=\"data row4 col6\" >0.335</td>\n",
-       "                        <td id=\"T_b338a_row4_col7\" class=\"data row4 col7\" >0.311</td>\n",
-       "                        <td id=\"T_b338a_row4_col8\" class=\"data row4 col8\" >0.546</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "                        <td id=\"T_f9fbf_row4_col0\" class=\"data row4 col0\" >0.286</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col1\" class=\"data row4 col1\" >0.314</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col2\" class=\"data row4 col2\" >0.323</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col3\" class=\"data row4 col3\" >0.316</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col5\" class=\"data row4 col5\" >0.304</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col6\" class=\"data row4 col6\" >0.335</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col7\" class=\"data row4 col7\" >0.311</td>\n",
+       "                        <td id=\"T_f9fbf_row4_col8\" class=\"data row4 col8\" >0.546</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_b338a_row5_col0\" class=\"data row5 col0\" >0.291</td>\n",
-       "                        <td id=\"T_b338a_row5_col1\" class=\"data row5 col1\" >0.305</td>\n",
-       "                        <td id=\"T_b338a_row5_col2\" class=\"data row5 col2\" >0.361</td>\n",
-       "                        <td id=\"T_b338a_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
-       "                        <td id=\"T_b338a_row5_col4\" class=\"data row5 col4\" >0.313</td>\n",
-       "                        <td id=\"T_b338a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row5_col6\" class=\"data row5 col6\" >0.372</td>\n",
-       "                        <td id=\"T_b338a_row5_col7\" class=\"data row5 col7\" >0.289</td>\n",
-       "                        <td id=\"T_b338a_row5_col8\" class=\"data row5 col8\" >0.608</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "                        <td id=\"T_f9fbf_row5_col0\" class=\"data row5 col0\" >0.291</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col1\" class=\"data row5 col1\" >0.305</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col2\" class=\"data row5 col2\" >0.361</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col4\" class=\"data row5 col4\" >0.313</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col6\" class=\"data row5 col6\" >0.372</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col7\" class=\"data row5 col7\" >0.289</td>\n",
+       "                        <td id=\"T_f9fbf_row5_col8\" class=\"data row5 col8\" >0.608</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_b338a_row6_col0\" class=\"data row6 col0\" >0.354</td>\n",
-       "                        <td id=\"T_b338a_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
-       "                        <td id=\"T_b338a_row6_col2\" class=\"data row6 col2\" >0.360</td>\n",
-       "                        <td id=\"T_b338a_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
-       "                        <td id=\"T_b338a_row6_col4\" class=\"data row6 col4\" >0.339</td>\n",
-       "                        <td id=\"T_b338a_row6_col5\" class=\"data row6 col5\" >0.367</td>\n",
-       "                        <td id=\"T_b338a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row6_col7\" class=\"data row6 col7\" >0.348</td>\n",
-       "                        <td id=\"T_b338a_row6_col8\" class=\"data row6 col8\" >0.567</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "                        <td id=\"T_f9fbf_row6_col0\" class=\"data row6 col0\" >0.354</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col2\" class=\"data row6 col2\" >0.360</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col4\" class=\"data row6 col4\" >0.339</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col5\" class=\"data row6 col5\" >0.367</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col7\" class=\"data row6 col7\" >0.348</td>\n",
+       "                        <td id=\"T_f9fbf_row6_col8\" class=\"data row6 col8\" >0.567</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_b338a_row7_col0\" class=\"data row7 col0\" >0.282</td>\n",
-       "                        <td id=\"T_b338a_row7_col1\" class=\"data row7 col1\" >0.296</td>\n",
-       "                        <td id=\"T_b338a_row7_col2\" class=\"data row7 col2\" >0.323</td>\n",
-       "                        <td id=\"T_b338a_row7_col3\" class=\"data row7 col3\" >0.344</td>\n",
-       "                        <td id=\"T_b338a_row7_col4\" class=\"data row7 col4\" >0.313</td>\n",
-       "                        <td id=\"T_b338a_row7_col5\" class=\"data row7 col5\" >0.282</td>\n",
-       "                        <td id=\"T_b338a_row7_col6\" class=\"data row7 col6\" >0.343</td>\n",
-       "                        <td id=\"T_b338a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "                        <td id=\"T_b338a_row7_col8\" class=\"data row7 col8\" >0.575</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "                        <td id=\"T_f9fbf_row7_col0\" class=\"data row7 col0\" >0.282</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col1\" class=\"data row7 col1\" >0.296</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col2\" class=\"data row7 col2\" >0.323</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col3\" class=\"data row7 col3\" >0.344</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col4\" class=\"data row7 col4\" >0.313</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col5\" class=\"data row7 col5\" >0.282</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col6\" class=\"data row7 col6\" >0.343</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "                        <td id=\"T_f9fbf_row7_col8\" class=\"data row7 col8\" >0.575</td>\n",
        "            </tr>\n",
        "            <tr>\n",
-       "                        <th id=\"T_b338a_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_b338a_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
-       "                        <td id=\"T_b338a_row8_col1\" class=\"data row8 col1\" >0.588</td>\n",
-       "                        <td id=\"T_b338a_row8_col2\" class=\"data row8 col2\" >0.563</td>\n",
-       "                        <td id=\"T_b338a_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
-       "                        <td id=\"T_b338a_row8_col4\" class=\"data row8 col4\" >0.550</td>\n",
-       "                        <td id=\"T_b338a_row8_col5\" class=\"data row8 col5\" >0.603</td>\n",
-       "                        <td id=\"T_b338a_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
-       "                        <td id=\"T_b338a_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
-       "                        <td id=\"T_b338a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "                        <th id=\"T_f9fbf_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "                        <td id=\"T_f9fbf_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col1\" class=\"data row8 col1\" >0.588</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col2\" class=\"data row8 col2\" >0.563</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col4\" class=\"data row8 col4\" >0.550</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col5\" class=\"data row8 col5\" >0.603</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
+       "                        <td id=\"T_f9fbf_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "            </tr>\n",
        "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f63654bf100>"
+       "<pandas.io.formats.style.Styler at 0x7f35feac9be0>"
       ]
      },
      "execution_count": 22,

From c5fd43c69d0020f6bd060474844dfc5e958ec20d Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Wed, 22 Sep 2021 10:43:22 +0200
Subject: [PATCH 064/158] minor adaptions to T028

---
 .../talktorial.ipynb                          | 104 +++++++++++++-----
 1 file changed, 76 insertions(+), 28 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index e137c26d..41a79e76 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -24,7 +24,7 @@
     "* **Talktorial T024**: Kinase pocket sequences (KLIFS pocket sequences)\n",
     "* **Talktorial T025**: Kinase pocket structures (KiSSim fingerprint based on KLIFS pocket residues)\n",
     "* **Talktorial T026**: Kinase-ligand interaction profiles (KLIFS IFPs based on KLIFS pocket residues)\n",
-    "* **Talktorial T027**: Ligand profiles"
+    "* **Talktorial T027**: Ligand profiling data"
    ]
   },
   {
@@ -34,7 +34,7 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor (considering 4 different aspects)"
    ]
   },
   {
@@ -78,7 +78,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which aimed to understand kinase similarities within different combinations of kinase on- and off-targets (also called anti-targets):\n",
+    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
     "\n",
     "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
     "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
@@ -103,7 +103,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: XXX\n",
+    "### Kinase similarity descriptor (considering 4 different aspects)\n",
     "\n",
     "Describe the dataset describing kinase similarity and how we use it.\n",
     "\n",
@@ -159,7 +159,9 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
-    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv\",\n",
+    "    #\"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv\",\n",
+    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_identity_matrix_sequence.csv\",\n",
+    "    #\"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv\",\n",
     "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
     "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
     "    \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv\"\n",
@@ -177,10 +179,38 @@
     "    kinase_distance_matrices[kinsim_perspective] = pd.read_csv(path, index_col=0).round(6)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check if the matrices have the same dimensions"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of sequence matrix: (9, 9)\n",
+      "Shape of kissim matrix: (9, 9)\n",
+      "Shape of ifp matrix: (9, 9)\n",
+      "Shape of ligand-profile matrix: (9, 9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in kinase_distance_matrix_paths.keys():\n",
+    "    print(f\"Shape of {key} matrix: {kinase_distance_matrices[key].shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -271,7 +301,7 @@
        " p110a  0.819209  0.000000  }"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,11 +321,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Choices of precalculated descriptors: dict_keys(['sequence', 'kissim', 'ifp', 'ligand-profile'])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Choices of precalculated descriptors: {kinase_distance_matrix_paths.keys()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix = kinase_distance_matrices[\"ifp\"]"
+    "descriptor_selection = \"sequence\"\n",
+    "kinase_distance_matrix = kinase_distance_matrices[descriptor_selection]"
    ]
   },
   {
@@ -311,12 +359,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -355,21 +403,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.3     , 0.277778, 0.393939, 0.363636, 0.32    , 0.368421,\n",
-       "       0.5     , 0.333333, 0.111111, 0.451613, 0.238095, 0.190476,\n",
-       "       0.133333, 0.347826, 0.277778, 0.258065, 0.238095, 0.166667,\n",
-       "       0.227273, 0.380952, 0.173913, 0.40625 , 0.419355, 0.382353,\n",
-       "       0.571429, 0.258065, 0.058824, 0.333333, 0.533333, 0.043478,\n",
-       "       0.25    , 0.375   , 0.190476, 0.428571, 0.352941, 0.473684])"
+       "array([0.105882, 0.623529, 0.682353, 0.552941, 0.541176, 0.611765,\n",
+       "       0.529412, 0.882353, 0.6     , 0.670588, 0.576471, 0.529412,\n",
+       "       0.6     , 0.564706, 0.882353, 0.670588, 0.611765, 0.623529,\n",
+       "       0.623529, 0.6     , 0.847059, 0.623529, 0.635294, 0.529412,\n",
+       "       0.658824, 0.894118, 0.6     , 0.611765, 0.564706, 0.858824,\n",
+       "       0.635294, 0.529412, 0.894118, 0.611765, 0.858824, 0.847059])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -389,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -405,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -421,12 +469,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -474,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -684,7 +732,7 @@
        "p38a   21.982655  38.529748   0.000000  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -695,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -759,7 +807,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

From 95c24b4e689ae0565e3089b41cc0824f811bfea9 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Wed, 22 Sep 2021 15:50:13 +0200
Subject: [PATCH 065/158] ideas for theory part

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 113 +++++++++++++-----
 1 file changed, 82 insertions(+), 31 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 29d5b255..8e759888 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -28,8 +28,13 @@
    "source": [
     "### Contents in *Theory*\n",
     "\n",
-    "* Kinase dataset\n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinases in a nutshell\n",
+    "    * The human kinome\n",
+    "    * Kinase structures and important motifs\n",
+    "    * Collection of kinase related information: KLIFS\n",
+    "    * Available bioactivity data\n",
+    "* Kinase-similarity: off-target, promiscuous binding\n",
+    "* Kinase dataset compilation"
    ]
   },
   {
@@ -66,88 +71,134 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase dataset"
+    "### Kinases in a nutshell"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "Kinases are established drug targets to combat cancer and inflammatory diseases ([TODO ref](https://onlinelibrary.wiley.com/doi/book/10.1002/9783527633470))\n",
+    "* Activate proteins by phosphorylation\n",
+    "* Most frequently mutated proteins in tumors\n",
+    "* 5782 x-ray structures of human kinases (as of Sept. 2021, see [KLIFS](https://klifs.net/) database\n",
+    "* 67 FDA approved small molecule protein kinase inhibitors on the market (as of Sept. 2021, see [link](http://www.brimr.org/PKI/PKIs.htm))\n",
+    "* Most of the approved drugs bind in the ATP-binding pocketand intermediate surroundings \n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+    "While there is so much research also going on in the field of kinases, there are still open challenges\n",
+    "* A large fraction of the kinome is un-/underexplored\n",
+    "* Many kinase inhibitors are promiscous binders -> off-target effects or polypharmacology\n",
+    "* Occurence of drug resistances due to mutations\n",
+    "\n",
+    "[TODO add a 3D figure of a kinase structure with ATP bound, and maybe DFG-in motif highlighted]\n",
+    "\n",
+    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development. \n",
+    "[TODO cite Kooistra, Volkamer, ARMC V.50, Elsevier, 2017, 153-192]"
    ]
   },
   {
-   "attachments": {
-    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
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"
-    }
-   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "#### The human kinome \n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+    "* Human kinome consists of ~540 protein kinases\n",
+    "    * [TODO: maybe comment on numbers differ depedning on the source, ref to kinodata]\n",
+    "* Grouped by Manning [TODO add ref] into eight major groups and one 'other' group\n",
+    "    * AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL, Other\n",
+    "    * Phylogenetic tree, clustered by overall sequence similarity\n",
+    "    \n",
+    "[TODO add a kinome tree figure from kinhub]\n",
+    "\n",
+    "[numbers taken from Molecules publication, maybe add some references]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: XXX\n",
+    "#### Kinase structures and important motifs\n",
     "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
+    "Important regions\n",
+    "* hinge region: key hydrogen bonds \n",
+    "* DFG motif: Flip between phenylalanine (F) and aspartate (D) driver for active and inactive state\n",
+    "* αC-helix: αC-in conformation -> salt bridge\n",
+    "* glycine-rich (G-rich) loop: stabilizes ATP binding\n",
     "\n",
-    "- XXX = KLIFS pocket sequence\n",
-    "- XXX = KiSSim fingerprint\n",
-    "- XXX = KLIFS IFP\n",
-    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+    "[Maybe a 3D figure, or include motifs already in the above mentioned figure]\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Compound activity measures"
+    "#### Collection of kinase related information: KLIFS\n",
+    "\n",
+    "TODO - short!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet."
+    "#### Available bioactivity data\n",
+    "\n",
+    "TODO - short !\n",
+    "* maybe an overview of data points per kinase on Chembl (from kinodata)\n",
+    "* other profiling data (as available in karaman, xxx)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "$pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $"
+    "### Kinase-similarity: off-target, promiscuous binding\n",
+    "\n",
+    "TODO Problem statement. Introduce\n",
+    "* problem of promiscuous binding (e.g. by profiling results for known inhibitors, (kinhub figure), example from molecules paper?)\n",
+    "* different perspectives: sequence, structure and ligand-profiling data\n",
+    "* that's why we investigate in similarity from the different perspectives\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-block alert-info\">\n",
-    "    \n",
-    "<b>Formulas</b>: Please use LaTeX to format formulas.\n",
-    "</div>"
+    "### Kinase dataset compilation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "\n",
+    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
    ]
   },
   {
+   "attachments": {
+    "814048cb-e723-4b10-b9f7-2b56848688d9.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If you place links, please link descriptive words.\n",
+    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
     "\n",
-    "> __Yes__: [ChEMBL](https://www.ebi.ac.uk/chembl/) is a manually curated database of bioactive molecules\n",
+    "*Figure 1:* \n",
+    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TODO\n",
     "\n",
-    "> __No__: ChEMBL ([here](https://www.ebi.ac.uk/chembl/)) is a manually curated database of bioactive molecules"
+    "Data set used throughout the following notebooks, maybe directly shaped in a way that it has the correct information per task in place\n"
    ]
   },
   {
@@ -302,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From 61a3f64982f0782a884a2060259b6de5dc6fb095 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 23 Sep 2021 15:11:29 +0200
Subject: [PATCH 066/158] =?UTF-8?q?=E2=9C=A8=20Add=20kinase=20selection=20?=
 =?UTF-8?q?as=20csv?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../T023_what_is_a_kinase/data/kinase_selection.csv    | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv
new file mode 100644
index 00000000..980dc600
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv
@@ -0,0 +1,10 @@
+Kinase,Synonyms,UniProt ID,Group,Full kinase name
+EGFR,ErbB1,P00533,TK,Epidermal growth factor receptor
+ErbB2,Her2,P04626,TK,Erythroblastic leukemia viral oncogene homolog 2
+PI3K,PI3KCA, p110a,P42336,Atypical,Phosphatidylinositol-3-kinase
+VEGFR2,KDR,P35968,TK,Vascular endothelial growth factor receptor 2
+BRAF,-,P15056,TKL,Rapidly accelerated fibrosarcoma isoform B
+CDK2,-,P24941,CMGC,Cyclic-dependent kinase 2
+LCK,-,P06239,TK,Lymphocyte-specific protein tyrosine kinase
+MET,-,P08581,TK,Mesenchymal-epithelial transition factor
+p38a,MAPK14,Q16539,CMGC,p38 mitogen activated protein kinase α

From e65b839aa0a1f85f4d6b67d2283bdc65f4df6bbf Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 23 Sep 2021 15:34:10 +0200
Subject: [PATCH 067/158] Update kinase data csv

---
 .../data/kinase_selection.csv                 |  18 +-
 .../T023_what_is_a_kinase/talktorial.ipynb    | 157 ++++++++++++++++--
 2 files changed, 152 insertions(+), 23 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv
index 980dc600..945ce89d 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinase_selection.csv
@@ -1,10 +1,10 @@
-Kinase,Synonyms,UniProt ID,Group,Full kinase name
-EGFR,ErbB1,P00533,TK,Epidermal growth factor receptor
-ErbB2,Her2,P04626,TK,Erythroblastic leukemia viral oncogene homolog 2
-PI3K,PI3KCA, p110a,P42336,Atypical,Phosphatidylinositol-3-kinase
+kinase,kinase_klifs,uniprot_id,group,full_kinase_name
+EGFR,EGFR,P00533,TK,Epidermal growth factor receptor
+ErbB2,ErbB2,P04626,TK,Erythroblastic leukemia viral oncogene homolog 2
+PI3K,p110a,P42336,Atypical,Phosphatidylinositol-3-kinase
 VEGFR2,KDR,P35968,TK,Vascular endothelial growth factor receptor 2
-BRAF,-,P15056,TKL,Rapidly accelerated fibrosarcoma isoform B
-CDK2,-,P24941,CMGC,Cyclic-dependent kinase 2
-LCK,-,P06239,TK,Lymphocyte-specific protein tyrosine kinase
-MET,-,P08581,TK,Mesenchymal-epithelial transition factor
-p38a,MAPK14,Q16539,CMGC,p38 mitogen activated protein kinase α
+BRAF,BRAF,P15056,TKL,Rapidly accelerated fibrosarcoma isoform B
+CDK2,CDK2,P24941,CMGC,Cyclic-dependent kinase 2
+LCK,LCK,P06239,TK,Lymphocyte-specific protein tyrosine kinase
+MET,MET,P08581,TK,Mesenchymal-epithelial transition factor
+p38a,p38a,Q16539,CMGC,p38 mitogen activated protein kinase alpha
diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 8e759888..e8763841 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -212,15 +212,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -246,10 +238,147 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_selection = pd.read_csv(DATA/\"kinase_selection.csv\")\n",
+    "kinase_selection"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -339,7 +468,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -353,7 +482,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 2946484758c18d0c33993d015e901ed4984e1f72 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 09:27:18 +0200
Subject: [PATCH 068/158] T023: Add KLIFS theory

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 23 +++++++++++++++----
 1 file changed, 19 insertions(+), 4 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index e8763841..201dc2ce 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -57,7 +57,11 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase similarity descriptor: XXX\n",
+    "* KLIFS\n",
+    "  * KLIFS URL: https://klifs.net/\n",
+    "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "  * KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)"
    ]
   },
   {
@@ -132,9 +136,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Collection of kinase related information: KLIFS\n",
+    "#### Collection of kinase related information: KLIFS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
     "\n",
-    "TODO - short!"
+    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinase binding site residues as defined by KLIFS.\n",
+    "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
   },
   {
@@ -482,7 +497,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From 8f06259d40142c43cca78addeb9a0e9409b9ae6b Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 10:32:37 +0200
Subject: [PATCH 069/158] T023: Add note about KLIFS IDs

---
 .../T023_what_is_a_kinase/talktorial.ipynb            | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 201dc2ce..88701d80 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -152,6 +152,17 @@
     "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each structure, kinase, and ligand in KLIFS is associated with an identifier:\n",
+    "\n",
+    "- Structure KLIFS ID\n",
+    "- Kinase KLIFS ID\n",
+    "- Ligand KLIFS ID"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

From faf15b18a195c414f357a655597598342577b05d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 11:22:01 +0200
Subject: [PATCH 070/158] T026: Add IFP figure; use kinase CSV; update variable
 names; proof-read

---
 .../images/T026_KLIFS_IFP.png                 | Bin 0 -> 20180 bytes
 .../talktorial.ipynb                          | 725 +++++++++++++++---
 2 files changed, 601 insertions(+), 124 deletions(-)
 create mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/images/T026_KLIFS_IFP.png

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/images/T026_KLIFS_IFP.png b/teachopencadd/talktorials/T026_kinase_similarity_ifp/images/T026_KLIFS_IFP.png
new file mode 100644
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HcmV?d00001

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 0cc1a99a..d389ca26 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -87,25 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
-    "\n",
-    "*Table 1:* \n",
-    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
-    "\n",
-    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
-    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
-    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
-    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
-    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
-    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
-    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
-    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
-    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
-    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
-    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+    "We use the kinase selection as defined in **Talktorial T023**."
    ]
   },
   {
@@ -114,32 +96,42 @@
    "source": [
     "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
-    "Interaction fingerprints encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowing about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once.\n",
-    "\n",
-    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. We will make use of pre-calculated interaction fingerprints provided by the kinase-focused resource KLIFS."
+    "Interaction fingerprints (IFPs) encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowledge about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
-    "\n",
-    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
-    "*Figure 1:* \n",
-    "Kinase binding site residues as defined by KLIFS.\n",
-    "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
+    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. \n",
+    "We will make use of the KLIFS IFP: For each kinase structure that is co-crystallized with a ligand, all interactions between the 85 KLIFS pocket residues (see more details in **Talktorial T023**) and the co-crystallized ligand are described using the IFP by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e))."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS authors calculate for each of these structures &mdash; that are co-crystallized with a ligand &mdash; an interaction fingerprint (IFP) as proposed by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e)). This is how the IFP is described in the respective KLIFS pulication [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w):\n",
+    "This is how the IFP is explained in the respective KLIFS publication [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w):\n",
     "\n",
-    "> For each amino acid in the catalytic cleft, seven types of protein−ligand interactions are determined. The presence of a certain type of interaction results in the type-setting ofa “1” in the bit-string; otherwise a “0” is used to indicate the absence of the interaction. The following seven types of interactions are summarized in one bit-string; bit 1 = hydrophobic contact (HYD); bit 2 = face to face aromatic interactions (F−F); bit 3 = face to edge aromatic interactions (F−E); bit 4 = protein H-bond donor (DON); bit 5 = protein H- bond acceptor (ACC); bit 6 = protein cationic interactions (ION+), and bit 7 = protein anionic interactions (ION−).\n",
+    "> For each amino acid in the catalytic cleft, seven types of protein−ligand interactions are determined. The presence of a certain type of interaction results in the type-setting ofa “1” in the bit-string; otherwise a “0” is used to indicate the absence of the interaction. The following seven types of interactions are summarized in one bit-string; bit 1 = hydrophobic contact (HYD); bit 2 = face to face aromatic interactions (F−F); bit 3 = face to edge aromatic interactions (F−E); bit 4 = protein H-bond donor (DON); bit 5 = protein H- bond acceptor (ACC); bit 6 = protein cationic interactions (ION+), and bit 7 = protein anionic interactions (ION−)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This results in a $85 \\times 7 = 595$ bit vector. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do now in the practical part of this tutorial."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![KLIFS IFP](images/T026_KLIFS_IFP.png)\n",
     "\n",
-    "This results in a $85 \\times 7 = 595$ bit vector, which describes the interactions seen in a ligand-kinase complex. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do now in the practical part of this tutorial."
+    "*Figure 1:* \n",
+    "KLIFS interaction fingerprint (IFP) definition: Seven interaction types are detected between each of a kinase structure's $85$ pocket residues and a co-crystallized ligand. Interaction types include: hydrophobic contact (HYD), face to face aromatic interactions (F−F), face to edge aromatic interactions (F−E), protein H-bond donor (DON), protein H- bond acceptor (ACC), protein cationic interactions (ION+), and protein anionic interactions (ION−).\n",
+    "Figure by Dominique Sydow, adapted from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
   },
   {
@@ -210,31 +202,151 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define our kinases of interest and all their synonyms as described in Table 1 in the global variable `query_kinases`."
+    "Let's load the kinase selection as defined in **Talktorial T023**."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "query_kinases = [\n",
-    "    \"EGFR\",\n",
-    "    \"ErbB2\",\n",
-    "    \"Her2\",\n",
-    "    \"PI3K\",\n",
-    "    \"PI3KCA\",\n",
-    "    \"p110a\",\n",
-    "    \"BRAF\",\n",
-    "    \"CDK2\",\n",
-    "    \"LCK\",\n",
-    "    \"MET\",\n",
-    "    \"p38a\",\n",
-    "    \"MAPK14\",\n",
-    "    \"VEGFR2\",\n",
-    "    \"KDR\",\n",
-    "]"
+    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
+    "kinase_selection_df"
    ]
   },
   {
@@ -301,15 +413,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 2484\n",
+      "Number of structures: 2486\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
    ],
    "source": [
-    "structures = klifs_session.structures.by_kinase_name(kinase_names=query_kinases)\n",
-    "print(f\"Number of structures: {len(structures)}\")\n",
-    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+    "# Get list of kinase names\n",
+    "kinase_names = kinase_selection_df[\"kinase_klifs\"].to_list()\n",
+    "\n",
+    "# Get all available structures for these kinases\n",
+    "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
+    "print(f\"Number of structures: {len(structures_df)}\")\n",
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
    ]
   },
   {
@@ -333,13 +449,6 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -350,14 +459,14 @@
     }
    ],
    "source": [
-    "structures = structures[\n",
-    "    (structures[\"species.klifs\"] == \"Human\")\n",
-    "    & (structures[\"structure.dfg\"] == \"in\")\n",
-    "    & (structures[\"structure.resolution\"] <= 3)\n",
-    "    & (structures[\"structure.qualityscore\"] >= 6)\n",
+    "structures_df = structures_df[\n",
+    "    (structures_df[\"species.klifs\"] == \"Human\")\n",
+    "    & (structures_df[\"structure.dfg\"] == \"in\")\n",
+    "    & (structures_df[\"structure.resolution\"] <= 3)\n",
+    "    & (structures_df[\"structure.qualityscore\"] >= 6)\n",
     "]\n",
-    "print(f\"Number of structures: {len(structures)}\")\n",
-    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+    "print(f\"Number of structures: {len(structures_df)}\")\n",
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
    ]
   },
   {
@@ -381,7 +490,7 @@
     }
    ],
    "source": [
-    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()\n",
+    "structure_klifs_ids = structures_df[\"structure.klifs_id\"].to_list()\n",
     "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
    ]
   },
@@ -415,7 +524,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -506,9 +614,9 @@
     }
    ],
    "source": [
-    "ifps = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
-    "print(f\"Number of structures with IFPs: {len(ifps)}\")\n",
-    "ifps.head()"
+    "ifps_df = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
+    "print(f\"Number of structures with IFPs: {len(ifps_df)}\")\n",
+    "ifps_df.head()"
    ]
   },
   {
@@ -752,9 +860,9 @@
     }
    ],
    "source": [
-    "structures_with_ifps = ifps.merge(structures, on=\"structure.klifs_id\", how=\"inner\")\n",
-    "print(f\"Number of structures with IFPs: {len(structures_with_ifps)}\")\n",
-    "structures_with_ifps.head()"
+    "structures_with_ifps_df = ifps_df.merge(structures_df, on=\"structure.klifs_id\", how=\"inner\")\n",
+    "print(f\"Number of structures with IFPs: {len(structures_with_ifps_df)}\")\n",
+    "structures_with_ifps_df.head()"
    ]
   },
   {
@@ -796,7 +904,7 @@
    ],
    "source": [
     "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
-    "n_structures_per_kinase = structures_with_ifps.groupby(\"kinase.klifs_name\").size().sort_values()\n",
+    "n_structures_per_kinase = structures_with_ifps_df.groupby(\"kinase.klifs_name\").size().sort_values()\n",
     "n_structures_per_kinase\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -815,7 +923,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -839,7 +947,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not: See e.g. CDK2 and EGFR vs. KDR and ErbB2. We will have to keep this in mind when interpreting our results later."
+    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not. We will have to keep this in mind when interpreting our results later."
    ]
   },
   {
@@ -884,7 +992,7 @@
    ],
    "source": [
     "# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\n",
-    "ifp_series = structures_with_ifps.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
+    "ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
     "ifp_series.head()"
    ]
   },
@@ -892,17 +1000,6 @@
    "cell_type": "code",
    "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cast string to list of \"0\" and \"1\"\n",
-    "# ifps_series = ifp_series.apply(list)\n",
-    "# ifp_series.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -916,7 +1013,7 @@
        "Name: interaction.fingerprint, dtype: object"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -929,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -944,7 +1041,7 @@
        "       [False, False, False, ..., False, False, False]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -968,16 +1065,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure_distance_matrix = pairwise_distances(ifps_array, metric=\"jaccard\")"
+    "structure_distance_matrix_array = pairwise_distances(ifps_array, metric=\"jaccard\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1079,7 +1176,7 @@
        "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1087,12 +1184,12 @@
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
     "structure_klifs_ids = ifp_series.index.get_level_values(0)\n",
-    "structure_distance_matrix = pd.DataFrame(\n",
-    "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
+    "structure_distance_matrix_df = pd.DataFrame(\n",
+    "    structure_distance_matrix_array, index=structure_klifs_ids, columns=structure_klifs_ids\n",
     ")\n",
-    "print(f\"Structure distance matrix size: {structure_distance_matrix.shape}\")\n",
+    "print(f\"Structure distance matrix size: {structure_distance_matrix_df.shape}\")\n",
     "print(\"Show matrix subset:\")\n",
-    "structure_distance_matrix.iloc[:5, :5]"
+    "structure_distance_matrix_df.iloc[:5, :5]"
    ]
   },
   {
@@ -1103,12 +1200,12 @@
    "source": [
     "### Map structure to kinase distance matrix\n",
     "\n",
-    "First &mdash; as an intermediate step &mdash; we will create a structure distance matrix but instead of labeling the data with structure KLIFS IDs we add the corresponding kinase name."
+    "First, as an intermediate step, we will create a structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we add the corresponding kinase name."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1209,20 +1306,20 @@
        "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# Copy distance matrix to kinase matrix\n",
-    "kinase_distance_matrix = structure_distance_matrix\n",
+    "kinase_distance_matrix_df = structure_distance_matrix_df.copy()\n",
     "# Replace structure KLIFS IDs with the structures' kinase names\n",
     "kinase_names = ifp_series.index.get_level_values(1)\n",
-    "kinase_distance_matrix.index = kinase_names\n",
-    "kinase_distance_matrix.columns = kinase_names\n",
+    "kinase_distance_matrix_df.index = kinase_names\n",
+    "kinase_distance_matrix_df.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
-    "kinase_distance_matrix.iloc[:5, :5]"
+    "kinase_distance_matrix_df.iloc[:5, :5]"
    ]
   },
   {
@@ -1234,12 +1331,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1252,7 +1349,7 @@
    ],
    "source": [
     "# Select EGFR-EGFR structure pairs only\n",
-    "D = kinase_distance_matrix.loc[\"EGFR\", \"EGFR\"]\n",
+    "D = kinase_distance_matrix_df.loc[\"EGFR\", \"EGFR\"]\n",
     "# Extract all pairwise distances without identical structure pairs\n",
     "# = lower triangular matrix without the diagonal\n",
     "D_condensed = distance.squareform(D)\n",
@@ -1269,7 +1366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1277,15 +1374,392 @@
     "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
     "# We take the minimum value in each kinase pair group\n",
     "# We unstack the remaining data points\n",
-    "kinase_distance_matrix = (\n",
-    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    "kinase_distance_matrix_df = (\n",
+    "    kinase_distance_matrix_df.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     ")\n",
-    "kinase_distance_matrix.index.name = None\n",
-    "kinase_distance_matrix.columns.name = None\n",
-    "\n",
+    "kinase_distance_matrix_df.index.name = None\n",
+    "kinase_distance_matrix_df.columns.name = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure matrix of shape (1466, 1466) reduced to kinase matrix of shape (9, 9).\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  background-color: #359a35;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_98eee_row8_col1 {\n",
+       "  background-color: #5aac5a;\n",
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+       "}\n",
+       "#T_98eee_row8_col4 {\n",
+       "  background-color: #d8ead8;\n",
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+       "}\n",
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+       "}\n",
+       "#T_98eee_row8_col7 {\n",
+       "  background-color: #289328;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_98eee_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_98eee_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_98eee_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_98eee_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_98eee_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_98eee_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_98eee_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_98eee_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_98eee_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_98eee_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_98eee_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_98eee_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_98eee_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_98eee_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_98eee_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_98eee_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_98eee_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_98eee_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_98eee_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_98eee_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_98eee_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_98eee_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_98eee_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_98eee_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_98eee_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_98eee_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_98eee_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_98eee_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_98eee_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_98eee_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_98eee_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_98eee_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_98eee_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_98eee_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_98eee_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_98eee_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_98eee_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_98eee_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_98eee_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_98eee_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_98eee_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_98eee_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_98eee_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_98eee_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_98eee_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_98eee_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_98eee_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_98eee_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_98eee_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_98eee_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_98eee_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_98eee_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_98eee_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_98eee_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_98eee_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_98eee_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_98eee_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_98eee_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_98eee_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_98eee_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_98eee_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_98eee_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_98eee_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_98eee_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_98eee_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_98eee_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_98eee_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_98eee_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_98eee_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_98eee_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_98eee_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_98eee_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_98eee_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_98eee_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_98eee_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_98eee_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fa8cb0c0640>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\") \n",
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\") \n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1300,11 +1774,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix.to_csv(DATA / \"kinase_distance_matrix_ifp.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_ifp.csv\")"
    ]
   },
   {
@@ -1318,9 +1792,12 @@
     "\n",
     "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity.\n",
     "\n",
-    "The approach shown here is a rather simple one. We only compare the two closest binding modes per kinase pair, although we have - at least for kinases such as EGFR and CDK2 - much more data available on binding modes. As an alternative, we could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
+    "We have to keep two things in mind:\n",
+    "\n",
+    "- Interaction fingerprints may miss important key interactions if the co-crystallized ligand(s) simply do not form the interaction but other ligands might.\n",
+    "- We only compare here the two closest binding modes per kinase pair, although we have &mdash; at least for kinases such as EGFR and CDK2 &mdash; much more data available on binding modes. As an alternative, one could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
     "\n",
-    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
+    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the interaction perspective we have talked about in this talktorial."
    ]
   },
   {
@@ -1344,7 +1821,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1358,7 +1835,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From b74e5b2df3e5dc275cadf598e0ee49a9ff6e0df6 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 11:24:06 +0200
Subject: [PATCH 071/158] T025: Use kinase CSV; update variable names;
 proof-read

---
 .../talktorial.ipynb                          | 1002 ++++++++++-------
 1 file changed, 570 insertions(+), 432 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index f2068fb8..871c2ae7 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -23,7 +23,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "We will assess the similarity between a set of kinases from a structural point of view using the [KiSSim](https://kissim.readthedocs.io/en/latest/) fingerprint. KiSSim describes the physicochemical and spatial properties in structurally resolved kinases."
+    "We will assess the similarity between a set of kinases from a structural point of view using the [KiSSim](https://kissim.readthedocs.io/en/latest/) fingerprint. This fingerprint describes the physicochemical and spatial properties in structurally resolved kinases."
    ]
   },
   {
@@ -95,25 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
-    "\n",
-    "*Table 1:* \n",
-    "Kinases used in this notebook, adapted from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, and kinase groups.\n",
-    "\n",
-    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
-    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
-    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
-    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
-    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
-    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
-    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
-    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
-    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
-    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
-    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+    "We use the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
@@ -124,31 +106,18 @@
     "\n",
     "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
     "\n",
-    "We here use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment described in the following."
+    "We here use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment of 85 pocket residues (see more details in __Talktorial T023__)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
-    "\n",
-    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
-    "\n",
-    "*Figure 1:* \n",
-    "Kinase binding site residues as defined by KLIFS.\n",
-    "Figure and description taken from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The KiSSim fingerprint encodes each of the 85 residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 2). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase.\n",
+    "The KiSSim fingerprint encodes each of the 85 residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 1). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase. See more details on structural motifs in kinases in __Talktorial T023__.\n",
     "\n",
     "![KiSSim fingerprint](https://raw.githubusercontent.com/volkamerlab/kissim/main/docs/_static/kissim_toc.png)\n",
     "\n",
-    "*Figure 2:* \n",
+    "*Figure 1:* \n",
     "The KiSSim fingerprint encodes physicochemical and spatial properties of a kinase binding site.\n",
     "Figure taken from: https://github.com/volkamerlab/kissim"
    ]
@@ -179,15 +148,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -220,31 +181,151 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's define our kinases of interest and all their synonyms as described in Table 1 in the global variable `query_kinases`."
+    "Let's load the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "query_kinases = [\n",
-    "    \"EGFR\",\n",
-    "    \"ErbB2\",\n",
-    "    \"Her2\",\n",
-    "    \"PI3K\",\n",
-    "    \"PI3KCA\",\n",
-    "    \"p110a\",\n",
-    "    \"BRAF\",\n",
-    "    \"CDK2\",\n",
-    "    \"LCK\",\n",
-    "    \"MET\",\n",
-    "    \"p38a\",\n",
-    "    \"MAPK14\",\n",
-    "    \"VEGFR2\",\n",
-    "    \"KDR\",\n",
-    "]"
+    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
+    "kinase_selection_df"
    ]
   },
   {
@@ -258,7 +339,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we query the KLIFS database using the `opencadd.databases.klifs` module to generate our IFP dataset."
+    "Now, we query the KLIFS database using the `opencadd.databases.klifs` module to fetch available structures in KLIFS."
    ]
   },
   {
@@ -281,16 +362,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -317,16 +389,20 @@
     }
    ],
    "source": [
-    "structures = klifs_session.structures.by_kinase_name(kinase_names=query_kinases)\n",
-    "print(f\"Number of structures: {len(structures)}\")\n",
-    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+    "# Get list of kinase names\n",
+    "kinase_names = kinase_selection_df[\"kinase_klifs\"].to_list()\n",
+    "\n",
+    "# Get all available structures for these kinases\n",
+    "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
+    "print(f\"Number of structures: {len(structures_df)}\")\n",
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's have a look at what is stored in the structures dataframe:"
+    "Let's have a look at what is stored in the structures' DataFrame:"
    ]
   },
   {
@@ -339,18 +415,20 @@
       "text/plain": [
        "Index(['structure.klifs_id', 'structure.pdb_id', 'structure.alternate_model',\n",
        "       'structure.chain', 'species.klifs', 'kinase.klifs_id',\n",
-       "       'kinase.klifs_name', 'kinase.family', 'kinase.group',\n",
+       "       'kinase.klifs_name', 'kinase.names', 'kinase.family', 'kinase.group',\n",
        "       'structure.pocket', 'ligand.expo_id', 'ligand_allosteric.expo_id',\n",
-       "       'ligand.name', 'ligand_allosteric.name', 'structure.dfg',\n",
-       "       'structure.ac_helix', 'structure.resolution', 'structure.qualityscore',\n",
+       "       'ligand.klifs_id', 'ligand_allosteric.klifs_id', 'ligand.name',\n",
+       "       'ligand_allosteric.name', 'structure.dfg', 'structure.ac_helix',\n",
+       "       'structure.resolution', 'structure.qualityscore',\n",
        "       'structure.missing_residues', 'structure.missing_atoms',\n",
-       "       'structure.rmsd1', 'structure.rmsd2', 'structure.front',\n",
-       "       'structure.gate', 'structure.back', 'structure.fp_i', 'structure.fp_ii',\n",
-       "       'structure.bp_i_a', 'structure.bp_i_b', 'structure.bp_ii_in',\n",
-       "       'structure.bp_ii_a_in', 'structure.bp_ii_b_in', 'structure.bp_ii_out',\n",
-       "       'structure.bp_ii_b', 'structure.bp_iii', 'structure.bp_iv',\n",
-       "       'structure.bp_v', 'structure.grich_distance', 'structure.grich_angle',\n",
-       "       'structure.grich_rotation', 'structure.filepath'],\n",
+       "       'structure.rmsd1', 'structure.rmsd2', 'interaction.fingerprint',\n",
+       "       'structure.front', 'structure.gate', 'structure.back', 'structure.fp_i',\n",
+       "       'structure.fp_ii', 'structure.bp_i_a', 'structure.bp_i_b',\n",
+       "       'structure.bp_ii_in', 'structure.bp_ii_a_in', 'structure.bp_ii_b_in',\n",
+       "       'structure.bp_ii_out', 'structure.bp_ii_b', 'structure.bp_iii',\n",
+       "       'structure.bp_iv', 'structure.bp_v', 'structure.grich_distance',\n",
+       "       'structure.grich_angle', 'structure.grich_rotation',\n",
+       "       'structure.filepath', 'structure.curation_flag'],\n",
        "      dtype='object')"
       ]
      },
@@ -360,7 +438,7 @@
     }
    ],
    "source": [
-    "structures.columns"
+    "structures_df.columns"
    ]
   },
   {
@@ -396,11 +474,10 @@
        "      <th>species.klifs</th>\n",
        "      <th>kinase.klifs_id</th>\n",
        "      <th>kinase.klifs_name</th>\n",
+       "      <th>kinase.names</th>\n",
        "      <th>kinase.family</th>\n",
        "      <th>kinase.group</th>\n",
-       "      <th>structure.pocket</th>\n",
        "      <th>...</th>\n",
-       "      <th>structure.bp_ii_b_in</th>\n",
        "      <th>structure.bp_ii_out</th>\n",
        "      <th>structure.bp_ii_b</th>\n",
        "      <th>structure.bp_iii</th>\n",
@@ -410,6 +487,7 @@
        "      <th>structure.grich_angle</th>\n",
        "      <th>structure.grich_rotation</th>\n",
        "      <th>structure.filepath</th>\n",
+       "      <th>structure.curation_flag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -424,18 +502,18 @@
        "      <td>CDK2</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
-       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>14.9071</td>\n",
        "      <td>50.806301</td>\n",
        "      <td>71.737297</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -448,18 +526,18 @@
        "      <td>CDK2</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
-       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>13.7758</td>\n",
        "      <td>47.825100</td>\n",
        "      <td>70.074600</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -472,18 +550,18 @@
        "      <td>CDK2</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
-       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>13.3718</td>\n",
        "      <td>46.579498</td>\n",
        "      <td>67.647102</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -496,18 +574,18 @@
        "      <td>CDK2</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
-       "      <td>EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>17.3950</td>\n",
        "      <td>59.113201</td>\n",
        "      <td>78.272400</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -520,22 +598,22 @@
        "      <td>CDK2</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
-       "      <td>EKIGEGTYGVVYKVA_KKITAIR_ISLLKE_NPNIVKLLDVYLVFE...</td>\n",
+       "      <td>&lt;NA&gt;</td>\n",
        "      <td>...</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>13.9936</td>\n",
        "      <td>49.519501</td>\n",
        "      <td>71.110497</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 41 columns</p>\n",
+       "<p>5 rows × 46 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -553,42 +631,35 @@
        "3               A         Human              198              CDK2   \n",
        "4               A         Human              198              CDK2   \n",
        "\n",
-       "  kinase.family kinase.group  \\\n",
-       "0          <NA>         <NA>   \n",
-       "1          <NA>         <NA>   \n",
-       "2          <NA>         <NA>   \n",
-       "3          <NA>         <NA>   \n",
-       "4          <NA>         <NA>   \n",
-       "\n",
-       "                                    structure.pocket  ...  \\\n",
-       "0  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
-       "1  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
-       "2  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
-       "3  EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFE...  ...   \n",
-       "4  EKIGEGTYGVVYKVA_KKITAIR_ISLLKE_NPNIVKLLDVYLVFE...  ...   \n",
+       "  kinase.names kinase.family kinase.group  ... structure.bp_ii_out  \\\n",
+       "0         <NA>          <NA>         <NA>  ...               False   \n",
+       "1         <NA>          <NA>         <NA>  ...               False   \n",
+       "2         <NA>          <NA>         <NA>  ...               False   \n",
+       "3         <NA>          <NA>         <NA>  ...               False   \n",
+       "4         <NA>          <NA>         <NA>  ...               False   \n",
        "\n",
-       "  structure.bp_ii_b_in structure.bp_ii_out structure.bp_ii_b structure.bp_iii  \\\n",
-       "0                False               False             False            False   \n",
-       "1                False               False             False            False   \n",
-       "2                False               False             False            False   \n",
-       "3                False               False             False            False   \n",
-       "4                False               False             False            False   \n",
+       "  structure.bp_ii_b structure.bp_iii  structure.bp_iv  structure.bp_v  \\\n",
+       "0             False            False            False           False   \n",
+       "1             False            False            False           False   \n",
+       "2             False            False            False           False   \n",
+       "3             False            False            False           False   \n",
+       "4             False            False            False           False   \n",
        "\n",
-       "  structure.bp_iv structure.bp_v  structure.grich_distance  \\\n",
-       "0           False          False                   14.9071   \n",
-       "1           False          False                   13.7758   \n",
-       "2           False          False                   13.3718   \n",
-       "3           False          False                   17.3950   \n",
-       "4           False          False                   13.9936   \n",
+       "  structure.grich_distance structure.grich_angle structure.grich_rotation  \\\n",
+       "0                  14.9071             50.806301                71.737297   \n",
+       "1                  13.7758             47.825100                70.074600   \n",
+       "2                  13.3718             46.579498                67.647102   \n",
+       "3                  17.3950             59.113201                78.272400   \n",
+       "4                  13.9936             49.519501                71.110497   \n",
        "\n",
-       "   structure.grich_angle  structure.grich_rotation  structure.filepath  \n",
-       "0              50.806301                 71.737297                <NA>  \n",
-       "1              47.825100                 70.074600                <NA>  \n",
-       "2              46.579498                 67.647102                <NA>  \n",
-       "3              59.113201                 78.272400                <NA>  \n",
-       "4              49.519501                 71.110497                <NA>  \n",
+       "  structure.filepath  structure.curation_flag  \n",
+       "0               <NA>                    False  \n",
+       "1               <NA>                    False  \n",
+       "2               <NA>                    False  \n",
+       "3               <NA>                    False  \n",
+       "4               <NA>                    False  \n",
        "\n",
-       "[5 rows x 41 columns]"
+       "[5 rows x 46 columns]"
       ]
      },
      "execution_count": 8,
@@ -597,7 +668,7 @@
     }
    ],
    "source": [
-    "structures.head()"
+    "structures_df.head()"
    ]
   },
   {
@@ -621,13 +692,6 @@
    "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -638,14 +702,14 @@
     }
    ],
    "source": [
-    "structures = structures[\n",
-    "    (structures[\"species.klifs\"] == \"Human\")\n",
-    "    & (structures[\"structure.dfg\"] == \"in\")\n",
-    "    & (structures[\"structure.resolution\"] <= 3)\n",
-    "    & (structures[\"structure.qualityscore\"] >= 6)\n",
+    "structures_df = structures_df[\n",
+    "    (structures_df[\"species.klifs\"] == \"Human\")\n",
+    "    & (structures_df[\"structure.dfg\"] == \"in\")\n",
+    "    & (structures_df[\"structure.resolution\"] <= 3)\n",
+    "    & (structures_df[\"structure.qualityscore\"] >= 6)\n",
     "]\n",
-    "print(f\"Number of structures: {len(structures)}\")\n",
-    "print(\"Kinases:\", *structures[\"kinase.klifs_name\"].unique())"
+    "print(f\"Number of structures: {len(structures_df)}\")\n",
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
    ]
   },
   {
@@ -669,7 +733,7 @@
     }
    ],
    "source": [
-    "structure_klifs_ids = structures[\"structure.klifs_id\"].to_list()\n",
+    "structure_klifs_ids = structures_df[\"structure.klifs_id\"].to_list()\n",
     "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
    ]
   },
@@ -677,7 +741,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please uncomment the cell below."
+    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please skip running the cell below (or change cell type from \"Code\" > \"Raw\")."
    ]
   },
   {
@@ -741,7 +805,7 @@
    ],
    "source": [
     "# Use pandas' groupby method to count the number of structures (rows) per kinase\n",
-    "n_structures_per_kinase = structures.groupby(\"kinase.klifs_name\").size().sort_values()\n",
+    "n_structures_per_kinase = structures_df.groupby(\"kinase.klifs_name\").size().sort_values()\n",
     "n_structures_per_kinase\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -760,7 +824,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -784,7 +848,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not: See e.g. CDK2 and EGFR vs. KDR and ErbB2. We will have to keep this in mind when interpreting our results later."
+    "We see that our dataset is highly imbalanced. While some kinases are structurally resolved very often, other kinases are not. We will have to keep this in mind when interpreting our results later."
    ]
   },
   {
@@ -798,14 +862,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use in this notebook pre-calculated KiSSim fingerprints for our kinase set. If you wanted to calculate the fingerprints yourself, you can use the following `kissim` API: \n",
+    "We will use in this notebook pre-calculated KiSSim fingerprints for our kinase set. If you wanted to calculate the fingerprints yourself, you could use the following `kissim` API: \n",
     "\n",
     "```python\n",
     "# Generate fingerprints\n",
     "from kissim.api import encode\n",
     "kissim_fingerprints = encode(structure_klifs_ids, n_cores=1)\n",
     "\n",
-    "# Save fingerprints as csv files\n",
+    "# Save fingerprints in csv file\n",
     "structure_klifs_ids = list(kissim_fingerprints.data.keys())\n",
     "kissim_fingerprints_array = [\n",
     "    fingerprint.values_array().tolist()\n",
@@ -841,10 +905,10 @@
     }
    ],
    "source": [
-    "kissim_fingerprints = pd.read_csv(DATA / \"kissim_fingerprints.csv\", index_col=0)\n",
-    "print(f\"Matrix shape: {kissim_fingerprints.shape}\")\n",
-    "print(f\"Number of fingerprints: {kissim_fingerprints.shape[0]}\")\n",
-    "print(f\"Number of fingerprint bits: {kissim_fingerprints.shape[1]}\")\n",
+    "kissim_fingerprints_df = pd.read_csv(DATA / \"kissim_fingerprints.csv\", index_col=0)\n",
+    "print(f\"Matrix shape: {kissim_fingerprints_df.shape}\")\n",
+    "print(f\"Number of fingerprints: {kissim_fingerprints_df.shape[0]}\")\n",
+    "print(f\"Number of fingerprint bits: {kissim_fingerprints_df.shape[1]}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -852,16 +916,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprinting when some structures cannot be encoded."
+    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprinting if any structures cannot be encoded."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's have a look at the fingerprint data frame. \n",
-    "* Kinase structures are stored by KLIFS Id (index)\n",
-    "* Each kinase is represented by 1032 features (85 residues * 8 physicohem. features + 85 residues * 4 distances + 12 moments)"
+    "Let's have a look at the fingerprint DataFrame. \n",
+    "\n",
+    "- Kinase structures are stored by KLIFS ID (index)\n",
+    "- Each kinase is represented by $1032$ features, i.e. $85$ residues * $8$ physicochem. features + $85$ residues * $4$ distances + $12$ moments"
    ]
   },
   {
@@ -1070,7 +1135,7 @@
     }
    ],
    "source": [
-    "kissim_fingerprints.head()"
+    "kissim_fingerprints_df.head()"
    ]
   },
   {
@@ -1096,7 +1161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "structure_distance_matrix = pairwise.nan_euclidean_distances(kissim_fingerprints.values)"
+    "structure_distance_matrix_array = pairwise.nan_euclidean_distances(kissim_fingerprints_df.values)"
    ]
   },
   {
@@ -1201,13 +1266,13 @@
    ],
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
-    "structure_klifs_ids = kissim_fingerprints.index.to_list()\n",
-    "structure_distance_matrix = pd.DataFrame(\n",
-    "    structure_distance_matrix, index=structure_klifs_ids, columns=structure_klifs_ids\n",
+    "structure_klifs_ids = kissim_fingerprints_df.index.to_list()\n",
+    "structure_distance_matrix_df = pd.DataFrame(\n",
+    "    structure_distance_matrix_array, index=structure_klifs_ids, columns=structure_klifs_ids\n",
     ")\n",
-    "print(f\"Structure distance matrix size: {structure_distance_matrix.shape}\")\n",
+    "print(f\"Structure distance matrix size: {structure_distance_matrix_df.shape}\")\n",
     "print(\"Show matrix subset:\")\n",
-    "structure_distance_matrix.iloc[:5, :5]"
+    "structure_distance_matrix_df.iloc[:5, :5]"
    ]
   },
   {
@@ -1218,7 +1283,7 @@
    "source": [
     "### Map structure to kinase distance matrix\n",
     "\n",
-    "First &mdash; as an intermediate step &mdash; we will create a structure distance matrix but instead of labeling the data with structure KLIFS IDs we add the corresponding kinase name."
+    "First, as an intermediate step, we will use the structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we add the corresponding kinase name."
    ]
   },
   {
@@ -1331,15 +1396,15 @@
    ],
    "source": [
     "# Copy distance matrix to kinase matrix\n",
-    "kinase_distance_matrix = structure_distance_matrix\n",
+    "kinase_distance_matrix_df = structure_distance_matrix_df.copy()\n",
     "# Replace structure KLIFS IDs with the structures' kinase names\n",
-    "kinase_names = structures.set_index(\"structure.klifs_id\").loc[\n",
+    "kinase_names = structures_df.set_index(\"structure.klifs_id\").loc[\n",
     "    structure_klifs_ids, \"kinase.klifs_name\"\n",
     "]\n",
-    "kinase_distance_matrix.index = kinase_names\n",
-    "kinase_distance_matrix.columns = kinase_names\n",
+    "kinase_distance_matrix_df.index = kinase_names\n",
+    "kinase_distance_matrix_df.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
-    "kinase_distance_matrix.iloc[:5, :5]"
+    "kinase_distance_matrix_df.iloc[:5, :5]"
    ]
   },
   {
@@ -1359,11 +1424,12 @@
     "# We group by kinase names (level=[0, 1] ensures that the order of the kinases is ignored\n",
     "# We take the minimum value in each kinase pair group\n",
     "# We unstack the remaining data points\n",
-    "kinase_distance_matrix = (\n",
-    "    kinase_distance_matrix.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
+    "kinase_distance_matrix_df = (\n",
+    "    kinase_distance_matrix_df.unstack().groupby(level=[0, 1]).min().unstack(level=1)\n",
     ")\n",
-    "kinase_distance_matrix.index.name = None\n",
-    "kinase_distance_matrix.columns.name = None"
+    "# Cosmetics: Remove the index and column names\n",
+    "kinase_distance_matrix_df.index.name = None\n",
+    "kinase_distance_matrix_df.columns.name = None"
    ]
   },
   {
@@ -1380,7 +1446,7 @@
     }
    ],
    "source": [
-    "print(f\"Structure matrix of shape {structure_distance_matrix.shape} reduced to kinase matrix of shape {kinase_distance_matrix.shape}.\")"
+    "print(f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\")"
    ]
   },
   {
@@ -1391,252 +1457,317 @@
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-       "        }</style><table id=\"T_71b38_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >BRAF</th>        <th class=\"col_heading level0 col1\" >CDK2</th>        <th class=\"col_heading level0 col2\" >EGFR</th>        <th class=\"col_heading level0 col3\" >ErbB2</th>        <th class=\"col_heading level0 col4\" >KDR</th>        <th class=\"col_heading level0 col5\" >LCK</th>        <th class=\"col_heading level0 col6\" >MET</th>        <th class=\"col_heading level0 col7\" >p110a</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_71b38_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "                        <td id=\"T_71b38_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "                        <td id=\"T_71b38_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "                        <td id=\"T_71b38_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "                        <td id=\"T_71b38_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "                        <td id=\"T_71b38_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "                        <td id=\"T_71b38_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "                        <td id=\"T_71b38_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "                        <td id=\"T_71b38_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "                        <td id=\"T_71b38_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "                        <td id=\"T_71b38_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "                        <td id=\"T_71b38_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "                        <td id=\"T_71b38_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "                        <td id=\"T_71b38_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "                        <td id=\"T_71b38_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "                        <td id=\"T_71b38_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "                        <td id=\"T_71b38_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "                        <td id=\"T_71b38_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "                        <td id=\"T_71b38_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "                        <td id=\"T_71b38_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "                        <td id=\"T_71b38_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "                        <td id=\"T_71b38_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "                        <td id=\"T_71b38_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "                        <td id=\"T_71b38_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "                        <td id=\"T_71b38_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "                        <td id=\"T_71b38_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "                        <td id=\"T_71b38_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "                        <td id=\"T_71b38_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "                        <td id=\"T_71b38_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "                        <td id=\"T_71b38_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "                        <td id=\"T_71b38_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "                        <td id=\"T_71b38_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "                        <td id=\"T_71b38_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "                        <td id=\"T_71b38_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "                        <td id=\"T_71b38_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "                        <td id=\"T_71b38_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "                        <td id=\"T_71b38_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "                        <td id=\"T_71b38_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "                        <td id=\"T_71b38_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "                        <td id=\"T_71b38_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "                        <td id=\"T_71b38_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "                        <td id=\"T_71b38_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "                        <td id=\"T_71b38_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "                        <td id=\"T_71b38_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "                        <td id=\"T_71b38_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "                        <td id=\"T_71b38_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "                        <td id=\"T_71b38_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "                        <td id=\"T_71b38_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "                        <td id=\"T_71b38_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "                        <td id=\"T_71b38_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "                        <td id=\"T_71b38_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "                        <td id=\"T_71b38_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "                        <td id=\"T_71b38_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "                        <td id=\"T_71b38_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "                        <td id=\"T_71b38_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "                        <td id=\"T_71b38_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "                        <td id=\"T_71b38_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "                        <td id=\"T_71b38_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "                        <td id=\"T_71b38_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "                        <td id=\"T_71b38_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "                        <td id=\"T_71b38_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "                        <td id=\"T_71b38_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "                        <td id=\"T_71b38_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "                        <td id=\"T_71b38_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "                        <td id=\"T_71b38_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "                        <td id=\"T_71b38_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_71b38_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "                        <td id=\"T_71b38_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "                        <td id=\"T_71b38_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "                        <td id=\"T_71b38_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "                        <td id=\"T_71b38_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "                        <td id=\"T_71b38_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "                        <td id=\"T_71b38_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "                        <td id=\"T_71b38_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "                        <td id=\"T_71b38_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "                        <td id=\"T_71b38_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
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+       "  background-color: #0a850a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9d518_row8_col1 {\n",
+       "  background-color: #6eb66e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9d518_row8_col2 {\n",
+       "  background-color: #5aac5a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9d518_row8_col3 {\n",
+       "  background-color: #5eae5e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9d518_row8_col6 {\n",
+       "  background-color: #68b368;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9d518_row8_col7 {\n",
+       "  background-color: #108810;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_9d518_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_9d518_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_9d518_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_9d518_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_9d518_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_9d518_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_9d518_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_9d518_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_9d518_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_9d518_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_9d518_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_9d518_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_9d518_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_9d518_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_9d518_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_9d518_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_9d518_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_9d518_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_9d518_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_9d518_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_9d518_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_9d518_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_9d518_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_9d518_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_9d518_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_9d518_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_9d518_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_9d518_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_9d518_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_9d518_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_9d518_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_9d518_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_9d518_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_9d518_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_9d518_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_9d518_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_9d518_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_9d518_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_9d518_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_9d518_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_9d518_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_9d518_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_9d518_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_9d518_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_9d518_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_9d518_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_9d518_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_9d518_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_9d518_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_9d518_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_9d518_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_9d518_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_9d518_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_9d518_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_9d518_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_9d518_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_9d518_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_9d518_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_9d518_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_9d518_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_9d518_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_9d518_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_9d518_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_9d518_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_9d518_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_9d518_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9d518_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_9d518_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_9d518_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_9d518_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_9d518_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_9d518_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_9d518_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_9d518_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_9d518_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_9d518_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f13fc2f1dc0>"
+       "<pandas.io.formats.style.Styler at 0x7f264522df70>"
       ]
      },
      "execution_count": 21,
@@ -1647,7 +1778,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1666,7 +1797,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix.to_csv(DATA / \"kinase_distance_matrix_kissim.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_kissim.csv\")"
    ]
   },
   {
@@ -1678,9 +1809,16 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we have assessed kinase similarity used KiSSim fingerprints, which describe physicochemical and spatial properties of pockets in kinase structures. We have reduced a structure distance matrix to a kinase distance matrix by selecting for each kinase pair the corresponding closest structure pair. \n",
+    "In this talktorial, we have assessed kinase similarity using the KiSSim fingerprints, which describe physicochemical and spatial properties of pockets in kinase structures. We have reduced a _structure_ distance matrix to a _kinase_ distance matrix by selecting for each _kinase pair_ the corresponding closest _structure pair_. \n",
+    "\n",
+    "We have to keep two things in mind:\n",
+    "\n",
+    "- Pocket fingerprints like KiSSim encode the full pocket although some residues might be more important for ligand binding than others. As an alternative, one could instead use a subset of residues that have been shown to interact with co-crystallized ligands frequently.\n",
+    "- We only compare here the two closest structures per kinase pair, although we have &mdash; at least for kinases such as EGFR and CDK2 &mdash; much more structural data available. Aggregating multiple structures could be a strategy but has two disadvantages: \n",
+    "  - Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
+    "  - Aggregated kinase fingerprints may cause too much averaging, making pairwise kinase comparison noisier.\n",
     "\n",
-    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
+    "The kinase distance matrix above will be reloaded in __Talktorial T028__, where we compare kinase similarities from different perspectives, including the pocket structure perspective we have talked about in this talktorial."
    ]
   },
   {
@@ -1704,7 +1842,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1718,7 +1856,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From c17d85f163329a1fe371dc52e8250c35dc07f1e8 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 11:42:22 +0200
Subject: [PATCH 072/158] T023: Add bioactivity datasets refs; add details to
 our kinase dataset

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 193 ++++++------------
 1 file changed, 61 insertions(+), 132 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 88701d80..85cbf48b 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "Add a short summary of this talktorial's content."
+    "In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available?"
    ]
   },
   {
@@ -28,13 +28,14 @@
    "source": [
     "### Contents in *Theory*\n",
     "\n",
-    "* Kinases in a nutshell\n",
-    "    * The human kinome\n",
-    "    * Kinase structures and important motifs\n",
-    "    * Collection of kinase related information: KLIFS\n",
-    "    * Available bioactivity data\n",
-    "* Kinase-similarity: off-target, promiscuous binding\n",
-    "* Kinase dataset compilation"
+    "- Kinases in a nutshell\n",
+    "    - The human kinome\n",
+    "    - Kinase structures and important motifs\n",
+    "- Kinase resources\n",
+    "    - Kinase structures and related information\n",
+    "    - Bioactivity data\n",
+    "- Kinase-similarity: off-target, promiscuous binding\n",
+    "- Kinase dataset compilation"
    ]
   },
   {
@@ -43,11 +44,11 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Retrieve and preprocess data\n",
-    "* Show kinase coverage\n",
-    "* Compare kinases\n",
-    "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
+    "- Retrieve and preprocess data\n",
+    "- Show kinase coverage\n",
+    "- Compare kinases\n",
+    "- Visualize similarity as kinase matrix\n",
+    "- Visualize similarity as phylogenetic tree"
    ]
   },
   {
@@ -56,12 +57,18 @@
    "source": [
     "### References\n",
     "\n",
-    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX\n",
-    "* KLIFS\n",
-    "  * KLIFS URL: https://klifs.net/\n",
-    "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
-    "  * KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)"
+    "- Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "- Kinase similarity descriptor: XXX\n",
+    "- Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "- KLIFS\n",
+    "  - KLIFS URL: https://klifs.net/\n",
+    "  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
+    "  - KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)\n",
+    "- Bioactivity data\n",
+    "  - Karaman et al. dataset: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
+    "  - Davis et al. dataset: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
+    "  - KIBA dataset: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
+    "  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)"
    ]
   },
   {
@@ -108,7 +115,7 @@
     "\n",
     "* Human kinome consists of ~540 protein kinases\n",
     "    * [TODO: maybe comment on numbers differ depedning on the source, ref to kinodata]\n",
-    "* Grouped by Manning [TODO add ref] into eight major groups and one 'other' group\n",
+    "* Grouped by Manning [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups and one 'other' group\n",
     "    * AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL, Other\n",
     "    * Phylogenetic tree, clustered by overall sequence similarity\n",
     "    \n",
@@ -136,7 +143,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Collection of kinase related information: KLIFS"
+    "### Kinase resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Kinase structures and related information: KLIFS"
    ]
   },
   {
@@ -167,11 +181,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Available bioactivity data\n",
+    "#### Bioactivity data\n",
     "\n",
     "TODO - short !\n",
     "* maybe an overview of data points per kinase on Chembl (from kinodata)\n",
-    "* other profiling data (as available in karaman, xxx)"
+    "* other profiling data (as available in karaman, xxx)\n",
+    "\n",
+    "- Karaman et al. dataset: TODO\n",
+    "  - Paper: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
+    "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Davis_profiling.js)\n",
+    "- Davis et al. dataset: TODO\n",
+    "  - Paper: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
+    "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Karaman_profiling.js)\n",
+    "- KIBA dataset: TODO\n",
+    "  - Paper: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
+    "  - Data: [SI data (XLSX)](https://ndownloader.figstatic.com/files/3950161)\n",
+    "- PKIS dataset: \n",
+    "  - Paper: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)\n",
+    "  - Data: [SI data (XLSX)](https://doi.org/10.1371/journal.pone.0181585.s004)"
    ]
   },
   {
@@ -197,41 +224,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "In the coure of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
     "\n",
     "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
     "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
    ]
   },
   {
-   "attachments": {
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"
-    }
-   },
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![image.png](attachment:814048cb-e723-4b10-b9f7-2b56848688d9.png)\n",
+    "We have collected information about these nine kinases in the CSV file `kinase_selection.csv`:\n",
     "\n",
-    "*Figure 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) (Table 1)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TODO\n",
-    "\n",
-    "Data set used throughout the following notebooks, maybe directly shaped in a way that it has the correct information per task in place\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Practical"
+    "- `kinase`: Kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
+    "- `kinase_klifs`: Kinase name as used in the KLIFS database\n",
+    "- `uniprot_id`: Kinase UniProt ID\n",
+    "- `group`: Kinase group as defined by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
    ]
   },
   {
@@ -255,16 +264,9 @@
     "DATA = HERE / \"data\""
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieve and preprocess data"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -396,94 +398,21 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_selection = pd.read_csv(DATA/\"kinase_selection.csv\")\n",
-    "kinase_selection"
+    "kinase_selection_df = pd.read_csv(DATA/\"kinase_selection.csv\")\n",
+    "kinase_selection_df"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": [
-    "### Show kinase coverage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Compare kinases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as kinase matrix"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visualize similarity as phylogenetic tree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Discussion\n",
-    "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Quiz\n",
-    "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

From 83c45c51c76f8a6fa96143ee6ba700abb155ee1e Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 11:48:15 +0200
Subject: [PATCH 073/158] T023: Condense details on Molecules dataset; add note
 on customized CSV

---
 .../T023_what_is_a_kinase/talktorial.ipynb        | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 85cbf48b..864e569a 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -224,10 +224,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the coure of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) because:\n",
+    "In the coure of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
     "\n",
-    "> We aggregated the investigated kinases in “profiles” (Table 2). Profile 1 combined EGFR and ErbB2 as targets (indicated by a ‘+’) and BRAF (from rapidly accelerated fibrosarcoma isoform B) as a (general) anti-target (designated by a ‘—’). Out of similar considerations, Profile 2 consisted of EGFR and PI3K as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and VEGFR2 as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases CDK2 (cyclic-dependent kinase 2), LCK (lymphocyte-specific protein tyrosine kinase), MET (mesenchymal-epithelial transition factor) and p38α (p38 mitogen activated protein kinase α) were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
+    "> - Profile 1 combined __EGFR__ and __ErbB2__ as targets and __BRAF__ as a (general) anti-target. \n",
+    "> - Out of similar considerations, Profile 2 consisted of EGFR and __PI3K__ as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. \n",
+    "> - Profile 3, comprised of EGFR and __VEGFR2__ as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
+    "> - To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases __CDK2__, __LCK__, __MET__ and __p38α__ were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets."
    ]
   },
   {
@@ -243,6 +245,13 @@
     "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

From 0e558cf25b4c095025db6a9ab39ad429bb99c486 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 12:32:18 +0200
Subject: [PATCH 074/158] T023: Move Molecules dataset to Practical; add
 transition to T024-T028

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 61 ++++++++++++-------
 1 file changed, 38 insertions(+), 23 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 864e569a..6ff5bcd6 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -224,7 +224,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the coure of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
+    "In the course of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
     "\n",
     "> - Profile 1 combined __EGFR__ and __ErbB2__ as targets and __BRAF__ as a (general) anti-target. \n",
     "> - Out of similar considerations, Profile 2 consisted of EGFR and __PI3K__ as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. \n",
@@ -236,20 +236,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have collected information about these nine kinases in the CSV file `kinase_selection.csv`:\n",
-    "\n",
-    "- `kinase`: Kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
-    "- `kinase_klifs`: Kinase name as used in the KLIFS database\n",
-    "- `uniprot_id`: Kinase UniProt ID\n",
-    "- `group`: Kinase group as defined by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
-    "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+    "## Practical"
    ]
   },
   {
@@ -273,6 +260,33 @@
     "DATA = HERE / \"data\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define the kinases of interest"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have collected information about these nine kinases in the CSV file `kinase_selection.csv`:\n",
+    "\n",
+    "- `kinase`: Kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
+    "- `kinase_klifs`: Kinase name as used in the KLIFS database\n",
+    "- `uniprot_id`: Kinase UniProt ID\n",
+    "- `group`: Kinase group as defined by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -420,14 +434,15 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "We will load this dataset in all downstream talktorials to assess kinase similarity from different perspectives:\n",
+    "\n",
+    "- **Talktorial T024**: Kinase similarity based on KLIFS pocket sequence\n",
+    "- **Talktorial T025**: Kinase similarity based on KiSSim pocket structure\n",
+    "- **Talktorial T026**: Kinase similarity based on KLIFS interaction fingerprint\n",
+    "- **Talktorial T027**: Kinase similarity based on ligand promiscuity (ChEMBL bioactivity data)\n",
+    "- **Talktorial T028**: Compare kinase similarity measures from **Talktorials T024-T027**"
+   ]
   }
  ],
  "metadata": {

From 24d00b454173bf203bba7f4f2f8cd1f0db3a1c8b Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 13:57:45 +0200
Subject: [PATCH 075/158] rescaling blosum matrix.

---
 ..._distance_substitution_matrix_sequence.csv |   10 +
 ...imilarity_substitution_matrix_sequence.csv |   10 +
 .../talktorial.ipynb                          | 1527 ++++++++++-------
 3 files changed, 914 insertions(+), 633 deletions(-)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
new file mode 100644
index 00000000..38662704
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,0.0,0.05903728309403988,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369,0.2839529624274032,0.5729382823021627
+ErbB2,0.05903728309403988,0.0,0.34539978367739244,0.36969159991343414,0.31492453290530287,0.3020325638294997,0.3648271324857587,0.297882231158642,0.586362347301078
+BRAF,0.34497247960673305,0.34539978367739244,0.0,0.35324546063925766,0.3270674806464644,0.3618419047625556,0.36208805042926506,0.3287322624812222,0.563540908147633
+CDK2,0.3515532811454096,0.36969159991343414,0.35324546063925766,0.0,0.31874681698629526,0.34397506329411764,0.2769069645096425,0.3466212151836624,0.5760058634630552
+LCK,0.2886787123597768,0.31492453290530287,0.3270674806464644,0.31874681698629526,0.0,0.3091212110335312,0.33741879744441083,0.31235243702199045,0.5486635309945707
+MET,0.28874208716346716,0.3020325638294997,0.3618419047625556,0.34397506329411764,0.3091212110335312,0.0,0.3706450713064281,0.28619407705453626,0.6063011403494588
+p38a,0.3553555545508369,0.3648271324857587,0.36208805042926506,0.2769069645096425,0.33741879744441083,0.3706450713064281,0.0,0.34655582404619856,0.5688055661744779
+KDR,0.2839529624274032,0.297882231158642,0.3287322624812222,0.3466212151836624,0.31235243702199045,0.28619407705453626,0.34655582404619856,0.0,0.5772950670526018
+p110a,0.5729382823021627,0.586362347301078,0.563540908147633,0.5760058634630552,0.5486635309945707,0.6063011403494588,0.5688055661744779,0.5772950670526018,0.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
new file mode 100644
index 00000000..f4d0f086
--- /dev/null
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
+EGFR,1.0,0.9409627169059601,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631,0.7160470375725968,0.42706171769783724
+ErbB2,0.9409627169059601,1.0,0.6546002163226076,0.6303084000865659,0.6850754670946971,0.6979674361705003,0.6351728675142413,0.702117768841358,0.413637652698922
+BRAF,0.655027520393267,0.6546002163226076,1.0,0.6467545393607423,0.6729325193535356,0.6381580952374444,0.6379119495707349,0.6712677375187778,0.43645909185236703
+CDK2,0.6484467188545904,0.6303084000865659,0.6467545393607423,1.0,0.6812531830137047,0.6560249367058824,0.7230930354903575,0.6533787848163376,0.4239941365369449
+LCK,0.7113212876402232,0.6850754670946971,0.6729325193535356,0.6812531830137047,1.0,0.6908787889664688,0.6625812025555892,0.6876475629780096,0.4513364690054293
+MET,0.7112579128365328,0.6979674361705003,0.6381580952374444,0.6560249367058824,0.6908787889664688,1.0,0.6293549286935719,0.7138059229454637,0.3936988596505412
+p38a,0.6446444454491631,0.6351728675142413,0.6379119495707349,0.7230930354903575,0.6625812025555892,0.6293549286935719,1.0,0.6534441759538014,0.43119443382552214
+KDR,0.7160470375725968,0.702117768841358,0.6712677375187778,0.6533787848163376,0.6876475629780096,0.7138059229454637,0.6534441759538014,1.0,0.4227049329473982
+p110a,0.42706171769783724,0.413637652698922,0.43645909185236703,0.4239941365369449,0.4513364690054293,0.3936988596505412,0.43119443382552214,0.4227049329473982,1.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 4611ecb4..38e7a505 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -35,7 +35,8 @@
     "* Kinase dataset\n",
     "* Kinase similarity descriptor: sequence\n",
     "    * Identity score\n",
-    "    * Substitution score"
+    "    * Substitution score\n",
+    "* From similarity matrix to distance matrix"
    ]
   },
   {
@@ -85,38 +86,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
-    " \n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
-    "\n",
-    " \n",
+    "We use the kinase selection as defined in __Talktorial T023__."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase similarity descriptor: sequence\n",
     "\n",
-    "*Table 1:*\n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
+    "As mentioned in the previous talktorial, sequence is often used in kinase similarity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**TODO**\n",
+    "Add figure here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "EGFR fasta:\n",
+    "- https://www.uniprot.org/uniprot/P00533.fasta\n",
     "\n",
-    " \n",
     "\n",
-    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
-    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
-    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
-    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
-    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
-    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
-    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
-    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
-    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
-    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
-    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+    "CDK2 fasta:\n",
+    "- https://www.uniprot.org/uniprot/P24941.fasta"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: sequence\n",
-    "\n",
     "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
     "1. The sequence is of fixed length (it contains 85 residues), which makes computation for pairwise similarity between two sequences easy.\n",
     "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an 85 residues sequence contains most relevant information?\n",
@@ -210,15 +215,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
+    "The BLOSUM substitution matrix is symmetric, as shown in the practical part.\n",
+    "For convenience, we will translate and rescale the matrix using the following:\n",
+    "\n",
+    "$$\n",
+    "SM' = SM - min(SM),\n",
+    "$$\n",
+    "\n",
+    "for translation, and\n",
+    "\n",
+    "for all $x, y$\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{1}{\\sum_i SM[i,i]} \\sum_{n}^{M} SM\\big((K_i[n], K_j[n])\\big),\n",
+    "SM''(x, y) = \\frac{SM'[x,y]}{\\sqrt{SM'[x,x]*SM'[y,y]}},\n",
     "$$\n",
     "\n",
-    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$.\n",
+    "for rescaling, such that\n",
+    "\n",
+    "$$\n",
+    "SM''[x, x] = 1 \\text{ for all } x,\n",
+    "$$ and\n",
+    "\n",
+    "$$\n",
+    "SM''[x, y] \\in [0, 1] \\text{ for all } x,y.\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
+    "\n",
+    "$$\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n}^{M} SM''\\big(K_i[n], K_j[n]\\big),\n",
+    "$$\n",
     "\n",
-    "For computational reasons, we will use the normalized version of this matrix, i.e. all entries are between $0$ and $1$."
+    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From similarity matrix to distance matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. A similarity matrix is not, by definition, a distance matrix. For example, the diagonal elements are not zero. However, if all entries of the are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
    ]
   },
   {
@@ -241,8 +288,7 @@
     "import seaborn as sns\n",
     "import requests\n",
     "import matplotlib.pyplot as plt\n",
-    "import biotite.sequence.align as align\n",
-    "from sklearn import preprocessing"
+    "import biotite.sequence.align as align"
    ]
   },
   {
@@ -540,10 +586,15 @@
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
     "    \n",
-    "    # Normalize values to be in (0,1)\n",
-    "    normalized_score_matrix = preprocessing.minmax_scale(score_matrix,\n",
-    "                                              feature_range=(0, 1),\n",
-    "                                              axis=1)\n",
+    "    # Translation of substitution matrix\n",
+    "    translated_matrix = score_matrix-np.min(score_matrix)\n",
+    "\n",
+    "    # Rescaling\n",
+    "    normalized_score_matrix = np.zeros(score_matrix.shape)\n",
+    "    for i in range(score_matrix.shape[0]):\n",
+    "        for j in range(score_matrix.shape[0]):\n",
+    "            normalized_score_matrix[i, j] = (translated_matrix[i, j]/\n",
+    "                                            np.sqrt(translated_matrix[i,i]*translated_matrix[j,j]))\n",
     "\n",
     "    # Retrieve the letter (amino acid)\n",
     "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
@@ -582,7 +633,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sequence_similarity(sequence_1, sequence_2, type_=\"identity\"):\n",
+    "def sequence_similarity(kinase_name1, kinase_name2, type_=\"identity\"):\n",
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
@@ -598,8 +649,8 @@
     "    float :\n",
     "        The similarity between the pocket sequences of the two kinases.\n",
     "    \"\"\"\n",
-    "    #sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
-    "    #sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
+    "    sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
+    "    sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
     "\n",
     "    # Replace possible unavailable residue\n",
     "    # noted in KLIFS with \"-\"\n",
@@ -627,6 +678,27 @@
     "        return similarity_normed"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pocket sequence similarity between EGFR itself: 1.00 using substitution.\n"
+     ]
+    }
+   ],
+   "source": [
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
+    "                                          \"EGFR\", \n",
+    "                                          type_=\"substitution\")\n",
+    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
+    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -636,45 +708,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EGFR : KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
+      "MET  : EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key in ('EGFR','MET'):\n",
+    "    print(f\"{key:5s}: {kinase_sequences[key]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "EGFR :KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
-      "MET  :EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n",
       "Pocket sequence similarity between EGFR and MET kinases: 0.46 using identity.\n"
      ]
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                              kinase_sequences[\"MET\"],\n",
+    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "                                              \"MET\",\n",
     "                                              \"identity\")\n",
-    "for key in ('EGFR','MET'):\n",
-    "    print(f\"{key:5s}:{kinase_sequences[key]}\")\n",
+    "\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
     "      f\"{EGFR_MET_seq_similarity:.2f} using identity.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pocket sequence similarity between EGFR and MET kinases: 0.72 using substitution.\n"
+      "Pocket sequence similarity between EGFR and MET kinases: 0.71 using substitution.\n"
      ]
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                              kinase_sequences[\"MET\"],\n",
+    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "                                              \"MET\",\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
     "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
@@ -689,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -701,15 +789,15 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"], \n",
-    "                                          kinase_sequences[\"EGFR\"])\n",
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
+    "                                          \"EGFR\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using identity.\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -721,8 +809,8 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"], \n",
-    "                                          kinase_sequences[\"EGFR\"], \n",
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
+    "                                          \"EGFR\", \n",
     "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
@@ -751,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -760,21 +848,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "kinase_similarity_matrix = np.zeros((len(query_kinases), len(query_kinases)))\n",
     "for i, kinase_name1 in enumerate(query_kinases):\n",
     "    for j, kinase_name2 in enumerate(query_kinases):\n",
-    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
-    "                                                             kinase_sequences[kinase_name2],\n",
+    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
+    "                                                             kinase_name2,\n",
     "                                                             type_=similarity_measure)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -813,109 +901,109 @@
        "    <tr>\n",
        "      <th>EGFR</th>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.940995</td>\n",
-       "      <td>0.658852</td>\n",
-       "      <td>0.650192</td>\n",
-       "      <td>0.710442</td>\n",
-       "      <td>0.715203</td>\n",
-       "      <td>0.644984</td>\n",
-       "      <td>0.715307</td>\n",
-       "      <td>0.429135</td>\n",
+       "      <td>0.940963</td>\n",
+       "      <td>0.655028</td>\n",
+       "      <td>0.648447</td>\n",
+       "      <td>0.711321</td>\n",
+       "      <td>0.711258</td>\n",
+       "      <td>0.644644</td>\n",
+       "      <td>0.716047</td>\n",
+       "      <td>0.427062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ErbB2</th>\n",
-       "      <td>0.941257</td>\n",
+       "      <td>0.940963</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.659334</td>\n",
-       "      <td>0.632521</td>\n",
-       "      <td>0.685368</td>\n",
-       "      <td>0.702937</td>\n",
-       "      <td>0.636820</td>\n",
-       "      <td>0.701761</td>\n",
-       "      <td>0.416966</td>\n",
+       "      <td>0.654600</td>\n",
+       "      <td>0.630308</td>\n",
+       "      <td>0.685075</td>\n",
+       "      <td>0.697967</td>\n",
+       "      <td>0.635173</td>\n",
+       "      <td>0.702118</td>\n",
+       "      <td>0.413638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BRAF</th>\n",
-       "      <td>0.653021</td>\n",
-       "      <td>0.651452</td>\n",
+       "      <td>0.655028</td>\n",
+       "      <td>0.654600</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.646301</td>\n",
-       "      <td>0.670291</td>\n",
-       "      <td>0.639468</td>\n",
-       "      <td>0.637391</td>\n",
-       "      <td>0.667424</td>\n",
-       "      <td>0.437786</td>\n",
+       "      <td>0.646755</td>\n",
+       "      <td>0.672933</td>\n",
+       "      <td>0.638158</td>\n",
+       "      <td>0.637912</td>\n",
+       "      <td>0.671268</td>\n",
+       "      <td>0.436459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDK2</th>\n",
-       "      <td>0.648024</td>\n",
-       "      <td>0.629135</td>\n",
-       "      <td>0.649093</td>\n",
+       "      <td>0.648447</td>\n",
+       "      <td>0.630308</td>\n",
+       "      <td>0.646755</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.679931</td>\n",
-       "      <td>0.658547</td>\n",
-       "      <td>0.724206</td>\n",
-       "      <td>0.652563</td>\n",
-       "      <td>0.425303</td>\n",
+       "      <td>0.681253</td>\n",
+       "      <td>0.656025</td>\n",
+       "      <td>0.723093</td>\n",
+       "      <td>0.653379</td>\n",
+       "      <td>0.423994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LCK</th>\n",
-       "      <td>0.713619</td>\n",
-       "      <td>0.685906</td>\n",
-       "      <td>0.677195</td>\n",
-       "      <td>0.684067</td>\n",
+       "      <td>0.711321</td>\n",
+       "      <td>0.685075</td>\n",
+       "      <td>0.672933</td>\n",
+       "      <td>0.681253</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.696126</td>\n",
-       "      <td>0.665214</td>\n",
-       "      <td>0.689147</td>\n",
-       "      <td>0.454323</td>\n",
+       "      <td>0.690879</td>\n",
+       "      <td>0.662581</td>\n",
+       "      <td>0.687648</td>\n",
+       "      <td>0.451336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MET</th>\n",
-       "      <td>0.709202</td>\n",
-       "      <td>0.694562</td>\n",
-       "      <td>0.638929</td>\n",
-       "      <td>0.655510</td>\n",
-       "      <td>0.687189</td>\n",
+       "      <td>0.711258</td>\n",
+       "      <td>0.697967</td>\n",
+       "      <td>0.638158</td>\n",
+       "      <td>0.656025</td>\n",
+       "      <td>0.690879</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.628056</td>\n",
-       "      <td>0.711115</td>\n",
-       "      <td>0.392443</td>\n",
+       "      <td>0.629355</td>\n",
+       "      <td>0.713806</td>\n",
+       "      <td>0.393699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p38a</th>\n",
-       "      <td>0.645767</td>\n",
-       "      <td>0.634558</td>\n",
-       "      <td>0.640101</td>\n",
-       "      <td>0.723386</td>\n",
-       "      <td>0.661388</td>\n",
-       "      <td>0.632620</td>\n",
+       "      <td>0.644644</td>\n",
+       "      <td>0.635173</td>\n",
+       "      <td>0.637912</td>\n",
+       "      <td>0.723093</td>\n",
+       "      <td>0.662581</td>\n",
+       "      <td>0.629355</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.652120</td>\n",
-       "      <td>0.432959</td>\n",
+       "      <td>0.653444</td>\n",
+       "      <td>0.431194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KDR</th>\n",
-       "      <td>0.718258</td>\n",
-       "      <td>0.703629</td>\n",
-       "      <td>0.676719</td>\n",
-       "      <td>0.656019</td>\n",
-       "      <td>0.687444</td>\n",
-       "      <td>0.718457</td>\n",
-       "      <td>0.656647</td>\n",
+       "      <td>0.716047</td>\n",
+       "      <td>0.702118</td>\n",
+       "      <td>0.671268</td>\n",
+       "      <td>0.653379</td>\n",
+       "      <td>0.687648</td>\n",
+       "      <td>0.713806</td>\n",
+       "      <td>0.653444</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.424930</td>\n",
+       "      <td>0.422705</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p110a</th>\n",
-       "      <td>0.427023</td>\n",
-       "      <td>0.412055</td>\n",
-       "      <td>0.437192</td>\n",
-       "      <td>0.424418</td>\n",
-       "      <td>0.450303</td>\n",
-       "      <td>0.397434</td>\n",
-       "      <td>0.431120</td>\n",
-       "      <td>0.422578</td>\n",
+       "      <td>0.427062</td>\n",
+       "      <td>0.413638</td>\n",
+       "      <td>0.436459</td>\n",
+       "      <td>0.423994</td>\n",
+       "      <td>0.451336</td>\n",
+       "      <td>0.393699</td>\n",
+       "      <td>0.431194</td>\n",
+       "      <td>0.422705</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -924,29 +1012,29 @@
       ],
       "text/plain": [
        "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       "EGFR   1.000000  0.940995  0.658852  0.650192  0.710442  0.715203  0.644984   \n",
-       "ErbB2  0.941257  1.000000  0.659334  0.632521  0.685368  0.702937  0.636820   \n",
-       "BRAF   0.653021  0.651452  1.000000  0.646301  0.670291  0.639468  0.637391   \n",
-       "CDK2   0.648024  0.629135  0.649093  1.000000  0.679931  0.658547  0.724206   \n",
-       "LCK    0.713619  0.685906  0.677195  0.684067  1.000000  0.696126  0.665214   \n",
-       "MET    0.709202  0.694562  0.638929  0.655510  0.687189  1.000000  0.628056   \n",
-       "p38a   0.645767  0.634558  0.640101  0.723386  0.661388  0.632620  1.000000   \n",
-       "KDR    0.718258  0.703629  0.676719  0.656019  0.687444  0.718457  0.656647   \n",
-       "p110a  0.427023  0.412055  0.437192  0.424418  0.450303  0.397434  0.431120   \n",
+       "EGFR   1.000000  0.940963  0.655028  0.648447  0.711321  0.711258  0.644644   \n",
+       "ErbB2  0.940963  1.000000  0.654600  0.630308  0.685075  0.697967  0.635173   \n",
+       "BRAF   0.655028  0.654600  1.000000  0.646755  0.672933  0.638158  0.637912   \n",
+       "CDK2   0.648447  0.630308  0.646755  1.000000  0.681253  0.656025  0.723093   \n",
+       "LCK    0.711321  0.685075  0.672933  0.681253  1.000000  0.690879  0.662581   \n",
+       "MET    0.711258  0.697967  0.638158  0.656025  0.690879  1.000000  0.629355   \n",
+       "p38a   0.644644  0.635173  0.637912  0.723093  0.662581  0.629355  1.000000   \n",
+       "KDR    0.716047  0.702118  0.671268  0.653379  0.687648  0.713806  0.653444   \n",
+       "p110a  0.427062  0.413638  0.436459  0.423994  0.451336  0.393699  0.431194   \n",
        "\n",
        "            KDR     p110a  \n",
-       "EGFR   0.715307  0.429135  \n",
-       "ErbB2  0.701761  0.416966  \n",
-       "BRAF   0.667424  0.437786  \n",
-       "CDK2   0.652563  0.425303  \n",
-       "LCK    0.689147  0.454323  \n",
-       "MET    0.711115  0.392443  \n",
-       "p38a   0.652120  0.432959  \n",
-       "KDR    1.000000  0.424930  \n",
-       "p110a  0.422578  1.000000  "
+       "EGFR   0.716047  0.427062  \n",
+       "ErbB2  0.702118  0.413638  \n",
+       "BRAF   0.671268  0.436459  \n",
+       "CDK2   0.653379  0.423994  \n",
+       "LCK    0.687648  0.451336  \n",
+       "MET    0.713806  0.393699  \n",
+       "p38a   0.653444  0.431194  \n",
+       "KDR    1.000000  0.422705  \n",
+       "p110a  0.422705  1.000000  "
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,254 +1046,355 @@
     "kinase_similarity_matrix_df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check that the matrix is symmetric:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(kinase_similarity_matrix_df.values == kinase_similarity_matrix_df.values.transpose()).all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "            color:  #000000;\n",
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-       "            background-color:  #7bbc7b;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row0_col5{\n",
-       "            background-color:  #6eb66e;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row0_col6,#T_efaac_row3_col2,#T_efaac_row6_col1{\n",
-       "            background-color:  #92c892;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row0_col7,#T_efaac_row1_col5{\n",
-       "            background-color:  #74b974;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row0_col8{\n",
-       "            background-color:  #ddecdd;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col0{\n",
-       "            background-color:  #188c18;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col2,#T_efaac_row3_col7,#T_efaac_row6_col7,#T_efaac_row7_col6{\n",
-       "            background-color:  #8ec58e;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col3,#T_efaac_row1_col6,#T_efaac_row2_col6,#T_efaac_row6_col2{\n",
-       "            background-color:  #96c996;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col4,#T_efaac_row4_col2{\n",
-       "            background-color:  #86c286;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col7,#T_efaac_row5_col1{\n",
-       "            background-color:  #79bc79;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row1_col8{\n",
-       "            background-color:  #e1eee1;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row2_col1{\n",
-       "            background-color:  #8bc48b;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row2_col3,#T_efaac_row3_col0,#T_efaac_row6_col4{\n",
-       "            background-color:  #90c790;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row2_col4,#T_efaac_row2_col5,#T_efaac_row5_col3{\n",
-       "            background-color:  #8dc58d;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row2_col7,#T_efaac_row7_col2{\n",
-       "            background-color:  #87c287;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row2_col8{\n",
-       "            background-color:  #d9ead9;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row3_col1{\n",
-       "            background-color:  #94c994;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row3_col4{\n",
-       "            background-color:  #89c389;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row3_col5,#T_efaac_row5_col4,#T_efaac_row7_col4{\n",
-       "            background-color:  #85c185;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row3_col6{\n",
-       "            background-color:  #72b872;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row3_col8,#T_efaac_row7_col8{\n",
-       "            background-color:  #dfeddf;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col0{\n",
-       "            background-color:  #75b975;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col1{\n",
-       "            background-color:  #7dbd7d;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col3{\n",
-       "            background-color:  #81bf81;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col5,#T_efaac_row5_col0,#T_efaac_row7_col1{\n",
-       "            background-color:  #77ba77;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col6{\n",
-       "            background-color:  #8ac48a;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col7{\n",
-       "            background-color:  #7ebe7e;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row4_col8{\n",
-       "            background-color:  #d3e7d3;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row5_col2{\n",
-       "            background-color:  #97ca97;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row5_col6{\n",
-       "            background-color:  #9acb9a;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row5_col7{\n",
-       "            background-color:  #76ba76;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row5_col8,#T_efaac_row8_col0,#T_efaac_row8_col1,#T_efaac_row8_col2,#T_efaac_row8_col3,#T_efaac_row8_col4,#T_efaac_row8_col5,#T_efaac_row8_col6,#T_efaac_row8_col7{\n",
-       "            background-color:  #ebf3eb;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row6_col0{\n",
-       "            background-color:  #91c791;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row6_col3{\n",
-       "            background-color:  #71b771;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row6_col8{\n",
-       "            background-color:  #dbebdb;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row7_col0{\n",
-       "            background-color:  #73b873;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row7_col3{\n",
-       "            background-color:  #8cc58c;\n",
-       "            color:  #000000;\n",
-       "        }#T_efaac_row7_col5{\n",
-       "            background-color:  #6db66d;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_efaac_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_efaac_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_efaac_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "                        <td id=\"T_efaac_row0_col2\" class=\"data row0 col2\" >0.659</td>\n",
-       "                        <td id=\"T_efaac_row0_col3\" class=\"data row0 col3\" >0.650</td>\n",
-       "                        <td id=\"T_efaac_row0_col4\" class=\"data row0 col4\" >0.710</td>\n",
-       "                        <td id=\"T_efaac_row0_col5\" class=\"data row0 col5\" >0.715</td>\n",
-       "                        <td id=\"T_efaac_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
-       "                        <td id=\"T_efaac_row0_col7\" class=\"data row0 col7\" >0.715</td>\n",
-       "                        <td id=\"T_efaac_row0_col8\" class=\"data row0 col8\" >0.429</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_efaac_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "                        <td id=\"T_efaac_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row1_col2\" class=\"data row1 col2\" >0.659</td>\n",
-       "                        <td id=\"T_efaac_row1_col3\" class=\"data row1 col3\" >0.633</td>\n",
-       "                        <td id=\"T_efaac_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
-       "                        <td id=\"T_efaac_row1_col5\" class=\"data row1 col5\" >0.703</td>\n",
-       "                        <td id=\"T_efaac_row1_col6\" class=\"data row1 col6\" >0.637</td>\n",
-       "                        <td id=\"T_efaac_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
-       "                        <td id=\"T_efaac_row1_col8\" class=\"data row1 col8\" >0.417</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_efaac_row2_col0\" class=\"data row2 col0\" >0.653</td>\n",
-       "                        <td id=\"T_efaac_row2_col1\" class=\"data row2 col1\" >0.651</td>\n",
-       "                        <td id=\"T_efaac_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row2_col3\" class=\"data row2 col3\" >0.646</td>\n",
-       "                        <td id=\"T_efaac_row2_col4\" class=\"data row2 col4\" >0.670</td>\n",
-       "                        <td id=\"T_efaac_row2_col5\" class=\"data row2 col5\" >0.639</td>\n",
-       "                        <td id=\"T_efaac_row2_col6\" class=\"data row2 col6\" >0.637</td>\n",
-       "                        <td id=\"T_efaac_row2_col7\" class=\"data row2 col7\" >0.667</td>\n",
-       "                        <td id=\"T_efaac_row2_col8\" class=\"data row2 col8\" >0.438</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_efaac_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
-       "                        <td id=\"T_efaac_row3_col1\" class=\"data row3 col1\" >0.629</td>\n",
-       "                        <td id=\"T_efaac_row3_col2\" class=\"data row3 col2\" >0.649</td>\n",
-       "                        <td id=\"T_efaac_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row3_col4\" class=\"data row3 col4\" >0.680</td>\n",
-       "                        <td id=\"T_efaac_row3_col5\" class=\"data row3 col5\" >0.659</td>\n",
-       "                        <td id=\"T_efaac_row3_col6\" class=\"data row3 col6\" >0.724</td>\n",
-       "                        <td id=\"T_efaac_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
-       "                        <td id=\"T_efaac_row3_col8\" class=\"data row3 col8\" >0.425</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_efaac_row4_col0\" class=\"data row4 col0\" >0.714</td>\n",
-       "                        <td id=\"T_efaac_row4_col1\" class=\"data row4 col1\" >0.686</td>\n",
-       "                        <td id=\"T_efaac_row4_col2\" class=\"data row4 col2\" >0.677</td>\n",
-       "                        <td id=\"T_efaac_row4_col3\" class=\"data row4 col3\" >0.684</td>\n",
-       "                        <td id=\"T_efaac_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row4_col5\" class=\"data row4 col5\" >0.696</td>\n",
-       "                        <td id=\"T_efaac_row4_col6\" class=\"data row4 col6\" >0.665</td>\n",
-       "                        <td id=\"T_efaac_row4_col7\" class=\"data row4 col7\" >0.689</td>\n",
-       "                        <td id=\"T_efaac_row4_col8\" class=\"data row4 col8\" >0.454</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_efaac_row5_col0\" class=\"data row5 col0\" >0.709</td>\n",
-       "                        <td id=\"T_efaac_row5_col1\" class=\"data row5 col1\" >0.695</td>\n",
-       "                        <td id=\"T_efaac_row5_col2\" class=\"data row5 col2\" >0.639</td>\n",
-       "                        <td id=\"T_efaac_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
-       "                        <td id=\"T_efaac_row5_col4\" class=\"data row5 col4\" >0.687</td>\n",
-       "                        <td id=\"T_efaac_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row5_col6\" class=\"data row5 col6\" >0.628</td>\n",
-       "                        <td id=\"T_efaac_row5_col7\" class=\"data row5 col7\" >0.711</td>\n",
-       "                        <td id=\"T_efaac_row5_col8\" class=\"data row5 col8\" >0.392</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_efaac_row6_col0\" class=\"data row6 col0\" >0.646</td>\n",
-       "                        <td id=\"T_efaac_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
-       "                        <td id=\"T_efaac_row6_col2\" class=\"data row6 col2\" >0.640</td>\n",
-       "                        <td id=\"T_efaac_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
-       "                        <td id=\"T_efaac_row6_col4\" class=\"data row6 col4\" >0.661</td>\n",
-       "                        <td id=\"T_efaac_row6_col5\" class=\"data row6 col5\" >0.633</td>\n",
-       "                        <td id=\"T_efaac_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row6_col7\" class=\"data row6 col7\" >0.652</td>\n",
-       "                        <td id=\"T_efaac_row6_col8\" class=\"data row6 col8\" >0.433</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_efaac_row7_col0\" class=\"data row7 col0\" >0.718</td>\n",
-       "                        <td id=\"T_efaac_row7_col1\" class=\"data row7 col1\" >0.704</td>\n",
-       "                        <td id=\"T_efaac_row7_col2\" class=\"data row7 col2\" >0.677</td>\n",
-       "                        <td id=\"T_efaac_row7_col3\" class=\"data row7 col3\" >0.656</td>\n",
-       "                        <td id=\"T_efaac_row7_col4\" class=\"data row7 col4\" >0.687</td>\n",
-       "                        <td id=\"T_efaac_row7_col5\" class=\"data row7 col5\" >0.718</td>\n",
-       "                        <td id=\"T_efaac_row7_col6\" class=\"data row7 col6\" >0.657</td>\n",
-       "                        <td id=\"T_efaac_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "                        <td id=\"T_efaac_row7_col8\" class=\"data row7 col8\" >0.425</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_efaac_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_efaac_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
-       "                        <td id=\"T_efaac_row8_col1\" class=\"data row8 col1\" >0.412</td>\n",
-       "                        <td id=\"T_efaac_row8_col2\" class=\"data row8 col2\" >0.437</td>\n",
-       "                        <td id=\"T_efaac_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
-       "                        <td id=\"T_efaac_row8_col4\" class=\"data row8 col4\" >0.450</td>\n",
-       "                        <td id=\"T_efaac_row8_col5\" class=\"data row8 col5\" >0.397</td>\n",
-       "                        <td id=\"T_efaac_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
-       "                        <td id=\"T_efaac_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
-       "                        <td id=\"T_efaac_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_144d8_row0_col0, #T_144d8_row1_col1, #T_144d8_row2_col2, #T_144d8_row3_col3, #T_144d8_row4_col4, #T_144d8_row5_col5, #T_144d8_row6_col6, #T_144d8_row7_col7, #T_144d8_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row0_col1 {\n",
+       "  background-color: #178b17;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row0_col2, #T_144d8_row0_col3, #T_144d8_row1_col2, #T_144d8_row2_col3, #T_144d8_row6_col5, #T_144d8_row7_col6 {\n",
+       "  background-color: #8fc68f;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row0_col4 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row0_col5 {\n",
+       "  background-color: #6fb76f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row0_col6, #T_144d8_row6_col1 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row0_col7 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row0_col8 {\n",
+       "  background-color: #deedde;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row1_col0 {\n",
+       "  background-color: #188c18;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row1_col3, #T_144d8_row1_col6, #T_144d8_row5_col2, #T_144d8_row6_col2 {\n",
+       "  background-color: #97ca97;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row1_col4 {\n",
+       "  background-color: #86c286;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row1_col5 {\n",
+       "  background-color: #75b975;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_144d8_row1_col7 {\n",
+       "  background-color: #79bc79;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row1_col8 {\n",
+       "  background-color: #e3efe3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col0, #T_144d8_row7_col3 {\n",
+       "  background-color: #8ec58e;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col1 {\n",
+       "  background-color: #8ac48a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col4, #T_144d8_row2_col5, #T_144d8_row5_col3 {\n",
+       "  background-color: #8cc58c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col6 {\n",
+       "  background-color: #95c995;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col7, #T_144d8_row3_col5, #T_144d8_row7_col4 {\n",
+       "  background-color: #85c185;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row2_col8 {\n",
+       "  background-color: #daebda;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row3_col0, #T_144d8_row6_col4 {\n",
+       "  background-color: #90c790;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row3_col1 {\n",
+       "  background-color: #94c994;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row3_col2 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row3_col4, #T_144d8_row4_col2 {\n",
+       "  background-color: #88c388;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_144d8_row3_col6 {\n",
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+       "#T_144d8_row5_col8, #T_144d8_row8_col0, #T_144d8_row8_col1, #T_144d8_row8_col2, #T_144d8_row8_col3, #T_144d8_row8_col4, #T_144d8_row8_col5, #T_144d8_row8_col6, #T_144d8_row8_col7 {\n",
+       "  background-color: #ebf3eb;\n",
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+       "#T_144d8_row6_col0 {\n",
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+       "  color: #000000;\n",
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+       "#T_144d8_row6_col3 {\n",
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+       "}\n",
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+       "#T_144d8_row7_col2 {\n",
+       "  background-color: #89c389;\n",
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+       "}\n",
+       "#T_144d8_row7_col5 {\n",
+       "  background-color: #6eb66e;\n",
+       "  color: #f1f1f1;\n",
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+       "</style>\n",
+       "<table id=\"T_144d8_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_144d8_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_144d8_row0_col2\" class=\"data row0 col2\" >0.655</td>\n",
+       "      <td id=\"T_144d8_row0_col3\" class=\"data row0 col3\" >0.648</td>\n",
+       "      <td id=\"T_144d8_row0_col4\" class=\"data row0 col4\" >0.711</td>\n",
+       "      <td id=\"T_144d8_row0_col5\" class=\"data row0 col5\" >0.711</td>\n",
+       "      <td id=\"T_144d8_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
+       "      <td id=\"T_144d8_row0_col7\" class=\"data row0 col7\" >0.716</td>\n",
+       "      <td id=\"T_144d8_row0_col8\" class=\"data row0 col8\" >0.427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_144d8_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_144d8_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row1_col2\" class=\"data row1 col2\" >0.655</td>\n",
+       "      <td id=\"T_144d8_row1_col3\" class=\"data row1 col3\" >0.630</td>\n",
+       "      <td id=\"T_144d8_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
+       "      <td id=\"T_144d8_row1_col5\" class=\"data row1 col5\" >0.698</td>\n",
+       "      <td id=\"T_144d8_row1_col6\" class=\"data row1 col6\" >0.635</td>\n",
+       "      <td id=\"T_144d8_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
+       "      <td id=\"T_144d8_row1_col8\" class=\"data row1 col8\" >0.414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_144d8_row2_col0\" class=\"data row2 col0\" >0.655</td>\n",
+       "      <td id=\"T_144d8_row2_col1\" class=\"data row2 col1\" >0.655</td>\n",
+       "      <td id=\"T_144d8_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row2_col3\" class=\"data row2 col3\" >0.647</td>\n",
+       "      <td id=\"T_144d8_row2_col4\" class=\"data row2 col4\" >0.673</td>\n",
+       "      <td id=\"T_144d8_row2_col5\" class=\"data row2 col5\" >0.638</td>\n",
+       "      <td id=\"T_144d8_row2_col6\" class=\"data row2 col6\" >0.638</td>\n",
+       "      <td id=\"T_144d8_row2_col7\" class=\"data row2 col7\" >0.671</td>\n",
+       "      <td id=\"T_144d8_row2_col8\" class=\"data row2 col8\" >0.436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_144d8_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
+       "      <td id=\"T_144d8_row3_col1\" class=\"data row3 col1\" >0.630</td>\n",
+       "      <td id=\"T_144d8_row3_col2\" class=\"data row3 col2\" >0.647</td>\n",
+       "      <td id=\"T_144d8_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row3_col4\" class=\"data row3 col4\" >0.681</td>\n",
+       "      <td id=\"T_144d8_row3_col5\" class=\"data row3 col5\" >0.656</td>\n",
+       "      <td id=\"T_144d8_row3_col6\" class=\"data row3 col6\" >0.723</td>\n",
+       "      <td id=\"T_144d8_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
+       "      <td id=\"T_144d8_row3_col8\" class=\"data row3 col8\" >0.424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_144d8_row4_col0\" class=\"data row4 col0\" >0.711</td>\n",
+       "      <td id=\"T_144d8_row4_col1\" class=\"data row4 col1\" >0.685</td>\n",
+       "      <td id=\"T_144d8_row4_col2\" class=\"data row4 col2\" >0.673</td>\n",
+       "      <td id=\"T_144d8_row4_col3\" class=\"data row4 col3\" >0.681</td>\n",
+       "      <td id=\"T_144d8_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row4_col5\" class=\"data row4 col5\" >0.691</td>\n",
+       "      <td id=\"T_144d8_row4_col6\" class=\"data row4 col6\" >0.663</td>\n",
+       "      <td id=\"T_144d8_row4_col7\" class=\"data row4 col7\" >0.688</td>\n",
+       "      <td id=\"T_144d8_row4_col8\" class=\"data row4 col8\" >0.451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_144d8_row5_col0\" class=\"data row5 col0\" >0.711</td>\n",
+       "      <td id=\"T_144d8_row5_col1\" class=\"data row5 col1\" >0.698</td>\n",
+       "      <td id=\"T_144d8_row5_col2\" class=\"data row5 col2\" >0.638</td>\n",
+       "      <td id=\"T_144d8_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
+       "      <td id=\"T_144d8_row5_col4\" class=\"data row5 col4\" >0.691</td>\n",
+       "      <td id=\"T_144d8_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row5_col6\" class=\"data row5 col6\" >0.629</td>\n",
+       "      <td id=\"T_144d8_row5_col7\" class=\"data row5 col7\" >0.714</td>\n",
+       "      <td id=\"T_144d8_row5_col8\" class=\"data row5 col8\" >0.394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_144d8_row6_col0\" class=\"data row6 col0\" >0.645</td>\n",
+       "      <td id=\"T_144d8_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
+       "      <td id=\"T_144d8_row6_col2\" class=\"data row6 col2\" >0.638</td>\n",
+       "      <td id=\"T_144d8_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
+       "      <td id=\"T_144d8_row6_col4\" class=\"data row6 col4\" >0.663</td>\n",
+       "      <td id=\"T_144d8_row6_col5\" class=\"data row6 col5\" >0.629</td>\n",
+       "      <td id=\"T_144d8_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row6_col7\" class=\"data row6 col7\" >0.653</td>\n",
+       "      <td id=\"T_144d8_row6_col8\" class=\"data row6 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_144d8_row7_col0\" class=\"data row7 col0\" >0.716</td>\n",
+       "      <td id=\"T_144d8_row7_col1\" class=\"data row7 col1\" >0.702</td>\n",
+       "      <td id=\"T_144d8_row7_col2\" class=\"data row7 col2\" >0.671</td>\n",
+       "      <td id=\"T_144d8_row7_col3\" class=\"data row7 col3\" >0.653</td>\n",
+       "      <td id=\"T_144d8_row7_col4\" class=\"data row7 col4\" >0.688</td>\n",
+       "      <td id=\"T_144d8_row7_col5\" class=\"data row7 col5\" >0.714</td>\n",
+       "      <td id=\"T_144d8_row7_col6\" class=\"data row7 col6\" >0.653</td>\n",
+       "      <td id=\"T_144d8_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_144d8_row7_col8\" class=\"data row7 col8\" >0.423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_144d8_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_144d8_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
+       "      <td id=\"T_144d8_row8_col1\" class=\"data row8 col1\" >0.414</td>\n",
+       "      <td id=\"T_144d8_row8_col2\" class=\"data row8 col2\" >0.436</td>\n",
+       "      <td id=\"T_144d8_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
+       "      <td id=\"T_144d8_row8_col4\" class=\"data row8 col4\" >0.451</td>\n",
+       "      <td id=\"T_144d8_row8_col5\" class=\"data row8 col5\" >0.394</td>\n",
+       "      <td id=\"T_144d8_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
+       "      <td id=\"T_144d8_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
+       "      <td id=\"T_144d8_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f35feba3f10>"
+       "<pandas.io.formats.style.Styler at 0x7fa2f304d970>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,7 +1416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1246,14 +1435,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. In the case of the similarity matrix above, this matrix is not a distance matrix. For example, the diagonal elements are not zero.\n",
-    "\n",
-    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
+    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1272,7 +1459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1281,252 +1468,326 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
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-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row4_col7{\n",
-       "            background-color:  #6db56d;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row4_col8{\n",
-       "            background-color:  #188c18;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row5_col2{\n",
-       "            background-color:  #54a954;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row5_col6{\n",
-       "            background-color:  #51a851;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row5_col7{\n",
-       "            background-color:  #75b975;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row5_col8,#T_f9fbf_row8_col0,#T_f9fbf_row8_col1,#T_f9fbf_row8_col2,#T_f9fbf_row8_col3,#T_f9fbf_row8_col4,#T_f9fbf_row8_col5,#T_f9fbf_row8_col6,#T_f9fbf_row8_col7{\n",
-       "            background-color:  #008000;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_f9fbf_row6_col0{\n",
-       "            background-color:  #59ac59;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row6_col3{\n",
-       "            background-color:  #79bc79;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row6_col5{\n",
-       "            background-color:  #5bad5b;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row6_col8{\n",
-       "            background-color:  #108810;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_f9fbf_row7_col3{\n",
-       "            background-color:  #5fae5f;\n",
-       "            color:  #000000;\n",
-       "        }#T_f9fbf_row7_col5{\n",
-       "            background-color:  #7dbd7d;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_f9fbf_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >BRAF</th>        <th class=\"col_heading level0 col3\" >CDK2</th>        <th class=\"col_heading level0 col4\" >LCK</th>        <th class=\"col_heading level0 col5\" >MET</th>        <th class=\"col_heading level0 col6\" >p38a</th>        <th class=\"col_heading level0 col7\" >KDR</th>        <th class=\"col_heading level0 col8\" >p110a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_f9fbf_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col2\" class=\"data row0 col2\" >0.341</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col3\" class=\"data row0 col3\" >0.350</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col4\" class=\"data row0 col4\" >0.290</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col5\" class=\"data row0 col5\" >0.285</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col7\" class=\"data row0 col7\" >0.285</td>\n",
-       "                        <td id=\"T_f9fbf_row0_col8\" class=\"data row0 col8\" >0.571</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_f9fbf_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col2\" class=\"data row1 col2\" >0.341</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col3\" class=\"data row1 col3\" >0.367</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col5\" class=\"data row1 col5\" >0.297</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col6\" class=\"data row1 col6\" >0.363</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
-       "                        <td id=\"T_f9fbf_row1_col8\" class=\"data row1 col8\" >0.583</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "                        <td id=\"T_f9fbf_row2_col0\" class=\"data row2 col0\" >0.347</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col1\" class=\"data row2 col1\" >0.349</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col3\" class=\"data row2 col3\" >0.354</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col4\" class=\"data row2 col4\" >0.330</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col5\" class=\"data row2 col5\" >0.361</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col6\" class=\"data row2 col6\" >0.363</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col7\" class=\"data row2 col7\" >0.333</td>\n",
-       "                        <td id=\"T_f9fbf_row2_col8\" class=\"data row2 col8\" >0.562</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "                        <td id=\"T_f9fbf_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col1\" class=\"data row3 col1\" >0.371</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col2\" class=\"data row3 col2\" >0.351</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col4\" class=\"data row3 col4\" >0.320</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col6\" class=\"data row3 col6\" >0.276</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
-       "                        <td id=\"T_f9fbf_row3_col8\" class=\"data row3 col8\" >0.575</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "                        <td id=\"T_f9fbf_row4_col0\" class=\"data row4 col0\" >0.286</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col1\" class=\"data row4 col1\" >0.314</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col2\" class=\"data row4 col2\" >0.323</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col3\" class=\"data row4 col3\" >0.316</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col5\" class=\"data row4 col5\" >0.304</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col6\" class=\"data row4 col6\" >0.335</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col7\" class=\"data row4 col7\" >0.311</td>\n",
-       "                        <td id=\"T_f9fbf_row4_col8\" class=\"data row4 col8\" >0.546</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "                        <td id=\"T_f9fbf_row5_col0\" class=\"data row5 col0\" >0.291</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col1\" class=\"data row5 col1\" >0.305</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col2\" class=\"data row5 col2\" >0.361</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col4\" class=\"data row5 col4\" >0.313</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col6\" class=\"data row5 col6\" >0.372</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col7\" class=\"data row5 col7\" >0.289</td>\n",
-       "                        <td id=\"T_f9fbf_row5_col8\" class=\"data row5 col8\" >0.608</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "                        <td id=\"T_f9fbf_row6_col0\" class=\"data row6 col0\" >0.354</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col2\" class=\"data row6 col2\" >0.360</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col4\" class=\"data row6 col4\" >0.339</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col5\" class=\"data row6 col5\" >0.367</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col7\" class=\"data row6 col7\" >0.348</td>\n",
-       "                        <td id=\"T_f9fbf_row6_col8\" class=\"data row6 col8\" >0.567</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "                        <td id=\"T_f9fbf_row7_col0\" class=\"data row7 col0\" >0.282</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col1\" class=\"data row7 col1\" >0.296</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col2\" class=\"data row7 col2\" >0.323</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col3\" class=\"data row7 col3\" >0.344</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col4\" class=\"data row7 col4\" >0.313</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col5\" class=\"data row7 col5\" >0.282</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col6\" class=\"data row7 col6\" >0.343</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "                        <td id=\"T_f9fbf_row7_col8\" class=\"data row7 col8\" >0.575</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_f9fbf_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "                        <td id=\"T_f9fbf_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col1\" class=\"data row8 col1\" >0.588</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col2\" class=\"data row8 col2\" >0.563</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col4\" class=\"data row8 col4\" >0.550</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col5\" class=\"data row8 col5\" >0.603</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
-       "                        <td id=\"T_f9fbf_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_d4786_row0_col0, #T_d4786_row1_col1, #T_d4786_row2_col2, #T_d4786_row3_col3, #T_d4786_row4_col4, #T_d4786_row5_col5, #T_d4786_row6_col6, #T_d4786_row7_col7, #T_d4786_row8_col8 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row0_col1 {\n",
+       "  background-color: #d4e8d4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row0_col2, #T_d4786_row0_col3, #T_d4786_row1_col2, #T_d4786_row2_col3, #T_d4786_row6_col5 {\n",
+       "  background-color: #5bad5b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row0_col4 {\n",
+       "  background-color: #6fb76f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row0_col5 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row0_col6, #T_d4786_row6_col1 {\n",
+       "  background-color: #58ab58;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row0_col7 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row0_col8 {\n",
+       "  background-color: #0d860d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row1_col0 {\n",
+       "  background-color: #d3e7d3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row1_col3, #T_d4786_row1_col6, #T_d4786_row5_col2, #T_d4786_row6_col2 {\n",
+       "  background-color: #54a954;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row1_col4 {\n",
+       "  background-color: #64b164;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row1_col5 {\n",
+       "  background-color: #76ba76;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row1_col7 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row1_col8 {\n",
+       "  background-color: #078407;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col0, #T_d4786_row7_col3 {\n",
+       "  background-color: #5dae5d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col1 {\n",
+       "  background-color: #61af61;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col4, #T_d4786_row2_col5, #T_d4786_row5_col3 {\n",
+       "  background-color: #5fae5f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col6, #T_d4786_row3_col1 {\n",
+       "  background-color: #56aa56;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col7, #T_d4786_row3_col5, #T_d4786_row7_col4 {\n",
+       "  background-color: #65b265;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row2_col8 {\n",
+       "  background-color: #118811;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row3_col0, #T_d4786_row6_col4 {\n",
+       "  background-color: #5aac5a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row3_col2 {\n",
+       "  background-color: #57ab57;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row3_col4, #T_d4786_row4_col2 {\n",
+       "  background-color: #62b062;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row3_col6 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row3_col7, #T_d4786_row6_col7 {\n",
+       "  background-color: #5eae5e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row3_col8, #T_d4786_row7_col8 {\n",
+       "  background-color: #0b850b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col0 {\n",
+       "  background-color: #75b975;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col1 {\n",
+       "  background-color: #6db56d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col3 {\n",
+       "  background-color: #69b369;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col5, #T_d4786_row7_col1 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col6 {\n",
+       "  background-color: #60af60;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col7 {\n",
+       "  background-color: #6cb56c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row4_col8 {\n",
+       "  background-color: #168b16;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col0 {\n",
+       "  background-color: #74b974;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col1 {\n",
+       "  background-color: #72b872;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col4 {\n",
+       "  background-color: #66b266;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col6 {\n",
+       "  background-color: #52a852;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col7, #T_d4786_row7_col0 {\n",
+       "  background-color: #77ba77;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row5_col8, #T_d4786_row8_col0, #T_d4786_row8_col1, #T_d4786_row8_col2, #T_d4786_row8_col3, #T_d4786_row8_col4, #T_d4786_row8_col5, #T_d4786_row8_col6, #T_d4786_row8_col7 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row6_col0 {\n",
+       "  background-color: #59ac59;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row6_col3 {\n",
+       "  background-color: #79bc79;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row6_col8 {\n",
+       "  background-color: #0e870e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row7_col2 {\n",
+       "  background-color: #61b061;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_d4786_row7_col5 {\n",
+       "  background-color: #7cbd7c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d4786_row7_col6 {\n",
+       "  background-color: #5cad5c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_d4786_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
+       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d4786_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_d4786_row0_col2\" class=\"data row0 col2\" >0.345</td>\n",
+       "      <td id=\"T_d4786_row0_col3\" class=\"data row0 col3\" >0.352</td>\n",
+       "      <td id=\"T_d4786_row0_col4\" class=\"data row0 col4\" >0.289</td>\n",
+       "      <td id=\"T_d4786_row0_col5\" class=\"data row0 col5\" >0.289</td>\n",
+       "      <td id=\"T_d4786_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
+       "      <td id=\"T_d4786_row0_col7\" class=\"data row0 col7\" >0.284</td>\n",
+       "      <td id=\"T_d4786_row0_col8\" class=\"data row0 col8\" >0.573</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d4786_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_d4786_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row1_col2\" class=\"data row1 col2\" >0.345</td>\n",
+       "      <td id=\"T_d4786_row1_col3\" class=\"data row1 col3\" >0.370</td>\n",
+       "      <td id=\"T_d4786_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
+       "      <td id=\"T_d4786_row1_col5\" class=\"data row1 col5\" >0.302</td>\n",
+       "      <td id=\"T_d4786_row1_col6\" class=\"data row1 col6\" >0.365</td>\n",
+       "      <td id=\"T_d4786_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
+       "      <td id=\"T_d4786_row1_col8\" class=\"data row1 col8\" >0.586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
+       "      <td id=\"T_d4786_row2_col0\" class=\"data row2 col0\" >0.345</td>\n",
+       "      <td id=\"T_d4786_row2_col1\" class=\"data row2 col1\" >0.345</td>\n",
+       "      <td id=\"T_d4786_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row2_col3\" class=\"data row2 col3\" >0.353</td>\n",
+       "      <td id=\"T_d4786_row2_col4\" class=\"data row2 col4\" >0.327</td>\n",
+       "      <td id=\"T_d4786_row2_col5\" class=\"data row2 col5\" >0.362</td>\n",
+       "      <td id=\"T_d4786_row2_col6\" class=\"data row2 col6\" >0.362</td>\n",
+       "      <td id=\"T_d4786_row2_col7\" class=\"data row2 col7\" >0.329</td>\n",
+       "      <td id=\"T_d4786_row2_col8\" class=\"data row2 col8\" >0.564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
+       "      <td id=\"T_d4786_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
+       "      <td id=\"T_d4786_row3_col1\" class=\"data row3 col1\" >0.370</td>\n",
+       "      <td id=\"T_d4786_row3_col2\" class=\"data row3 col2\" >0.353</td>\n",
+       "      <td id=\"T_d4786_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row3_col4\" class=\"data row3 col4\" >0.319</td>\n",
+       "      <td id=\"T_d4786_row3_col5\" class=\"data row3 col5\" >0.344</td>\n",
+       "      <td id=\"T_d4786_row3_col6\" class=\"data row3 col6\" >0.277</td>\n",
+       "      <td id=\"T_d4786_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
+       "      <td id=\"T_d4786_row3_col8\" class=\"data row3 col8\" >0.576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
+       "      <td id=\"T_d4786_row4_col0\" class=\"data row4 col0\" >0.289</td>\n",
+       "      <td id=\"T_d4786_row4_col1\" class=\"data row4 col1\" >0.315</td>\n",
+       "      <td id=\"T_d4786_row4_col2\" class=\"data row4 col2\" >0.327</td>\n",
+       "      <td id=\"T_d4786_row4_col3\" class=\"data row4 col3\" >0.319</td>\n",
+       "      <td id=\"T_d4786_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row4_col5\" class=\"data row4 col5\" >0.309</td>\n",
+       "      <td id=\"T_d4786_row4_col6\" class=\"data row4 col6\" >0.337</td>\n",
+       "      <td id=\"T_d4786_row4_col7\" class=\"data row4 col7\" >0.312</td>\n",
+       "      <td id=\"T_d4786_row4_col8\" class=\"data row4 col8\" >0.549</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
+       "      <td id=\"T_d4786_row5_col0\" class=\"data row5 col0\" >0.289</td>\n",
+       "      <td id=\"T_d4786_row5_col1\" class=\"data row5 col1\" >0.302</td>\n",
+       "      <td id=\"T_d4786_row5_col2\" class=\"data row5 col2\" >0.362</td>\n",
+       "      <td id=\"T_d4786_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
+       "      <td id=\"T_d4786_row5_col4\" class=\"data row5 col4\" >0.309</td>\n",
+       "      <td id=\"T_d4786_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row5_col6\" class=\"data row5 col6\" >0.371</td>\n",
+       "      <td id=\"T_d4786_row5_col7\" class=\"data row5 col7\" >0.286</td>\n",
+       "      <td id=\"T_d4786_row5_col8\" class=\"data row5 col8\" >0.606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
+       "      <td id=\"T_d4786_row6_col0\" class=\"data row6 col0\" >0.355</td>\n",
+       "      <td id=\"T_d4786_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
+       "      <td id=\"T_d4786_row6_col2\" class=\"data row6 col2\" >0.362</td>\n",
+       "      <td id=\"T_d4786_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
+       "      <td id=\"T_d4786_row6_col4\" class=\"data row6 col4\" >0.337</td>\n",
+       "      <td id=\"T_d4786_row6_col5\" class=\"data row6 col5\" >0.371</td>\n",
+       "      <td id=\"T_d4786_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row6_col7\" class=\"data row6 col7\" >0.347</td>\n",
+       "      <td id=\"T_d4786_row6_col8\" class=\"data row6 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
+       "      <td id=\"T_d4786_row7_col0\" class=\"data row7 col0\" >0.284</td>\n",
+       "      <td id=\"T_d4786_row7_col1\" class=\"data row7 col1\" >0.298</td>\n",
+       "      <td id=\"T_d4786_row7_col2\" class=\"data row7 col2\" >0.329</td>\n",
+       "      <td id=\"T_d4786_row7_col3\" class=\"data row7 col3\" >0.347</td>\n",
+       "      <td id=\"T_d4786_row7_col4\" class=\"data row7 col4\" >0.312</td>\n",
+       "      <td id=\"T_d4786_row7_col5\" class=\"data row7 col5\" >0.286</td>\n",
+       "      <td id=\"T_d4786_row7_col6\" class=\"data row7 col6\" >0.347</td>\n",
+       "      <td id=\"T_d4786_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_d4786_row7_col8\" class=\"data row7 col8\" >0.577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4786_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
+       "      <td id=\"T_d4786_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
+       "      <td id=\"T_d4786_row8_col1\" class=\"data row8 col1\" >0.586</td>\n",
+       "      <td id=\"T_d4786_row8_col2\" class=\"data row8 col2\" >0.564</td>\n",
+       "      <td id=\"T_d4786_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
+       "      <td id=\"T_d4786_row8_col4\" class=\"data row8 col4\" >0.549</td>\n",
+       "      <td id=\"T_d4786_row8_col5\" class=\"data row8 col5\" >0.606</td>\n",
+       "      <td id=\"T_d4786_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
+       "      <td id=\"T_d4786_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
+       "      <td id=\"T_d4786_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f35feac9be0>"
+       "<pandas.io.formats.style.Styler at 0x7fa2f32f8610>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1546,7 +1807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1579,7 +1840,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1593,7 +1854,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 6a2a3632a8aed57626f0257781da82f644865e2e Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 14:14:45 +0200
Subject: [PATCH 076/158] T024: Add sequence logo figure

---
 .../images/sequence_logo.png                    | Bin 0 -> 70458 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/images/sequence_logo.png

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/images/sequence_logo.png b/teachopencadd/talktorials/T024_kinase_similarity_sequence/images/sequence_logo.png
new file mode 100644
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From 5d307974f5b6a0f25c83161af4289634b3f1b1c3 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 14:18:04 +0200
Subject: [PATCH 077/158] T23: Add kinase structure figure; add KinMap data
 generation

---
 .../data/kinmap_n_structures_per_kinase.csv   | 312 ++++++++++++++++++
 .../images/T023_kinase_structure.png          | Bin 0 -> 123975 bytes
 .../T023_what_is_a_kinase/talktorial.ipynb    | 157 +++++++--
 3 files changed, 434 insertions(+), 35 deletions(-)
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/images/T023_kinase_structure.png

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
new file mode 100644
index 00000000..1b3f8da8
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
@@ -0,0 +1,312 @@
+xName,n_structures,shape,fill,stroke,size
+AAK1,5,circle,LimeGreen,LightGray,10.470588235294118
+ABL1,58,circle,LimeGreen,LightGray,16.705882352941178
+ABL2,4,circle,LimeGreen,LightGray,10.352941176470589
+ACK,11,circle,LimeGreen,LightGray,11.176470588235293
+ACTR2,3,circle,LimeGreen,LightGray,10.235294117647058
+ACTR2B,1,circle,LimeGreen,LightGray,10.0
+ADCK3,2,circle,LimeGreen,LightGray,10.117647058823529
+AKT1,29,circle,LimeGreen,LightGray,13.294117647058824
+AKT2,16,circle,LimeGreen,LightGray,11.764705882352942
+ALK,61,circle,LimeGreen,LightGray,17.058823529411764
+ALK1,1,circle,LimeGreen,LightGray,10.0
+ALK2,73,circle,LimeGreen,LightGray,18.470588235294116
+AMPKa1,11,circle,LimeGreen,LightGray,11.176470588235293
+AMPKa2,11,circle,LimeGreen,LightGray,11.176470588235293
+ANKRD3,5,circle,LimeGreen,LightGray,10.470588235294118
+ATM,7,circle,LimeGreen,LightGray,10.705882352941176
+ATR,1,circle,LimeGreen,LightGray,10.0
+AXL,1,circle,LimeGreen,LightGray,10.0
+AurA,174,circle,LimeGreen,LightGray,30.352941176470587
+AurB,1,circle,LimeGreen,LightGray,10.0
+AurC,2,circle,LimeGreen,LightGray,10.117647058823529
+BARK1,18,circle,LimeGreen,LightGray,12.0
+BIKE,5,circle,LimeGreen,LightGray,10.470588235294118
+BMPR1B,1,circle,LimeGreen,LightGray,10.0
+BMPR2,2,circle,LimeGreen,LightGray,10.117647058823529
+BMX,3,circle,LimeGreen,LightGray,10.235294117647058
+BRAF,97,circle,LimeGreen,LightGray,21.294117647058826
+BRK,6,circle,LimeGreen,LightGray,10.588235294117647
+BRSK2,2,circle,LimeGreen,LightGray,10.117647058823529
+BTK,93,circle,LimeGreen,LightGray,20.823529411764703
+BUB1,4,circle,LimeGreen,LightGray,10.352941176470589
+BUBR1,1,circle,LimeGreen,LightGray,10.0
+CASK,13,circle,LimeGreen,LightGray,11.411764705882353
+CCK4,1,circle,LimeGreen,LightGray,10.0
+CDC2,11,circle,LimeGreen,LightGray,11.176470588235293
+CDC7,7,circle,LimeGreen,LightGray,10.705882352941176
+CDK2,426,circle,LimeGreen,LightGray,60.0
+CDK4,13,circle,LimeGreen,LightGray,11.411764705882353
+CDK5,6,circle,LimeGreen,LightGray,10.588235294117647
+CDK6,18,circle,LimeGreen,LightGray,12.0
+CDK7,7,circle,LimeGreen,LightGray,10.705882352941176
+CDK8,31,circle,LimeGreen,LightGray,13.529411764705882
+CDK9,25,circle,LimeGreen,LightGray,12.823529411764707
+CDKL1,1,circle,LimeGreen,LightGray,10.0
+CDKL2,2,circle,LimeGreen,LightGray,10.117647058823529
+CDKL3,1,circle,LimeGreen,LightGray,10.0
+CDKL5,1,circle,LimeGreen,LightGray,10.0
+CHED,2,circle,LimeGreen,LightGray,10.117647058823529
+CHK1,148,circle,LimeGreen,LightGray,27.294117647058822
+CHK2,36,circle,LimeGreen,LightGray,14.117647058823529
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diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/images/T023_kinase_structure.png b/teachopencadd/talktorials/T023_what_is_a_kinase/images/T023_kinase_structure.png
new file mode 100644
index 0000000000000000000000000000000000000000..742998571ea571c469c5c8beffd5bef4425f71f7
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diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 6ff5bcd6..dd91bceb 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -44,11 +44,7 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "- Retrieve and preprocess data\n",
-    "- Show kinase coverage\n",
-    "- Compare kinases\n",
-    "- Visualize similarity as kinase matrix\n",
-    "- Visualize similarity as phylogenetic tree"
+    "- Define the kinases of interest"
    ]
   },
   {
@@ -57,8 +53,7 @@
    "source": [
     "### References\n",
     "\n",
-    "- Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "- Kinase similarity descriptor: XXX\n",
+    "- Kinases as drug targets: [<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)\n",
     "- Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
     "- KLIFS\n",
     "  - KLIFS URL: https://klifs.net/\n",
@@ -68,7 +63,8 @@
     "  - Karaman et al. dataset: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
     "  - Davis et al. dataset: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - KIBA dataset: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
-    "  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)"
+    "  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)\n",
+    "- Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) "
    ]
   },
   {
@@ -89,22 +85,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kinases are established drug targets to combat cancer and inflammatory diseases ([TODO ref](https://onlinelibrary.wiley.com/doi/book/10.1002/9783527633470))\n",
-    "* Activate proteins by phosphorylation\n",
-    "* Most frequently mutated proteins in tumors\n",
-    "* 5782 x-ray structures of human kinases (as of Sept. 2021, see [KLIFS](https://klifs.net/) database\n",
-    "* 67 FDA approved small molecule protein kinase inhibitors on the market (as of Sept. 2021, see [link](http://www.brimr.org/PKI/PKIs.htm))\n",
-    "* Most of the approved drugs bind in the ATP-binding pocketand intermediate surroundings \n",
+    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating and thereby activating themselves or other proteins and are most frequently mutated proteins in tumors.\n",
     "\n",
-    "While there is so much research also going on in the field of kinases, there are still open challenges\n",
-    "* A large fraction of the kinome is un-/underexplored\n",
-    "* Many kinase inhibitors are promiscous binders -> off-target effects or polypharmacology\n",
-    "* Occurence of drug resistances due to mutations\n",
+    "As of Sep 2021, 5782 X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and 67 FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
     "\n",
-    "[TODO add a 3D figure of a kinase structure with ATP bound, and maybe DFG-in motif highlighted]\n",
+    "Despite of decades of kinase research, there are still many open challenges:\n",
     "\n",
-    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development. \n",
-    "[TODO cite Kooistra, Volkamer, ARMC V.50, Elsevier, 2017, 153-192]"
+    "- A large fraction of the kinome is un-/underexplored\n",
+    "- Many kinase inhibitors are promiscous binders causing off-target effects or enabling polypharmacology\n",
+    "- Occurence of drug resistances due to mutations"
    ]
   },
   {
@@ -113,37 +102,82 @@
    "source": [
     "#### The human kinome \n",
     "\n",
-    "* Human kinome consists of ~540 protein kinases\n",
-    "    * [TODO: maybe comment on numbers differ depedning on the source, ref to kinodata]\n",
-    "* Grouped by Manning [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups and one 'other' group\n",
-    "    * AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL, Other\n",
-    "    * Phylogenetic tree, clustered by overall sequence similarity\n",
+    "Human kinome consists of over 500 protein kinases (number varies depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
+    "\n",
+    "Manning et al. clustered the human protein kinases based on their sequence into [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as Manning kinome tree. The kinase resource KinMap enables mapping of kinase data onto that tree, e.g. the number of of X-ray structures per kinase as shown in Figure 1.\n",
+    "\n",
+    "\n",
+    " Phylogenetic tree, clustered by overall sequence similarity\n",
     "    \n",
     "[TODO add a kinome tree figure from kinhub]\n",
     "\n",
-    "[numbers taken from Molecules publication, maybe add some references]"
+    "<!---\n",
+    "\n",
+    "--->"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "#### Kinase structures and important motifs\n",
     "\n",
-    "Important regions\n",
-    "* hinge region: key hydrogen bonds \n",
-    "* DFG motif: Flip between phenylalanine (F) and aspartate (D) driver for active and inactive state\n",
-    "* αC-helix: αC-in conformation -> salt bridge\n",
-    "* glycine-rich (G-rich) loop: stabilizes ATP binding\n",
+    "Kinases sequences and structures are highly conserved. Important regions in the kinase pocket include:\n",
+    "\n",
+    "- Hinge region: Forms key hydrogen bonds to ligands \n",
+    "- DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state\n",
+    "- αC-helix: Forms in the αC-in conformation a salt bridge between highly conserved lysine and glutamine\n",
+    "- Glycine-rich (G-rich) loop: Stabilizes ATP binding\n",
     "\n",
-    "[Maybe a 3D figure, or include motifs already in the above mentioned figure]\n"
+    "![Kinase structure with key motifs](images/T023_kinase_structure.png)\n",
+    "\n",
+    "*Figure 1:* \n",
+    "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop (example: CDK2, PDB ID: 1FIN)\n",
+    "\n",
+    "<!---\n",
+    "from opencadd.structure.pocket import PocketKlifs, PocketViewer\n",
+    "pocket = PocketKlifs.from_structure_klifs_id(4367)\n",
+    "viewer = PocketViewer()\n",
+    "viewer.add_pocket(\n",
+    "    pocket,\n",
+    "    ligand_expo_id=\"ATP\",\n",
+    "    show_pocket_center=False\n",
+    ")\n",
+    "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
+    "-->"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase resources"
+    "### Kinase resources\n",
+    "\n",
+    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development. \n",
+    "[TODO cite Kooistra, Volkamer, ARMC V.50, Elsevier, 2017, 153-192]"
    ]
   },
   {
@@ -443,6 +477,59 @@
     "- **Talktorial T027**: Kinase similarity based on ligand promiscuity (ChEMBL bioactivity data)\n",
     "- **Talktorial T028**: Compare kinase similarity measures from **Talktorials T024-T027**"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate data for number of structures per kinase in the KinMap format to be mapped onto the kinome tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from opencadd.databases.klifs import setup_remote\n",
+    "klifs = setup_remote()\n",
+    "structures_df = klifs.structures.all_structures()\n",
+    "\n",
+    "min_scaled = 10\n",
+    "max_scaled = 50\n",
+    "\n",
+    "n_structures_per_kinase = structures_df.groupby(\n",
+    "    [\"structure.pdb_id\", \"kinase.klifs_name\"]\n",
+    ").first().reset_index().groupby(\"kinase.klifs_name\").size()\n",
+    "n_structures_per_kinase.index.name = \"xName\"\n",
+    "n_structures_per_kinase.name = \"n_structures\"\n",
+    "n_structures_per_kinase = n_structures_per_kinase.reset_index()\n",
+    "n_structures_per_kinase[\"shape\"] = \"circle\"\n",
+    "n_structures_per_kinase[\"fill\"] = \"LimeGreen\"\n",
+    "n_structures_per_kinase[\"stroke\"] = \"LightGray\"\n",
+    "min_ = n_structures_per_kinase[\"n_structures\"].min()\n",
+    "max_ = n_structures_per_kinase[\"n_structures\"].max()\n",
+    "n_structures_per_kinase[\"size\"] = n_structures_per_kinase[\"n_structures\"].apply(\n",
+    "    lambda x: ((x - min_)/(max_ - min_) * max_scaled) + min_scaled\n",
+    ")\n",
+    "\n",
+    "n_structures_per_kinase.to_csv(DATA / \"kinmap_n_structures_per_kinase.csv\", index=None)\n",
+    "# Some kinases will not be resolved in KinMap and will be simply dropped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 306fc6f57c176a0ca2ac81eead7e4c6fb30f222f Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 15:20:05 +0200
Subject: [PATCH 078/158] Add logo, address AV comments.

---
 ..._distance_substitution_matrix_sequence.csv |   20 +-
 ...imilarity_substitution_matrix_sequence.csv |   20 +-
 .../talktorial.ipynb                          | 1287 ++++++++++-------
 3 files changed, 766 insertions(+), 561 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
index 38662704..4205a1a5 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
-EGFR,0.0,0.05903728309403988,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369,0.2839529624274032,0.5729382823021627
-ErbB2,0.05903728309403988,0.0,0.34539978367739244,0.36969159991343414,0.31492453290530287,0.3020325638294997,0.3648271324857587,0.297882231158642,0.586362347301078
-BRAF,0.34497247960673305,0.34539978367739244,0.0,0.35324546063925766,0.3270674806464644,0.3618419047625556,0.36208805042926506,0.3287322624812222,0.563540908147633
-CDK2,0.3515532811454096,0.36969159991343414,0.35324546063925766,0.0,0.31874681698629526,0.34397506329411764,0.2769069645096425,0.3466212151836624,0.5760058634630552
-LCK,0.2886787123597768,0.31492453290530287,0.3270674806464644,0.31874681698629526,0.0,0.3091212110335312,0.33741879744441083,0.31235243702199045,0.5486635309945707
-MET,0.28874208716346716,0.3020325638294997,0.3618419047625556,0.34397506329411764,0.3091212110335312,0.0,0.3706450713064281,0.28619407705453626,0.6063011403494588
-p38a,0.3553555545508369,0.3648271324857587,0.36208805042926506,0.2769069645096425,0.33741879744441083,0.3706450713064281,0.0,0.34655582404619856,0.5688055661744779
-KDR,0.2839529624274032,0.297882231158642,0.3287322624812222,0.3466212151836624,0.31235243702199045,0.28619407705453626,0.34655582404619856,0.0,0.5772950670526018
-p110a,0.5729382823021627,0.586362347301078,0.563540908147633,0.5760058634630552,0.5486635309945707,0.6063011403494588,0.5688055661744779,0.5772950670526018,0.0
+kinase_klifs,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.0,0.05903728309403988,0.5729382823021627,0.2839529624274032,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369
+ErbB2,0.05903728309403988,0.0,0.586362347301078,0.297882231158642,0.34539978367739244,0.36969159991343414,0.31492453290530287,0.3020325638294997,0.3648271324857587
+p110a,0.5729382823021627,0.586362347301078,0.0,0.5772950670526018,0.563540908147633,0.5760058634630552,0.5486635309945707,0.6063011403494588,0.5688055661744779
+KDR,0.2839529624274032,0.297882231158642,0.5772950670526018,0.0,0.3287322624812222,0.3466212151836624,0.31235243702199045,0.28619407705453626,0.34655582404619856
+BRAF,0.34497247960673305,0.34539978367739244,0.563540908147633,0.3287322624812222,0.0,0.35324546063925766,0.3270674806464644,0.3618419047625556,0.36208805042926506
+CDK2,0.3515532811454096,0.36969159991343414,0.5760058634630552,0.3466212151836624,0.35324546063925766,0.0,0.31874681698629526,0.34397506329411764,0.2769069645096425
+LCK,0.2886787123597768,0.31492453290530287,0.5486635309945707,0.31235243702199045,0.3270674806464644,0.31874681698629526,0.0,0.3091212110335312,0.33741879744441083
+MET,0.28874208716346716,0.3020325638294997,0.6063011403494588,0.28619407705453626,0.3618419047625556,0.34397506329411764,0.3091212110335312,0.0,0.3706450713064281
+p38a,0.3553555545508369,0.3648271324857587,0.5688055661744779,0.34655582404619856,0.36208805042926506,0.2769069645096425,0.33741879744441083,0.3706450713064281,0.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
index f4d0f086..c197d5c2 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
-EGFR,1.0,0.9409627169059601,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631,0.7160470375725968,0.42706171769783724
-ErbB2,0.9409627169059601,1.0,0.6546002163226076,0.6303084000865659,0.6850754670946971,0.6979674361705003,0.6351728675142413,0.702117768841358,0.413637652698922
-BRAF,0.655027520393267,0.6546002163226076,1.0,0.6467545393607423,0.6729325193535356,0.6381580952374444,0.6379119495707349,0.6712677375187778,0.43645909185236703
-CDK2,0.6484467188545904,0.6303084000865659,0.6467545393607423,1.0,0.6812531830137047,0.6560249367058824,0.7230930354903575,0.6533787848163376,0.4239941365369449
-LCK,0.7113212876402232,0.6850754670946971,0.6729325193535356,0.6812531830137047,1.0,0.6908787889664688,0.6625812025555892,0.6876475629780096,0.4513364690054293
-MET,0.7112579128365328,0.6979674361705003,0.6381580952374444,0.6560249367058824,0.6908787889664688,1.0,0.6293549286935719,0.7138059229454637,0.3936988596505412
-p38a,0.6446444454491631,0.6351728675142413,0.6379119495707349,0.7230930354903575,0.6625812025555892,0.6293549286935719,1.0,0.6534441759538014,0.43119443382552214
-KDR,0.7160470375725968,0.702117768841358,0.6712677375187778,0.6533787848163376,0.6876475629780096,0.7138059229454637,0.6534441759538014,1.0,0.4227049329473982
-p110a,0.42706171769783724,0.413637652698922,0.43645909185236703,0.4239941365369449,0.4513364690054293,0.3936988596505412,0.43119443382552214,0.4227049329473982,1.0
+kinase_klifs,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,1.0,0.9409627169059601,0.42706171769783724,0.7160470375725968,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631
+ErbB2,0.9409627169059601,1.0,0.413637652698922,0.702117768841358,0.6546002163226076,0.6303084000865659,0.6850754670946971,0.6979674361705003,0.6351728675142413
+p110a,0.42706171769783724,0.413637652698922,1.0,0.4227049329473982,0.43645909185236703,0.4239941365369449,0.4513364690054293,0.3936988596505412,0.43119443382552214
+KDR,0.7160470375725968,0.702117768841358,0.4227049329473982,1.0,0.6712677375187778,0.6533787848163376,0.6876475629780096,0.7138059229454637,0.6534441759538014
+BRAF,0.655027520393267,0.6546002163226076,0.43645909185236703,0.6712677375187778,1.0,0.6467545393607423,0.6729325193535356,0.6381580952374444,0.6379119495707349
+CDK2,0.6484467188545904,0.6303084000865659,0.4239941365369449,0.6533787848163376,0.6467545393607423,1.0,0.6812531830137047,0.6560249367058824,0.7230930354903575
+LCK,0.7113212876402232,0.6850754670946971,0.4513364690054293,0.6876475629780096,0.6729325193535356,0.6812531830137047,1.0,0.6908787889664688,0.6625812025555892
+MET,0.7112579128365328,0.6979674361705003,0.3936988596505412,0.7138059229454637,0.6381580952374444,0.6560249367058824,0.6908787889664688,1.0,0.6293549286935719
+p38a,0.6446444454491631,0.6351728675142413,0.43119443382552214,0.6534441759538014,0.6379119495707349,0.7230930354903575,0.6625812025555892,0.6293549286935719,1.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 38e7a505..41c4e279 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -22,7 +22,7 @@
     "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the 85 residues pocket sequence for each kinase. \n",
     "\n",
     "Two similarity measures are implemented:\n",
-    "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy\n",
+    "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.\n",
     "   2. Sequence similarity, i.e., the similarity which is based on a substitution matrix, thus, reflecting similarities between amino acids."
    ]
   },
@@ -65,7 +65,12 @@
     "* KLIFS\n",
     "  * KLIFS URL: https://klifs.net/\n",
     "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
-    "* Substitution matrix: [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915)"
+    "* Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "* Substitution matrix: [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915)\n",
+    "* Biotite\n",
+    "    * documentation: https://www.biotite-python.org/index.html\n",
+    "    * [Blosum matrix](https://www.biotite-python.org/examples/gallery/sequence/blosum_dendrogram.html?highlight=blosum)\n",
+    "    "
    ]
   },
   {
@@ -95,37 +100,44 @@
    "source": [
     "### Kinase similarity descriptor: sequence\n",
     "\n",
-    "As mentioned in the previous talktorial, sequence is often used in kinase similarity."
+    "As mentioned in the previous talktorial, sequence is often used in kinase similarity (see the phylogenetic tree developped by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**TODO**\n",
-    "Add figure here"
+    "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
+    "\n",
+    "1. The sequence is of fixed length (it contains 85 residues), which makes computation for pairwise similarity between two sequences easy.\n",
+    "\n",
+    "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an 85 residues sequence contains most relevant information?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "EGFR fasta:\n",
-    "- https://www.uniprot.org/uniprot/P00533.fasta\n",
+    "Missing residues in KLIFS are noted using the symbol \"-\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![sequence_logo](images/sequence_logo.png)\n",
     "\n",
+    "_Figure 1:_\n",
+    "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion.\n",
     "\n",
-    "CDK2 fasta:\n",
-    "- https://www.uniprot.org/uniprot/P24941.fasta"
+    "**Note for reproducibility:**\n",
+    "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to 85 and _Page size_ to 1200 x 480."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
-    "1. The sequence is of fixed length (it contains 85 residues), which makes computation for pairwise similarity between two sequences easy.\n",
-    "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an 85 residues sequence contains most relevant information?\n",
-    "\n",
     "We now describe two ways to compare pocket sequences."
    ]
   },
@@ -268,6 +280,16 @@
     "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. A similarity matrix is not, by definition, a distance matrix. For example, the diagonal elements are not zero. However, if all entries of the are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext pycodestyle_magic\n",
+    "%pycodestyle_on"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -277,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,24 +334,152 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We start by listing the kinases of interest."
+    "Let's load the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "query_kinases = ['EGFR',\n",
-    "                 'ErbB2',\n",
-    "                 'BRAF',\n",
-    "                 'CDK2',\n",
-    "                 'LCK',\n",
-    "                 'MET',\n",
-    "                 'p38a',\n",
-    "                 'KDR',\n",
-    "                 'p110a']"
+    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/\\\n",
+    "data/kinase_selection.csv\")\n",
+    "kinase_selection_df"
    ]
   },
   {
@@ -348,18 +498,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def klifs_pocket_sequence(kinase_name):\n",
+    "def klifs_pocket_sequence(klifs_kinase_name):\n",
     "    \"\"\"\n",
     "    Retrieves the pocket sequence from KLIFS using the API.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    kinase_name : str\n",
-    "        The name of the kinase of interest.\n",
+    "    klifs_kinase_name : str\n",
+    "        The KLIFS' name of the kinase of interest.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -368,13 +518,13 @@
     "        if the kinase name is valid, None otherwise.\n",
     "    \"\"\"\n",
     "    response = requests.get(f\"https://klifs.net/api/\"\n",
-    "                            f\"kinase_ID?kinase_name={kinase_name}\"\n",
+    "                            f\"kinase_ID?kinase_name={klifs_kinase_name}\"\n",
     "                            f\"&species=HUMAN\")\n",
     "\n",
     "    if response.status_code == 200:\n",
     "        return response.json()[0]['pocket']\n",
     "    else:\n",
-    "        print(f'KLIFS failed for kinase {kinase_name}')\n",
+    "        print(f'KLIFS failed for kinase {klifs_kinase_name}')\n",
     "        return None"
    ]
   },
@@ -387,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -395,27 +545,34 @@
       "text/plain": [
        "{'EGFR': 'KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA',\n",
        " 'ErbB2': 'KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA',\n",
+       " 'p110a': 'CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF',\n",
+       " 'KDR': 'KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA',\n",
        " 'BRAF': 'QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA',\n",
        " 'CDK2': 'EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA',\n",
        " 'LCK': 'ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA',\n",
        " 'MET': 'EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA',\n",
-       " 'p38a': 'SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA',\n",
-       " 'KDR': 'KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA',\n",
-       " 'p110a': 'CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF'}"
+       " 'p38a': 'SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA'}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "kinase_sequences = {}\n",
-    "for kinase in query_kinases:\n",
+    "for kinase in kinase_selection_df.kinase_klifs:\n",
     "    kinase_sequences[kinase] = klifs_pocket_sequence(kinase)\n",
     "kinase_sequences"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As shown in the cell above, some sequences have missing residues, denoted by \"-\"."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -440,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -486,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +701,7 @@
        "True"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -555,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -564,7 +721,7 @@
     "                       substitution_matrix=align.\n",
     "                       SubstitutionMatrix.std_protein_matrix()):\n",
     "    \"\"\"\n",
-    "    Retrieve the match score given the substitution matrix\n",
+    "    Retrieve the match score given the substitution matrix.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -585,7 +742,7 @@
     "    \"\"\"\n",
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
-    "    \n",
+    "\n",
     "    # Translation of substitution matrix\n",
     "    translated_matrix = score_matrix-np.min(score_matrix)\n",
     "\n",
@@ -593,8 +750,9 @@
     "    normalized_score_matrix = np.zeros(score_matrix.shape)\n",
     "    for i in range(score_matrix.shape[0]):\n",
     "        for j in range(score_matrix.shape[0]):\n",
-    "            normalized_score_matrix[i, j] = (translated_matrix[i, j]/\n",
-    "                                            np.sqrt(translated_matrix[i,i]*translated_matrix[j,j]))\n",
+    "            normalized_score_matrix[i, j] = (translated_matrix[i, j] /\n",
+    "                                             np.sqrt(translated_matrix[i, i] *\n",
+    "                                                     translated_matrix[j, j]))\n",
     "\n",
     "    # Retrieve the letter (amino acid)\n",
     "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
@@ -604,13 +762,13 @@
     "    for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
     "        dict_letters[letter] = i\n",
     "\n",
-    "    match_score  = np.zeros(len(sequence1))\n",
+    "    match_score_array = np.zeros(len(sequence1))\n",
     "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1,\n",
     "                                                             sequence2)):\n",
     "        ind1 = dict_letters[character_seq1]\n",
     "        ind2 = dict_letters[character_seq2]\n",
-    "        match_score[i] = normalized_score_matrix[ind1, ind2]\n",
-    "    return match_score"
+    "        match_score_array[i] = normalized_score_matrix[ind1, ind2]\n",
+    "    return match_score_array"
    ]
   },
   {
@@ -629,16 +787,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sequence_similarity(kinase_name1, kinase_name2, type_=\"identity\"):\n",
+    "def sequence_similarity(klifs_kinase_name1,\n",
+    "                        klifs_kinase_name2,\n",
+    "                        type_=\"identity\"):\n",
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
-    "    kinase_name1, kinase_name2 : str\n",
-    "        The two names of the kinases for comparison.\n",
+    "    klifs_kinase_name1, klifs_kinase_name2 : str\n",
+    "        The two KLIFS names of the kinases for comparison.\n",
     "\n",
     "    type_ : str\n",
     "        The type of metric to compute the similarity.\n",
@@ -649,8 +809,8 @@
     "    float :\n",
     "        The similarity between the pocket sequences of the two kinases.\n",
     "    \"\"\"\n",
-    "    sequence_1 = klifs_pocket_sequence(kinase_name1)\n",
-    "    sequence_2 = klifs_pocket_sequence(kinase_name2)\n",
+    "    sequence_1 = klifs_pocket_sequence(klifs_kinase_name1)\n",
+    "    sequence_2 = klifs_pocket_sequence(klifs_kinase_name2)\n",
     "\n",
     "    # Replace possible unavailable residue\n",
     "    # noted in KLIFS with \"-\"\n",
@@ -666,11 +826,11 @@
     "        seq_array2 = np.array(list(sequence_2))\n",
     "\n",
     "        if type_ == \"identity\":\n",
-    "            is_match = identity_score(seq_array1, seq_array2)\n",
-    "            similarity_normed = sum(is_match)/len(sequence_1)\n",
+    "            is_match_array = identity_score(seq_array1, seq_array2)\n",
+    "            similarity_normed = sum(is_match_array)/len(sequence_1)\n",
     "        elif type_ == \"substitution\":\n",
-    "            match_score = substitution_score(seq_array1, seq_array2)\n",
-    "            similarity_normed = sum(match_score)/len(sequence_1)\n",
+    "            match_score_array = substitution_score(seq_array1, seq_array2)\n",
+    "            similarity_normed = sum(match_score_array)/len(sequence_1)\n",
     "        else:\n",
     "            print(f\"Type {type_} not defined.\")\n",
     "            return None\n",
@@ -678,27 +838,6 @@
     "        return similarity_normed"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pocket sequence similarity between EGFR itself: 1.00 using substitution.\n"
-     ]
-    }
-   ],
-   "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
-    "                                          \"EGFR\", \n",
-    "                                          type_=\"substitution\")\n",
-    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -708,26 +847,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The sequences are:\n",
+      "\n",
       "EGFR : KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
       "MET  : EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n"
      ]
     }
    ],
    "source": [
-    "for key in ('EGFR','MET'):\n",
+    "print(\"The sequences are:\\n\")\n",
+    "for key in ('EGFR', 'MET'):\n",
     "    print(f\"{key:5s}: {kinase_sequences[key]}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -749,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -777,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -789,7 +931,7 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                          \"EGFR\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using identity.\")"
@@ -797,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -809,8 +951,8 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\", \n",
-    "                                          \"EGFR\", \n",
+    "EGFR_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "                                          \"EGFR\",\n",
     "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
@@ -839,30 +981,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "similarity_measure=\"substitution\" "
+    "similarity_measure = \"substitution\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Note_: The cell below takes a few seconds to execute."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "7:80: E501 line too long (80 > 79 characters)\n"
+     ]
+    }
+   ],
    "source": [
-    "kinase_similarity_matrix = np.zeros((len(query_kinases), len(query_kinases)))\n",
-    "for i, kinase_name1 in enumerate(query_kinases):\n",
-    "    for j, kinase_name2 in enumerate(query_kinases):\n",
+    "kinase_similarity_matrix = np.zeros((len(kinase_selection_df.kinase_klifs),\n",
+    "                                     len(kinase_selection_df.kinase_klifs)))\n",
+    "for i, kinase_name1 in enumerate(kinase_selection_df.kinase_klifs):\n",
+    "    for j, kinase_name2 in enumerate(kinase_selection_df.kinase_klifs):\n",
     "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
     "                                                             kinase_name2,\n",
-    "                                                             type_=similarity_measure)"
+    "                                                             similarity_measure)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -885,16 +1043,28 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
+       "      <th>kinase_klifs</th>\n",
        "      <th>EGFR</th>\n",
        "      <th>ErbB2</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>KDR</th>\n",
        "      <th>BRAF</th>\n",
        "      <th>CDK2</th>\n",
        "      <th>LCK</th>\n",
        "      <th>MET</th>\n",
        "      <th>p38a</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>p110a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -902,147 +1072,157 @@
        "      <th>EGFR</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.940963</td>\n",
+       "      <td>0.427062</td>\n",
+       "      <td>0.716047</td>\n",
        "      <td>0.655028</td>\n",
        "      <td>0.648447</td>\n",
        "      <td>0.711321</td>\n",
        "      <td>0.711258</td>\n",
        "      <td>0.644644</td>\n",
-       "      <td>0.716047</td>\n",
-       "      <td>0.427062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ErbB2</th>\n",
        "      <td>0.940963</td>\n",
        "      <td>1.000000</td>\n",
+       "      <td>0.413638</td>\n",
+       "      <td>0.702118</td>\n",
        "      <td>0.654600</td>\n",
        "      <td>0.630308</td>\n",
        "      <td>0.685075</td>\n",
        "      <td>0.697967</td>\n",
        "      <td>0.635173</td>\n",
-       "      <td>0.702118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.427062</td>\n",
        "      <td>0.413638</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.422705</td>\n",
+       "      <td>0.436459</td>\n",
+       "      <td>0.423994</td>\n",
+       "      <td>0.451336</td>\n",
+       "      <td>0.393699</td>\n",
+       "      <td>0.431194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.716047</td>\n",
+       "      <td>0.702118</td>\n",
+       "      <td>0.422705</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.671268</td>\n",
+       "      <td>0.653379</td>\n",
+       "      <td>0.687648</td>\n",
+       "      <td>0.713806</td>\n",
+       "      <td>0.653444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>BRAF</th>\n",
        "      <td>0.655028</td>\n",
        "      <td>0.654600</td>\n",
+       "      <td>0.436459</td>\n",
+       "      <td>0.671268</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.646755</td>\n",
        "      <td>0.672933</td>\n",
        "      <td>0.638158</td>\n",
        "      <td>0.637912</td>\n",
-       "      <td>0.671268</td>\n",
-       "      <td>0.436459</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDK2</th>\n",
        "      <td>0.648447</td>\n",
        "      <td>0.630308</td>\n",
+       "      <td>0.423994</td>\n",
+       "      <td>0.653379</td>\n",
        "      <td>0.646755</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.681253</td>\n",
        "      <td>0.656025</td>\n",
        "      <td>0.723093</td>\n",
-       "      <td>0.653379</td>\n",
-       "      <td>0.423994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LCK</th>\n",
        "      <td>0.711321</td>\n",
        "      <td>0.685075</td>\n",
+       "      <td>0.451336</td>\n",
+       "      <td>0.687648</td>\n",
        "      <td>0.672933</td>\n",
        "      <td>0.681253</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.690879</td>\n",
        "      <td>0.662581</td>\n",
-       "      <td>0.687648</td>\n",
-       "      <td>0.451336</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MET</th>\n",
        "      <td>0.711258</td>\n",
        "      <td>0.697967</td>\n",
+       "      <td>0.393699</td>\n",
+       "      <td>0.713806</td>\n",
        "      <td>0.638158</td>\n",
        "      <td>0.656025</td>\n",
        "      <td>0.690879</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.629355</td>\n",
-       "      <td>0.713806</td>\n",
-       "      <td>0.393699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>p38a</th>\n",
        "      <td>0.644644</td>\n",
        "      <td>0.635173</td>\n",
+       "      <td>0.431194</td>\n",
+       "      <td>0.653444</td>\n",
        "      <td>0.637912</td>\n",
        "      <td>0.723093</td>\n",
        "      <td>0.662581</td>\n",
        "      <td>0.629355</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>0.653444</td>\n",
-       "      <td>0.431194</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>0.716047</td>\n",
-       "      <td>0.702118</td>\n",
-       "      <td>0.671268</td>\n",
-       "      <td>0.653379</td>\n",
-       "      <td>0.687648</td>\n",
-       "      <td>0.713806</td>\n",
-       "      <td>0.653444</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.422705</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>0.427062</td>\n",
-       "      <td>0.413638</td>\n",
-       "      <td>0.436459</td>\n",
-       "      <td>0.423994</td>\n",
-       "      <td>0.451336</td>\n",
-       "      <td>0.393699</td>\n",
-       "      <td>0.431194</td>\n",
-       "      <td>0.422705</td>\n",
-       "      <td>1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "           EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       "EGFR   1.000000  0.940963  0.655028  0.648447  0.711321  0.711258  0.644644   \n",
-       "ErbB2  0.940963  1.000000  0.654600  0.630308  0.685075  0.697967  0.635173   \n",
-       "BRAF   0.655028  0.654600  1.000000  0.646755  0.672933  0.638158  0.637912   \n",
-       "CDK2   0.648447  0.630308  0.646755  1.000000  0.681253  0.656025  0.723093   \n",
-       "LCK    0.711321  0.685075  0.672933  0.681253  1.000000  0.690879  0.662581   \n",
-       "MET    0.711258  0.697967  0.638158  0.656025  0.690879  1.000000  0.629355   \n",
-       "p38a   0.644644  0.635173  0.637912  0.723093  0.662581  0.629355  1.000000   \n",
-       "KDR    0.716047  0.702118  0.671268  0.653379  0.687648  0.713806  0.653444   \n",
-       "p110a  0.427062  0.413638  0.436459  0.423994  0.451336  0.393699  0.431194   \n",
+       "kinase_klifs      EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
+       "kinase_klifs                                                               \n",
+       "EGFR          1.000000  0.940963  0.427062  0.716047  0.655028  0.648447   \n",
+       "ErbB2         0.940963  1.000000  0.413638  0.702118  0.654600  0.630308   \n",
+       "p110a         0.427062  0.413638  1.000000  0.422705  0.436459  0.423994   \n",
+       "KDR           0.716047  0.702118  0.422705  1.000000  0.671268  0.653379   \n",
+       "BRAF          0.655028  0.654600  0.436459  0.671268  1.000000  0.646755   \n",
+       "CDK2          0.648447  0.630308  0.423994  0.653379  0.646755  1.000000   \n",
+       "LCK           0.711321  0.685075  0.451336  0.687648  0.672933  0.681253   \n",
+       "MET           0.711258  0.697967  0.393699  0.713806  0.638158  0.656025   \n",
+       "p38a          0.644644  0.635173  0.431194  0.653444  0.637912  0.723093   \n",
        "\n",
-       "            KDR     p110a  \n",
-       "EGFR   0.716047  0.427062  \n",
-       "ErbB2  0.702118  0.413638  \n",
-       "BRAF   0.671268  0.436459  \n",
-       "CDK2   0.653379  0.423994  \n",
-       "LCK    0.687648  0.451336  \n",
-       "MET    0.713806  0.393699  \n",
-       "p38a   0.653444  0.431194  \n",
-       "KDR    1.000000  0.422705  \n",
-       "p110a  0.422705  1.000000  "
+       "kinase_klifs       LCK       MET      p38a  \n",
+       "kinase_klifs                                \n",
+       "EGFR          0.711321  0.711258  0.644644  \n",
+       "ErbB2         0.685075  0.697967  0.635173  \n",
+       "p110a         0.451336  0.393699  0.431194  \n",
+       "KDR           0.687648  0.713806  0.653444  \n",
+       "BRAF          0.672933  0.638158  0.637912  \n",
+       "CDK2          0.681253  0.656025  0.723093  \n",
+       "LCK           1.000000  0.690879  0.662581  \n",
+       "MET           0.690879  1.000000  0.629355  \n",
+       "p38a          0.662581  0.629355  1.000000  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2:80: E501 line too long (82 > 79 characters)\n",
+      "3:80: E501 line too long (84 > 79 characters)\n"
+     ]
     }
    ],
    "source": [
     "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                        index=query_kinases,\n",
-    "                                        columns=query_kinases)\n",
+    "                                           index=kinase_selection_df.kinase_klifs,\n",
+    "                                           columns=kinase_selection_df.kinase_klifs)\n",
     "kinase_similarity_matrix_df"
    ]
   },
@@ -1055,7 +1235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1064,337 +1244,350 @@
        "True"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(kinase_similarity_matrix_df.values == kinase_similarity_matrix_df.values.transpose()).all()"
+    "(kinase_similarity_matrix_df.values ==\n",
+    " kinase_similarity_matrix_df.values.transpose()).all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "#T_144d8_row1_col8 {\n",
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        "}\n",
-       "#T_144d8_row2_col0, #T_144d8_row7_col3 {\n",
+       "#T_d46bb_row3_col4 {\n",
+       "  background-color: #89c389;\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row2_col1 {\n",
-       "  background-color: #8ac48a;\n",
+       "#T_d46bb_row3_col6, #T_d46bb_row4_col3, #T_d46bb_row5_col7 {\n",
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-       "  background-color: #8cc58c;\n",
+       "#T_d46bb_row3_col7 {\n",
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-       "#T_144d8_row2_col6 {\n",
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        "}\n",
-       "#T_144d8_row2_col7, #T_144d8_row3_col5, #T_144d8_row7_col4 {\n",
-       "  background-color: #85c185;\n",
+       "#T_d46bb_row4_col6, #T_d46bb_row4_col7, #T_d46bb_row7_col5 {\n",
+       "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row2_col8 {\n",
-       "  background-color: #daebda;\n",
+       "#T_d46bb_row4_col8 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row3_col0, #T_144d8_row6_col4 {\n",
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        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row3_col1 {\n",
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        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row3_col2 {\n",
+       "#T_d46bb_row5_col3, #T_d46bb_row8_col3 {\n",
+       "  background-color: #8dc58d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_d46bb_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row3_col4, #T_144d8_row4_col2 {\n",
+       "#T_d46bb_row5_col6, #T_d46bb_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row3_col6 {\n",
+       "#T_d46bb_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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-       "  background-color: #8dc58d;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_144d8_row3_col8, #T_144d8_row7_col8 {\n",
-       "  background-color: #e0eedf;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_144d8_row4_col0 {\n",
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        "  background-color: #76ba76;\n",
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        "}\n",
-       "#T_144d8_row4_col1 {\n",
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        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row4_col3 {\n",
-       "  background-color: #82c082;\n",
+       "#T_d46bb_row6_col2 {\n",
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-       "}\n",
-       "#T_144d8_row4_col6 {\n",
-       "  background-color: #8bc48b;\n",
+       "#T_d46bb_row6_col3 {\n",
+       "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row4_col7 {\n",
-       "  background-color: #7fbe7f;\n",
+       "#T_d46bb_row6_col5 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row4_col8 {\n",
-       "  background-color: #d4e8d4;\n",
+       "#T_d46bb_row6_col8 {\n",
+       "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row5_col0 {\n",
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        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_144d8_row5_col1 {\n",
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        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row5_col4 {\n",
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        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row5_col6 {\n",
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        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row5_col7, #T_144d8_row7_col0 {\n",
-       "  background-color: #74b974;\n",
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-       "  background-color: #ebf3eb;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_144d8_row6_col0 {\n",
+       "#T_d46bb_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row6_col3 {\n",
-       "  background-color: #71b771;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_144d8_row6_col8 {\n",
+       "#T_d46bb_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_144d8_row7_col2 {\n",
-       "  background-color: #89c389;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_144d8_row7_col5 {\n",
-       "  background-color: #6eb66e;\n",
+       "#T_d46bb_row8_col5 {\n",
+       "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_144d8_\">\n",
+       "<table id=\"T_d46bb_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_144d8_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_144d8_row0_col2\" class=\"data row0 col2\" >0.655</td>\n",
-       "      <td id=\"T_144d8_row0_col3\" class=\"data row0 col3\" >0.648</td>\n",
-       "      <td id=\"T_144d8_row0_col4\" class=\"data row0 col4\" >0.711</td>\n",
-       "      <td id=\"T_144d8_row0_col5\" class=\"data row0 col5\" >0.711</td>\n",
-       "      <td id=\"T_144d8_row0_col6\" class=\"data row0 col6\" >0.645</td>\n",
-       "      <td id=\"T_144d8_row0_col7\" class=\"data row0 col7\" >0.716</td>\n",
-       "      <td id=\"T_144d8_row0_col8\" class=\"data row0 col8\" >0.427</td>\n",
+       "      <th id=\"T_d46bb_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d46bb_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_d46bb_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_d46bb_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_d46bb_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_d46bb_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_d46bb_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_d46bb_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_d46bb_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_144d8_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_144d8_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row1_col2\" class=\"data row1 col2\" >0.655</td>\n",
-       "      <td id=\"T_144d8_row1_col3\" class=\"data row1 col3\" >0.630</td>\n",
-       "      <td id=\"T_144d8_row1_col4\" class=\"data row1 col4\" >0.685</td>\n",
-       "      <td id=\"T_144d8_row1_col5\" class=\"data row1 col5\" >0.698</td>\n",
-       "      <td id=\"T_144d8_row1_col6\" class=\"data row1 col6\" >0.635</td>\n",
-       "      <td id=\"T_144d8_row1_col7\" class=\"data row1 col7\" >0.702</td>\n",
-       "      <td id=\"T_144d8_row1_col8\" class=\"data row1 col8\" >0.414</td>\n",
+       "      <th id=\"T_d46bb_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d46bb_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_d46bb_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_d46bb_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_d46bb_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_d46bb_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_d46bb_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_d46bb_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_d46bb_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_144d8_row2_col0\" class=\"data row2 col0\" >0.655</td>\n",
-       "      <td id=\"T_144d8_row2_col1\" class=\"data row2 col1\" >0.655</td>\n",
-       "      <td id=\"T_144d8_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row2_col3\" class=\"data row2 col3\" >0.647</td>\n",
-       "      <td id=\"T_144d8_row2_col4\" class=\"data row2 col4\" >0.673</td>\n",
-       "      <td id=\"T_144d8_row2_col5\" class=\"data row2 col5\" >0.638</td>\n",
-       "      <td id=\"T_144d8_row2_col6\" class=\"data row2 col6\" >0.638</td>\n",
-       "      <td id=\"T_144d8_row2_col7\" class=\"data row2 col7\" >0.671</td>\n",
-       "      <td id=\"T_144d8_row2_col8\" class=\"data row2 col8\" >0.436</td>\n",
+       "      <th id=\"T_d46bb_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_d46bb_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_d46bb_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_d46bb_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_d46bb_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_d46bb_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_d46bb_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_d46bb_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_d46bb_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_144d8_row3_col0\" class=\"data row3 col0\" >0.648</td>\n",
-       "      <td id=\"T_144d8_row3_col1\" class=\"data row3 col1\" >0.630</td>\n",
-       "      <td id=\"T_144d8_row3_col2\" class=\"data row3 col2\" >0.647</td>\n",
-       "      <td id=\"T_144d8_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row3_col4\" class=\"data row3 col4\" >0.681</td>\n",
-       "      <td id=\"T_144d8_row3_col5\" class=\"data row3 col5\" >0.656</td>\n",
-       "      <td id=\"T_144d8_row3_col6\" class=\"data row3 col6\" >0.723</td>\n",
-       "      <td id=\"T_144d8_row3_col7\" class=\"data row3 col7\" >0.653</td>\n",
-       "      <td id=\"T_144d8_row3_col8\" class=\"data row3 col8\" >0.424</td>\n",
+       "      <th id=\"T_d46bb_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_d46bb_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_d46bb_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_d46bb_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_d46bb_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_d46bb_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_d46bb_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_d46bb_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_d46bb_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_144d8_row4_col0\" class=\"data row4 col0\" >0.711</td>\n",
-       "      <td id=\"T_144d8_row4_col1\" class=\"data row4 col1\" >0.685</td>\n",
-       "      <td id=\"T_144d8_row4_col2\" class=\"data row4 col2\" >0.673</td>\n",
-       "      <td id=\"T_144d8_row4_col3\" class=\"data row4 col3\" >0.681</td>\n",
-       "      <td id=\"T_144d8_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row4_col5\" class=\"data row4 col5\" >0.691</td>\n",
-       "      <td id=\"T_144d8_row4_col6\" class=\"data row4 col6\" >0.663</td>\n",
-       "      <td id=\"T_144d8_row4_col7\" class=\"data row4 col7\" >0.688</td>\n",
-       "      <td id=\"T_144d8_row4_col8\" class=\"data row4 col8\" >0.451</td>\n",
+       "      <th id=\"T_d46bb_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_d46bb_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_d46bb_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_d46bb_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_d46bb_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_d46bb_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_d46bb_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_d46bb_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_d46bb_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_144d8_row5_col0\" class=\"data row5 col0\" >0.711</td>\n",
-       "      <td id=\"T_144d8_row5_col1\" class=\"data row5 col1\" >0.698</td>\n",
-       "      <td id=\"T_144d8_row5_col2\" class=\"data row5 col2\" >0.638</td>\n",
-       "      <td id=\"T_144d8_row5_col3\" class=\"data row5 col3\" >0.656</td>\n",
-       "      <td id=\"T_144d8_row5_col4\" class=\"data row5 col4\" >0.691</td>\n",
-       "      <td id=\"T_144d8_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row5_col6\" class=\"data row5 col6\" >0.629</td>\n",
-       "      <td id=\"T_144d8_row5_col7\" class=\"data row5 col7\" >0.714</td>\n",
-       "      <td id=\"T_144d8_row5_col8\" class=\"data row5 col8\" >0.394</td>\n",
+       "      <th id=\"T_d46bb_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_d46bb_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_d46bb_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_d46bb_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_d46bb_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_d46bb_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_d46bb_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_d46bb_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_d46bb_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_144d8_row6_col0\" class=\"data row6 col0\" >0.645</td>\n",
-       "      <td id=\"T_144d8_row6_col1\" class=\"data row6 col1\" >0.635</td>\n",
-       "      <td id=\"T_144d8_row6_col2\" class=\"data row6 col2\" >0.638</td>\n",
-       "      <td id=\"T_144d8_row6_col3\" class=\"data row6 col3\" >0.723</td>\n",
-       "      <td id=\"T_144d8_row6_col4\" class=\"data row6 col4\" >0.663</td>\n",
-       "      <td id=\"T_144d8_row6_col5\" class=\"data row6 col5\" >0.629</td>\n",
-       "      <td id=\"T_144d8_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row6_col7\" class=\"data row6 col7\" >0.653</td>\n",
-       "      <td id=\"T_144d8_row6_col8\" class=\"data row6 col8\" >0.431</td>\n",
+       "      <th id=\"T_d46bb_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_d46bb_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_d46bb_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_d46bb_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_d46bb_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_d46bb_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_d46bb_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_d46bb_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_d46bb_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_144d8_row7_col0\" class=\"data row7 col0\" >0.716</td>\n",
-       "      <td id=\"T_144d8_row7_col1\" class=\"data row7 col1\" >0.702</td>\n",
-       "      <td id=\"T_144d8_row7_col2\" class=\"data row7 col2\" >0.671</td>\n",
-       "      <td id=\"T_144d8_row7_col3\" class=\"data row7 col3\" >0.653</td>\n",
-       "      <td id=\"T_144d8_row7_col4\" class=\"data row7 col4\" >0.688</td>\n",
-       "      <td id=\"T_144d8_row7_col5\" class=\"data row7 col5\" >0.714</td>\n",
-       "      <td id=\"T_144d8_row7_col6\" class=\"data row7 col6\" >0.653</td>\n",
-       "      <td id=\"T_144d8_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_144d8_row7_col8\" class=\"data row7 col8\" >0.423</td>\n",
+       "      <th id=\"T_d46bb_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_d46bb_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_d46bb_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_d46bb_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_d46bb_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_d46bb_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_d46bb_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_d46bb_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_d46bb_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_d46bb_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_144d8_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_144d8_row8_col0\" class=\"data row8 col0\" >0.427</td>\n",
-       "      <td id=\"T_144d8_row8_col1\" class=\"data row8 col1\" >0.414</td>\n",
-       "      <td id=\"T_144d8_row8_col2\" class=\"data row8 col2\" >0.436</td>\n",
-       "      <td id=\"T_144d8_row8_col3\" class=\"data row8 col3\" >0.424</td>\n",
-       "      <td id=\"T_144d8_row8_col4\" class=\"data row8 col4\" >0.451</td>\n",
-       "      <td id=\"T_144d8_row8_col5\" class=\"data row8 col5\" >0.394</td>\n",
-       "      <td id=\"T_144d8_row8_col6\" class=\"data row8 col6\" >0.431</td>\n",
-       "      <td id=\"T_144d8_row8_col7\" class=\"data row8 col7\" >0.423</td>\n",
-       "      <td id=\"T_144d8_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_d46bb_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d46bb_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_d46bb_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_d46bb_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_d46bb_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_d46bb_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_d46bb_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_d46bb_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_d46bb_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_d46bb_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa2f304d970>"
+       "<pandas.io.formats.style.Styler at 0x7fd3c816ed30>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1416,7 +1609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1440,7 +1633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1459,7 +1652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1468,326 +1661,338 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_d4786_row0_col0, #T_d4786_row1_col1, #T_d4786_row2_col2, #T_d4786_row3_col3, #T_d4786_row4_col4, #T_d4786_row5_col5, #T_d4786_row6_col6, #T_d4786_row7_col7, #T_d4786_row8_col8 {\n",
+       "#T_9f36b_row0_col0, #T_9f36b_row1_col1, #T_9f36b_row2_col2, #T_9f36b_row3_col3, #T_9f36b_row4_col4, #T_9f36b_row5_col5, #T_9f36b_row6_col6, #T_9f36b_row7_col7, #T_9f36b_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row0_col1 {\n",
+       "#T_9f36b_row0_col1 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row0_col2, #T_d4786_row0_col3, #T_d4786_row1_col2, #T_d4786_row2_col3, #T_d4786_row6_col5 {\n",
+       "#T_9f36b_row0_col2 {\n",
+       "  background-color: #0d860d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row0_col3 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row0_col4, #T_9f36b_row0_col5, #T_9f36b_row1_col4, #T_9f36b_row4_col5, #T_9f36b_row8_col7 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row0_col4 {\n",
+       "#T_9f36b_row0_col6 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row0_col5 {\n",
+       "#T_9f36b_row0_col7 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row0_col6, #T_d4786_row6_col1 {\n",
+       "#T_9f36b_row0_col8, #T_9f36b_row8_col1 {\n",
        "  background-color: #58ab58;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row0_col7 {\n",
-       "  background-color: #78bb78;\n",
-       "  color: #f1f1f1;\n",
+       "#T_9f36b_row1_col0 {\n",
+       "  background-color: #d3e7d3;\n",
+       "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row0_col8 {\n",
-       "  background-color: #0d860d;\n",
+       "#T_9f36b_row1_col2 {\n",
+       "  background-color: #078407;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col0 {\n",
-       "  background-color: #d3e7d3;\n",
-       "  color: #000000;\n",
+       "#T_9f36b_row1_col3 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col3, #T_d4786_row1_col6, #T_d4786_row5_col2, #T_d4786_row6_col2 {\n",
+       "#T_9f36b_row1_col5, #T_9f36b_row1_col8, #T_9f36b_row7_col4, #T_9f36b_row8_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col4 {\n",
+       "#T_9f36b_row1_col6 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col5 {\n",
+       "#T_9f36b_row1_col7 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col7 {\n",
-       "  background-color: #71b771;\n",
+       "#T_9f36b_row2_col0, #T_9f36b_row2_col1, #T_9f36b_row2_col3, #T_9f36b_row2_col4, #T_9f36b_row2_col5, #T_9f36b_row2_col6, #T_9f36b_row2_col7, #T_9f36b_row2_col8, #T_9f36b_row7_col2 {\n",
+       "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row1_col8 {\n",
-       "  background-color: #078407;\n",
+       "#T_9f36b_row3_col0, #T_9f36b_row7_col3 {\n",
+       "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col0, #T_d4786_row7_col3 {\n",
-       "  background-color: #5dae5d;\n",
+       "#T_9f36b_row3_col1, #T_9f36b_row6_col7 {\n",
+       "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col1 {\n",
-       "  background-color: #61af61;\n",
+       "#T_9f36b_row3_col2, #T_9f36b_row5_col2 {\n",
+       "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col4, #T_d4786_row2_col5, #T_d4786_row5_col3 {\n",
-       "  background-color: #5fae5f;\n",
+       "#T_9f36b_row3_col4 {\n",
+       "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col6, #T_d4786_row3_col1 {\n",
-       "  background-color: #56aa56;\n",
+       "#T_9f36b_row3_col5, #T_9f36b_row4_col0 {\n",
+       "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col7, #T_d4786_row3_col5, #T_d4786_row7_col4 {\n",
+       "#T_9f36b_row3_col6, #T_9f36b_row4_col3, #T_9f36b_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row2_col8 {\n",
+       "#T_9f36b_row3_col7 {\n",
+       "  background-color: #7cbd7c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_9f36b_row3_col8 {\n",
+       "  background-color: #5cad5c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row4_col1 {\n",
+       "  background-color: #61af61;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row3_col0, #T_d4786_row6_col4 {\n",
+       "#T_9f36b_row4_col6, #T_9f36b_row4_col7, #T_9f36b_row7_col5 {\n",
+       "  background-color: #5fae5f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row4_col8, #T_9f36b_row5_col1 {\n",
+       "  background-color: #56aa56;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row5_col0, #T_9f36b_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row3_col2 {\n",
+       "#T_9f36b_row5_col3, #T_9f36b_row8_col3 {\n",
+       "  background-color: #5eae5e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_9f36b_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row3_col4, #T_d4786_row4_col2 {\n",
+       "#T_9f36b_row5_col6, #T_9f36b_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row3_col6 {\n",
+       "#T_9f36b_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row3_col7, #T_d4786_row6_col7 {\n",
-       "  background-color: #5eae5e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row3_col8, #T_d4786_row7_col8 {\n",
-       "  background-color: #0b850b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row4_col0 {\n",
+       "#T_9f36b_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row4_col1 {\n",
+       "#T_9f36b_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row4_col3 {\n",
-       "  background-color: #69b369;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row4_col5, #T_d4786_row7_col1 {\n",
-       "  background-color: #73b873;\n",
+       "#T_9f36b_row6_col2 {\n",
+       "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row4_col6 {\n",
-       "  background-color: #60af60;\n",
+       "#T_9f36b_row6_col3 {\n",
+       "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row4_col7 {\n",
-       "  background-color: #6cb56c;\n",
+       "#T_9f36b_row6_col5 {\n",
+       "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row4_col8 {\n",
-       "  background-color: #168b16;\n",
+       "#T_9f36b_row6_col8 {\n",
+       "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row5_col0 {\n",
+       "#T_9f36b_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row5_col1 {\n",
+       "#T_9f36b_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row5_col4 {\n",
+       "#T_9f36b_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row5_col6 {\n",
+       "#T_9f36b_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row5_col7, #T_d4786_row7_col0 {\n",
-       "  background-color: #77ba77;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row5_col8, #T_d4786_row8_col0, #T_d4786_row8_col1, #T_d4786_row8_col2, #T_d4786_row8_col3, #T_d4786_row8_col4, #T_d4786_row8_col5, #T_d4786_row8_col6, #T_d4786_row8_col7 {\n",
-       "  background-color: #008000;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row6_col0 {\n",
+       "#T_9f36b_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row6_col3 {\n",
-       "  background-color: #79bc79;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_d4786_row6_col8 {\n",
+       "#T_9f36b_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4786_row7_col2 {\n",
-       "  background-color: #61b061;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_d4786_row7_col5 {\n",
-       "  background-color: #7cbd7c;\n",
+       "#T_9f36b_row8_col5 {\n",
+       "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4786_row7_col6 {\n",
-       "  background-color: #5cad5c;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
        "</style>\n",
-       "<table id=\"T_d4786_\">\n",
+       "<table id=\"T_9f36b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col3\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col5\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col6\" >p38a</th>\n",
-       "      <th class=\"col_heading level0 col7\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d4786_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_d4786_row0_col2\" class=\"data row0 col2\" >0.345</td>\n",
-       "      <td id=\"T_d4786_row0_col3\" class=\"data row0 col3\" >0.352</td>\n",
-       "      <td id=\"T_d4786_row0_col4\" class=\"data row0 col4\" >0.289</td>\n",
-       "      <td id=\"T_d4786_row0_col5\" class=\"data row0 col5\" >0.289</td>\n",
-       "      <td id=\"T_d4786_row0_col6\" class=\"data row0 col6\" >0.355</td>\n",
-       "      <td id=\"T_d4786_row0_col7\" class=\"data row0 col7\" >0.284</td>\n",
-       "      <td id=\"T_d4786_row0_col8\" class=\"data row0 col8\" >0.573</td>\n",
+       "      <th id=\"T_9f36b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_9f36b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_9f36b_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_9f36b_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_9f36b_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_9f36b_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_9f36b_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_9f36b_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_9f36b_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d4786_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_d4786_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row1_col2\" class=\"data row1 col2\" >0.345</td>\n",
-       "      <td id=\"T_d4786_row1_col3\" class=\"data row1 col3\" >0.370</td>\n",
-       "      <td id=\"T_d4786_row1_col4\" class=\"data row1 col4\" >0.315</td>\n",
-       "      <td id=\"T_d4786_row1_col5\" class=\"data row1 col5\" >0.302</td>\n",
-       "      <td id=\"T_d4786_row1_col6\" class=\"data row1 col6\" >0.365</td>\n",
-       "      <td id=\"T_d4786_row1_col7\" class=\"data row1 col7\" >0.298</td>\n",
-       "      <td id=\"T_d4786_row1_col8\" class=\"data row1 col8\" >0.586</td>\n",
+       "      <th id=\"T_9f36b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_9f36b_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_9f36b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_9f36b_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_9f36b_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_9f36b_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_9f36b_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_9f36b_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_9f36b_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row2\" class=\"row_heading level0 row2\" >BRAF</th>\n",
-       "      <td id=\"T_d4786_row2_col0\" class=\"data row2 col0\" >0.345</td>\n",
-       "      <td id=\"T_d4786_row2_col1\" class=\"data row2 col1\" >0.345</td>\n",
-       "      <td id=\"T_d4786_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row2_col3\" class=\"data row2 col3\" >0.353</td>\n",
-       "      <td id=\"T_d4786_row2_col4\" class=\"data row2 col4\" >0.327</td>\n",
-       "      <td id=\"T_d4786_row2_col5\" class=\"data row2 col5\" >0.362</td>\n",
-       "      <td id=\"T_d4786_row2_col6\" class=\"data row2 col6\" >0.362</td>\n",
-       "      <td id=\"T_d4786_row2_col7\" class=\"data row2 col7\" >0.329</td>\n",
-       "      <td id=\"T_d4786_row2_col8\" class=\"data row2 col8\" >0.564</td>\n",
+       "      <th id=\"T_9f36b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_9f36b_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_9f36b_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_9f36b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_9f36b_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_9f36b_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_9f36b_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_9f36b_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_9f36b_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row3\" class=\"row_heading level0 row3\" >CDK2</th>\n",
-       "      <td id=\"T_d4786_row3_col0\" class=\"data row3 col0\" >0.352</td>\n",
-       "      <td id=\"T_d4786_row3_col1\" class=\"data row3 col1\" >0.370</td>\n",
-       "      <td id=\"T_d4786_row3_col2\" class=\"data row3 col2\" >0.353</td>\n",
-       "      <td id=\"T_d4786_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row3_col4\" class=\"data row3 col4\" >0.319</td>\n",
-       "      <td id=\"T_d4786_row3_col5\" class=\"data row3 col5\" >0.344</td>\n",
-       "      <td id=\"T_d4786_row3_col6\" class=\"data row3 col6\" >0.277</td>\n",
-       "      <td id=\"T_d4786_row3_col7\" class=\"data row3 col7\" >0.347</td>\n",
-       "      <td id=\"T_d4786_row3_col8\" class=\"data row3 col8\" >0.576</td>\n",
+       "      <th id=\"T_9f36b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_9f36b_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_9f36b_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_9f36b_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_9f36b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_9f36b_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_9f36b_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_9f36b_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_9f36b_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row4\" class=\"row_heading level0 row4\" >LCK</th>\n",
-       "      <td id=\"T_d4786_row4_col0\" class=\"data row4 col0\" >0.289</td>\n",
-       "      <td id=\"T_d4786_row4_col1\" class=\"data row4 col1\" >0.315</td>\n",
-       "      <td id=\"T_d4786_row4_col2\" class=\"data row4 col2\" >0.327</td>\n",
-       "      <td id=\"T_d4786_row4_col3\" class=\"data row4 col3\" >0.319</td>\n",
-       "      <td id=\"T_d4786_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row4_col5\" class=\"data row4 col5\" >0.309</td>\n",
-       "      <td id=\"T_d4786_row4_col6\" class=\"data row4 col6\" >0.337</td>\n",
-       "      <td id=\"T_d4786_row4_col7\" class=\"data row4 col7\" >0.312</td>\n",
-       "      <td id=\"T_d4786_row4_col8\" class=\"data row4 col8\" >0.549</td>\n",
+       "      <th id=\"T_9f36b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_9f36b_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_9f36b_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_9f36b_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_9f36b_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_9f36b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_9f36b_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_9f36b_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_9f36b_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row5\" class=\"row_heading level0 row5\" >MET</th>\n",
-       "      <td id=\"T_d4786_row5_col0\" class=\"data row5 col0\" >0.289</td>\n",
-       "      <td id=\"T_d4786_row5_col1\" class=\"data row5 col1\" >0.302</td>\n",
-       "      <td id=\"T_d4786_row5_col2\" class=\"data row5 col2\" >0.362</td>\n",
-       "      <td id=\"T_d4786_row5_col3\" class=\"data row5 col3\" >0.344</td>\n",
-       "      <td id=\"T_d4786_row5_col4\" class=\"data row5 col4\" >0.309</td>\n",
-       "      <td id=\"T_d4786_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row5_col6\" class=\"data row5 col6\" >0.371</td>\n",
-       "      <td id=\"T_d4786_row5_col7\" class=\"data row5 col7\" >0.286</td>\n",
-       "      <td id=\"T_d4786_row5_col8\" class=\"data row5 col8\" >0.606</td>\n",
+       "      <th id=\"T_9f36b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_9f36b_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_9f36b_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_9f36b_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_9f36b_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_9f36b_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_9f36b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_9f36b_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_9f36b_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row6\" class=\"row_heading level0 row6\" >p38a</th>\n",
-       "      <td id=\"T_d4786_row6_col0\" class=\"data row6 col0\" >0.355</td>\n",
-       "      <td id=\"T_d4786_row6_col1\" class=\"data row6 col1\" >0.365</td>\n",
-       "      <td id=\"T_d4786_row6_col2\" class=\"data row6 col2\" >0.362</td>\n",
-       "      <td id=\"T_d4786_row6_col3\" class=\"data row6 col3\" >0.277</td>\n",
-       "      <td id=\"T_d4786_row6_col4\" class=\"data row6 col4\" >0.337</td>\n",
-       "      <td id=\"T_d4786_row6_col5\" class=\"data row6 col5\" >0.371</td>\n",
-       "      <td id=\"T_d4786_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row6_col7\" class=\"data row6 col7\" >0.347</td>\n",
-       "      <td id=\"T_d4786_row6_col8\" class=\"data row6 col8\" >0.569</td>\n",
+       "      <th id=\"T_9f36b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_9f36b_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_9f36b_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_9f36b_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_9f36b_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_9f36b_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_9f36b_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_9f36b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_9f36b_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row7\" class=\"row_heading level0 row7\" >KDR</th>\n",
-       "      <td id=\"T_d4786_row7_col0\" class=\"data row7 col0\" >0.284</td>\n",
-       "      <td id=\"T_d4786_row7_col1\" class=\"data row7 col1\" >0.298</td>\n",
-       "      <td id=\"T_d4786_row7_col2\" class=\"data row7 col2\" >0.329</td>\n",
-       "      <td id=\"T_d4786_row7_col3\" class=\"data row7 col3\" >0.347</td>\n",
-       "      <td id=\"T_d4786_row7_col4\" class=\"data row7 col4\" >0.312</td>\n",
-       "      <td id=\"T_d4786_row7_col5\" class=\"data row7 col5\" >0.286</td>\n",
-       "      <td id=\"T_d4786_row7_col6\" class=\"data row7 col6\" >0.347</td>\n",
-       "      <td id=\"T_d4786_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_d4786_row7_col8\" class=\"data row7 col8\" >0.577</td>\n",
+       "      <th id=\"T_9f36b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_9f36b_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_9f36b_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_9f36b_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_9f36b_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_9f36b_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_9f36b_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_9f36b_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_9f36b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_9f36b_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4786_level0_row8\" class=\"row_heading level0 row8\" >p110a</th>\n",
-       "      <td id=\"T_d4786_row8_col0\" class=\"data row8 col0\" >0.573</td>\n",
-       "      <td id=\"T_d4786_row8_col1\" class=\"data row8 col1\" >0.586</td>\n",
-       "      <td id=\"T_d4786_row8_col2\" class=\"data row8 col2\" >0.564</td>\n",
-       "      <td id=\"T_d4786_row8_col3\" class=\"data row8 col3\" >0.576</td>\n",
-       "      <td id=\"T_d4786_row8_col4\" class=\"data row8 col4\" >0.549</td>\n",
-       "      <td id=\"T_d4786_row8_col5\" class=\"data row8 col5\" >0.606</td>\n",
-       "      <td id=\"T_d4786_row8_col6\" class=\"data row8 col6\" >0.569</td>\n",
-       "      <td id=\"T_d4786_row8_col7\" class=\"data row8 col7\" >0.577</td>\n",
-       "      <td id=\"T_d4786_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_9f36b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_9f36b_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_9f36b_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_9f36b_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_9f36b_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_9f36b_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_9f36b_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_9f36b_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_9f36b_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_9f36b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa2f32f8610>"
+       "<pandas.io.formats.style.Styler at 0x7fd3c84b1d30>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1807,7 +2012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [

From 5a7e53c375012c8de56ef637161cce17fe309252 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 15:31:41 +0200
Subject: [PATCH 079/158] final checks.

---
 .../talktorial.ipynb                          | 651 +++++++++---------
 1 file changed, 313 insertions(+), 338 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 41c4e279..8ada386d 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the 85 residues pocket sequence for each kinase. \n",
+    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the $85$ residues pocket sequence for each kinase. \n",
     "\n",
     "Two similarity measures are implemented:\n",
     "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.\n",
@@ -70,6 +70,7 @@
     "* Biotite\n",
     "    * documentation: https://www.biotite-python.org/index.html\n",
     "    * [Blosum matrix](https://www.biotite-python.org/examples/gallery/sequence/blosum_dendrogram.html?highlight=blosum)\n",
+    "* Sequence logo: http://www.cbs.dtu.dk/biotools/Seq2Logo/\n",
     "    "
    ]
   },
@@ -100,7 +101,7 @@
    "source": [
     "### Kinase similarity descriptor: sequence\n",
     "\n",
-    "As mentioned in the previous talktorial, sequence is often used in kinase similarity (see the phylogenetic tree developped by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
+    "As mentioned in the previous talktorial, sequence is often used in kinase similarity (see the phylogenetic tree developed by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
    ]
   },
   {
@@ -109,9 +110,9 @@
    "source": [
     "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
     "\n",
-    "1. The sequence is of fixed length (it contains 85 residues), which makes computation for pairwise similarity between two sequences easy.\n",
+    "1. The sequence is of fixed length (it contains $85$ residues), which makes computation for pairwise similarity between two sequences easy.\n",
     "\n",
-    "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an 85 residues sequence contains most relevant information?"
+    "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an $85$ residues sequence contains most relevant information?"
    ]
   },
   {
@@ -131,7 +132,7 @@
     "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion.\n",
     "\n",
     "**Note for reproducibility:**\n",
-    "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to 85 and _Page size_ to 1200 x 480."
+    "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to $85$ and _Page size_ to $1200 \\times 480$."
    ]
   },
   {
@@ -280,16 +281,6 @@
     "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. A similarity matrix is not, by definition, a distance matrix. For example, the diagonal elements are not zero. However, if all entries of the are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext pycodestyle_magic\n",
-    "%pycodestyle_on"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -299,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -315,7 +306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -339,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -471,7 +462,7 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -554,7 +545,7 @@
        " 'p38a': 'SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA'}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -643,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -692,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -701,7 +692,7 @@
        "True"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -712,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -787,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -847,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -869,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -891,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -919,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -939,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -981,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -997,17 +988,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "7:80: E501 line too long (80 > 79 characters)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "kinase_similarity_matrix = np.zeros((len(kinase_selection_df.kinase_klifs),\n",
     "                                     len(kinase_selection_df.kinase_klifs)))\n",
@@ -1020,7 +1003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1206,17 +1189,9 @@
        "p38a          0.662581  0.629355  1.000000  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2:80: E501 line too long (82 > 79 characters)\n",
-      "3:80: E501 line too long (84 > 79 characters)\n"
-     ]
     }
    ],
    "source": [
@@ -1235,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1244,7 +1219,7 @@
        "True"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1256,195 +1231,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
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-       "#T_d46bb_row1_col6 {\n",
+       "#T_72c5e_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row1_col7 {\n",
+       "#T_72c5e_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row2_col0, #T_d46bb_row2_col1, #T_d46bb_row2_col3, #T_d46bb_row2_col4, #T_d46bb_row2_col5, #T_d46bb_row2_col6, #T_d46bb_row2_col7, #T_d46bb_row2_col8, #T_d46bb_row7_col2 {\n",
+       "#T_72c5e_row2_col0, #T_72c5e_row2_col1, #T_72c5e_row2_col3, #T_72c5e_row2_col4, #T_72c5e_row2_col5, #T_72c5e_row2_col6, #T_72c5e_row2_col7, #T_72c5e_row2_col8, #T_72c5e_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row3_col0, #T_d46bb_row7_col3 {\n",
+       "#T_72c5e_row3_col0, #T_72c5e_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row3_col1, #T_d46bb_row6_col7 {\n",
+       "#T_72c5e_row3_col1, #T_72c5e_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row3_col2, #T_d46bb_row5_col2 {\n",
+       "#T_72c5e_row3_col2, #T_72c5e_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row3_col4 {\n",
+       "#T_72c5e_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row3_col5, #T_d46bb_row4_col0 {\n",
+       "#T_72c5e_row3_col5, #T_72c5e_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row3_col6, #T_d46bb_row4_col3, #T_d46bb_row5_col7 {\n",
+       "#T_72c5e_row3_col6, #T_72c5e_row4_col3, #T_72c5e_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row3_col7 {\n",
+       "#T_72c5e_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row4_col1 {\n",
+       "#T_72c5e_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row4_col2 {\n",
+       "#T_72c5e_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row4_col6, #T_d46bb_row4_col7, #T_d46bb_row7_col5 {\n",
+       "#T_72c5e_row4_col6, #T_72c5e_row4_col7, #T_72c5e_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row4_col8 {\n",
+       "#T_72c5e_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col0, #T_d46bb_row8_col6 {\n",
+       "#T_72c5e_row5_col0, #T_72c5e_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col1 {\n",
+       "#T_72c5e_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col3, #T_d46bb_row8_col3 {\n",
+       "#T_72c5e_row5_col3, #T_72c5e_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col4 {\n",
+       "#T_72c5e_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col6, #T_d46bb_row6_col4 {\n",
+       "#T_72c5e_row5_col6, #T_72c5e_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row5_col8 {\n",
+       "#T_72c5e_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row6_col0 {\n",
+       "#T_72c5e_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row6_col1 {\n",
+       "#T_72c5e_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row6_col2 {\n",
+       "#T_72c5e_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row6_col3 {\n",
+       "#T_72c5e_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row6_col5 {\n",
+       "#T_72c5e_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row6_col8 {\n",
+       "#T_72c5e_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row7_col0 {\n",
+       "#T_72c5e_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d46bb_row7_col1 {\n",
+       "#T_72c5e_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row7_col6 {\n",
+       "#T_72c5e_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row7_col8 {\n",
+       "#T_72c5e_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row8_col0 {\n",
+       "#T_72c5e_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row8_col2 {\n",
+       "#T_72c5e_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d46bb_row8_col5 {\n",
+       "#T_72c5e_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d46bb_\">\n",
+       "<table id=\"T_72c5e_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
@@ -1473,121 +1448,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d46bb_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_d46bb_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_d46bb_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_d46bb_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_d46bb_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_d46bb_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_d46bb_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_d46bb_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "      <th id=\"T_72c5e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_72c5e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_72c5e_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_72c5e_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_72c5e_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_72c5e_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_72c5e_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_72c5e_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_72c5e_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d46bb_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_d46bb_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_d46bb_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_d46bb_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_d46bb_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_d46bb_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_d46bb_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_d46bb_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "      <th id=\"T_72c5e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_72c5e_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_72c5e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_72c5e_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_72c5e_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_72c5e_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_72c5e_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_72c5e_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_72c5e_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_d46bb_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_d46bb_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_d46bb_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_d46bb_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_d46bb_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_d46bb_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_d46bb_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_d46bb_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "      <th id=\"T_72c5e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_72c5e_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_72c5e_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_72c5e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_72c5e_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_72c5e_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_72c5e_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_72c5e_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_72c5e_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_d46bb_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_d46bb_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_d46bb_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_d46bb_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_d46bb_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_d46bb_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_d46bb_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_d46bb_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "      <th id=\"T_72c5e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_72c5e_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_72c5e_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_72c5e_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_72c5e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_72c5e_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_72c5e_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_72c5e_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_72c5e_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_d46bb_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_d46bb_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_d46bb_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_d46bb_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_d46bb_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_d46bb_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_d46bb_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_d46bb_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "      <th id=\"T_72c5e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_72c5e_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_72c5e_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_72c5e_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_72c5e_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_72c5e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_72c5e_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_72c5e_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_72c5e_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_d46bb_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_d46bb_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_d46bb_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_d46bb_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_d46bb_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_d46bb_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_d46bb_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_d46bb_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "      <th id=\"T_72c5e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_72c5e_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_72c5e_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_72c5e_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_72c5e_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_72c5e_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_72c5e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_72c5e_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_72c5e_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_d46bb_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_d46bb_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_d46bb_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_d46bb_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_d46bb_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_d46bb_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_d46bb_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_d46bb_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "      <th id=\"T_72c5e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_72c5e_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_72c5e_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_72c5e_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_72c5e_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_72c5e_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_72c5e_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_72c5e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_72c5e_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_d46bb_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_d46bb_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_d46bb_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_d46bb_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_d46bb_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_d46bb_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_d46bb_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_d46bb_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_d46bb_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "      <th id=\"T_72c5e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_72c5e_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_72c5e_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_72c5e_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_72c5e_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_72c5e_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_72c5e_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_72c5e_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_72c5e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_72c5e_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d46bb_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d46bb_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_d46bb_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_d46bb_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_d46bb_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_d46bb_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_d46bb_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_d46bb_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_d46bb_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_d46bb_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_72c5e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_72c5e_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_72c5e_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_72c5e_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_72c5e_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_72c5e_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_72c5e_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_72c5e_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_72c5e_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_72c5e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd3c816ed30>"
+       "<pandas.io.formats.style.Styler at 0x7fb414e3fd90>"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1609,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1633,7 +1608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1652,7 +1627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1661,195 +1636,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
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+       "#T_a23fa_row0_col0, #T_a23fa_row1_col1, #T_a23fa_row2_col2, #T_a23fa_row3_col3, #T_a23fa_row4_col4, #T_a23fa_row5_col5, #T_a23fa_row6_col6, #T_a23fa_row7_col7, #T_a23fa_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9f36b_row0_col1 {\n",
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        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
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-       "#T_9f36b_row0_col2 {\n",
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        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
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-       "#T_9f36b_row0_col3 {\n",
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        "  background-color: #6fb76f;\n",
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-       "#T_9f36b_row0_col8, #T_9f36b_row8_col1 {\n",
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        "  background-color: #d3e7d3;\n",
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-       "#T_9f36b_row1_col2 {\n",
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        "  background-color: #078407;\n",
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-       "#T_9f36b_row1_col3 {\n",
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-       "#T_9f36b_row1_col5, #T_9f36b_row1_col8, #T_9f36b_row7_col4, #T_9f36b_row8_col4 {\n",
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        "  background-color: #54a954;\n",
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-       "#T_9f36b_row1_col6 {\n",
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        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row1_col7 {\n",
+       "#T_a23fa_row1_col7 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
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-       "#T_9f36b_row2_col0, #T_9f36b_row2_col1, #T_9f36b_row2_col3, #T_9f36b_row2_col4, #T_9f36b_row2_col5, #T_9f36b_row2_col6, #T_9f36b_row2_col7, #T_9f36b_row2_col8, #T_9f36b_row7_col2 {\n",
+       "#T_a23fa_row2_col0, #T_a23fa_row2_col1, #T_a23fa_row2_col3, #T_a23fa_row2_col4, #T_a23fa_row2_col5, #T_a23fa_row2_col6, #T_a23fa_row2_col7, #T_a23fa_row2_col8, #T_a23fa_row7_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col0, #T_9f36b_row7_col3 {\n",
+       "#T_a23fa_row3_col0, #T_a23fa_row7_col3 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col1, #T_9f36b_row6_col7 {\n",
+       "#T_a23fa_row3_col1, #T_a23fa_row6_col7 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col2, #T_9f36b_row5_col2 {\n",
+       "#T_a23fa_row3_col2, #T_a23fa_row5_col2 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col4 {\n",
+       "#T_a23fa_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col5, #T_9f36b_row4_col0 {\n",
+       "#T_a23fa_row3_col5, #T_a23fa_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col6, #T_9f36b_row4_col3, #T_9f36b_row5_col7 {\n",
+       "#T_a23fa_row3_col6, #T_a23fa_row4_col3, #T_a23fa_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row3_col7 {\n",
+       "#T_a23fa_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9f36b_row3_col8 {\n",
+       "#T_a23fa_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row4_col1 {\n",
+       "#T_a23fa_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row4_col2 {\n",
+       "#T_a23fa_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row4_col6, #T_9f36b_row4_col7, #T_9f36b_row7_col5 {\n",
+       "#T_a23fa_row4_col6, #T_a23fa_row4_col7, #T_a23fa_row7_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row4_col8, #T_9f36b_row5_col1 {\n",
+       "#T_a23fa_row4_col8, #T_a23fa_row5_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row5_col0, #T_9f36b_row8_col6 {\n",
+       "#T_a23fa_row5_col0, #T_a23fa_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row5_col3, #T_9f36b_row8_col3 {\n",
+       "#T_a23fa_row5_col3, #T_a23fa_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row5_col4 {\n",
+       "#T_a23fa_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row5_col6, #T_9f36b_row6_col4 {\n",
+       "#T_a23fa_row5_col6, #T_a23fa_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row5_col8 {\n",
+       "#T_a23fa_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9f36b_row6_col0 {\n",
+       "#T_a23fa_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row6_col1 {\n",
+       "#T_a23fa_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row6_col2 {\n",
+       "#T_a23fa_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row6_col3 {\n",
+       "#T_a23fa_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row6_col5 {\n",
+       "#T_a23fa_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row6_col8 {\n",
+       "#T_a23fa_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row7_col0 {\n",
+       "#T_a23fa_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row7_col1 {\n",
+       "#T_a23fa_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row7_col6 {\n",
+       "#T_a23fa_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row7_col8 {\n",
+       "#T_a23fa_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row8_col0 {\n",
+       "#T_a23fa_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row8_col2 {\n",
+       "#T_a23fa_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9f36b_row8_col5 {\n",
+       "#T_a23fa_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_9f36b_\">\n",
+       "<table id=\"T_a23fa_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
@@ -1878,121 +1853,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_9f36b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_9f36b_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_9f36b_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_9f36b_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_9f36b_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_9f36b_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_9f36b_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_9f36b_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "      <th id=\"T_a23fa_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_a23fa_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_a23fa_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_a23fa_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_a23fa_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_a23fa_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_a23fa_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_a23fa_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_a23fa_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_9f36b_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_9f36b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_9f36b_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_9f36b_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_9f36b_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_9f36b_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_9f36b_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_9f36b_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "      <th id=\"T_a23fa_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_a23fa_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_a23fa_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_a23fa_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_a23fa_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_a23fa_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_a23fa_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_a23fa_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_a23fa_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_9f36b_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_9f36b_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_9f36b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_9f36b_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_9f36b_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_9f36b_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_9f36b_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_9f36b_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "      <th id=\"T_a23fa_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_a23fa_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_a23fa_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_a23fa_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_a23fa_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_a23fa_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_a23fa_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_a23fa_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_a23fa_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_9f36b_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_9f36b_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_9f36b_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_9f36b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_9f36b_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_9f36b_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_9f36b_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_9f36b_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "      <th id=\"T_a23fa_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_a23fa_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_a23fa_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_a23fa_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_a23fa_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_a23fa_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_a23fa_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_a23fa_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_a23fa_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_9f36b_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_9f36b_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_9f36b_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_9f36b_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_9f36b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_9f36b_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_9f36b_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_9f36b_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "      <th id=\"T_a23fa_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_a23fa_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_a23fa_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_a23fa_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_a23fa_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_a23fa_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_a23fa_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_a23fa_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_a23fa_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_9f36b_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_9f36b_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_9f36b_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_9f36b_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_9f36b_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_9f36b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_9f36b_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_9f36b_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "      <th id=\"T_a23fa_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_a23fa_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_a23fa_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_a23fa_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_a23fa_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_a23fa_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_a23fa_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_a23fa_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_a23fa_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_9f36b_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_9f36b_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_9f36b_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_9f36b_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_9f36b_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_9f36b_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_9f36b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_9f36b_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "      <th id=\"T_a23fa_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_a23fa_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_a23fa_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_a23fa_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_a23fa_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_a23fa_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_a23fa_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_a23fa_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_a23fa_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_9f36b_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_9f36b_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_9f36b_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_9f36b_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_9f36b_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_9f36b_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_9f36b_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_9f36b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_9f36b_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "      <th id=\"T_a23fa_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_a23fa_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_a23fa_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_a23fa_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_a23fa_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_a23fa_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_a23fa_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_a23fa_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_a23fa_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_a23fa_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9f36b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_9f36b_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_9f36b_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_9f36b_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_9f36b_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_9f36b_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_9f36b_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_9f36b_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_9f36b_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_9f36b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_a23fa_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_a23fa_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_a23fa_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_a23fa_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_a23fa_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_a23fa_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_a23fa_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_a23fa_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_a23fa_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_a23fa_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd3c84b1d30>"
+       "<pandas.io.formats.style.Styler at 0x7fb41520fbb0>"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2012,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [

From f7ad096760107e0366d1a18e5d4a7a93d1a05e4c Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 15:37:26 +0200
Subject: [PATCH 080/158] T023: Add KinMap trees (#bioactivities, #structures)

---
 .../data/kinmap_n_activities_per_kinase.csv   | 385 +++++++++++
 .../data/kinmap_n_structures_per_kinase.csv   | 624 +++++++++---------
 .../images/kinmap_n_activities_per_kinase.png | Bin 0 -> 157979 bytes
 .../images/kinmap_n_structures_per_kinase.png | Bin 0 -> 155668 bytes
 .../T023_what_is_a_kinase/talktorial.ipynb    | 159 +++--
 5 files changed, 810 insertions(+), 358 deletions(-)
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_activities_per_kinase.csv
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_n_activities_per_kinase.png
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_n_structures_per_kinase.png

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_activities_per_kinase.csv b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_activities_per_kinase.csv
new file mode 100644
index 00000000..20c3608b
--- /dev/null
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_activities_per_kinase.csv
@@ -0,0 +1,385 @@
+xName,size
+AAK1,10.487934705464868
+ABL1,18.365862313697658
+ABL2,10.124201561391057
+ACK,11.197657913413769
+ACTR2,10.0
+ACTR2B,10.017743080198722
+AKT1,22.863733144073812
+AKT2,14.027679205110008
+AKT3,12.093683463449254
+ALK,19.412704045422284
+ALK1,10.248403122782115
+ALK2,11.091199432221433
+ALK4,10.221788502484031
+AMPKa1,10.026614620298083
+ANKRD3,10.008871540099362
+ANPa,10.044357700496807
+ARAF,10.221788502484031
+ATM,11.34847409510291
+ATR,11.99609652235628
+AXL,13.726046841731725
+AurA,24.48722498225692
+AurB,20.5305180979418
+AurC,10.275017743080198
+BARK1,11.792051100070973
+BARK2,10.062100780695529
+BCR,10.017743080198722
+BLK,10.585521646557842
+BMPR1A,10.159687721788503
+BMPR1B,10.07984386089425
+BMPR2,10.062100780695529
+BMX,11.011355571327183
+BRAF,27.414833215046134
+BRD2,11.658977998580554
+BRD3,11.366217175301632
+BRD4,25.773598296664304
+BRDT,10.523420865862313
+BRK,11.21540099361249
+BRSK1,10.0
+BRSK2,10.026614620298083
+BTK,21.21362668559262
+BUB1,10.044357700496807
+CDC2,17.851312987934705
+CDC7,12.48403122782115
+CDK11,10.026614620298083
+CDK2,21.240241305890702
+CDK3,10.026614620298083
+CDK4,12.439673527324343
+CDK5,11.286373314407381
+CDK6,10.585521646557842
+CDK7,10.78069552874379
+CDK8,11.729950319375444
+CDK9,10.718594748048261
+CDKL1,10.0
+CHED,10.008871540099362
+CHK1,24.380766501064585
+CHK2,12.84776437189496
+CK1a,10.523420865862313
+CK1d,12.120298083747338
+CK1e,10.913768630234209
+CK1g1,10.07984386089425
+CK1g2,10.230660042583393
+CK1g3,10.026614620298083
+CK2a1,11.48154719659333
+CK2a2,10.381476224272534
+CLK1,12.510645848119234
+CLK2,10.887154009936125
+CLK3,10.35486160397445
+CLK4,11.729950319375444
+COT,11.898509581263307
+CRK7,10.044357700496807
+CSK,10.505677785663591
+CTK,10.0
+CaMK1a,10.053229240596167
+CaMK1b,10.017743080198722
+CaMK1d,10.150816181689141
+CaMK1g,10.008871540099362
+CaMK2a,10.14194464158978
+CaMK2b,10.035486160397445
+CaMK2d,10.816181689141235
+CaMK2g,10.008871540099362
+CaMK4,10.035486160397445
+CaMKK1,10.0
+CaMKK2,10.576650106458482
+DAPK1,10.257274662881477
+DAPK2,10.07984386089425
+DAPK3,10.266146202980838
+DCLK1,10.266146202980838
+DDR1,11.446061036195884
+DDR2,11.454932576295246
+DLK,10.0
+DMPK1,10.017743080198722
+DRAK1,10.292760823278922
+DRAK2,10.541163946061037
+DYRK1A,15.60681334279631
+DYRK1B,12.315471965933286
+DYRK2,11.357345635202272
+DYRK3,10.56777856635912
+DYRK4,10.017743080198722
+EGFR,60.0
+EphA1,10.053229240596167
+EphA2,10.789567068843152
+EphA3,10.115330021291696
+EphA4,10.248403122782115
+EphA5,10.017743080198722
+EphA6,10.008871540099362
+EphA7,10.044357700496807
+EphA8,10.07984386089425
+EphB1,10.053229240596167
+EphB2,10.28388928317956
+EphB3,10.35486160397445
+EphB4,14.347054648687012
+EphB6,10.008871540099362
+ErbB2,23.537970191625263
+ErbB3,10.399219304471256
+ErbB4,11.437189496096522
+Erk1,11.037970191625266
+Erk2,14.6930447125621
+Erk5,10.674237047551454
+Erk7,10.035486160397445
+FAK,14.604329311568488
+FER,10.204045422285308
+FES,10.07097232079489
+FGFR1,20.317601135557133
+FGFR2,11.91625266146203
+FGFR3,11.295244854506743
+FGFR4,11.56139105748758
+FGR,10.337118523775727
+FLT1,16.511710432931157
+FLT3,24.726756564939677
+FLT4,12.244499645138395
+FMS,16.502838892831797
+FRAP,22.677430801987224
+FRK,10.088715400993612
+FYN,11.259758694109298
+GAK,10.275017743080198
+GCK,10.674237047551454
+GCN2,10.168559261887864
+GPRK4,10.053229240596167
+GPRK5,11.100070972320795
+GPRK6,10.035486160397445
+GPRK7,10.035486160397445
+GSK3A,12.661462029808375
+GSK3B,29.508516678495386
+HCK,13.016323633782825
+HGK,10.896025550035485
+HH498,11.330731014904188
+HIPK1,10.13307310149042
+HIPK2,10.07097232079489
+HIPK3,10.008871540099362
+HIPK4,10.008871540099362
+HPK1,10.0
+HRI,10.292760823278922
+Haspin,10.275017743080198
+IGF1R,20.01596877217885
+IKKa,12.892122072391768
+IKKb,18.481192334989352
+IKKe,11.304116394606103
+ILK,10.0
+INSR,15.660042583392478
+IRAK1,10.177430801987224
+IRAK3,10.0
+IRAK4,14.74627395315827
+IRE1,10.523420865862313
+IRE2,10.0
+IRR,10.008871540099362
+ITK,15.420511000709723
+JAK1,19.21753016323634
+JAK2,24.41625266146203
+JAK3,23.369410929737402
+JNK1,18.481192334989352
+JNK2,14.524485450674238
+JNK3,16.44960965223563
+KDR,59.272533711852375
+KHS1,10.035486160397445
+KHS2,10.017743080198722
+KIT,17.82469836763662
+KSR1,10.0
+KSR2,10.0
+LCK,23.520227111426543
+LIMK1,12.04932576295245
+LIMK2,11.623491838183108
+LMR3,10.062100780695529
+LRRK2,14.116394606103619
+LTK,10.13307310149042
+LYN,12.111426543647976
+MAP2K1,15.660042583392478
+MAP2K2,10.07984386089425
+MAP2K3,10.035486160397445
+MAP2K4,10.0
+MAP2K5,10.053229240596167
+MAP2K6,10.07097232079489
+MAP2K7,10.008871540099362
+MAP3K1,10.026614620298083
+MAP3K2,10.035486160397445
+MAP3K5,11.588005677785663
+MAP3K6,10.0
+MAPKAPK2,16.831085876508162
+MAPKAPK3,10.088715400993612
+MAPKAPK5,10.319375443577005
+MARK1,10.053229240596167
+MARK2,10.017743080198722
+MARK3,10.532292405961675
+MARK4,10.186302342086586
+MAST3,10.044357700496807
+MELK,10.78069552874379
+MER,12.226756564939674
+MET,32.17885024840312
+MINK,10.14194464158978
+MLK1,10.505677785663591
+MLK2,10.195173882185948
+MLK3,10.452448545067424
+MLK4,10.0
+MNK1,12.235628105039034
+MNK2,11.268630234208658
+MPSK1,10.017743080198722
+MRCKa,10.230660042583393
+MSK1,10.674237047551454
+MSK2,10.026614620298083
+MSSK1,10.0
+MST2,10.159687721788503
+MST3,10.0
+MST4,10.14194464158978
+MUSK,10.008871540099362
+MYT1,10.044357700496807
+Map4k2,10.408090844570618
+NEK1,10.088715400993612
+NEK2,11.082327892122072
+NEK3,10.008871540099362
+NEK4,10.053229240596167
+NEK5,10.0
+NEK6,10.159687721788503
+NEK7,10.0
+NEK8,10.0
+NIK,11.082327892122072
+NLK,10.0
+NuaK1,10.275017743080198
+PAK1,10.975869410929738
+PAK2,10.07097232079489
+PAK3,10.062100780695529
+PAK4,10.93151171043293
+PAK5,10.017743080198722
+PAK6,10.0
+PASK,10.044357700496807
+PBK,10.337118523775727
+PDGFRa,13.575230660042584
+PDGFRb,18.720723917672107
+PDHK1,11.037970191625266
+PDHK2,10.523420865862313
+PDHK3,10.195173882185948
+PDHK4,10.328246983676367
+PDK1,12.581618168914122
+PEK,10.860539389638042
+PFTAIRE1,10.062100780695529
+PHKg1,10.008871540099362
+PHKg2,10.008871540099362
+PI4K2A,10.0
+PI4KA,10.745209368346345
+PI4KB,12.510645848119234
+PIK3C2A,10.150816181689141
+PIK3C2B,10.42583392476934
+PIK3C2G,10.062100780695529
+PIK3C3,10.479063165365508
+PIK3R4,10.0
+PIM1,19.137686302342086
+PIM2,15.429382540809083
+PIM3,12.900993612491128
+PIP4K2A,10.345990063875089
+PIP4K2B,10.28388928317956
+PIP4K2C,10.017743080198722
+PIP5K1C,10.13307310149042
+PITSLRE,10.0
+PKACa,10.452448545067424
+PKACb,10.008871540099362
+PKACg,10.0
+PKCa,14.33818310858765
+PKCb,13.105039034776437
+PKCd,14.098651525904897
+PKCe,12.421930447125622
+PKCg,11.4194464158978
+PKCh,11.70333569907736
+PKCi,10.629879347054649
+PKCt,14.817246273953158
+PKCz,11.641234918381832
+PKD1,10.904897090134847
+PKD2,10.399219304471256
+PKD3,10.026614620298083
+PKG1,10.035486160397445
+PKG2,10.0
+PKN1,10.177430801987224
+PKN2,10.186302342086586
+PKN3,10.0
+PKR,10.621007806955287
+PLK1,16.289921930447125
+PLK2,11.446061036195884
+PLK3,11.4194464158978
+PLK4,11.13555713271824
+PRKX,10.008871540099362
+PYK2,10.798438608942513
+QIK,10.026614620298083
+QSK,10.0
+RAF1,15.003548616039744
+RET,14.524485450674238
+RHOK,10.124201561391057
+RIPK1,11.783179559971611
+RIPK2,10.93151171043293
+RIPK3,10.221788502484031
+RNAseL,10.088715400993612
+ROCK1,15.305180979418026
+ROCK2,18.286018452803404
+RON,10.674237047551454
+ROS,11.1444286728176
+RSK1,10.443577004968063
+RSK2,12.20901348474095
+RSK3,10.550035486160397
+RSK4,10.177430801987224
+SGK1,10.860539389638042
+SGK2,10.017743080198722
+SGK3,10.008871540099362
+SIK,10.026614620298083
+SLK,10.026614620298083
+SMG1,10.177430801987224
+SRC,31.80624556422995
+SRM,10.044357700496807
+SRPK1,10.062100780695529
+SSTK,10.0
+STK33,10.195173882185948
+SYK,15.988289567068843
+SgK085,10.0
+SgK269,10.0
+SgK496,10.0
+TAF1,10.913768630234209
+TAF1L,10.008871540099362
+TAK1,11.055713271823988
+TAO1,10.008871540099362
+TAO2,10.07097232079489
+TAO3,10.0
+TBK1,11.658977998580554
+TEC,10.443577004968063
+TESK1,10.035486160397445
+TESK2,10.044357700496807
+TGFbR1,16.662526614620297
+TGFbR2,10.718594748048261
+TIE1,10.026614620298083
+TIE2,16.431866572036906
+TIF1a,10.514549325762953
+TIF1g,10.0
+TNIK,10.275017743080198
+TRKA,15.429382540809083
+TRKB,11.960610361958835
+TRKC,10.771823988644428
+TSSK1,10.07984386089425
+TSSK2,10.026614620298083
+TSSK3,10.017743080198722
+TTBK1,10.008871540099362
+TTBK2,10.008871540099362
+TTK,13.575230660042584
+TXK,10.514549325762953
+TYK2,15.172107877927608
+TYRO3,12.48403122782115
+ULK1,10.372604684173172
+ULK2,10.044357700496807
+VRK1,10.044357700496807
+VRK2,10.0
+Wee1,12.359829666430091
+Wnk1,10.097586941092974
+Wnk2,10.008871540099362
+Wnk3,10.026614620298083
+YES,10.798438608942513
+YSK1,10.0
+ZAK,10.452448545067424
+ZAP70,11.863023420865863
+eEF2K,10.576650106458482
+p110a,36.69446415897799
+p110b,23.626685592618877
+p110d,29.082682753726047
+p110g,27.11320085166785
+p38a,40.642299503193755
+p38b,11.588005677785663
+p38d,10.408090844570618
+p38g,10.21291696238467
+p70S6K,11.1444286728176
+p70S6Kb,10.13307310149042
+skMLCK,10.035486160397445
+smMLCK,10.612136266855925
diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
index 1b3f8da8..40110e4f 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/data/kinmap_n_structures_per_kinase.csv
@@ -1,312 +1,312 @@
-xName,n_structures,shape,fill,stroke,size
-AAK1,5,circle,LimeGreen,LightGray,10.470588235294118
-ABL1,58,circle,LimeGreen,LightGray,16.705882352941178
-ABL2,4,circle,LimeGreen,LightGray,10.352941176470589
-ACK,11,circle,LimeGreen,LightGray,11.176470588235293
-ACTR2,3,circle,LimeGreen,LightGray,10.235294117647058
-ACTR2B,1,circle,LimeGreen,LightGray,10.0
-ADCK3,2,circle,LimeGreen,LightGray,10.117647058823529
-AKT1,29,circle,LimeGreen,LightGray,13.294117647058824
-AKT2,16,circle,LimeGreen,LightGray,11.764705882352942
-ALK,61,circle,LimeGreen,LightGray,17.058823529411764
-ALK1,1,circle,LimeGreen,LightGray,10.0
-ALK2,73,circle,LimeGreen,LightGray,18.470588235294116
-AMPKa1,11,circle,LimeGreen,LightGray,11.176470588235293
-AMPKa2,11,circle,LimeGreen,LightGray,11.176470588235293
-ANKRD3,5,circle,LimeGreen,LightGray,10.470588235294118
-ATM,7,circle,LimeGreen,LightGray,10.705882352941176
-ATR,1,circle,LimeGreen,LightGray,10.0
-AXL,1,circle,LimeGreen,LightGray,10.0
-AurA,174,circle,LimeGreen,LightGray,30.352941176470587
-AurB,1,circle,LimeGreen,LightGray,10.0
-AurC,2,circle,LimeGreen,LightGray,10.117647058823529
-BARK1,18,circle,LimeGreen,LightGray,12.0
-BIKE,5,circle,LimeGreen,LightGray,10.470588235294118
-BMPR1B,1,circle,LimeGreen,LightGray,10.0
-BMPR2,2,circle,LimeGreen,LightGray,10.117647058823529
-BMX,3,circle,LimeGreen,LightGray,10.235294117647058
-BRAF,97,circle,LimeGreen,LightGray,21.294117647058826
-BRK,6,circle,LimeGreen,LightGray,10.588235294117647
-BRSK2,2,circle,LimeGreen,LightGray,10.117647058823529
-BTK,93,circle,LimeGreen,LightGray,20.823529411764703
-BUB1,4,circle,LimeGreen,LightGray,10.352941176470589
-BUBR1,1,circle,LimeGreen,LightGray,10.0
-CASK,13,circle,LimeGreen,LightGray,11.411764705882353
-CCK4,1,circle,LimeGreen,LightGray,10.0
-CDC2,11,circle,LimeGreen,LightGray,11.176470588235293
-CDC7,7,circle,LimeGreen,LightGray,10.705882352941176
-CDK2,426,circle,LimeGreen,LightGray,60.0
-CDK4,13,circle,LimeGreen,LightGray,11.411764705882353
-CDK5,6,circle,LimeGreen,LightGray,10.588235294117647
-CDK6,18,circle,LimeGreen,LightGray,12.0
-CDK7,7,circle,LimeGreen,LightGray,10.705882352941176
-CDK8,31,circle,LimeGreen,LightGray,13.529411764705882
-CDK9,25,circle,LimeGreen,LightGray,12.823529411764707
-CDKL1,1,circle,LimeGreen,LightGray,10.0
-CDKL2,2,circle,LimeGreen,LightGray,10.117647058823529
-CDKL3,1,circle,LimeGreen,LightGray,10.0
-CDKL5,1,circle,LimeGreen,LightGray,10.0
-CHED,2,circle,LimeGreen,LightGray,10.117647058823529
-CHK1,148,circle,LimeGreen,LightGray,27.294117647058822
-CHK2,36,circle,LimeGreen,LightGray,14.117647058823529
-CK1a,2,circle,LimeGreen,LightGray,10.117647058823529
-CK1d,33,circle,LimeGreen,LightGray,13.76470588235294
-CK1e,2,circle,LimeGreen,LightGray,10.117647058823529
-CK1g1,1,circle,LimeGreen,LightGray,10.0
-CK1g2,1,circle,LimeGreen,LightGray,10.0
-CK1g3,10,circle,LimeGreen,LightGray,11.058823529411764
-CK2a1,202,circle,LimeGreen,LightGray,33.64705882352941
-CK2a2,24,circle,LimeGreen,LightGray,12.705882352941176
-CLK1,34,circle,LimeGreen,LightGray,13.882352941176471
-CLK2,6,circle,LimeGreen,LightGray,10.588235294117647
-CLK3,19,circle,LimeGreen,LightGray,12.117647058823529
-CLK4,1,circle,LimeGreen,LightGray,10.0
-COT,3,circle,LimeGreen,LightGray,10.235294117647058
-CRK7,8,circle,LimeGreen,LightGray,10.823529411764707
-CSK,3,circle,LimeGreen,LightGray,10.235294117647058
-CaMK1a,4,circle,LimeGreen,LightGray,10.352941176470589
-CaMK1d,5,circle,LimeGreen,LightGray,10.470588235294118
-CaMK1g,1,circle,LimeGreen,LightGray,10.0
-CaMK2a,18,circle,LimeGreen,LightGray,12.0
-CaMK2b,1,circle,LimeGreen,LightGray,10.0
-CaMK2d,5,circle,LimeGreen,LightGray,10.470588235294118
-CaMK2g,1,circle,LimeGreen,LightGray,10.0
-CaMK4,1,circle,LimeGreen,LightGray,10.0
-CaMKK1,2,circle,LimeGreen,LightGray,10.117647058823529
-CaMKK2,16,circle,LimeGreen,LightGray,11.764705882352942
-DAPK1,58,circle,LimeGreen,LightGray,16.705882352941178
-DAPK2,10,circle,LimeGreen,LightGray,11.058823529411764
-DAPK3,7,circle,LimeGreen,LightGray,10.705882352941176
-DCLK1,5,circle,LimeGreen,LightGray,10.470588235294118
-DDR1,23,circle,LimeGreen,LightGray,12.588235294117647
-DDR2,2,circle,LimeGreen,LightGray,10.117647058823529
-DLK,6,circle,LimeGreen,LightGray,10.588235294117647
-DMPK1,1,circle,LimeGreen,LightGray,10.0
-DNAPK,22,circle,LimeGreen,LightGray,12.470588235294118
-DRAK2,7,circle,LimeGreen,LightGray,10.705882352941176
-DYRK1A,71,circle,LimeGreen,LightGray,18.235294117647058
-DYRK2,11,circle,LimeGreen,LightGray,11.176470588235293
-DYRK3,1,circle,LimeGreen,LightGray,10.0
-EGFR,199,circle,LimeGreen,LightGray,33.294117647058826
-EphA2,56,circle,LimeGreen,LightGray,16.47058823529412
-EphA3,27,circle,LimeGreen,LightGray,13.058823529411764
-EphA4,4,circle,LimeGreen,LightGray,10.352941176470589
-EphA5,1,circle,LimeGreen,LightGray,10.0
-EphA7,5,circle,LimeGreen,LightGray,10.470588235294118
-EphA8,1,circle,LimeGreen,LightGray,10.0
-EphB1,6,circle,LimeGreen,LightGray,10.588235294117647
-EphB2,3,circle,LimeGreen,LightGray,10.235294117647058
-EphB3,3,circle,LimeGreen,LightGray,10.235294117647058
-EphB4,19,circle,LimeGreen,LightGray,12.117647058823529
-ErbB2,3,circle,LimeGreen,LightGray,10.235294117647058
-ErbB3,7,circle,LimeGreen,LightGray,10.705882352941176
-ErbB4,4,circle,LimeGreen,LightGray,10.352941176470589
-Erk1,3,circle,LimeGreen,LightGray,10.235294117647058
-Erk2,111,circle,LimeGreen,LightGray,22.94117647058824
-Erk3,5,circle,LimeGreen,LightGray,10.470588235294118
-Erk5,11,circle,LimeGreen,LightGray,11.176470588235293
-FAK,24,circle,LimeGreen,LightGray,12.705882352941176
-FES,5,circle,LimeGreen,LightGray,10.470588235294118
-FGFR1,56,circle,LimeGreen,LightGray,16.47058823529412
-FGFR2,30,circle,LimeGreen,LightGray,13.411764705882353
-FGFR3,4,circle,LimeGreen,LightGray,10.352941176470589
-FGFR4,29,circle,LimeGreen,LightGray,13.294117647058824
-FLT1,1,circle,LimeGreen,LightGray,10.0
-FLT3,6,circle,LimeGreen,LightGray,10.588235294117647
-FMS,17,circle,LimeGreen,LightGray,11.882352941176471
-FRAP,22,circle,LimeGreen,LightGray,12.470588235294118
-FYN,1,circle,LimeGreen,LightGray,10.0
-GAK,7,circle,LimeGreen,LightGray,10.705882352941176
-GCN2,3,circle,LimeGreen,LightGray,10.235294117647058
-GPRK4,1,circle,LimeGreen,LightGray,10.0
-GPRK5,3,circle,LimeGreen,LightGray,10.235294117647058
-GPRK6,3,circle,LimeGreen,LightGray,10.235294117647058
-GSK3B,74,circle,LimeGreen,LightGray,18.588235294117645
-HCK,25,circle,LimeGreen,LightGray,12.823529411764707
-HGK,17,circle,LimeGreen,LightGray,11.882352941176471
-HH498,3,circle,LimeGreen,LightGray,10.235294117647058
-HIPK2,2,circle,LimeGreen,LightGray,10.117647058823529
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-Haspin,29,circle,LimeGreen,LightGray,13.294117647058824
-IGF1R,23,circle,LimeGreen,LightGray,12.588235294117647
-IKKa,4,circle,LimeGreen,LightGray,10.352941176470589
-IKKb,2,circle,LimeGreen,LightGray,10.117647058823529
-ILK,4,circle,LimeGreen,LightGray,10.352941176470589
-INSR,23,circle,LimeGreen,LightGray,12.588235294117647
-IRAK1,1,circle,LimeGreen,LightGray,10.0
-IRAK3,2,circle,LimeGreen,LightGray,10.117647058823529
-IRAK4,59,circle,LimeGreen,LightGray,16.823529411764707
-IRE1,23,circle,LimeGreen,LightGray,12.588235294117647
-ITK,33,circle,LimeGreen,LightGray,13.76470588235294
-JAK1,43,circle,LimeGreen,LightGray,14.941176470588236
-JAK1-b,2,circle,LimeGreen,LightGray,10.117647058823529
-JAK2,85,circle,LimeGreen,LightGray,19.88235294117647
-JAK2-b,28,circle,LimeGreen,LightGray,13.176470588235293
-JAK3,38,circle,LimeGreen,LightGray,14.352941176470587
-JNK1,34,circle,LimeGreen,LightGray,13.882352941176471
-JNK2,3,circle,LimeGreen,LightGray,10.235294117647058
-JNK3,57,circle,LimeGreen,LightGray,16.58823529411765
-KDR,42,circle,LimeGreen,LightGray,14.823529411764707
-KHS2,1,circle,LimeGreen,LightGray,10.0
-KIT,22,circle,LimeGreen,LightGray,12.470588235294118
-KSR1,6,circle,LimeGreen,LightGray,10.588235294117647
-KSR2,8,circle,LimeGreen,LightGray,10.823529411764707
-LCK,35,circle,LimeGreen,LightGray,14.0
-LIMK1,6,circle,LimeGreen,LightGray,10.588235294117647
-LIMK2,2,circle,LimeGreen,LightGray,10.117647058823529
-LKB1,1,circle,LimeGreen,LightGray,10.0
-LMR3,1,circle,LimeGreen,LightGray,10.0
-LOK,13,circle,LimeGreen,LightGray,11.411764705882353
-LRRK2,8,circle,LimeGreen,LightGray,10.823529411764707
-LYN,6,circle,LimeGreen,LightGray,10.588235294117647
-MAP2K1,65,circle,LimeGreen,LightGray,17.52941176470588
-MAP2K2,1,circle,LimeGreen,LightGray,10.0
-MAP2K4,3,circle,LimeGreen,LightGray,10.235294117647058
-MAP2K6,3,circle,LimeGreen,LightGray,10.235294117647058
-MAP2K7,29,circle,LimeGreen,LightGray,13.294117647058824
-MAP3K5,22,circle,LimeGreen,LightGray,12.470588235294118
-MAPKAPK2,26,circle,LimeGreen,LightGray,12.941176470588236
-MAPKAPK3,4,circle,LimeGreen,LightGray,10.352941176470589
-MARK1,2,circle,LimeGreen,LightGray,10.117647058823529
-MARK2,4,circle,LimeGreen,LightGray,10.352941176470589
-MARK3,4,circle,LimeGreen,LightGray,10.352941176470589
-MARK4,1,circle,LimeGreen,LightGray,10.0
-MASTL,1,circle,LimeGreen,LightGray,10.0
-MELK,33,circle,LimeGreen,LightGray,13.76470588235294
-MER,32,circle,LimeGreen,LightGray,13.647058823529411
-MET,83,circle,LimeGreen,LightGray,19.647058823529413
-MLK1,2,circle,LimeGreen,LightGray,10.117647058823529
-MLK4,1,circle,LimeGreen,LightGray,10.0
-MLKL,13,circle,LimeGreen,LightGray,11.411764705882353
-MNK1,2,circle,LimeGreen,LightGray,10.117647058823529
-MNK2,12,circle,LimeGreen,LightGray,11.294117647058824
-MPSK1,1,circle,LimeGreen,LightGray,10.0
-MRCKb,6,circle,LimeGreen,LightGray,10.588235294117647
-MSK1,1,circle,LimeGreen,LightGray,10.0
-MSK1-b,2,circle,LimeGreen,LightGray,10.117647058823529
-MST1,2,circle,LimeGreen,LightGray,10.117647058823529
-MST2,4,circle,LimeGreen,LightGray,10.352941176470589
-MST3,34,circle,LimeGreen,LightGray,13.882352941176471
-MST4,5,circle,LimeGreen,LightGray,10.470588235294118
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-NDR1,1,circle,LimeGreen,LightGray,10.0
-NEK1,2,circle,LimeGreen,LightGray,10.117647058823529
-NEK2,30,circle,LimeGreen,LightGray,13.411764705882353
-NEK7,8,circle,LimeGreen,LightGray,10.823529411764707
-NIK,17,circle,LimeGreen,LightGray,11.882352941176471
-OSR1,2,circle,LimeGreen,LightGray,10.117647058823529
-PAK1,27,circle,LimeGreen,LightGray,13.058823529411764
-PAK3,1,circle,LimeGreen,LightGray,10.0
-PAK4,39,circle,LimeGreen,LightGray,14.470588235294118
-PAK5,1,circle,LimeGreen,LightGray,10.0
-PAK6,3,circle,LimeGreen,LightGray,10.235294117647058
-PASK,1,circle,LimeGreen,LightGray,10.0
-PBK,1,circle,LimeGreen,LightGray,10.0
-PCTAIRE1,2,circle,LimeGreen,LightGray,10.117647058823529
-PDGFRa,7,circle,LimeGreen,LightGray,10.705882352941176
-PDK1,64,circle,LimeGreen,LightGray,17.41176470588235
-PEK,11,circle,LimeGreen,LightGray,11.176470588235293
-PHKg2,1,circle,LimeGreen,LightGray,10.0
-PI4K2A,6,circle,LimeGreen,LightGray,10.588235294117647
-PI4K2B,1,circle,LimeGreen,LightGray,10.0
-PI4KA,1,circle,LimeGreen,LightGray,10.0
-PI4KB,13,circle,LimeGreen,LightGray,11.411764705882353
-PIK3C3,14,circle,LimeGreen,LightGray,11.529411764705882
-PIK3R4,1,circle,LimeGreen,LightGray,10.0
-PIM1,171,circle,LimeGreen,LightGray,30.0
-PIM2,2,circle,LimeGreen,LightGray,10.117647058823529
-PKACa,118,circle,LimeGreen,LightGray,23.764705882352942
-PKCa,2,circle,LimeGreen,LightGray,10.117647058823529
-PKCb,1,circle,LimeGreen,LightGray,10.0
-PKCh,1,circle,LimeGreen,LightGray,10.0
-PKCi,9,circle,LimeGreen,LightGray,10.941176470588236
-PKCt,5,circle,LimeGreen,LightGray,10.470588235294118
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-PKN2,1,circle,LimeGreen,LightGray,10.0
-PKR,5,circle,LimeGreen,LightGray,10.470588235294118
-PLK1,14,circle,LimeGreen,LightGray,11.529411764705882
-PLK2,5,circle,LimeGreen,LightGray,10.470588235294118
-PLK3,1,circle,LimeGreen,LightGray,10.0
-PLK4,3,circle,LimeGreen,LightGray,10.235294117647058
-PRP4,7,circle,LimeGreen,LightGray,10.705882352941176
-PRPK,1,circle,LimeGreen,LightGray,10.0
-PYK2,12,circle,LimeGreen,LightGray,11.294117647058824
-RAF1,1,circle,LimeGreen,LightGray,10.0
-RET,21,circle,LimeGreen,LightGray,12.352941176470589
-RIOK1,8,circle,LimeGreen,LightGray,10.823529411764707
-RIOK2,6,circle,LimeGreen,LightGray,10.588235294117647
-RIPK1,14,circle,LimeGreen,LightGray,11.529411764705882
-RIPK2,23,circle,LimeGreen,LightGray,12.588235294117647
-RIPK3,3,circle,LimeGreen,LightGray,10.235294117647058
-RNAseL,2,circle,LimeGreen,LightGray,10.117647058823529
-ROCK1,24,circle,LimeGreen,LightGray,12.705882352941176
-ROCK2,9,circle,LimeGreen,LightGray,10.941176470588236
-RON,1,circle,LimeGreen,LightGray,10.0
-ROR1,1,circle,LimeGreen,LightGray,10.0
-ROR2,2,circle,LimeGreen,LightGray,10.117647058823529
-ROS,2,circle,LimeGreen,LightGray,10.117647058823529
-RSK2,9,circle,LimeGreen,LightGray,10.941176470588236
-RSK2-b,10,circle,LimeGreen,LightGray,11.058823529411764
-RSK3,3,circle,LimeGreen,LightGray,10.235294117647058
-RSK3-b,3,circle,LimeGreen,LightGray,10.235294117647058
-RSK4,3,circle,LimeGreen,LightGray,10.235294117647058
-RYK,1,circle,LimeGreen,LightGray,10.0
-SGK1,3,circle,LimeGreen,LightGray,10.235294117647058
-SGK196,2,circle,LimeGreen,LightGray,10.117647058823529
-SLK,6,circle,LimeGreen,LightGray,10.588235294117647
-SMG1,4,circle,LimeGreen,LightGray,10.352941176470589
-SNRK,1,circle,LimeGreen,LightGray,10.0
-SRC,20,circle,LimeGreen,LightGray,12.235294117647058
-SRPK1,9,circle,LimeGreen,LightGray,10.941176470588236
-SRPK2,2,circle,LimeGreen,LightGray,10.117647058823529
-STLK3,2,circle,LimeGreen,LightGray,10.117647058823529
-STLK5,2,circle,LimeGreen,LightGray,10.117647058823529
-SYK,77,circle,LimeGreen,LightGray,18.941176470588236
-SgK085,1,circle,LimeGreen,LightGray,10.0
-SgK223,1,circle,LimeGreen,LightGray,10.0
-SgK269,1,circle,LimeGreen,LightGray,10.0
-SgK495,1,circle,LimeGreen,LightGray,10.0
-TAK1,22,circle,LimeGreen,LightGray,12.470588235294118
-TAO3,1,circle,LimeGreen,LightGray,10.0
-TBK1,24,circle,LimeGreen,LightGray,12.705882352941176
-TGFbR1,34,circle,LimeGreen,LightGray,13.882352941176471
-TGFbR2,6,circle,LimeGreen,LightGray,10.588235294117647
-TIE2,8,circle,LimeGreen,LightGray,10.823529411764707
-TLK2,1,circle,LimeGreen,LightGray,10.0
-TNIK,6,circle,LimeGreen,LightGray,10.588235294117647
-TRKA,45,circle,LimeGreen,LightGray,15.176470588235293
-TRKB,4,circle,LimeGreen,LightGray,10.352941176470589
-TRKC,4,circle,LimeGreen,LightGray,10.352941176470589
-TTBK1,7,circle,LimeGreen,LightGray,10.705882352941176
-TTBK2,2,circle,LimeGreen,LightGray,10.117647058823529
-TTK,74,circle,LimeGreen,LightGray,18.588235294117645
-TTN,3,circle,LimeGreen,LightGray,10.235294117647058
-TYK2,26,circle,LimeGreen,LightGray,12.941176470588236
-TYK2-b,16,circle,LimeGreen,LightGray,11.764705882352942
-TYRO3,3,circle,LimeGreen,LightGray,10.235294117647058
-Trb1,3,circle,LimeGreen,LightGray,10.235294117647058
-ULK1,5,circle,LimeGreen,LightGray,10.470588235294118
-ULK2,4,circle,LimeGreen,LightGray,10.352941176470589
-ULK3,2,circle,LimeGreen,LightGray,10.117647058823529
-ULK4,2,circle,LimeGreen,LightGray,10.117647058823529
-VRK1,23,circle,LimeGreen,LightGray,12.588235294117647
-VRK2,4,circle,LimeGreen,LightGray,10.352941176470589
-VRK3,1,circle,LimeGreen,LightGray,10.0
-Wee1,21,circle,LimeGreen,LightGray,12.352941176470589
-Wee1B,1,circle,LimeGreen,LightGray,10.0
-Wnk1,4,circle,LimeGreen,LightGray,10.352941176470589
-Wnk3,6,circle,LimeGreen,LightGray,10.588235294117647
-YANK1,1,circle,LimeGreen,LightGray,10.0
-YSK1,2,circle,LimeGreen,LightGray,10.117647058823529
-ZAK,4,circle,LimeGreen,LightGray,10.352941176470589
-ZAP70,3,circle,LimeGreen,LightGray,10.235294117647058
-p110a,56,circle,LimeGreen,LightGray,16.47058823529412
-p110b,2,circle,LimeGreen,LightGray,10.117647058823529
-p110d,71,circle,LimeGreen,LightGray,18.235294117647058
-p110g,102,circle,LimeGreen,LightGray,21.88235294117647
-p38a,360,circle,LimeGreen,LightGray,52.23529411764706
-p38b,3,circle,LimeGreen,LightGray,10.235294117647058
-p38d,7,circle,LimeGreen,LightGray,10.705882352941176
-p38g,3,circle,LimeGreen,LightGray,10.235294117647058
-p70S6K,18,circle,LimeGreen,LightGray,12.0
+xName,size
+AAK1,10.470588235294118
+ABL1,16.705882352941178
+ABL2,10.352941176470589
+ACK,11.176470588235293
+ACTR2,10.235294117647058
+ACTR2B,10.0
+ADCK3,10.117647058823529
+AKT1,13.294117647058824
+AKT2,11.764705882352942
+ALK,17.058823529411764
+ALK1,10.0
+ALK2,18.470588235294116
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+ATM,10.705882352941176
+ATR,10.0
+AXL,10.0
+AurA,30.352941176470587
+AurB,10.0
+AurC,10.117647058823529
+BARK1,12.0
+BIKE,10.470588235294118
+BMPR1B,10.0
+BMPR2,10.117647058823529
+BMX,10.235294117647058
+BRAF,21.294117647058826
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+BRSK2,10.117647058823529
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+CASK,11.411764705882353
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+CDK2,60.0
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+CDK8,13.529411764705882
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+CDKL5,10.0
+CHED,10.117647058823529
+CHK1,27.294117647058822
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+CK1g2,10.0
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+CK2a1,33.64705882352941
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+COT,10.235294117647058
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+FES,10.470588235294118
+FGFR1,16.47058823529412
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+GPRK4,10.0
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+GSK3B,18.588235294117645
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+IKKa,10.352941176470589
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+PIM1,30.0
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+RET,12.352941176470589
+RIOK1,10.823529411764707
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diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_n_activities_per_kinase.png b/teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_n_activities_per_kinase.png
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diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index dd91bceb..3c42b0d2 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -106,37 +106,15 @@
     "\n",
     "Manning et al. clustered the human protein kinases based on their sequence into [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as Manning kinome tree. The kinase resource KinMap enables mapping of kinase data onto that tree, e.g. the number of of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
+    "![Manning tree with number of structures per kinase (KinMap)](images/kinmap_n_structures_per_kinase.png)\n",
     "\n",
-    " Phylogenetic tree, clustered by overall sequence similarity\n",
-    "    \n",
-    "[TODO add a kinome tree figure from kinhub]\n",
-    "\n",
+    "*Figure 1:* \n",
+    "Number of PDB structures per kinase mapped onto the Manning kinome tree using KinMap.\n",
     "<!---\n",
-    "\n",
+    "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -145,7 +123,7 @@
    "source": [
     "#### Kinase structures and important motifs\n",
     "\n",
-    "Kinases sequences and structures are highly conserved. Important regions in the kinase pocket include:\n",
+    "Kinases sequences and structures are highly conserved. Important regions in the kinase pocket include (Figure 2):\n",
     "\n",
     "- Hinge region: Forms key hydrogen bonds to ligands \n",
     "- DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state\n",
@@ -154,7 +132,7 @@
     "\n",
     "![Kinase structure with key motifs](images/T023_kinase_structure.png)\n",
     "\n",
-    "*Figure 1:* \n",
+    "*Figure 2:* \n",
     "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop (example: CDK2, PDB ID: 1FIN)\n",
     "\n",
     "<!---\n",
@@ -221,6 +199,19 @@
     "* maybe an overview of data points per kinase on Chembl (from kinodata)\n",
     "* other profiling data (as available in karaman, xxx)\n",
     "\n",
+    "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
+    "\n",
+    "*Figure 3:* \n",
+    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "<!---\n",
+    "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
+    "--->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "- Karaman et al. dataset: TODO\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Davis_profiling.js)\n",
@@ -489,7 +480,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Generate data for number of structures per kinase in the KinMap format to be mapped onto the kinome tree."
+    "### KinMap data\n",
+    "\n",
+    "There are some KinMap trees shown in this notebook. Please find in the appendix the code to generate the KinMap CSV files to be uploaded to KinMap:\n",
+    "http://www.kinhub.org/kinmap\n",
+    "\n",
+    "Note: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 xxx.svg xxx.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
    ]
   },
   {
@@ -497,39 +493,110 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "def format_for_kinmap(kinase_names, kinase_values, size_min=10, size_max=50):\n",
+    "    \"\"\"\n",
+    "    Take kinase names and some associated values and generate a KinMap data file\n",
+    "    that will display values as circles of size [`size_min`, `size_max`].\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_names : list of str\n",
+    "        Kinase names.\n",
+    "    kinase_values : list of float\n",
+    "        Some associated values.\n",
+    "    size_min : int\n",
+    "        Minimum circle size on KinMap tree (minimum input value will be scaled to `size_min`).\n",
+    "    size_max : int\n",
+    "        Maximum circle size on KinMap tree (maximum input value will be scaled to `size_min`).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    pandas.DataFrame\n",
+    "        KinMap data with columns `xName` (kinase name), `size` (circle size for KinMap tree).\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    data = pd.DataFrame({\"xName\": kinase_names, \"values\": kinase_values})\n",
+    "    min_ = data[\"values\"].min()\n",
+    "    max_ = data[\"values\"].max()\n",
+    "    data[\"size\"] = data[\"values\"].apply(\n",
+    "        lambda x: ((x - min_)/(max_ - min_) * size_max) + size_min\n",
+    "    )\n",
+    "    return data[[\"xName\", \"size\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of PDB structures per kinase\n",
+    "\n",
+    "Generate data for number of structures per kinase in the KinMap format to be mapped onto the kinome tree."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
+    "\n",
     "klifs = setup_remote()\n",
     "structures_df = klifs.structures.all_structures()\n",
     "\n",
-    "min_scaled = 10\n",
-    "max_scaled = 50\n",
-    "\n",
+    "# Get number of structures per kinase\n",
     "n_structures_per_kinase = structures_df.groupby(\n",
     "    [\"structure.pdb_id\", \"kinase.klifs_name\"]\n",
     ").first().reset_index().groupby(\"kinase.klifs_name\").size()\n",
-    "n_structures_per_kinase.index.name = \"xName\"\n",
-    "n_structures_per_kinase.name = \"n_structures\"\n",
-    "n_structures_per_kinase = n_structures_per_kinase.reset_index()\n",
-    "n_structures_per_kinase[\"shape\"] = \"circle\"\n",
-    "n_structures_per_kinase[\"fill\"] = \"LimeGreen\"\n",
-    "n_structures_per_kinase[\"stroke\"] = \"LightGray\"\n",
-    "min_ = n_structures_per_kinase[\"n_structures\"].min()\n",
-    "max_ = n_structures_per_kinase[\"n_structures\"].max()\n",
-    "n_structures_per_kinase[\"size\"] = n_structures_per_kinase[\"n_structures\"].apply(\n",
-    "    lambda x: ((x - min_)/(max_ - min_) * max_scaled) + min_scaled\n",
-    ")\n",
     "\n",
-    "n_structures_per_kinase.to_csv(DATA / \"kinmap_n_structures_per_kinase.csv\", index=None)\n",
+    "# Save in KinMap format\n",
+    "kinmap_n_structures_per_kinase = format_for_kinmap(n_structures_per_kinase.index, n_structures_per_kinase.values)\n",
+    "kinmap_n_structures_per_kinase.to_csv(DATA / \"kinmap_n_structures_per_kinase.csv\", index=None)\n",
     "# Some kinases will not be resolved in KinMap and will be simply dropped"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Number of ChEMBL bioactivities per kinase\n",
+    "\n",
+    "Generate data for number of ChEMBL bioactivities per kinase in the KinMap format to be mapped onto the kinome tree."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from opencadd.databases.klifs import setup_remote\n",
+    "\n",
+    "# Get bioactivity data\n",
+    "path = \"https://github.com/openkinome/kinodata/releases/download/v0.3/activities-chembl29_v0.3.zip\"\n",
+    "data = pd.read_csv(path, index_col=None)\n",
+    "data = data[data[\"activities.standard_type\"] == \"pIC50\"]\n",
+    "data = data.dropna()\n",
+    "\n",
+    "# Get kinase data\n",
+    "klifs = setup_remote()\n",
+    "kinases_df = klifs.kinases.all_kinases()\n",
+    "kinases_df = kinases_df[kinases_df[\"kinase.uniprot\"] != \"0\"]\n",
+    "# Some UniProt IDs have several names in KLIFS, keep only first\n",
+    "kinases_df = kinases_df.groupby(\"kinase.uniprot\").first()\n",
+    "\n",
+    "# Map UniProt ID > kinase KLIFS name\n",
+    "data = pd.merge(data, kinases_df, left_on=\"UniprotID\", right_on=\"kinase.uniprot\", how=\"left\")\n",
+    "\n",
+    "# Get number of activities per kinase\n",
+    "n_activities_per_kinase = data.groupby(\"kinase.klifs_name\").size()\n",
+    "\n",
+    "# Save in KinMap format\n",
+    "kinmap_n_activities_per_kinase = format_for_kinmap(n_activities_per_kinase.index, n_activities_per_kinase.values)\n",
+    "kinmap_n_activities_per_kinase.to_csv(DATA / \"kinmap_n_activities_per_kinase.csv\", index=None)\n",
+    "# Some kinases will not be resolved in KinMap and will be simply dropped"
+   ]
   }
  ],
  "metadata": {

From d6cdf6974ba88d4ace6066feaebe14899a602230 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 15:43:51 +0200
Subject: [PATCH 081/158] T023/25/26: Satisfy black-nb

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 28 ++++++++++++-------
 .../talktorial.ipynb                          |  4 ++-
 .../talktorial.ipynb                          | 14 +++++++---
 3 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 3c42b0d2..5d73256b 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -452,7 +452,7 @@
     }
    ],
    "source": [
-    "kinase_selection_df = pd.read_csv(DATA/\"kinase_selection.csv\")\n",
+    "kinase_selection_df = pd.read_csv(DATA / \"kinase_selection.csv\")\n",
     "kinase_selection_df"
    ]
   },
@@ -498,7 +498,7 @@
     "    \"\"\"\n",
     "    Take kinase names and some associated values and generate a KinMap data file\n",
     "    that will display values as circles of size [`size_min`, `size_max`].\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    kinase_names : list of str\n",
@@ -509,18 +509,18 @@
     "        Minimum circle size on KinMap tree (minimum input value will be scaled to `size_min`).\n",
     "    size_max : int\n",
     "        Maximum circle size on KinMap tree (maximum input value will be scaled to `size_min`).\n",
-    "    \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    pandas.DataFrame\n",
     "        KinMap data with columns `xName` (kinase name), `size` (circle size for KinMap tree).\n",
     "    \"\"\"\n",
-    "    \n",
+    "\n",
     "    data = pd.DataFrame({\"xName\": kinase_names, \"values\": kinase_values})\n",
     "    min_ = data[\"values\"].min()\n",
     "    max_ = data[\"values\"].max()\n",
     "    data[\"size\"] = data[\"values\"].apply(\n",
-    "        lambda x: ((x - min_)/(max_ - min_) * size_max) + size_min\n",
+    "        lambda x: ((x - min_) / (max_ - min_) * size_max) + size_min\n",
     "    )\n",
     "    return data[[\"xName\", \"size\"]]"
    ]
@@ -546,12 +546,18 @@
     "structures_df = klifs.structures.all_structures()\n",
     "\n",
     "# Get number of structures per kinase\n",
-    "n_structures_per_kinase = structures_df.groupby(\n",
-    "    [\"structure.pdb_id\", \"kinase.klifs_name\"]\n",
-    ").first().reset_index().groupby(\"kinase.klifs_name\").size()\n",
+    "n_structures_per_kinase = (\n",
+    "    structures_df.groupby([\"structure.pdb_id\", \"kinase.klifs_name\"])\n",
+    "    .first()\n",
+    "    .reset_index()\n",
+    "    .groupby(\"kinase.klifs_name\")\n",
+    "    .size()\n",
+    ")\n",
     "\n",
     "# Save in KinMap format\n",
-    "kinmap_n_structures_per_kinase = format_for_kinmap(n_structures_per_kinase.index, n_structures_per_kinase.values)\n",
+    "kinmap_n_structures_per_kinase = format_for_kinmap(\n",
+    "    n_structures_per_kinase.index, n_structures_per_kinase.values\n",
+    ")\n",
     "kinmap_n_structures_per_kinase.to_csv(DATA / \"kinmap_n_structures_per_kinase.csv\", index=None)\n",
     "# Some kinases will not be resolved in KinMap and will be simply dropped"
    ]
@@ -593,7 +599,9 @@
     "n_activities_per_kinase = data.groupby(\"kinase.klifs_name\").size()\n",
     "\n",
     "# Save in KinMap format\n",
-    "kinmap_n_activities_per_kinase = format_for_kinmap(n_activities_per_kinase.index, n_activities_per_kinase.values)\n",
+    "kinmap_n_activities_per_kinase = format_for_kinmap(\n",
+    "    n_activities_per_kinase.index, n_activities_per_kinase.values\n",
+    ")\n",
     "kinmap_n_activities_per_kinase.to_csv(DATA / \"kinmap_n_activities_per_kinase.csv\", index=None)\n",
     "# Some kinases will not be resolved in KinMap and will be simply dropped"
    ]
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 871c2ae7..7af60c5a 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -1446,7 +1446,9 @@
     }
    ],
    "source": [
-    "print(f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\")"
+    "print(\n",
+    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
+    ")"
    ]
   },
   {
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index d389ca26..e174f9ac 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -516,7 +516,9 @@
    ],
    "source": [
     "# Load frozen dataset\n",
-    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\"structure.klifs_id\"].to_list()\n",
+    "structure_klifs_ids = pd.read_csv(DATA / \"frozen_structure_klifs_ids.csv\")[\n",
+    "    \"structure.klifs_id\"\n",
+    "].to_list()\n",
     "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -992,7 +994,9 @@
    ],
    "source": [
     "# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\n",
-    "ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\"interaction.fingerprint\"]\n",
+    "ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n",
+    "    \"interaction.fingerprint\"\n",
+    "]\n",
     "ifp_series.head()"
    ]
   },
@@ -1395,7 +1399,9 @@
     }
    ],
    "source": [
-    "print(f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\")"
+    "print(\n",
+    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
+    ")"
    ]
   },
   {
@@ -1759,7 +1765,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\") \n",
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },

From 462b8d4ed1ec8d31d8e0a34a3a18f1ebbc2d400f Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 16:07:58 +0200
Subject: [PATCH 082/158] Update headers.

---
 .../T024_kinase_similarity_sequence/talktorial.ipynb          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 8ada386d..90cf6a9a 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -766,7 +766,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kinase similarity"
+    "### Kinase similarity"
    ]
   },
   {
@@ -1596,7 +1596,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kinase distance matrix"
+    "### Kinase distance matrix"
    ]
   },
   {

From 03847bc5f92e0769c8abd5d4b36235cd54644acf Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 16:09:16 +0200
Subject: [PATCH 083/158] Revert "Update headers."

This reverts commit 462b8d4ed1ec8d31d8e0a34a3a18f1ebbc2d400f.
---
 .../T024_kinase_similarity_sequence/talktorial.ipynb          | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 90cf6a9a..8ada386d 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -766,7 +766,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity"
+    "## Kinase similarity"
    ]
   },
   {
@@ -1596,7 +1596,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase distance matrix"
+    "## Kinase distance matrix"
    ]
   },
   {

From e6415fd961541f58ec8cbc87c8720567ea97548c Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 16:15:49 +0200
Subject: [PATCH 084/158] address some comments of AV.

---
 .../talktorial.ipynb                          | 2573 +++++++++++++++--
 1 file changed, 2401 insertions(+), 172 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index c41ef2d7..5a7579d1 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -32,7 +32,8 @@
     "* Bioactivity data\n",
     "* Kinase similarity descriptor: ligand-profile\n",
     "    * Kinase similarity\n",
-    "    * Kinase promiscuity"
+    "    * Kinase promiscuity\n",
+    "* From similarity matrix to distance matrix"
    ]
   },
   {
@@ -60,7 +61,8 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* [ChEMBL](https://www.ebi.ac.uk/chembl/) database"
+    "* [ChEMBL](https://www.ebi.ac.uk/chembl/) database\n",
+    "* Kinmap: http://www.kinhub.org/kinmap/"
    ]
   },
   {
@@ -81,31 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
-    "\n",
-    " \n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
-    "\n",
-    " \n",
-    "\n",
-    "*Table 1:* \n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
-    "\n",
-    " \n",
-    "\n",
-    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
-    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
-    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
-    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
-    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
-    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
-    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
-    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
-    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
-    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
-    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+    "We use the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
@@ -119,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL28 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
+    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL29 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
     "For more details on querying the ChEMBL database, please refer to [Talktorial T001](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb)."
    ]
   },
@@ -171,6 +149,15 @@
     "Computing the similarity between a kinase and itself may be interpreted as kinase promiscuity, where the similarity described above would therefore represent the fraction of active compounds over all tested compounds."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From similarity matrix to distance matrix\n",
+    "\n",
+    "As discussed in __Talktorial T024__, we use convert the similarity matrix to a distance matrix."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -205,24 +192,398 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Retrieve the data"
+    "### Define the kinases of interest"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.28 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.2."
+    "Let's load the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/\\\n",
+    "data/kinase_selection.csv\")\n",
+    "kinase_selection_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
+   "source": [
+    "### Retrieve the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.29 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (190634, 17)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>activities.activity_id</th>\n",
+       "      <th>assays.chembl_id</th>\n",
+       "      <th>target_dictionary.chembl_id</th>\n",
+       "      <th>molecule_dictionary.chembl_id</th>\n",
+       "      <th>molecule_dictionary.max_phase</th>\n",
+       "      <th>activities.standard_type</th>\n",
+       "      <th>activities.standard_value</th>\n",
+       "      <th>activities.standard_units</th>\n",
+       "      <th>compound_structures.canonical_smiles</th>\n",
+       "      <th>compound_structures.standard_inchi</th>\n",
+       "      <th>component_sequences.sequence</th>\n",
+       "      <th>assays.confidence_score</th>\n",
+       "      <th>docs.chembl_id</th>\n",
+       "      <th>docs.year</th>\n",
+       "      <th>docs.authors</th>\n",
+       "      <th>UniprotID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>96698</td>\n",
+       "      <td>16291323</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666724</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.096910</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>94326</td>\n",
+       "      <td>16264754</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666728</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>98119</td>\n",
+       "      <td>16306943</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL1968705</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>101161</td>\n",
+       "      <td>16340050</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL1997433</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>13.958607</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>96324</td>\n",
+       "      <td>16287186</td>\n",
+       "      <td>CHEMBL3705523</td>\n",
+       "      <td>CHEMBL2973</td>\n",
+       "      <td>CHEMBL3666721</td>\n",
+       "      <td>0</td>\n",
+       "      <td>pIC50</td>\n",
+       "      <td>13.920819</td>\n",
+       "      <td>nM</td>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...</td>\n",
+       "      <td>MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>CHEMBL3639077</td>\n",
+       "      <td>2014.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  activities.activity_id assays.chembl_id  \\\n",
+       "0       96698                16291323    CHEMBL3705523   \n",
+       "1       94326                16264754    CHEMBL3705523   \n",
+       "2       98119                16306943    CHEMBL3705523   \n",
+       "3      101161                16340050    CHEMBL3705523   \n",
+       "4       96324                16287186    CHEMBL3705523   \n",
+       "\n",
+       "  target_dictionary.chembl_id molecule_dictionary.chembl_id  \\\n",
+       "0                  CHEMBL2973                 CHEMBL3666724   \n",
+       "1                  CHEMBL2973                 CHEMBL3666728   \n",
+       "2                  CHEMBL2973                 CHEMBL1968705   \n",
+       "3                  CHEMBL2973                 CHEMBL1997433   \n",
+       "4                  CHEMBL2973                 CHEMBL3666721   \n",
+       "\n",
+       "   molecule_dictionary.max_phase activities.standard_type  \\\n",
+       "0                              0                    pIC50   \n",
+       "1                              0                    pIC50   \n",
+       "2                              0                    pIC50   \n",
+       "3                              0                    pIC50   \n",
+       "4                              0                    pIC50   \n",
+       "\n",
+       "   activities.standard_value activities.standard_units  \\\n",
+       "0                  14.096910                        nM   \n",
+       "1                  14.000000                        nM   \n",
+       "2                  14.000000                        nM   \n",
+       "3                  13.958607                        nM   \n",
+       "4                  13.920819                        nM   \n",
+       "\n",
+       "                compound_structures.canonical_smiles  \\\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
+       "\n",
+       "                  compound_structures.standard_inchi  \\\n",
+       "0  InChI=1S/C31H33N7O3/c1-2-4-29(40)33-22-6-3-5-2...   \n",
+       "1  InChI=1S/C34H40N8O3/c1-5-7-32(43)36-24-9-6-8-2...   \n",
+       "2  InChI=1S/C31H33N7O2/c1-2-6-29(39)33-23-8-5-7-2...   \n",
+       "3  InChI=1S/C28H28N6O3/c1-3-5-26(35)30-20-7-4-6-1...   \n",
+       "4  InChI=1S/C32H35N7O2/c1-2-7-30(40)34-24-9-6-8-2...   \n",
+       "\n",
+       "                        component_sequences.sequence  assays.confidence_score  \\\n",
+       "0  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "1  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "2  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "3  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "4  MSRPPPTGKMPGAPETAPGDGAGASRQRKLEALIRDPRSPINVESL...                        9   \n",
+       "\n",
+       "  docs.chembl_id  docs.year docs.authors UniprotID  \n",
+       "0  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "1  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "2  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "3  CHEMBL3639077     2014.0          NaN    O75116  \n",
+       "4  CHEMBL3639077     2014.0          NaN    O75116  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "path = \"https://github.com/openkinome/kinodata/releases/download/\\\n",
-    "v0.2/activities-chembl28_v0.2.zip\"\n",
+    "v0.3/activities-chembl29_v0.3.zip\"\n",
     "data = pd.read_csv(path, index_col=None)\n",
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
@@ -244,9 +605,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Activities: {'pKi', 'pKd', 'pIC50'}\n",
+      "Units: {'nM'}\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
     "      f\"Units: {set(data['activities.standard_units'])}\")"
@@ -261,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,9 +640,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'activities.activity_id', 'assays.chembl_id',\n",
+       "       'target_dictionary.chembl_id', 'molecule_dictionary.chembl_id',\n",
+       "       'molecule_dictionary.max_phase', 'activities.standard_type',\n",
+       "       'activities.standard_value', 'activities.standard_units',\n",
+       "       'compound_structures.canonical_smiles',\n",
+       "       'compound_structures.standard_inchi', 'component_sequences.sequence',\n",
+       "       'assays.confidence_score', 'docs.chembl_id', 'docs.year',\n",
+       "       'docs.authors', 'UniprotID'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data.columns"
    ]
@@ -287,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -295,14 +684,96 @@
     "             \"activities.standard_value\",\n",
     "             \"UniprotID\"]]\n",
     "data = data.rename(columns={\"compound_structures.canonical_smiles\": \"smiles\",\n",
-    "                            \"activities.standard_value\": \"activity_value\"})"
+    "                            \"activities.standard_value\": \"pIC50\"})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (160857, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>pIC50</th>\n",
+       "      <th>UniprotID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.096910</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.958607</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...</td>\n",
+       "      <td>13.920819</td>\n",
+       "      <td>O75116</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              smiles      pIC50 UniprotID\n",
+       "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  14.096910    O75116\n",
+       "1  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  14.000000    O75116\n",
+       "2  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  14.000000    O75116\n",
+       "3  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  13.958607    O75116\n",
+       "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  13.920819    O75116"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
@@ -317,9 +788,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (160703, 3)\n"
+     ]
+    }
+   ],
    "source": [
     "data = data.dropna()\n",
     "print(f\"Current shape of data: {data.shape}\")"
@@ -329,50 +808,107 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Kinases of interest\n",
-    "\n",
-    "We focus on the kinases of interest and map them to their [UniProt](https://www.uniprot.org/) IDs, using Table 1 from the theory."
+    "We keep the data for the query kinases only:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "name_to_uniprot = {'EGFR': 'P00533',\n",
-    "                   'ErbB2': 'P04626',\n",
-    "                   'BRAF': 'P15056',\n",
-    "                   'CDK2': 'P24941',\n",
-    "                   'LCK': 'P06239',\n",
-    "                   'MET': 'P08581',\n",
-    "                   'p38a': 'Q16539',\n",
-    "                   'KDR': 'P35968',\n",
-    "                   'p110a': 'P42336'}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We keep data for these kinases only:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = data[data[\"UniprotID\"].isin(name_to_uniprot.values())]\n",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (33427, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = data[data[\"UniprotID\"].isin(kinase_selection_df.uniprot_id)]\n",
     "print(f\"Current shape of data: {data.shape}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>pIC50</th>\n",
+       "      <th>UniprotID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1</td>\n",
+       "      <td>11.522879</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
+       "      <td>11.221849</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC</td>\n",
+       "      <td>11.221849</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1</td>\n",
+       "      <td>11.096910</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1</td>\n",
+       "      <td>11.096910</td>\n",
+       "      <td>P00533</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  smiles      pIC50 UniprotID\n",
+       "58     Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1  11.522879    P00533\n",
+       "98    CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1  11.221849    P00533\n",
+       "99   CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC  11.221849    P00533\n",
+       "140      CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1  11.096910    P00533\n",
+       "141  Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1  11.096910    P00533"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data.head()"
    ]
@@ -386,11 +922,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "EGFR_data = data[data[\"UniprotID\"] == \"P00533\"]"
+    "EGFR_data = data[data[\"UniprotID\"] == kinase_selection_df.uniprot_id[0]]"
    ]
   },
   {
@@ -402,9 +938,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 39),\n",
+       " ('C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 27),\n",
+       " ('C=CC(=O)Nc1cc(Nc2nccc(-c3cn(C)c4ccccc34)n2)c(OC)cc1N(C)CCN(C)C', 15),\n",
+       " ('C=CC(=O)Nc1cccc(Oc2nc(Nc3ccc(N4CCN(C)CC4)cc3OC)ncc2Cl)c1', 11),\n",
+       " ('CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1', 8)]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "measured_compounds = Counter(EGFR_data[\"smiles\"])\n",
     "measured_compounds.most_common()[0:5]"
@@ -419,12 +970,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>pIC50</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
+       "      <td>5.336488</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
+       "      <td>5.996539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
+       "      <td>8.397940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>7.207608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>8.420216</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  UniprotID                                             smiles     pIC50\n",
+       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...  5.336488\n",
+       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...  5.996539\n",
+       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...  8.397940\n",
+       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  7.207608\n",
+       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  8.420216"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "data = data.groupby([\"UniprotID\",\n",
-    "                     \"smiles\"])['activity_value'].max().reset_index()\n",
+    "                     \"smiles\"])[\"pIC50\"].max().reset_index()\n",
     "data.head()"
    ]
   },
@@ -444,7 +1070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -481,19 +1107,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data[\"activity_binary\"] = data[\"activity_value\"].apply(binarize_pic50,\n",
+    "data[\"activity_binary\"] = data[\"pIC50\"].apply(binarize_pic50,\n",
     "                                                       args=(cutoff, ))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current shape of data: (32916, 4)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UniprotID</th>\n",
+       "      <th>smiles</th>\n",
+       "      <th>pIC50</th>\n",
+       "      <th>activity_binary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...</td>\n",
+       "      <td>5.336488</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...</td>\n",
+       "      <td>5.996539</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...</td>\n",
+       "      <td>8.397940</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>7.207608</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>P00533</td>\n",
+       "      <td>Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...</td>\n",
+       "      <td>8.420216</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  UniprotID                                             smiles     pIC50  \\\n",
+       "0    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1ccc(C(...  5.336488   \n",
+       "1    P00533  Br.CC(Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1cccc2c...  5.996539   \n",
+       "2    P00533  Br.CC[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1...  8.397940   \n",
+       "3    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  7.207608   \n",
+       "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  8.420216   \n",
+       "\n",
+       "   activity_binary  \n",
+       "0                0  \n",
+       "1                0  \n",
+       "2                1  \n",
+       "3                1  \n",
+       "4                1  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
@@ -521,7 +1242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -566,11 +1287,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uniprot_id = name_to_uniprot[\"EGFR\"]\n",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total number of measured compounds: \t5965 \n",
+      "Number of active compounds: \t\t3635 \n",
+      "Fraction of active compounds: \t\t0.61 \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "uniprot_id = kinase_selection_df.uniprot_id[0]\n",
     "EGFR_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
     "print(f\"Total number of measured compounds: \\t\"\n",
     "      f\"{EGFR_metrics[0]} \\n\"\n",
@@ -589,11 +1321,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def from_numbers_to_table(activity_dataframe):\n",
+    "def promiscuity_table(activity_dataframe):\n",
     "    \"\"\"\n",
     "    Create a table with all three values for all kinases.\n",
     "\n",
@@ -607,9 +1339,10 @@
     "    promiscuity_table : pd.DataFrame\n",
     "        A dataframe with the kinases as rows and values as columns.\n",
     "    \"\"\"\n",
-    "    promiscuity_table = pd.DataFrame(index=name_to_uniprot.keys(),\n",
+    "    promiscuity_table = pd.DataFrame(index=kinase_selection_df.kinase_klifs,\n",
     "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
-    "    for name, uniprot_id in name_to_uniprot.items():\n",
+    "    for name, uniprot_id in zip(kinase_selection_df.kinase_klifs,\n",
+    "                                kinase_selection_df.uniprot_id):\n",
     "        values = kinase_to_activity_numbers(uniprot_id, activity_dataframe)\n",
     "        promiscuity_table.loc[name] = values\n",
     "    return promiscuity_table"
@@ -617,12 +1350,122 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kinase_promiscuity_table = from_numbers_to_table(data)\n",
-    "kinase_promiscuity_table"
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total</th>\n",
+       "      <th>actives</th>\n",
+       "      <th>fraction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>5965</td>\n",
+       "      <td>3635</td>\n",
+       "      <td>0.609388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>1700</td>\n",
+       "      <td>1031</td>\n",
+       "      <td>0.606471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>4393</td>\n",
+       "      <td>2827</td>\n",
+       "      <td>0.643524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>7641</td>\n",
+       "      <td>5328</td>\n",
+       "      <td>0.697291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>3688</td>\n",
+       "      <td>2992</td>\n",
+       "      <td>0.81128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>1500</td>\n",
+       "      <td>815</td>\n",
+       "      <td>0.543333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>1560</td>\n",
+       "      <td>935</td>\n",
+       "      <td>0.599359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>2832</td>\n",
+       "      <td>2248</td>\n",
+       "      <td>0.793785</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>3637</td>\n",
+       "      <td>2778</td>\n",
+       "      <td>0.763816</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             total actives  fraction\n",
+       "kinase_klifs                        \n",
+       "EGFR          5965    3635  0.609388\n",
+       "ErbB2         1700    1031  0.606471\n",
+       "p110a         4393    2827  0.643524\n",
+       "KDR           7641    5328  0.697291\n",
+       "BRAF          3688    2992   0.81128\n",
+       "CDK2          1500     815  0.543333\n",
+       "LCK           1560     935  0.599359\n",
+       "MET           2832    2248  0.793785\n",
+       "p38a          3637    2778  0.763816"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_promiscuity_df = promiscuity_table(data)\n",
+    "kinase_promiscuity_df"
    ]
   },
   {
@@ -634,11 +1477,136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kinase_promiscuity_table.style.\\\n",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_64427_row0_col2 {\n",
+       "  background-color: #dadaeb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_64427_row1_col2 {\n",
+       "  background-color: #dcdcec;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_64427_row2_col2 {\n",
+       "  background-color: #bdbedc;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_64427_row3_col2 {\n",
+       "  background-color: #8c88bf;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_64427_row4_col2 {\n",
+       "  background-color: #3f007d;\n",
+       "  color: #f1f1f1;\n",
+       "  background-color: red;\n",
+       "}\n",
+       "#T_64427_row5_col2 {\n",
+       "  background-color: #fcfbfd;\n",
+       "  color: #000000;\n",
+       "  background-color: yellow;\n",
+       "}\n",
+       "#T_64427_row6_col2 {\n",
+       "  background-color: #e1e0ee;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_64427_row7_col2 {\n",
+       "  background-color: #4a1486;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_64427_row8_col2 {\n",
+       "  background-color: #5d3897;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_64427_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >total</th>\n",
+       "      <th class=\"col_heading level0 col1\" >actives</th>\n",
+       "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_64427_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_64427_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_64427_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_64427_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_64427_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_64427_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_64427_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_64427_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_64427_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_64427_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_64427_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_64427_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_64427_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_64427_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_64427_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_64427_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_64427_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_64427_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_64427_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_64427_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_64427_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_64427_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_64427_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_64427_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_64427_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_64427_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_64427_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_64427_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f99f109bf70>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_promiscuity_df.style.\\\n",
     "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
     "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
     "    highlight_min(color=\"yellow\", axis=None, subset=[\"fraction\"]).\\\n",
@@ -649,7 +1617,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the table, we notice that CDK2 and BRAF are the least and most promiscuous kinases, respectively."
+    "From the table, we notice that CDK2 and BRAF are the least (in yellow) and most (in red) promiscuous kinases, respectively."
    ]
   },
   {
@@ -668,7 +1636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,13 +1704,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "uniprot_id1=name_to_uniprot[\"EGFR\"]\n",
-    "uniprot_id2=name_to_uniprot[\"MET\"]\n",
-    "similarity_EGFR_MET = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)\n",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ligand profile similarity between EGFR and MET: 0.23.\n"
+     ]
+    }
+   ],
+   "source": [
+    "similarity_EGFR_MET = similarity_ligand_profile(kinase_selection_df.uniprot_id[0],\n",
+    "                                                kinase_selection_df.uniprot_id[7],\n",
+    "                                                data)\n",
     "print(f\"Ligand profile similarity between EGFR and MET: \"\n",
     "      f\"{similarity_EGFR_MET:.2f}.\")"
    ]
@@ -756,16 +1732,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kinase_similarity_matrix = np.zeros((len(name_to_uniprot),\n",
-    "                                     len(name_to_uniprot)))\n",
-    "for i, kinase_name1 in enumerate(name_to_uniprot):\n",
-    "    for j, kinase_name2 in enumerate(name_to_uniprot):\n",
-    "        uniprot_id1=name_to_uniprot[kinase_name1]\n",
-    "        uniprot_id2=name_to_uniprot[kinase_name2]\n",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No compounds were measured on both kinases, namely P04626 and P42336.\n",
+      "No compounds were measured on both kinases, namely P42336 and P04626.\n"
+     ]
+    }
+   ],
+   "source": [
+    "kinase_similarity_matrix = np.zeros((len(kinase_selection_df),\n",
+    "                                     len(kinase_selection_df)))\n",
+    "for i, uniprot_id1 in enumerate(kinase_selection_df.uniprot_id):\n",
+    "    for j, uniprot_id2 in enumerate(kinase_selection_df.uniprot_id):\n",
     "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(\n",
     "            uniprot_id1,\n",
     "            uniprot_id2,\n",
@@ -774,21 +1757,562 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.609388</td>\n",
+       "      <td>0.565812</td>\n",
+       "      <td>0.072626</td>\n",
+       "      <td>0.350504</td>\n",
+       "      <td>0.457627</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.246032</td>\n",
+       "      <td>0.228261</td>\n",
+       "      <td>0.346154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.565812</td>\n",
+       "      <td>0.606471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.148148</td>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.072626</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.643524</td>\n",
+       "      <td>0.183908</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.350504</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.183908</td>\n",
+       "      <td>0.697291</td>\n",
+       "      <td>0.759542</td>\n",
+       "      <td>0.617391</td>\n",
+       "      <td>0.433414</td>\n",
+       "      <td>0.541176</td>\n",
+       "      <td>0.516393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.457627</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.759542</td>\n",
+       "      <td>0.811280</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.115385</td>\n",
+       "      <td>0.707317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.148148</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.617391</td>\n",
+       "      <td>0.076923</td>\n",
+       "      <td>0.543333</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.246032</td>\n",
+       "      <td>0.151515</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.433414</td>\n",
+       "      <td>0.550000</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.599359</td>\n",
+       "      <td>0.269841</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.228261</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.541176</td>\n",
+       "      <td>0.115385</td>\n",
+       "      <td>0.090909</td>\n",
+       "      <td>0.269841</td>\n",
+       "      <td>0.793785</td>\n",
+       "      <td>0.050000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.346154</td>\n",
+       "      <td>0.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.516393</td>\n",
+       "      <td>0.707317</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.763816</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase_klifs      EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
+       "kinase_klifs                                                               \n",
+       "EGFR          0.609388  0.565812  0.072626  0.350504  0.457627  0.125000   \n",
+       "ErbB2         0.565812  0.606471       NaN  0.400000  0.250000  0.148148   \n",
+       "p110a         0.072626       NaN  0.643524  0.183908  0.333333  0.333333   \n",
+       "KDR           0.350504  0.400000  0.183908  0.697291  0.759542  0.617391   \n",
+       "BRAF          0.457627  0.250000  0.333333  0.759542  0.811280  0.076923   \n",
+       "CDK2          0.125000  0.148148  0.333333  0.617391  0.076923  0.543333   \n",
+       "LCK           0.246032  0.151515  0.000000  0.433414  0.550000  0.111111   \n",
+       "MET           0.228261  0.035714  0.000000  0.541176  0.115385  0.090909   \n",
+       "p38a          0.346154  0.125000  0.000000  0.516393  0.707317  0.111111   \n",
+       "\n",
+       "kinase_klifs       LCK       MET      p38a  \n",
+       "kinase_klifs                                \n",
+       "EGFR          0.246032  0.228261  0.346154  \n",
+       "ErbB2         0.151515  0.035714  0.125000  \n",
+       "p110a         0.000000  0.000000  0.000000  \n",
+       "KDR           0.433414  0.541176  0.516393  \n",
+       "BRAF          0.550000  0.115385  0.707317  \n",
+       "CDK2          0.111111  0.090909  0.111111  \n",
+       "LCK           0.599359  0.269841  0.500000  \n",
+       "MET           0.269841  0.793785  0.050000  \n",
+       "p38a          0.500000  0.050000  0.763816  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                        index=name_to_uniprot.keys(),\n",
-    "                                        columns=name_to_uniprot.keys())\n",
+    "                                        index=kinase_selection_df.kinase_klifs,\n",
+    "                                        columns=kinase_selection_df.kinase_klifs)\n",
     "kinase_similarity_matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
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+       "<table id=\"T_889e4_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_889e4_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_889e4_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_889e4_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_889e4_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_889e4_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_889e4_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_889e4_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_889e4_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_889e4_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_889e4_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_889e4_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_889e4_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_889e4_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_889e4_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_889e4_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_889e4_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_889e4_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_889e4_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_889e4_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_889e4_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_889e4_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_889e4_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_889e4_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_889e4_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_889e4_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_889e4_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_889e4_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_889e4_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_889e4_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_889e4_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_889e4_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_889e4_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_889e4_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_889e4_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_889e4_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_889e4_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_889e4_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_889e4_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_889e4_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_889e4_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_889e4_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_889e4_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_889e4_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_889e4_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_889e4_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_889e4_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_889e4_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_889e4_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_889e4_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_889e4_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_889e4_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_889e4_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_889e4_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_889e4_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_889e4_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_889e4_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_889e4_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_889e4_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_889e4_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_889e4_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_889e4_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_889e4_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_889e4_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_889e4_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_889e4_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_889e4_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_889e4_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_889e4_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_889e4_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_889e4_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_889e4_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_889e4_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_889e4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_889e4_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_889e4_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_889e4_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_889e4_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_889e4_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_889e4_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_889e4_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_889e4_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_889e4_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f99f16feb50>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
@@ -810,9 +2334,366 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_b9388_row0_col0, #T_b9388_row1_col1, #T_b9388_row2_col2, #T_b9388_row3_col5, #T_b9388_row4_col3, #T_b9388_row4_col4, #T_b9388_row6_col6, #T_b9388_row7_col7, #T_b9388_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row0_col1 {\n",
+       "  background-color: #108810;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row0_col2 {\n",
+       "  background-color: #d1e6d1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row0_col3, #T_b9388_row7_col0 {\n",
+       "  background-color: #a6d2a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row0_col4, #T_b9388_row3_col0, #T_b9388_row4_col2, #T_b9388_row5_col2 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row0_col5 {\n",
+       "  background-color: #d6e9d6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row0_col6, #T_b9388_row4_col1 {\n",
+       "  background-color: #8ac48a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row0_col7, #T_b9388_row3_col2 {\n",
+       "  background-color: #a7d2a7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row0_col8 {\n",
+       "  background-color: #80bf80;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col0 {\n",
+       "  background-color: #128912;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row1_col2, #T_b9388_row2_col0, #T_b9388_row2_col1, #T_b9388_row2_col3, #T_b9388_row2_col6, #T_b9388_row2_col7, #T_b9388_row2_col8, #T_b9388_row4_col5, #T_b9388_row5_col4, #T_b9388_row6_col2, #T_b9388_row7_col2, #T_b9388_row8_col2 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col3 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col4 {\n",
+       "  background-color: #b3d8b3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col5 {\n",
+       "  background-color: #cce4cc;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col6 {\n",
+       "  background-color: #b0d6b0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col7 {\n",
+       "  background-color: #e0eee0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row1_col8 {\n",
+       "  background-color: #c5e0c5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row2_col4 {\n",
+       "  background-color: #99cb99;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row2_col5 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row3_col1 {\n",
+       "  background-color: #50a750;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row3_col3 {\n",
+       "  background-color: #198c19;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row3_col4, #T_b9388_row4_col8 {\n",
+       "  background-color: #118811;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row3_col6 {\n",
+       "  background-color: #40a040;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row3_col7 {\n",
+       "  background-color: #4ba54a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row3_col8 {\n",
+       "  background-color: #4ba54b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row4_col0 {\n",
+       "  background-color: #42a042;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row4_col6 {\n",
+       "  background-color: #138913;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row4_col7, #T_b9388_row5_col8 {\n",
+       "  background-color: #c9e2c8;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row5_col0 {\n",
+       "  background-color: #d4e8d4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row5_col1 {\n",
+       "  background-color: #b2d7b1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row5_col3 {\n",
+       "  background-color: #3a9c3a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row5_col5 {\n",
+       "  background-color: #209020;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row5_col6 {\n",
+       "  background-color: #bfdebf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row5_col7 {\n",
+       "  background-color: #d0e6d0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col0 {\n",
+       "  background-color: #9fce9f;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col1 {\n",
+       "  background-color: #b1d7b1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col3 {\n",
+       "  background-color: #85c185;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col4 {\n",
+       "  background-color: #54a954;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row6_col5, #T_b9388_row7_col8, #T_b9388_row8_col5, #T_b9388_row8_col7 {\n",
+       "  background-color: #dcecdc;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col7 {\n",
+       "  background-color: #9bcc9b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row6_col8 {\n",
+       "  background-color: #51a851;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row7_col1 {\n",
+       "  background-color: #ddecdd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row7_col3 {\n",
+       "  background-color: #59ac59;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row7_col4 {\n",
+       "  background-color: #dfeddf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row7_col5 {\n",
+       "  background-color: #e5f0e5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row7_col6 {\n",
+       "  background-color: #81bf81;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row8_col0 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row8_col1 {\n",
+       "  background-color: #bbdcbb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_b9388_row8_col3 {\n",
+       "  background-color: #63b163;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row8_col4 {\n",
+       "  background-color: #219021;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_b9388_row8_col6 {\n",
+       "  background-color: #279327;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_b9388_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_b9388_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_b9388_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_b9388_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_b9388_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_b9388_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_b9388_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_b9388_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_b9388_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_b9388_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_b9388_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_b9388_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_b9388_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_b9388_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_b9388_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_b9388_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_b9388_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_b9388_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_b9388_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_b9388_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_b9388_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_b9388_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_b9388_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_b9388_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_b9388_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_b9388_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_b9388_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_b9388_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_b9388_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_b9388_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_b9388_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_b9388_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_b9388_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_b9388_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_b9388_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_b9388_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_b9388_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_b9388_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_b9388_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_b9388_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_b9388_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_b9388_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_b9388_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_b9388_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_b9388_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_b9388_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_b9388_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_b9388_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_b9388_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_b9388_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_b9388_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_b9388_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_b9388_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_b9388_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_b9388_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_b9388_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_b9388_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_b9388_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_b9388_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_b9388_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_b9388_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_b9388_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_b9388_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_b9388_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_b9388_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_b9388_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_b9388_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b9388_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_b9388_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_b9388_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_b9388_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_b9388_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_b9388_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_b9388_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_b9388_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_b9388_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_b9388_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f99f16fe610>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kinase_similarity_matrix = kinase_similarity_matrix.fillna(0)\n",
     "\n",
@@ -830,7 +2711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -849,16 +2730,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. In the case of the similarity matrix above, this matrix is not a distance matrix. For example, the diagonal elements are not zero.\n",
-    "\n",
-    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
+    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The values of the similarity matrix lie between: 0.00 and 0.81\n"
+     ]
+    }
+   ],
    "source": [
     "print(f\"The values of the similarity matrix lie between: \"\n",
     "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
@@ -867,7 +2754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -883,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -892,9 +2779,358 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
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+       "<table id=\"T_54bb3_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "      <th class=\"blank col5\" >&nbsp;</th>\n",
+       "      <th class=\"blank col6\" >&nbsp;</th>\n",
+       "      <th class=\"blank col7\" >&nbsp;</th>\n",
+       "      <th class=\"blank col8\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_54bb3_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_54bb3_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_54bb3_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_54bb3_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_54bb3_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_54bb3_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_54bb3_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_54bb3_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_54bb3_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_54bb3_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_54bb3_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_54bb3_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_54bb3_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_54bb3_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_54bb3_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_54bb3_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_54bb3_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_54bb3_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_54bb3_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_54bb3_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_54bb3_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_54bb3_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_54bb3_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_54bb3_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_54bb3_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_54bb3_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_54bb3_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_54bb3_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_54bb3_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_54bb3_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_54bb3_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_54bb3_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_54bb3_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_54bb3_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_54bb3_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_54bb3_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_54bb3_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_54bb3_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_54bb3_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_54bb3_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_54bb3_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_54bb3_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_54bb3_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_54bb3_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_54bb3_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_54bb3_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_54bb3_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_54bb3_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_54bb3_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_54bb3_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_54bb3_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_54bb3_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_54bb3_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_54bb3_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_54bb3_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_54bb3_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_54bb3_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_54bb3_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_54bb3_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_54bb3_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_54bb3_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_54bb3_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_54bb3_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_54bb3_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_54bb3_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_54bb3_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_54bb3_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_54bb3_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_54bb3_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f99f1701e20>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kinase_distance_matrix.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
@@ -910,7 +3146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -939,18 +3175,11 @@
     "2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few? \n",
     "3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of $0$, as is the case for p110a and LCK?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -964,7 +3193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 90267f76dffe4bfc7c48fa1e8be192dc8583b821 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 16:25:22 +0200
Subject: [PATCH 085/158] T023: Add KinMap-Erlotinib figure

---
 .../images/kinmap_erlotinib_karaman.png        | Bin 0 -> 216956 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_erlotinib_karaman.png

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_erlotinib_karaman.png b/teachopencadd/talktorials/T023_what_is_a_kinase/images/kinmap_erlotinib_karaman.png
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From 2d7721ec55e455bf6491021fd45922b7c0fbb104 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 27 Sep 2021 16:26:09 +0200
Subject: [PATCH 086/158] T023: Add kinase similarity section

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 172 +++++++++++++-----
 1 file changed, 131 insertions(+), 41 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 5d73256b..133a9241 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -34,7 +34,7 @@
     "- Kinase resources\n",
     "    - Kinase structures and related information\n",
     "    - Bioactivity data\n",
-    "- Kinase-similarity: off-target, promiscuous binding\n",
+    "- Kinase similarity: Off-targets and promiscuous binding\n",
     "- Kinase dataset compilation"
    ]
   },
@@ -55,6 +55,9 @@
     "\n",
     "- Kinases as drug targets: [<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)\n",
     "- Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "- KinMap: \n",
+    "  - Paper: [<i>BMC Bioinformatics</i> (2017), <b>18</b>, 16](https://dx.doi.org/10.1186%2Fs12859-016-1433-7)\n",
+    "  - Website: http://www.kinhub.org/kinmap/\n",
     "- KLIFS\n",
     "  - KLIFS URL: https://klifs.net/\n",
     "  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
@@ -85,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating and thereby activating themselves or other proteins and are most frequently mutated proteins in tumors.\n",
+    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating and thereby activating themselves or other proteins and are the most frequently mutated proteins in tumors.\n",
     "\n",
     "As of Sep 2021, 5782 X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and 67 FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
     "\n",
@@ -104,12 +107,12 @@
     "\n",
     "Human kinome consists of over 500 protein kinases (number varies depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
     "\n",
-    "Manning et al. clustered the human protein kinases based on their sequence into [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as Manning kinome tree. The kinase resource KinMap enables mapping of kinase data onto that tree, e.g. the number of of X-ray structures per kinase as shown in Figure 1.\n",
+    "Manning _et al._ clustered the human protein kinases based on their sequence into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases ([<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)). The kinase clustering is visualized as Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree ([<i>BMC Bioinformatics</i> (2017), <b>18</b>, 16](https://dx.doi.org/10.1186%2Fs12859-016-1433-7)), e.g. the number of of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
     "![Manning tree with number of structures per kinase (KinMap)](images/kinmap_n_structures_per_kinase.png)\n",
     "\n",
     "*Figure 1:* \n",
-    "Number of PDB structures per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "Number of PDB structures per kinase mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). Check the appendix of this notebook on how to generate this figure.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -133,19 +136,7 @@
     "![Kinase structure with key motifs](images/T023_kinase_structure.png)\n",
     "\n",
     "*Figure 2:* \n",
-    "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop (example: CDK2, PDB ID: 1FIN)\n",
-    "\n",
-    "<!---\n",
-    "from opencadd.structure.pocket import PocketKlifs, PocketViewer\n",
-    "pocket = PocketKlifs.from_structure_klifs_id(4367)\n",
-    "viewer = PocketViewer()\n",
-    "viewer.add_pocket(\n",
-    "    pocket,\n",
-    "    ligand_expo_id=\"ATP\",\n",
-    "    show_pocket_center=False\n",
-    ")\n",
-    "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
-    "-->"
+    "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop. The example structure shown here represents CDK2, PDB ID: 1FIN. Check the appendix on how to generate this visualization with `opencadd`."
    ]
   },
   {
@@ -182,27 +173,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Each structure, kinase, and ligand in KLIFS is associated with an identifier:\n",
+    "Each structure, kinase, and ligand in KLIFS is associated with an identifier (we will use those at times in the downstream talktorials):\n",
     "\n",
     "- Structure KLIFS ID\n",
     "- Kinase KLIFS ID\n",
     "- Ligand KLIFS ID"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "KLIFS contains not only kinase structures and their pocket definitions (used in __Talktorials T024 and T025__) but also a lot of structure-, kinase-, and/or ligand-associated data:\n",
+    "\n",
+    "- Interaction fingerprints (used in __Talktorial T026__)\n",
+    "- Structure conformations\n",
+    "- Bioactivity data from ChEMBL\n",
+    "- Approved drugs\n",
+    "- Subpocket coverage by co-crystallized ligands"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Bioactivity data\n",
     "\n",
-    "TODO - short !\n",
-    "* maybe an overview of data points per kinase on Chembl (from kinodata)\n",
-    "* other profiling data (as available in karaman, xxx)\n",
+    "There are many resources for bioactivity data, the ChEMBL database being on the most popular resource. The `kinodata` project collects all bioactivities measured on kinases in a GitHub repository and will be used in __Talktorial T027__:\n",
+    "\n",
+    "- `kinodata` GitHub repository: https://github.com/openkinome/kinodata\n",
+    "- `kinodata` ChEMBL 29 release: https://github.com/openkinome/kinodata/releases/tag/v0.3 (`activities-chembl29_v0.3.zip`)\n",
+    "\n",
+    "As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spend on certain kinases (Figure 3).\n",
     "\n",
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
     "*Figure 3:* \n",
-    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). Check the appendix of this notebook on how to generate this figure.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -212,16 +219,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Karaman et al. dataset: TODO\n",
+    "Other kinase-specific resources for kinase bioactivity data are:\n",
+    "\n",
+    "- Karaman _et al._ dataset\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Davis_profiling.js)\n",
-    "- Davis et al. dataset: TODO\n",
+    "- Davis _et al._ dataset\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Karaman_profiling.js)\n",
-    "- KIBA dataset: TODO\n",
+    "- KIBA dataset\n",
     "  - Paper: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
     "  - Data: [SI data (XLSX)](https://ndownloader.figstatic.com/files/3950161)\n",
-    "- PKIS dataset: \n",
+    "- PKIS dataset\n",
     "  - Paper: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)\n",
     "  - Data: [SI data (XLSX)](https://doi.org/10.1371/journal.pone.0181585.s004)"
    ]
@@ -230,12 +239,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase-similarity: off-target, promiscuous binding\n",
+    "### Kinase similarity: Off-targets and promiscuous binding\n",
+    "\n",
+    "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also other targets (off-targets). This promiscuous binding can cause mild to severe side effects.\n",
+    "\n",
+    "Predicting such side effects is a non-trivial task because some off-targets are not obvious: For example, the EGFR inhibitor Erlotinib shows side effects in the sequentially highly similar TK kinase group, however also shows the off-targets GAK, LOK, and SLK in more remote kinase groups (Figure 4). \n",
+    "\n",
+    "![Erlotinib profiling data from Karaman dataset (KinMap)](images/kinmap_erlotinib_karaman.png)\n",
     "\n",
-    "TODO Problem statement. Introduce\n",
-    "* problem of promiscuous binding (e.g. by profiling results for known inhibitors, (kinhub figure), example from molecules paper?)\n",
-    "* different perspectives: sequence, structure and ligand-profiling data\n",
-    "* that's why we investigate in similarity from the different perspectives\n"
+    "*Figure 4:* \n",
+    "Profiling data for EGFR inhibitor Erlobinib from the Karaman _et al._ dataset ([<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)) mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). Check the appendix of this notebook on how to generate this figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following __Talktorials T024-027__, we will assess kinase similarity from different perspectives, which we compare with each other in __Talktorial T028__:\n",
+    "\n",
+    "- __Talktorial T024__: Kinase similarity based on KLIFS pocket sequence\n",
+    "- __Talktorial T025__: Kinase similarity based on KiSSim pocket structure\n",
+    "- __Talktorial T026__: Kinase similarity based on KLIFS interaction fingerprint\n",
+    "- __Talktorial T027__: Kinase similarity based on ligand promiscuity (ChEMBL bioactivity data)\n",
+    "- __Talktorial T028__: Compare kinase similarity measures from __Talktorials T024-T027__"
    ]
   },
   {
@@ -249,7 +275,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the course of the kinase similarity talktorials (**Talktorials T024-T028**), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
+    "In the course of the kinase similarity talktorials (__Talktorials T024-T028__), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
     "\n",
     "> - Profile 1 combined __EGFR__ and __ErbB2__ as targets and __BRAF__ as a (general) anti-target. \n",
     "> - Out of similar considerations, Profile 2 consisted of EGFR and __PI3K__ as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. \n",
@@ -301,7 +327,7 @@
     "- `kinase`: Kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
     "- `kinase_klifs`: Kinase name as used in the KLIFS database\n",
     "- `uniprot_id`: Kinase UniProt ID\n",
-    "- `group`: Kinase group as defined by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "- `group`: Kinase group as defined by Manning _et al._ [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
     "- `full_kinase_name`: Full kinase name as used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
    ]
   },
@@ -309,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+    "Note: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
    ]
   },
   {
@@ -460,13 +486,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will load this dataset in all downstream talktorials to assess kinase similarity from different perspectives:\n",
-    "\n",
-    "- **Talktorial T024**: Kinase similarity based on KLIFS pocket sequence\n",
-    "- **Talktorial T025**: Kinase similarity based on KiSSim pocket structure\n",
-    "- **Talktorial T026**: Kinase similarity based on KLIFS interaction fingerprint\n",
-    "- **Talktorial T027**: Kinase similarity based on ligand promiscuity (ChEMBL bioactivity data)\n",
-    "- **Talktorial T028**: Compare kinase similarity measures from **Talktorials T024-T027**"
+    "We will load this dataset in all downstream talktorials to assess kinase similarity from different perspectives."
    ]
   },
   {
@@ -605,6 +625,76 @@
     "kinmap_n_activities_per_kinase.to_csv(DATA / \"kinmap_n_activities_per_kinase.csv\", index=None)\n",
     "# Some kinases will not be resolved in KinMap and will be simply dropped"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Erlotinib profiling data from Karaman _et al._ dataset\n",
+    "\n",
+    "- Go to http://www.kinhub.org/kinmap/\n",
+    "- Select \"Data Source\": Profiling\n",
+    "- Select \"Data type\": Karaman et al., 2018\n",
+    "- Select \"Karaman et al., 2018\": Erlotinib\n",
+    "- Click \"Add source\"\n",
+    "- Click \"Apply\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinase structure visualization with `opencadd`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73a1e0c53d74415ab788e27e8822fea4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2c15064772547ee8f358ec9b3972436",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "NGLWidget()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from opencadd.structure.pocket import PocketKlifs, PocketViewer\n",
+    "\n",
+    "# Get structure and pocket\n",
+    "pocket = PocketKlifs.from_structure_klifs_id(4367)\n",
+    "\n",
+    "# Show pocket\n",
+    "viewer = PocketViewer()\n",
+    "viewer.add_pocket(\n",
+    "    pocket,\n",
+    "    ligand_expo_id=\"ATP\",\n",
+    "    show_pocket_center=False\n",
+    ")\n",
+    "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
+    "viewer.viewer"
+   ]
   }
  ],
  "metadata": {

From 6d05309137efa0bcb83da06fb5de616e8304722a Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 21:08:16 +0200
Subject: [PATCH 087/158] address some comments of AV.

---
 .../talktorial.ipynb                          | 1283 ++++++++---------
 1 file changed, 622 insertions(+), 661 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 5a7579d1..b1699854 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -105,7 +105,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In drug design, it is common to binarize the activity of a compound against a target of interest as a \"hit\" or \"non-hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active (hit), and inactive (non-hit) otherwise."
+    "In drug design, it is common to binarize the activity of a compound against a target of interest as a \"hit\" or \"non-hit\". Practically speaking, this is done using a cutoff value for measured activity. If the activity is greater than the cutoff, the compound is labeled as active (hit), and inactive (non-hit) otherwise.\n",
+    "\n",
+    "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
+    "\n",
+    "\n",
+    "*Figure 3:* \n",
+    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. The figure is generated in __Talktorial T023__."
    ]
   },
   {
@@ -134,7 +140,7 @@
     "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on } K_i \\text{ and } K_j}\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on both } K_i \\text{ and } K_j}\n",
     "{\\#\\text{ of compounds that were tested on both } K_i \\text{ and } K_j}.\n",
     "$$\n",
     "\n",
@@ -358,7 +364,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.29 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
+    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.29 freely available at Openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
    ]
   },
   {
@@ -612,7 +618,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKi', 'pKd', 'pIC50'}\n",
+      "Activities: {'pIC50', 'pKi', 'pKd'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -783,7 +789,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We also drop NA values."
+    "NA values are dropped."
    ]
   },
   {
@@ -808,7 +814,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We keep the data for the query kinases only:"
+    "We only keep the data for the query kinases:"
    ]
   },
   {
@@ -917,7 +923,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at EGFR data:"
+    "Let's look at EGFR data (which corresponds to the first row in the kinase selection dataframe):"
    ]
   },
   {
@@ -965,7 +971,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., highest pIC50 value here."
+    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., highest pIC50 value."
    ]
   },
   {
@@ -1282,7 +1288,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see what information we get for EGFR."
+    "Let's see what information we get for EGFR (which corresponds to the first row in the dataframe):"
    ]
   },
   {
@@ -1325,12 +1331,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def promiscuity_table(activity_dataframe):\n",
+    "def promiscuity_table(kinase_selection, activity_dataframe):\n",
     "    \"\"\"\n",
     "    Create a table with all three values for all kinases.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
+    "    kinase_selection :  pd.DataFrame\n",
+    "        The dataframe for the chosen kinases.\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
     "\n",
@@ -1339,10 +1347,13 @@
     "    promiscuity_table : pd.DataFrame\n",
     "        A dataframe with the kinases as rows and values as columns.\n",
     "    \"\"\"\n",
-    "    promiscuity_table = pd.DataFrame(index=kinase_selection_df.kinase_klifs,\n",
+    "    promiscuity_table = pd.DataFrame(index=kinase_selection.kinase_klifs,\n",
     "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
-    "    for name, uniprot_id in zip(kinase_selection_df.kinase_klifs,\n",
-    "                                kinase_selection_df.uniprot_id):\n",
+    "    promiscuity_table.index.name = None\n",
+    "    promiscuity_table.columns.name = None\n",
+    "\n",
+    "    for name, uniprot_id in zip(kinase_selection.kinase_klifs,\n",
+    "                                kinase_selection.uniprot_id):\n",
     "        values = kinase_to_activity_numbers(uniprot_id, activity_dataframe)\n",
     "        promiscuity_table.loc[name] = values\n",
     "    return promiscuity_table"
@@ -1378,12 +1389,6 @@
        "      <th>actives</th>\n",
        "      <th>fraction</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase_klifs</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1445,17 +1450,16 @@
        "</div>"
       ],
       "text/plain": [
-       "             total actives  fraction\n",
-       "kinase_klifs                        \n",
-       "EGFR          5965    3635  0.609388\n",
-       "ErbB2         1700    1031  0.606471\n",
-       "p110a         4393    2827  0.643524\n",
-       "KDR           7641    5328  0.697291\n",
-       "BRAF          3688    2992   0.81128\n",
-       "CDK2          1500     815  0.543333\n",
-       "LCK           1560     935  0.599359\n",
-       "MET           2832    2248  0.793785\n",
-       "p38a          3637    2778  0.763816"
+       "      total actives  fraction\n",
+       "EGFR   5965    3635  0.609388\n",
+       "ErbB2  1700    1031  0.606471\n",
+       "p110a  4393    2827  0.643524\n",
+       "KDR    7641    5328  0.697291\n",
+       "BRAF   3688    2992   0.81128\n",
+       "CDK2   1500     815  0.543333\n",
+       "LCK    1560     935  0.599359\n",
+       "MET    2832    2248  0.793785\n",
+       "p38a   3637    2778  0.763816"
       ]
      },
      "execution_count": 23,
@@ -1464,7 +1468,7 @@
     }
    ],
    "source": [
-    "kinase_promiscuity_df = promiscuity_table(data)\n",
+    "kinase_promiscuity_df = promiscuity_table(kinase_selection_df, data)\n",
     "kinase_promiscuity_df"
    ]
   },
@@ -1484,46 +1488,46 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_64427_row0_col2 {\n",
+       "#T_c4bc4_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_64427_row1_col2 {\n",
+       "#T_c4bc4_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_64427_row2_col2 {\n",
+       "#T_c4bc4_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_64427_row3_col2 {\n",
+       "#T_c4bc4_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_64427_row4_col2 {\n",
+       "#T_c4bc4_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_64427_row5_col2 {\n",
+       "#T_c4bc4_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_64427_row6_col2 {\n",
+       "#T_c4bc4_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_64427_row7_col2 {\n",
+       "#T_c4bc4_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_64427_row8_col2 {\n",
+       "#T_c4bc4_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_64427_\">\n",
+       "<table id=\"T_c4bc4_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1531,73 +1535,67 @@
        "      <th class=\"col_heading level0 col1\" >actives</th>\n",
        "      <th class=\"col_heading level0 col2\" >fraction</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_64427_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_64427_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_64427_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_c4bc4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_c4bc4_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_c4bc4_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_c4bc4_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_64427_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_64427_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_64427_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_c4bc4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_c4bc4_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_c4bc4_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_c4bc4_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_64427_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_64427_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_64427_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_c4bc4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_c4bc4_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_c4bc4_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_c4bc4_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_64427_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_64427_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_64427_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_c4bc4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_c4bc4_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_c4bc4_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_c4bc4_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_64427_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_64427_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_64427_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_c4bc4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_c4bc4_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_c4bc4_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_c4bc4_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_64427_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_64427_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_64427_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_c4bc4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_c4bc4_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_c4bc4_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_c4bc4_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_64427_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_64427_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_64427_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_c4bc4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_c4bc4_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_c4bc4_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_c4bc4_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_64427_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_64427_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_64427_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_c4bc4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_c4bc4_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_c4bc4_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_c4bc4_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_64427_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_64427_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_64427_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_64427_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_c4bc4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c4bc4_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_c4bc4_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_c4bc4_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f99f109bf70>"
+       "<pandas.io.formats.style.Styler at 0x7fcaf2bf4310>"
       ]
      },
      "execution_count": 24,
@@ -1650,23 +1648,22 @@
     "        Uniprot id of first kinase of interest.\n",
     "    uniprot_id2 : str\n",
     "        Uniprot id of second kinase of interest.\n",
-    "\n",
     "    activity_dataframe :  pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    fraction : float\n",
-    "        The metric for kinase similariy,\n",
-    "        i.e. number of active compounds on both kinases\n",
-    "        over number of measured compounds on both kinases.\n",
+    "    tuple : (int, int, float)\n",
+    "        The three metrics:\n",
+    "        1. The total number of measured compounds against both kinases\n",
+    "        2. The number of active compounds against both kinases.\n",
+    "        3. The metric for kinase similarity,\n",
+    "                i.e. number of active compounds on both kinases\n",
+    "                over number of measured compounds on both kinases.\n",
     "    \"\"\"\n",
     "    if uniprot_id1 == uniprot_id2:\n",
-    "        (total_compounds,\n",
-    "         active_compounds,\n",
-    "         fraction) = kinase_to_activity_numbers(uniprot_id1,\n",
-    "                                                activity_dataframe)\n",
-    "        return fraction\n",
+    "        return kinase_to_activity_numbers(uniprot_id1,\n",
+    "                                          activity_dataframe)\n",
     "    else:\n",
     "        # Data for the two kinases only\n",
     "        reduced_data = activity_dataframe[activity_dataframe\n",
@@ -1692,14 +1689,14 @@
     "            print(f\"No compounds were measured on both kinases, \"\n",
     "                  f\"namely {uniprot_id1} and {uniprot_id2}.\")\n",
     "            fraction = np.nan\n",
-    "        return fraction"
+    "        return (measured_compounds_on_both[0], active_compounds_on_both[0], fraction)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at the similarity between EGFR and MET:"
+    "Let's look at the values and similarity between EGFR and MET (which corresponds to the first and 7th row in the dataframe)"
    ]
   },
   {
@@ -1711,7 +1708,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Ligand profile similarity between EGFR and MET: 0.23.\n"
+      "Values for EGFR and MET: \n",
+      "\n",
+      "Total number of measured compounds: \t92 \n",
+      "Number of active compounds: \t\t21 \n",
+      "Fraction of active compounds or \n",
+      "ligand profile similarity between EGFR and MET: \t\t0.23 \n",
+      "\n"
      ]
     }
    ],
@@ -1719,8 +1722,14 @@
     "similarity_EGFR_MET = similarity_ligand_profile(kinase_selection_df.uniprot_id[0],\n",
     "                                                kinase_selection_df.uniprot_id[7],\n",
     "                                                data)\n",
-    "print(f\"Ligand profile similarity between EGFR and MET: \"\n",
-    "      f\"{similarity_EGFR_MET:.2f}.\")"
+    "print(f\"Values for EGFR and MET: \\n\\n\"\n",
+    "      f\"Total number of measured compounds: \\t\"\n",
+    "      f\"{similarity_EGFR_MET[0]} \\n\"\n",
+    "      f\"Number of active compounds: \\t\\t\"\n",
+    "      f\"{similarity_EGFR_MET[1]} \\n\"\n",
+    "      f\"Fraction of active compounds or \\n\"\n",
+    "      f\"ligand profile similarity between EGFR and MET: \\t\\t\"\n",
+    "      f\"{similarity_EGFR_MET[2]:.2f} \\n\")"
    ]
   },
   {
@@ -1752,7 +1761,7 @@
     "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(\n",
     "            uniprot_id1,\n",
     "            uniprot_id2,\n",
-    "            data)"
+    "            data)[2]"
    ]
   },
   {
@@ -1780,7 +1789,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase_klifs</th>\n",
+       "      <th></th>\n",
        "      <th>EGFR</th>\n",
        "      <th>ErbB2</th>\n",
        "      <th>p110a</th>\n",
@@ -1791,18 +1800,6 @@
        "      <th>MET</th>\n",
        "      <th>p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase_klifs</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1918,29 +1915,27 @@
        "</div>"
       ],
       "text/plain": [
-       "kinase_klifs      EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
-       "kinase_klifs                                                               \n",
-       "EGFR          0.609388  0.565812  0.072626  0.350504  0.457627  0.125000   \n",
-       "ErbB2         0.565812  0.606471       NaN  0.400000  0.250000  0.148148   \n",
-       "p110a         0.072626       NaN  0.643524  0.183908  0.333333  0.333333   \n",
-       "KDR           0.350504  0.400000  0.183908  0.697291  0.759542  0.617391   \n",
-       "BRAF          0.457627  0.250000  0.333333  0.759542  0.811280  0.076923   \n",
-       "CDK2          0.125000  0.148148  0.333333  0.617391  0.076923  0.543333   \n",
-       "LCK           0.246032  0.151515  0.000000  0.433414  0.550000  0.111111   \n",
-       "MET           0.228261  0.035714  0.000000  0.541176  0.115385  0.090909   \n",
-       "p38a          0.346154  0.125000  0.000000  0.516393  0.707317  0.111111   \n",
+       "           EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       "EGFR   0.609388  0.565812  0.072626  0.350504  0.457627  0.125000  0.246032   \n",
+       "ErbB2  0.565812  0.606471       NaN  0.400000  0.250000  0.148148  0.151515   \n",
+       "p110a  0.072626       NaN  0.643524  0.183908  0.333333  0.333333  0.000000   \n",
+       "KDR    0.350504  0.400000  0.183908  0.697291  0.759542  0.617391  0.433414   \n",
+       "BRAF   0.457627  0.250000  0.333333  0.759542  0.811280  0.076923  0.550000   \n",
+       "CDK2   0.125000  0.148148  0.333333  0.617391  0.076923  0.543333  0.111111   \n",
+       "LCK    0.246032  0.151515  0.000000  0.433414  0.550000  0.111111  0.599359   \n",
+       "MET    0.228261  0.035714  0.000000  0.541176  0.115385  0.090909  0.269841   \n",
+       "p38a   0.346154  0.125000  0.000000  0.516393  0.707317  0.111111  0.500000   \n",
        "\n",
-       "kinase_klifs       LCK       MET      p38a  \n",
-       "kinase_klifs                                \n",
-       "EGFR          0.246032  0.228261  0.346154  \n",
-       "ErbB2         0.151515  0.035714  0.125000  \n",
-       "p110a         0.000000  0.000000  0.000000  \n",
-       "KDR           0.433414  0.541176  0.516393  \n",
-       "BRAF          0.550000  0.115385  0.707317  \n",
-       "CDK2          0.111111  0.090909  0.111111  \n",
-       "LCK           0.599359  0.269841  0.500000  \n",
-       "MET           0.269841  0.793785  0.050000  \n",
-       "p38a          0.500000  0.050000  0.763816  "
+       "            MET      p38a  \n",
+       "EGFR   0.228261  0.346154  \n",
+       "ErbB2  0.035714  0.125000  \n",
+       "p110a  0.000000  0.000000  \n",
+       "KDR    0.541176  0.516393  \n",
+       "BRAF   0.115385  0.707317  \n",
+       "CDK2   0.090909  0.111111  \n",
+       "LCK    0.269841  0.500000  \n",
+       "MET    0.793785  0.050000  \n",
+       "p38a   0.050000  0.763816  "
       ]
      },
      "execution_count": 28,
@@ -1949,10 +1944,12 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix = pd.DataFrame(data=kinase_similarity_matrix,\n",
+    "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
     "                                        index=kinase_selection_df.kinase_klifs,\n",
     "                                        columns=kinase_selection_df.kinase_klifs)\n",
-    "kinase_similarity_matrix"
+    "kinase_similarity_matrix_df.index.name = None\n",
+    "kinase_similarity_matrix_df.columns.name = None\n",
+    "kinase_similarity_matrix_df"
    ]
   },
   {
@@ -1964,211 +1961,211 @@
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        "}\n",
-       "#T_889e4_row7_col3 {\n",
+       "#T_c384a_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_889e4_row7_col4 {\n",
+       "#T_c384a_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_889e4_row7_col5 {\n",
+       "#T_c384a_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_889e4_row7_col6 {\n",
+       "#T_c384a_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_889e4_row8_col0 {\n",
+       "#T_c384a_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_889e4_row8_col1 {\n",
+       "#T_c384a_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_889e4_row8_col3 {\n",
+       "#T_c384a_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_889e4_row8_col4 {\n",
+       "#T_c384a_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_889e4_row8_col6 {\n",
+       "#T_c384a_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_889e4_\">\n",
+       "<table id=\"T_c384a_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
        "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
@@ -2179,133 +2176,121 @@
        "      <th class=\"col_heading level0 col7\" >MET</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_889e4_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_889e4_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_889e4_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_889e4_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_889e4_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_889e4_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_889e4_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_889e4_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_889e4_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_889e4_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_889e4_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_889e4_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_889e4_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_889e4_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_889e4_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_889e4_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_889e4_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_889e4_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_889e4_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_889e4_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_889e4_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_889e4_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_889e4_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_889e4_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_889e4_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_889e4_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_889e4_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_889e4_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_889e4_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_889e4_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_889e4_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_889e4_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_889e4_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_889e4_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_889e4_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_889e4_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_889e4_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_889e4_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_889e4_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_889e4_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_889e4_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_889e4_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_889e4_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_889e4_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_889e4_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_889e4_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_889e4_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_889e4_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_889e4_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_889e4_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_889e4_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_889e4_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_889e4_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_889e4_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_889e4_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_889e4_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_889e4_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_889e4_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_889e4_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_889e4_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_889e4_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_889e4_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_889e4_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_889e4_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_889e4_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_889e4_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_889e4_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_889e4_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_889e4_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_889e4_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_889e4_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_889e4_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_889e4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_889e4_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_889e4_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_889e4_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_889e4_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_889e4_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_889e4_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_889e4_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_889e4_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_889e4_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_c384a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_c384a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_c384a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_c384a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_c384a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_c384a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_c384a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_c384a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_c384a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_c384a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_c384a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_c384a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_c384a_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_c384a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_c384a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_c384a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_c384a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_c384a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_c384a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_c384a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_c384a_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_c384a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_c384a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_c384a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_c384a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_c384a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_c384a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_c384a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_c384a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_c384a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_c384a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_c384a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_c384a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_c384a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_c384a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_c384a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_c384a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_c384a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_c384a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_c384a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_c384a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_c384a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_c384a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_c384a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_c384a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_c384a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_c384a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_c384a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_c384a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_c384a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_c384a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_c384a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_c384a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_c384a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_c384a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_c384a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_c384a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_c384a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_c384a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_c384a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_c384a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_c384a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_c384a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_c384a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_c384a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_c384a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_c384a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_c384a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_c384a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_c384a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_c384a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_c384a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_c384a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c384a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c384a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_c384a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_c384a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_c384a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_c384a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_c384a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_c384a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_c384a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_c384a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f99f16feb50>"
+       "<pandas.io.formats.style.Styler at 0x7fcaf25004c0>"
       ]
      },
      "execution_count": 29,
@@ -2316,7 +2301,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix.style.\\\n",
+    "kinase_similarity_matrix_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
@@ -2327,7 +2312,7 @@
    "source": [
     "Note that the diagonal contains the previously discussed promiscuity values.\n",
     "\n",
-    "As mentioned above, no compounds were measured on both ErbB2 and p110a and therefore create a `np.nan` entry which can be problematic for algorithmic reason.\n",
+    "As mentioned above, no compounds were measured on both ErbB2 and p110a and therefore creates a `np.nan` entry which can be problematic for algorithmic reason.\n",
     "\n",
     "As a simple workaround, we will fill the NA values with zero."
    ]
@@ -2341,215 +2326,215 @@
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        "  background-color: #108810;\n",
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        "  background-color: #92c892;\n",
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        "}\n",
-       "#T_b9388_row1_col4 {\n",
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        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
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-       "#T_b9388_row1_col6 {\n",
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        "  background-color: #b0d6b0;\n",
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-       "#T_b9388_row1_col7 {\n",
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-       "#T_b9388_row1_col8 {\n",
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        "  background-color: #c5e0c5;\n",
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-       "#T_b9388_row2_col4 {\n",
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        "  background-color: #99cb99;\n",
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-       "#T_b9388_row2_col5 {\n",
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        "  background-color: #7bbc7b;\n",
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-       "#T_b9388_row3_col1 {\n",
+       "#T_aefc8_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row3_col3 {\n",
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        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row3_col4, #T_b9388_row4_col8 {\n",
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-       "#T_b9388_row3_col7 {\n",
+       "#T_aefc8_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row3_col8 {\n",
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        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row4_col0 {\n",
+       "#T_aefc8_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row4_col6 {\n",
+       "#T_aefc8_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row4_col7, #T_b9388_row5_col8 {\n",
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        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row5_col0 {\n",
+       "#T_aefc8_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row5_col1 {\n",
+       "#T_aefc8_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row5_col3 {\n",
+       "#T_aefc8_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row5_col5 {\n",
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        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row5_col6 {\n",
+       "#T_aefc8_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row5_col7 {\n",
+       "#T_aefc8_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col0 {\n",
+       "#T_aefc8_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col1 {\n",
+       "#T_aefc8_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col3 {\n",
+       "#T_aefc8_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col4 {\n",
+       "#T_aefc8_row6_col4 {\n",
        "  background-color: #54a954;\n",
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        "}\n",
-       "#T_b9388_row6_col5, #T_b9388_row7_col8, #T_b9388_row8_col5, #T_b9388_row8_col7 {\n",
+       "#T_aefc8_row6_col5, #T_aefc8_row7_col8, #T_aefc8_row8_col5, #T_aefc8_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col7 {\n",
+       "#T_aefc8_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row6_col8 {\n",
+       "#T_aefc8_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row7_col1 {\n",
+       "#T_aefc8_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row7_col3 {\n",
+       "#T_aefc8_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row7_col4 {\n",
+       "#T_aefc8_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row7_col5 {\n",
+       "#T_aefc8_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row7_col6 {\n",
+       "#T_aefc8_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row8_col0 {\n",
+       "#T_aefc8_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row8_col1 {\n",
+       "#T_aefc8_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b9388_row8_col3 {\n",
+       "#T_aefc8_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row8_col4 {\n",
+       "#T_aefc8_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b9388_row8_col6 {\n",
+       "#T_aefc8_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_b9388_\">\n",
+       "<table id=\"T_aefc8_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
        "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
@@ -2560,133 +2545,121 @@
        "      <th class=\"col_heading level0 col7\" >MET</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_b9388_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_b9388_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_b9388_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_b9388_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_b9388_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_b9388_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_b9388_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_b9388_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_b9388_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_b9388_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_b9388_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_b9388_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_b9388_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_b9388_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_b9388_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_b9388_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_b9388_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_b9388_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_b9388_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_b9388_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_b9388_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_b9388_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_b9388_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_b9388_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_b9388_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_b9388_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_b9388_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_b9388_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_b9388_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_b9388_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_b9388_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_b9388_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_b9388_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_b9388_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_b9388_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_b9388_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_b9388_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_b9388_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_b9388_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_b9388_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_b9388_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_b9388_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_b9388_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_b9388_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_b9388_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_b9388_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_b9388_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_b9388_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_b9388_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_b9388_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_b9388_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_b9388_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_b9388_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_b9388_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_b9388_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_b9388_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_b9388_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_b9388_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_b9388_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_b9388_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_b9388_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_b9388_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_b9388_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_b9388_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_b9388_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_b9388_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b9388_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_b9388_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_b9388_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_b9388_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_b9388_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_b9388_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_b9388_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_b9388_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_b9388_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_b9388_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_aefc8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_aefc8_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_aefc8_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_aefc8_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_aefc8_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_aefc8_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_aefc8_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_aefc8_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_aefc8_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_aefc8_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_aefc8_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_aefc8_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_aefc8_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_aefc8_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_aefc8_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_aefc8_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_aefc8_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_aefc8_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_aefc8_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_aefc8_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_aefc8_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_aefc8_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_aefc8_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_aefc8_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_aefc8_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_aefc8_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_aefc8_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_aefc8_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_aefc8_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_aefc8_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_aefc8_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_aefc8_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_aefc8_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_aefc8_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_aefc8_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_aefc8_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_aefc8_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_aefc8_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_aefc8_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_aefc8_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_aefc8_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_aefc8_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_aefc8_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_aefc8_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_aefc8_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_aefc8_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_aefc8_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_aefc8_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_aefc8_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_aefc8_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_aefc8_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_aefc8_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_aefc8_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_aefc8_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_aefc8_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_aefc8_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_aefc8_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_aefc8_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_aefc8_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_aefc8_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_aefc8_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_aefc8_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_aefc8_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_aefc8_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_aefc8_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_aefc8_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_aefc8_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aefc8_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_aefc8_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_aefc8_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_aefc8_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_aefc8_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_aefc8_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_aefc8_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_aefc8_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_aefc8_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_aefc8_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f99f16fe610>"
+       "<pandas.io.formats.style.Styler at 0x7fcaf24f08e0>"
       ]
      },
      "execution_count": 30,
@@ -2695,9 +2668,9 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix = kinase_similarity_matrix.fillna(0)\n",
+    "kinase_similarity_matrix_df = kinase_similarity_matrix_df.fillna(0)\n",
     "\n",
-    "kinase_similarity_matrix.style.\\\n",
+    "kinase_similarity_matrix_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
@@ -2715,7 +2688,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_similarity_matrix.to_csv(DATA /\n",
+    "kinase_similarity_matrix_df.to_csv(DATA /\n",
     "                                \"kinase_similarity_matrix_ligand_profile.csv\")"
    ]
   },
@@ -2748,8 +2721,8 @@
    ],
    "source": [
     "print(f\"The values of the similarity matrix lie between: \"\n",
-    "      f\"{kinase_similarity_matrix.min().min():.2f}\"\n",
-    "      f\" and {kinase_similarity_matrix.max().max():.2f}\")"
+    "      f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
+    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")"
    ]
   },
   {
@@ -2758,7 +2731,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix = 1 - kinase_similarity_matrix"
+    "kinase_distance_matrix_df = 1 - kinase_similarity_matrix_df"
    ]
   },
   {
@@ -2774,7 +2747,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "np.fill_diagonal(kinase_distance_matrix.values, 0)"
+    "np.fill_diagonal(kinase_distance_matrix_df.values, 0)"
    ]
   },
   {
@@ -2786,207 +2759,207 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_54bb3_row0_col0, #T_54bb3_row1_col1, #T_54bb3_row2_col2, #T_54bb3_row3_col3, #T_54bb3_row4_col4, #T_54bb3_row5_col5, #T_54bb3_row6_col6, #T_54bb3_row7_col7, #T_54bb3_row8_col8 {\n",
+       "#T_f86a3_row0_col0, #T_f86a3_row1_col1, #T_f86a3_row2_col2, #T_f86a3_row3_col3, #T_f86a3_row4_col4, #T_f86a3_row5_col5, #T_f86a3_row6_col6, #T_f86a3_row7_col7, #T_f86a3_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row0_col1 {\n",
+       "#T_f86a3_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row0_col2 {\n",
+       "#T_f86a3_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col3 {\n",
+       "#T_f86a3_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col4 {\n",
+       "#T_f86a3_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col5 {\n",
+       "#T_f86a3_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col6 {\n",
+       "#T_f86a3_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col7 {\n",
+       "#T_f86a3_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row0_col8 {\n",
+       "#T_f86a3_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col0 {\n",
+       "#T_f86a3_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row1_col2, #T_54bb3_row2_col0, #T_54bb3_row2_col1, #T_54bb3_row2_col3, #T_54bb3_row2_col6, #T_54bb3_row2_col7, #T_54bb3_row2_col8, #T_54bb3_row4_col5, #T_54bb3_row5_col4, #T_54bb3_row6_col2, #T_54bb3_row7_col2, #T_54bb3_row8_col2 {\n",
+       "#T_f86a3_row1_col2, #T_f86a3_row2_col0, #T_f86a3_row2_col1, #T_f86a3_row2_col3, #T_f86a3_row2_col6, #T_f86a3_row2_col7, #T_f86a3_row2_col8, #T_f86a3_row4_col5, #T_f86a3_row5_col4, #T_f86a3_row6_col2, #T_f86a3_row7_col2, #T_f86a3_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col3 {\n",
+       "#T_f86a3_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col4, #T_54bb3_row3_col2, #T_54bb3_row6_col0 {\n",
+       "#T_f86a3_row1_col4, #T_f86a3_row3_col2, #T_f86a3_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col5 {\n",
+       "#T_f86a3_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col6, #T_54bb3_row6_col1 {\n",
+       "#T_f86a3_row1_col6, #T_f86a3_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col7, #T_54bb3_row6_col5, #T_54bb3_row7_col1, #T_54bb3_row8_col5 {\n",
+       "#T_f86a3_row1_col7, #T_f86a3_row6_col5, #T_f86a3_row7_col1, #T_f86a3_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row1_col8, #T_54bb3_row8_col1 {\n",
+       "#T_f86a3_row1_col8, #T_f86a3_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row2_col4, #T_54bb3_row2_col5 {\n",
+       "#T_f86a3_row2_col4, #T_f86a3_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row3_col0 {\n",
+       "#T_f86a3_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row3_col1 {\n",
+       "#T_f86a3_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row3_col4 {\n",
+       "#T_f86a3_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row3_col5 {\n",
+       "#T_f86a3_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row3_col6 {\n",
+       "#T_f86a3_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row3_col7 {\n",
+       "#T_f86a3_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row3_col8 {\n",
+       "#T_f86a3_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row4_col0 {\n",
+       "#T_f86a3_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row4_col1 {\n",
+       "#T_f86a3_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row4_col2, #T_54bb3_row5_col2 {\n",
+       "#T_f86a3_row4_col2, #T_f86a3_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row4_col3 {\n",
+       "#T_f86a3_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row4_col6 {\n",
+       "#T_f86a3_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row4_col7 {\n",
+       "#T_f86a3_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row4_col8 {\n",
+       "#T_f86a3_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row5_col0 {\n",
+       "#T_f86a3_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row5_col1 {\n",
+       "#T_f86a3_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row5_col3 {\n",
+       "#T_f86a3_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row5_col6, #T_54bb3_row5_col8 {\n",
+       "#T_f86a3_row5_col6, #T_f86a3_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row5_col7 {\n",
+       "#T_f86a3_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row6_col3 {\n",
+       "#T_f86a3_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row6_col4 {\n",
+       "#T_f86a3_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_54bb3_row6_col7, #T_54bb3_row7_col6 {\n",
+       "#T_f86a3_row6_col7, #T_f86a3_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row6_col8, #T_54bb3_row8_col6 {\n",
+       "#T_f86a3_row6_col8, #T_f86a3_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row7_col0 {\n",
+       "#T_f86a3_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row7_col3 {\n",
+       "#T_f86a3_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row7_col4 {\n",
+       "#T_f86a3_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row7_col5 {\n",
+       "#T_f86a3_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row7_col8, #T_54bb3_row8_col7 {\n",
+       "#T_f86a3_row7_col8, #T_f86a3_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row8_col0 {\n",
+       "#T_f86a3_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row8_col3 {\n",
+       "#T_f86a3_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_54bb3_row8_col4 {\n",
+       "#T_f86a3_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_54bb3_\">\n",
+       "<table id=\"T_f86a3_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
        "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
@@ -2997,133 +2970,121 @@
        "      <th class=\"col_heading level0 col7\" >MET</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_54bb3_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_54bb3_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_54bb3_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_54bb3_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_54bb3_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_54bb3_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_54bb3_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_54bb3_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_54bb3_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_54bb3_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_54bb3_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_54bb3_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_54bb3_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_54bb3_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_54bb3_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_54bb3_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_54bb3_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_54bb3_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_54bb3_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_54bb3_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_54bb3_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_54bb3_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_54bb3_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_54bb3_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_54bb3_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_54bb3_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_54bb3_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_54bb3_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_54bb3_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_54bb3_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_54bb3_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_54bb3_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_54bb3_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_54bb3_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_54bb3_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_54bb3_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_54bb3_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_54bb3_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_54bb3_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_54bb3_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_54bb3_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_54bb3_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_54bb3_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_54bb3_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_54bb3_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_54bb3_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_54bb3_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_54bb3_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_54bb3_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_54bb3_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_54bb3_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_54bb3_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_54bb3_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_54bb3_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_54bb3_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_54bb3_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_54bb3_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_54bb3_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_54bb3_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_54bb3_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_54bb3_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_54bb3_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_54bb3_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_54bb3_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_54bb3_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_54bb3_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_54bb3_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_54bb3_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_54bb3_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_f86a3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_f86a3_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_f86a3_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_f86a3_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_f86a3_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_f86a3_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_f86a3_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_f86a3_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_f86a3_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_f86a3_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_f86a3_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_f86a3_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_f86a3_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_f86a3_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_f86a3_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_f86a3_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_f86a3_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_f86a3_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_f86a3_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_f86a3_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_f86a3_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_f86a3_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_f86a3_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_f86a3_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_f86a3_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_f86a3_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_f86a3_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_f86a3_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_f86a3_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_f86a3_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_f86a3_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_f86a3_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_f86a3_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_f86a3_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_f86a3_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_f86a3_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_f86a3_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_f86a3_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_f86a3_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_f86a3_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_f86a3_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_f86a3_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_f86a3_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_f86a3_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_f86a3_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_f86a3_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_f86a3_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_f86a3_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_f86a3_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_f86a3_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_f86a3_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_f86a3_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_f86a3_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_f86a3_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_f86a3_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_f86a3_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_f86a3_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_f86a3_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_f86a3_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_f86a3_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f86a3_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_f86a3_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_f86a3_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_f86a3_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_f86a3_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_f86a3_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_f86a3_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_f86a3_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_f86a3_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_f86a3_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f99f1701e20>"
+       "<pandas.io.formats.style.Styler at 0x7fcaf24d7760>"
       ]
      },
      "execution_count": 35,
@@ -3132,7 +3093,7 @@
     }
    ],
    "source": [
-    "kinase_distance_matrix.style.\\\n",
+    "kinase_distance_matrix_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
@@ -3150,7 +3111,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix.to_csv(DATA /\n",
+    "kinase_distance_matrix_df.to_csv(DATA /\n",
     "                              \"kinase_distance_matrix_ligand_profile.csv\")"
    ]
   },

From 6e3eec0815af6ebabcfa844fa01e9c9ee918c6f6 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 22:15:15 +0200
Subject: [PATCH 088/158] Add dataframe for non reduced fraction

---
 .../talktorial.ipynb                          | 1301 ++++++++++-------
 1 file changed, 761 insertions(+), 540 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index b1699854..9915a25a 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -1488,46 +1488,46 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_c4bc4_row0_col2 {\n",
+       "#T_925af_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c4bc4_row1_col2 {\n",
+       "#T_925af_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c4bc4_row2_col2 {\n",
+       "#T_925af_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c4bc4_row3_col2 {\n",
+       "#T_925af_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c4bc4_row4_col2 {\n",
+       "#T_925af_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_c4bc4_row5_col2 {\n",
+       "#T_925af_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_c4bc4_row6_col2 {\n",
+       "#T_925af_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c4bc4_row7_col2 {\n",
+       "#T_925af_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c4bc4_row8_col2 {\n",
+       "#T_925af_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c4bc4_\">\n",
+       "<table id=\"T_925af_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1538,64 +1538,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_c4bc4_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_c4bc4_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_c4bc4_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_925af_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_925af_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_925af_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_925af_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_c4bc4_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_c4bc4_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_c4bc4_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_925af_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_925af_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_925af_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_925af_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_c4bc4_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_c4bc4_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_c4bc4_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_925af_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_925af_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_925af_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_925af_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_c4bc4_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_c4bc4_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_c4bc4_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_925af_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_925af_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_925af_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_925af_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_c4bc4_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_c4bc4_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_c4bc4_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_925af_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_925af_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_925af_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_925af_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_c4bc4_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_c4bc4_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_c4bc4_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_925af_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_925af_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_925af_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_925af_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_c4bc4_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_c4bc4_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_c4bc4_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_925af_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_925af_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_925af_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_925af_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_c4bc4_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_c4bc4_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_c4bc4_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_925af_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_925af_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_925af_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_925af_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c4bc4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c4bc4_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_c4bc4_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_c4bc4_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_925af_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_925af_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_925af_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_925af_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fcaf2bf4310>"
+       "<pandas.io.formats.style.Styler at 0x7f889ca20670>"
       ]
      },
      "execution_count": 24,
@@ -1675,21 +1675,23 @@
     "        # Group by compounds\n",
     "        compounds = active_entries.groupby(\"smiles\").size()\n",
     "        # Look at the number of active compounds measured on both kinases\n",
-    "        active_compounds_on_both = compounds[compounds == 2].shape\n",
+    "        active_compounds_on_both = compounds[compounds == 2].shape[0]\n",
     "\n",
     "        # Look at all tested compounds\n",
     "        compounds = reduced_data.groupby(\"smiles\").size()\n",
     "        # Look at the number of compounds measured on both kinases\n",
-    "        measured_compounds_on_both = compounds[compounds == 2].shape\n",
+    "        measured_compounds_on_both = compounds[compounds == 2].shape[0]\n",
     "\n",
-    "        if measured_compounds_on_both[0] > 0:\n",
-    "            fraction = (active_compounds_on_both[0] /\n",
-    "                        measured_compounds_on_both[0])\n",
+    "        if measured_compounds_on_both > 0:\n",
+    "            fraction = (active_compounds_on_both /\n",
+    "                        measured_compounds_on_both)\n",
     "        else:\n",
     "            print(f\"No compounds were measured on both kinases, \"\n",
     "                  f\"namely {uniprot_id1} and {uniprot_id2}.\")\n",
     "            fraction = np.nan\n",
-    "        return (measured_compounds_on_both[0], active_compounds_on_both[0], fraction)"
+    "            measured_compounds_on_both = np.nan\n",
+    "            active_compounds_on_both = np.nan\n",
+    "        return (measured_compounds_on_both, active_compounds_on_both, fraction)"
    ]
   },
   {
@@ -1739,10 +1741,229 @@
     "### Visualize similarity as kinase matrix"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first look at the non-reduced fraction of number of active compound against total number of compounds to have an idea of the counts."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No compounds were measured on both kinases, namely P04626 and P42336.\n",
+      "No compounds were measured on both kinases, namely P42336 and P04626.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>3635/5965</td>\n",
+       "      <td>662/1170</td>\n",
+       "      <td>13/179</td>\n",
+       "      <td>313/893</td>\n",
+       "      <td>27/59</td>\n",
+       "      <td>5/40</td>\n",
+       "      <td>31/126</td>\n",
+       "      <td>21/92</td>\n",
+       "      <td>18/52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>662/1170</td>\n",
+       "      <td>1031/1700</td>\n",
+       "      <td>nan/nan</td>\n",
+       "      <td>72/180</td>\n",
+       "      <td>4/16</td>\n",
+       "      <td>4/27</td>\n",
+       "      <td>5/33</td>\n",
+       "      <td>1/28</td>\n",
+       "      <td>2/16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>13/179</td>\n",
+       "      <td>nan/nan</td>\n",
+       "      <td>2827/4393</td>\n",
+       "      <td>32/174</td>\n",
+       "      <td>1/3</td>\n",
+       "      <td>4/12</td>\n",
+       "      <td>0/3</td>\n",
+       "      <td>0/1</td>\n",
+       "      <td>0/5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>313/893</td>\n",
+       "      <td>72/180</td>\n",
+       "      <td>32/174</td>\n",
+       "      <td>5328/7641</td>\n",
+       "      <td>199/262</td>\n",
+       "      <td>71/115</td>\n",
+       "      <td>179/413</td>\n",
+       "      <td>184/340</td>\n",
+       "      <td>63/122</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>27/59</td>\n",
+       "      <td>4/16</td>\n",
+       "      <td>1/3</td>\n",
+       "      <td>199/262</td>\n",
+       "      <td>2992/3688</td>\n",
+       "      <td>1/13</td>\n",
+       "      <td>22/40</td>\n",
+       "      <td>3/26</td>\n",
+       "      <td>29/41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>5/40</td>\n",
+       "      <td>4/27</td>\n",
+       "      <td>4/12</td>\n",
+       "      <td>71/115</td>\n",
+       "      <td>1/13</td>\n",
+       "      <td>815/1500</td>\n",
+       "      <td>2/18</td>\n",
+       "      <td>2/22</td>\n",
+       "      <td>1/9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>31/126</td>\n",
+       "      <td>5/33</td>\n",
+       "      <td>0/3</td>\n",
+       "      <td>179/413</td>\n",
+       "      <td>22/40</td>\n",
+       "      <td>2/18</td>\n",
+       "      <td>935/1560</td>\n",
+       "      <td>17/63</td>\n",
+       "      <td>69/138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>21/92</td>\n",
+       "      <td>1/28</td>\n",
+       "      <td>0/1</td>\n",
+       "      <td>184/340</td>\n",
+       "      <td>3/26</td>\n",
+       "      <td>2/22</td>\n",
+       "      <td>17/63</td>\n",
+       "      <td>2248/2832</td>\n",
+       "      <td>1/20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>18/52</td>\n",
+       "      <td>2/16</td>\n",
+       "      <td>0/5</td>\n",
+       "      <td>63/122</td>\n",
+       "      <td>29/41</td>\n",
+       "      <td>1/9</td>\n",
+       "      <td>69/138</td>\n",
+       "      <td>1/20</td>\n",
+       "      <td>2778/3637</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            EGFR      ErbB2      p110a        KDR       BRAF      CDK2  \\\n",
+       "EGFR   3635/5965   662/1170     13/179    313/893      27/59      5/40   \n",
+       "ErbB2   662/1170  1031/1700    nan/nan     72/180       4/16      4/27   \n",
+       "p110a     13/179    nan/nan  2827/4393     32/174        1/3      4/12   \n",
+       "KDR      313/893     72/180     32/174  5328/7641    199/262    71/115   \n",
+       "BRAF       27/59       4/16        1/3    199/262  2992/3688      1/13   \n",
+       "CDK2        5/40       4/27       4/12     71/115       1/13  815/1500   \n",
+       "LCK       31/126       5/33        0/3    179/413      22/40      2/18   \n",
+       "MET        21/92       1/28        0/1    184/340       3/26      2/22   \n",
+       "p38a       18/52       2/16        0/5     63/122      29/41       1/9   \n",
+       "\n",
+       "            LCK        MET       p38a  \n",
+       "EGFR     31/126      21/92      18/52  \n",
+       "ErbB2      5/33       1/28       2/16  \n",
+       "p110a       0/3        0/1        0/5  \n",
+       "KDR     179/413    184/340     63/122  \n",
+       "BRAF      22/40       3/26      29/41  \n",
+       "CDK2       2/18       2/22        1/9  \n",
+       "LCK    935/1560      17/63     69/138  \n",
+       "MET       17/63  2248/2832       1/20  \n",
+       "p38a     69/138       1/20  2778/3637  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_counts_matrix = pd.DataFrame(index=kinase_selection_df.kinase_klifs,\n",
+    "                                        columns=kinase_selection_df.kinase_klifs)\n",
+    "kinase_counts_matrix.index.name = None\n",
+    "kinase_counts_matrix.columns.name = None\n",
+    "\n",
+    "for i, (uniprot_id1, klifs_name1) in enumerate(\n",
+    "    zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)):\n",
+    "    for j, (uniprot_id2, klifs_name2) in enumerate(\n",
+    "        zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)):\n",
+    "        total, actives, _ = similarity_ligand_profile(uniprot_id1,\n",
+    "                                                      uniprot_id2,\n",
+    "                                                      data)\n",
+    "        integer_ratio = f\"{str(actives)}/{str(total)}\"\n",
+    "        kinase_counts_matrix[klifs_name1][klifs_name2] = integer_ratio\n",
+    "kinase_counts_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's look at the similarity, in this case, the reduced fraction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1766,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1938,7 +2159,7 @@
        "p38a   0.050000  0.763816  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1954,215 +2175,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
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        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c384a_row7_col4 {\n",
+       "#T_99488_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c384a_row7_col5 {\n",
+       "#T_99488_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c384a_row7_col6 {\n",
+       "#T_99488_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c384a_row8_col0 {\n",
+       "#T_99488_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c384a_row8_col1 {\n",
+       "#T_99488_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c384a_row8_col3 {\n",
+       "#T_99488_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c384a_row8_col4 {\n",
+       "#T_99488_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c384a_row8_col6 {\n",
+       "#T_99488_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c384a_\">\n",
+       "<table id=\"T_99488_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2179,121 +2400,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_c384a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_c384a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_c384a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_c384a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_c384a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_c384a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_c384a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_c384a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_c384a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_c384a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_c384a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_c384a_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_c384a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_c384a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_c384a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_c384a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_c384a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_c384a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_c384a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_c384a_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_c384a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_c384a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_c384a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_c384a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_c384a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_c384a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_c384a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_c384a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_c384a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_c384a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_c384a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_c384a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_c384a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_c384a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_c384a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_c384a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_c384a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_c384a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_c384a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_c384a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_c384a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_c384a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_c384a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_c384a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_c384a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_c384a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_c384a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_c384a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_c384a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_c384a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_c384a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_c384a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_c384a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_c384a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_c384a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_c384a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_c384a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_c384a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_c384a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_c384a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_c384a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_c384a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_c384a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_c384a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_c384a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_c384a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_c384a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_c384a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_c384a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_c384a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_c384a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_c384a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c384a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c384a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_c384a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_c384a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_c384a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_c384a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_c384a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_c384a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_c384a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_c384a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_99488_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_99488_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_99488_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_99488_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_99488_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_99488_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_99488_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_99488_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_99488_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_99488_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_99488_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_99488_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_99488_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_99488_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_99488_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_99488_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_99488_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_99488_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_99488_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_99488_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_99488_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_99488_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_99488_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_99488_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_99488_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_99488_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_99488_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_99488_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_99488_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_99488_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_99488_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_99488_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_99488_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_99488_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_99488_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_99488_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_99488_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_99488_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_99488_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_99488_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_99488_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_99488_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_99488_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_99488_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_99488_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_99488_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_99488_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_99488_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_99488_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_99488_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_99488_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_99488_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_99488_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_99488_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_99488_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_99488_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_99488_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_99488_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_99488_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_99488_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_99488_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_99488_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_99488_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_99488_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_99488_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_99488_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_99488_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_99488_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_99488_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_99488_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_99488_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_99488_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_99488_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_99488_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_99488_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_99488_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_99488_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_99488_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_99488_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_99488_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_99488_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_99488_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_99488_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fcaf25004c0>"
+       "<pandas.io.formats.style.Styler at 0x7f889cd059a0>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2319,219 +2540,219 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_aefc8_row0_col0, #T_aefc8_row1_col1, #T_aefc8_row2_col2, #T_aefc8_row3_col5, #T_aefc8_row4_col3, #T_aefc8_row4_col4, #T_aefc8_row6_col6, #T_aefc8_row7_col7, #T_aefc8_row8_col8 {\n",
+       "#T_40d7b_row0_col0, #T_40d7b_row1_col1, #T_40d7b_row2_col2, #T_40d7b_row3_col5, #T_40d7b_row4_col3, #T_40d7b_row4_col4, #T_40d7b_row6_col6, #T_40d7b_row7_col7, #T_40d7b_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row0_col1 {\n",
+       "#T_40d7b_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row0_col2 {\n",
+       "#T_40d7b_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row0_col3, #T_aefc8_row7_col0 {\n",
+       "#T_40d7b_row0_col3, #T_40d7b_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row0_col4, #T_aefc8_row3_col0, #T_aefc8_row4_col2, #T_aefc8_row5_col2 {\n",
+       "#T_40d7b_row0_col4, #T_40d7b_row3_col0, #T_40d7b_row4_col2, #T_40d7b_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row0_col5 {\n",
+       "#T_40d7b_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row0_col6, #T_aefc8_row4_col1 {\n",
+       "#T_40d7b_row0_col6, #T_40d7b_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row0_col7, #T_aefc8_row3_col2 {\n",
+       "#T_40d7b_row0_col7, #T_40d7b_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row0_col8 {\n",
+       "#T_40d7b_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col0 {\n",
+       "#T_40d7b_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row1_col2, #T_aefc8_row2_col0, #T_aefc8_row2_col1, #T_aefc8_row2_col3, #T_aefc8_row2_col6, #T_aefc8_row2_col7, #T_aefc8_row2_col8, #T_aefc8_row4_col5, #T_aefc8_row5_col4, #T_aefc8_row6_col2, #T_aefc8_row7_col2, #T_aefc8_row8_col2 {\n",
+       "#T_40d7b_row1_col2, #T_40d7b_row2_col0, #T_40d7b_row2_col1, #T_40d7b_row2_col3, #T_40d7b_row2_col6, #T_40d7b_row2_col7, #T_40d7b_row2_col8, #T_40d7b_row4_col5, #T_40d7b_row5_col4, #T_40d7b_row6_col2, #T_40d7b_row7_col2, #T_40d7b_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col3 {\n",
+       "#T_40d7b_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col4 {\n",
+       "#T_40d7b_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col5 {\n",
+       "#T_40d7b_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col6 {\n",
+       "#T_40d7b_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col7 {\n",
+       "#T_40d7b_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row1_col8 {\n",
+       "#T_40d7b_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row2_col4 {\n",
+       "#T_40d7b_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row2_col5 {\n",
+       "#T_40d7b_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row3_col1 {\n",
+       "#T_40d7b_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row3_col3 {\n",
+       "#T_40d7b_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row3_col4, #T_aefc8_row4_col8 {\n",
+       "#T_40d7b_row3_col4, #T_40d7b_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row3_col6 {\n",
+       "#T_40d7b_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row3_col7 {\n",
+       "#T_40d7b_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row3_col8 {\n",
+       "#T_40d7b_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row4_col0 {\n",
+       "#T_40d7b_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row4_col6 {\n",
+       "#T_40d7b_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row4_col7, #T_aefc8_row5_col8 {\n",
+       "#T_40d7b_row4_col7, #T_40d7b_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row5_col0 {\n",
+       "#T_40d7b_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row5_col1 {\n",
+       "#T_40d7b_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row5_col3 {\n",
+       "#T_40d7b_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row5_col5 {\n",
+       "#T_40d7b_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row5_col6 {\n",
+       "#T_40d7b_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row5_col7 {\n",
+       "#T_40d7b_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col0 {\n",
+       "#T_40d7b_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col1 {\n",
+       "#T_40d7b_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col3 {\n",
+       "#T_40d7b_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col4 {\n",
+       "#T_40d7b_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row6_col5, #T_aefc8_row7_col8, #T_aefc8_row8_col5, #T_aefc8_row8_col7 {\n",
+       "#T_40d7b_row6_col5, #T_40d7b_row7_col8, #T_40d7b_row8_col5, #T_40d7b_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col7 {\n",
+       "#T_40d7b_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row6_col8 {\n",
+       "#T_40d7b_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row7_col1 {\n",
+       "#T_40d7b_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row7_col3 {\n",
+       "#T_40d7b_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row7_col4 {\n",
+       "#T_40d7b_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row7_col5 {\n",
+       "#T_40d7b_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row7_col6 {\n",
+       "#T_40d7b_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row8_col0 {\n",
+       "#T_40d7b_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row8_col1 {\n",
+       "#T_40d7b_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_aefc8_row8_col3 {\n",
+       "#T_40d7b_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row8_col4 {\n",
+       "#T_40d7b_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_aefc8_row8_col6 {\n",
+       "#T_40d7b_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_aefc8_\">\n",
+       "<table id=\"T_40d7b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2548,121 +2769,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_aefc8_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_aefc8_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_aefc8_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_aefc8_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_aefc8_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_aefc8_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_aefc8_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_aefc8_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_aefc8_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_aefc8_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_aefc8_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_aefc8_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_aefc8_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_aefc8_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_aefc8_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_aefc8_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_aefc8_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_aefc8_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_aefc8_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_aefc8_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_aefc8_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_aefc8_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_aefc8_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_aefc8_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_aefc8_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_aefc8_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_aefc8_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_aefc8_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_aefc8_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_aefc8_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_aefc8_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_aefc8_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_aefc8_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_aefc8_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_aefc8_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_aefc8_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_aefc8_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_aefc8_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_aefc8_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_aefc8_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_aefc8_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_aefc8_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_aefc8_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_aefc8_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_aefc8_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_aefc8_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_aefc8_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_aefc8_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_aefc8_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_aefc8_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_aefc8_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_aefc8_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_aefc8_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_aefc8_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_aefc8_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_aefc8_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_aefc8_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_aefc8_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_aefc8_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_aefc8_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_aefc8_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_aefc8_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_aefc8_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_aefc8_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_aefc8_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_aefc8_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aefc8_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_aefc8_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_aefc8_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_aefc8_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_aefc8_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_aefc8_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_aefc8_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_aefc8_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_aefc8_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_aefc8_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_40d7b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_40d7b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_40d7b_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_40d7b_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_40d7b_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_40d7b_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_40d7b_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_40d7b_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_40d7b_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_40d7b_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_40d7b_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_40d7b_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_40d7b_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_40d7b_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_40d7b_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_40d7b_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_40d7b_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_40d7b_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_40d7b_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_40d7b_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_40d7b_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_40d7b_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_40d7b_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_40d7b_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_40d7b_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_40d7b_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_40d7b_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_40d7b_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_40d7b_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_40d7b_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_40d7b_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_40d7b_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_40d7b_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_40d7b_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_40d7b_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_40d7b_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_40d7b_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_40d7b_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_40d7b_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_40d7b_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_40d7b_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_40d7b_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_40d7b_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_40d7b_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_40d7b_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_40d7b_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_40d7b_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_40d7b_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_40d7b_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_40d7b_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_40d7b_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_40d7b_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_40d7b_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_40d7b_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_40d7b_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_40d7b_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_40d7b_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_40d7b_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_40d7b_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_40d7b_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_40d7b_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_40d7b_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_40d7b_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_40d7b_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_40d7b_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_40d7b_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_40d7b_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_40d7b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_40d7b_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_40d7b_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_40d7b_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_40d7b_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_40d7b_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_40d7b_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_40d7b_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_40d7b_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_40d7b_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fcaf24f08e0>"
+       "<pandas.io.formats.style.Styler at 0x7f889cd05a90>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2684,7 +2905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2708,7 +2929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2727,7 +2948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2743,7 +2964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2752,211 +2973,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_f86a3_row0_col0, #T_f86a3_row1_col1, #T_f86a3_row2_col2, #T_f86a3_row3_col3, #T_f86a3_row4_col4, #T_f86a3_row5_col5, #T_f86a3_row6_col6, #T_f86a3_row7_col7, #T_f86a3_row8_col8 {\n",
+       "#T_48848_row0_col0, #T_48848_row1_col1, #T_48848_row2_col2, #T_48848_row3_col3, #T_48848_row4_col4, #T_48848_row5_col5, #T_48848_row6_col6, #T_48848_row7_col7, #T_48848_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row0_col1 {\n",
+       "#T_48848_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row0_col2 {\n",
+       "#T_48848_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col3 {\n",
+       "#T_48848_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col4 {\n",
+       "#T_48848_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col5 {\n",
+       "#T_48848_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col6 {\n",
+       "#T_48848_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col7 {\n",
+       "#T_48848_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row0_col8 {\n",
+       "#T_48848_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col0 {\n",
+       "#T_48848_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row1_col2, #T_f86a3_row2_col0, #T_f86a3_row2_col1, #T_f86a3_row2_col3, #T_f86a3_row2_col6, #T_f86a3_row2_col7, #T_f86a3_row2_col8, #T_f86a3_row4_col5, #T_f86a3_row5_col4, #T_f86a3_row6_col2, #T_f86a3_row7_col2, #T_f86a3_row8_col2 {\n",
+       "#T_48848_row1_col2, #T_48848_row2_col0, #T_48848_row2_col1, #T_48848_row2_col3, #T_48848_row2_col6, #T_48848_row2_col7, #T_48848_row2_col8, #T_48848_row4_col5, #T_48848_row5_col4, #T_48848_row6_col2, #T_48848_row7_col2, #T_48848_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col3 {\n",
+       "#T_48848_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col4, #T_f86a3_row3_col2, #T_f86a3_row6_col0 {\n",
+       "#T_48848_row1_col4, #T_48848_row3_col2, #T_48848_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col5 {\n",
+       "#T_48848_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col6, #T_f86a3_row6_col1 {\n",
+       "#T_48848_row1_col6, #T_48848_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col7, #T_f86a3_row6_col5, #T_f86a3_row7_col1, #T_f86a3_row8_col5 {\n",
+       "#T_48848_row1_col7, #T_48848_row6_col5, #T_48848_row7_col1, #T_48848_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row1_col8, #T_f86a3_row8_col1 {\n",
+       "#T_48848_row1_col8, #T_48848_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row2_col4, #T_f86a3_row2_col5 {\n",
+       "#T_48848_row2_col4, #T_48848_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row3_col0 {\n",
+       "#T_48848_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row3_col1 {\n",
+       "#T_48848_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row3_col4 {\n",
+       "#T_48848_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row3_col5 {\n",
+       "#T_48848_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row3_col6 {\n",
+       "#T_48848_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row3_col7 {\n",
+       "#T_48848_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row3_col8 {\n",
+       "#T_48848_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row4_col0 {\n",
+       "#T_48848_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row4_col1 {\n",
+       "#T_48848_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row4_col2, #T_f86a3_row5_col2 {\n",
+       "#T_48848_row4_col2, #T_48848_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row4_col3 {\n",
+       "#T_48848_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row4_col6 {\n",
+       "#T_48848_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row4_col7 {\n",
+       "#T_48848_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row4_col8 {\n",
+       "#T_48848_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row5_col0 {\n",
+       "#T_48848_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row5_col1 {\n",
+       "#T_48848_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row5_col3 {\n",
+       "#T_48848_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row5_col6, #T_f86a3_row5_col8 {\n",
+       "#T_48848_row5_col6, #T_48848_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row5_col7 {\n",
+       "#T_48848_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row6_col3 {\n",
+       "#T_48848_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row6_col4 {\n",
+       "#T_48848_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f86a3_row6_col7, #T_f86a3_row7_col6 {\n",
+       "#T_48848_row6_col7, #T_48848_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row6_col8, #T_f86a3_row8_col6 {\n",
+       "#T_48848_row6_col8, #T_48848_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row7_col0 {\n",
+       "#T_48848_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row7_col3 {\n",
+       "#T_48848_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row7_col4 {\n",
+       "#T_48848_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row7_col5 {\n",
+       "#T_48848_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row7_col8, #T_f86a3_row8_col7 {\n",
+       "#T_48848_row7_col8, #T_48848_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row8_col0 {\n",
+       "#T_48848_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row8_col3 {\n",
+       "#T_48848_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f86a3_row8_col4 {\n",
+       "#T_48848_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_f86a3_\">\n",
+       "<table id=\"T_48848_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2973,121 +3194,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_f86a3_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_f86a3_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_f86a3_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_f86a3_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_f86a3_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_f86a3_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_f86a3_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_f86a3_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_f86a3_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_f86a3_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_f86a3_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_f86a3_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_f86a3_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_f86a3_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_f86a3_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_f86a3_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_f86a3_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_f86a3_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_f86a3_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_f86a3_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_f86a3_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_f86a3_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_f86a3_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_f86a3_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_f86a3_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_f86a3_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_f86a3_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_f86a3_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_f86a3_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_f86a3_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_f86a3_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_f86a3_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_f86a3_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_f86a3_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_f86a3_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_f86a3_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_f86a3_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_f86a3_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_f86a3_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_f86a3_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_f86a3_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_f86a3_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_f86a3_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_f86a3_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_f86a3_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_f86a3_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_f86a3_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_f86a3_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_f86a3_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_f86a3_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_f86a3_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_f86a3_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_f86a3_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_f86a3_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_f86a3_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_f86a3_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_f86a3_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_f86a3_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_f86a3_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f86a3_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_f86a3_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_f86a3_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_f86a3_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_f86a3_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_f86a3_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_f86a3_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_f86a3_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_f86a3_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_f86a3_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_48848_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_48848_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_48848_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_48848_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_48848_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_48848_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_48848_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_48848_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_48848_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_48848_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_48848_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_48848_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_48848_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_48848_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_48848_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_48848_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_48848_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_48848_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_48848_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_48848_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_48848_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_48848_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_48848_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_48848_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_48848_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_48848_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_48848_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_48848_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_48848_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_48848_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_48848_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_48848_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_48848_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_48848_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_48848_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_48848_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_48848_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_48848_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_48848_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_48848_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_48848_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_48848_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_48848_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_48848_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_48848_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_48848_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_48848_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_48848_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_48848_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_48848_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_48848_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_48848_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_48848_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_48848_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_48848_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_48848_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_48848_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_48848_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_48848_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_48848_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_48848_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_48848_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_48848_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_48848_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_48848_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_48848_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_48848_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_48848_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_48848_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_48848_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_48848_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_48848_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_48848_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_48848_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_48848_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_48848_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_48848_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_48848_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_48848_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_48848_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_48848_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_48848_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_48848_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fcaf24d7760>"
+       "<pandas.io.formats.style.Styler at 0x7f889cf93970>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3107,7 +3328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [

From 195c405e90cfdbc65b3bc101c6a01cc8e880589e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 22:18:59 +0200
Subject: [PATCH 089/158] add kinmap figure from T023 in T027.

---
 .../images/kinmap_n_activities_per_kinase.png  | Bin 0 -> 157979 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/kinmap_n_activities_per_kinase.png

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/kinmap_n_activities_per_kinase.png b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images/kinmap_n_activities_per_kinase.png
new file mode 100644
index 0000000000000000000000000000000000000000..fe3bd3a27e0e0bfdaca9bc160d21634a7ca81010
GIT binary patch
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From 81cdb5c50d26c05b995e64edaad1745cd0c2429f Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 27 Sep 2021 22:24:42 +0200
Subject: [PATCH 090/158] Run of notebook for pipeline.

---
 ..._distance_substitution_matrix_sequence.csv |    2 +-
 ...imilarity_substitution_matrix_sequence.csv |    2 +-
 .../talktorial.ipynb                          |  634 +++++-----
 .../kinase_distance_matrix_ligand_profile.csv |   20 +-
 ...inase_similarity_matrix_ligand_profile.csv |   20 +-
 .../talktorial.ipynb                          | 1044 ++++++++---------
 .../talktorial.ipynb                          |  110 +-
 7 files changed, 899 insertions(+), 933 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
index 4205a1a5..3acff1d6 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
@@ -1,4 +1,4 @@
-kinase_klifs,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
 EGFR,0.0,0.05903728309403988,0.5729382823021627,0.2839529624274032,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369
 ErbB2,0.05903728309403988,0.0,0.586362347301078,0.297882231158642,0.34539978367739244,0.36969159991343414,0.31492453290530287,0.3020325638294997,0.3648271324857587
 p110a,0.5729382823021627,0.586362347301078,0.0,0.5772950670526018,0.563540908147633,0.5760058634630552,0.5486635309945707,0.6063011403494588,0.5688055661744779
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
index c197d5c2..59da49d6 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
@@ -1,4 +1,4 @@
-kinase_klifs,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
 EGFR,1.0,0.9409627169059601,0.42706171769783724,0.7160470375725968,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631
 ErbB2,0.9409627169059601,1.0,0.413637652698922,0.702117768841358,0.6546002163226076,0.6303084000865659,0.6850754670946971,0.6979674361705003,0.6351728675142413
 p110a,0.42706171769783724,0.413637652698922,1.0,0.4227049329473982,0.43645909185236703,0.4239941365369449,0.4513364690054293,0.3936988596505412,0.43119443382552214
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 8ada386d..02e1cc45 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -1026,7 +1026,7 @@
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase_klifs</th>\n",
+       "      <th></th>\n",
        "      <th>EGFR</th>\n",
        "      <th>ErbB2</th>\n",
        "      <th>p110a</th>\n",
@@ -1037,18 +1037,6 @@
        "      <th>MET</th>\n",
        "      <th>p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase_klifs</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1164,29 +1152,27 @@
        "</div>"
       ],
       "text/plain": [
-       "kinase_klifs      EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
-       "kinase_klifs                                                               \n",
-       "EGFR          1.000000  0.940963  0.427062  0.716047  0.655028  0.648447   \n",
-       "ErbB2         0.940963  1.000000  0.413638  0.702118  0.654600  0.630308   \n",
-       "p110a         0.427062  0.413638  1.000000  0.422705  0.436459  0.423994   \n",
-       "KDR           0.716047  0.702118  0.422705  1.000000  0.671268  0.653379   \n",
-       "BRAF          0.655028  0.654600  0.436459  0.671268  1.000000  0.646755   \n",
-       "CDK2          0.648447  0.630308  0.423994  0.653379  0.646755  1.000000   \n",
-       "LCK           0.711321  0.685075  0.451336  0.687648  0.672933  0.681253   \n",
-       "MET           0.711258  0.697967  0.393699  0.713806  0.638158  0.656025   \n",
-       "p38a          0.644644  0.635173  0.431194  0.653444  0.637912  0.723093   \n",
+       "           EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       "EGFR   1.000000  0.940963  0.427062  0.716047  0.655028  0.648447  0.711321   \n",
+       "ErbB2  0.940963  1.000000  0.413638  0.702118  0.654600  0.630308  0.685075   \n",
+       "p110a  0.427062  0.413638  1.000000  0.422705  0.436459  0.423994  0.451336   \n",
+       "KDR    0.716047  0.702118  0.422705  1.000000  0.671268  0.653379  0.687648   \n",
+       "BRAF   0.655028  0.654600  0.436459  0.671268  1.000000  0.646755  0.672933   \n",
+       "CDK2   0.648447  0.630308  0.423994  0.653379  0.646755  1.000000  0.681253   \n",
+       "LCK    0.711321  0.685075  0.451336  0.687648  0.672933  0.681253  1.000000   \n",
+       "MET    0.711258  0.697967  0.393699  0.713806  0.638158  0.656025  0.690879   \n",
+       "p38a   0.644644  0.635173  0.431194  0.653444  0.637912  0.723093  0.662581   \n",
        "\n",
-       "kinase_klifs       LCK       MET      p38a  \n",
-       "kinase_klifs                                \n",
-       "EGFR          0.711321  0.711258  0.644644  \n",
-       "ErbB2         0.685075  0.697967  0.635173  \n",
-       "p110a         0.451336  0.393699  0.431194  \n",
-       "KDR           0.687648  0.713806  0.653444  \n",
-       "BRAF          0.672933  0.638158  0.637912  \n",
-       "CDK2          0.681253  0.656025  0.723093  \n",
-       "LCK           1.000000  0.690879  0.662581  \n",
-       "MET           0.690879  1.000000  0.629355  \n",
-       "p38a          0.662581  0.629355  1.000000  "
+       "            MET      p38a  \n",
+       "EGFR   0.711258  0.644644  \n",
+       "ErbB2  0.697967  0.635173  \n",
+       "p110a  0.393699  0.431194  \n",
+       "KDR    0.713806  0.653444  \n",
+       "BRAF   0.638158  0.637912  \n",
+       "CDK2   0.656025  0.723093  \n",
+       "LCK    0.690879  0.662581  \n",
+       "MET    1.000000  0.629355  \n",
+       "p38a   0.629355  1.000000  "
       ]
      },
      "execution_count": 18,
@@ -1198,6 +1184,8 @@
     "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
     "                                           index=kinase_selection_df.kinase_klifs,\n",
     "                                           columns=kinase_selection_df.kinase_klifs)\n",
+    "kinase_similarity_matrix_df.index.name = None\n",
+    "kinase_similarity_matrix_df.columns.name = None\n",
     "kinase_similarity_matrix_df"
    ]
   },
@@ -1238,191 +1226,191 @@
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        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row3_col1, #T_72c5e_row6_col7 {\n",
+       "#T_d0463_row3_col1, #T_d0463_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row3_col2, #T_72c5e_row5_col2 {\n",
+       "#T_d0463_row3_col2, #T_d0463_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row3_col4 {\n",
+       "#T_d0463_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row3_col5, #T_72c5e_row4_col0 {\n",
+       "#T_d0463_row3_col5, #T_d0463_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row3_col6, #T_72c5e_row4_col3, #T_72c5e_row5_col7 {\n",
+       "#T_d0463_row3_col6, #T_d0463_row4_col3, #T_d0463_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row3_col7 {\n",
+       "#T_d0463_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row4_col1 {\n",
+       "#T_d0463_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row4_col2 {\n",
+       "#T_d0463_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row4_col6, #T_72c5e_row4_col7, #T_72c5e_row7_col5 {\n",
+       "#T_d0463_row4_col6, #T_d0463_row4_col7, #T_d0463_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row4_col8 {\n",
+       "#T_d0463_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col0, #T_72c5e_row8_col6 {\n",
+       "#T_d0463_row5_col0, #T_d0463_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col1 {\n",
+       "#T_d0463_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col3, #T_72c5e_row8_col3 {\n",
+       "#T_d0463_row5_col3, #T_d0463_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col4 {\n",
+       "#T_d0463_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col6, #T_72c5e_row6_col4 {\n",
+       "#T_d0463_row5_col6, #T_d0463_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row5_col8 {\n",
+       "#T_d0463_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row6_col0 {\n",
+       "#T_d0463_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row6_col1 {\n",
+       "#T_d0463_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row6_col2 {\n",
+       "#T_d0463_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row6_col3 {\n",
+       "#T_d0463_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row6_col5 {\n",
+       "#T_d0463_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row6_col8 {\n",
+       "#T_d0463_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row7_col0 {\n",
+       "#T_d0463_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_72c5e_row7_col1 {\n",
+       "#T_d0463_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row7_col6 {\n",
+       "#T_d0463_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row7_col8 {\n",
+       "#T_d0463_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row8_col0 {\n",
+       "#T_d0463_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row8_col2 {\n",
+       "#T_d0463_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_72c5e_row8_col5 {\n",
+       "#T_d0463_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_72c5e_\">\n",
+       "<table id=\"T_d0463_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
        "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
@@ -1433,133 +1421,121 @@
        "      <th class=\"col_heading level0 col7\" >MET</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_72c5e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_72c5e_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_72c5e_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_72c5e_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_72c5e_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_72c5e_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_72c5e_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_72c5e_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "      <th id=\"T_d0463_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d0463_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_d0463_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_d0463_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_d0463_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_d0463_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_d0463_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_d0463_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_d0463_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_72c5e_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_72c5e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_72c5e_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_72c5e_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_72c5e_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_72c5e_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_72c5e_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_72c5e_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "      <th id=\"T_d0463_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d0463_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_d0463_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_d0463_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_d0463_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_d0463_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_d0463_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_d0463_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_d0463_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_72c5e_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_72c5e_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_72c5e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_72c5e_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_72c5e_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_72c5e_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_72c5e_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_72c5e_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "      <th id=\"T_d0463_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_d0463_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_d0463_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_d0463_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_d0463_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_d0463_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_d0463_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_d0463_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_d0463_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_72c5e_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_72c5e_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_72c5e_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_72c5e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_72c5e_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_72c5e_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_72c5e_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_72c5e_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "      <th id=\"T_d0463_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_d0463_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_d0463_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_d0463_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_d0463_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_d0463_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_d0463_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_d0463_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_d0463_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_72c5e_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_72c5e_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_72c5e_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_72c5e_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_72c5e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_72c5e_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_72c5e_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_72c5e_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "      <th id=\"T_d0463_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_d0463_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_d0463_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_d0463_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_d0463_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_d0463_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_d0463_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_d0463_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_d0463_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_72c5e_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_72c5e_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_72c5e_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_72c5e_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_72c5e_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_72c5e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_72c5e_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_72c5e_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "      <th id=\"T_d0463_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_d0463_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_d0463_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_d0463_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_d0463_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_d0463_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_d0463_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_d0463_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_d0463_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_72c5e_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_72c5e_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_72c5e_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_72c5e_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_72c5e_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_72c5e_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_72c5e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_72c5e_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "      <th id=\"T_d0463_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_d0463_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_d0463_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_d0463_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_d0463_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_d0463_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_d0463_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_d0463_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_d0463_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_72c5e_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_72c5e_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_72c5e_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_72c5e_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_72c5e_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_72c5e_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_72c5e_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_72c5e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_72c5e_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "      <th id=\"T_d0463_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_d0463_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_d0463_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_d0463_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_d0463_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_d0463_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_d0463_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_d0463_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_d0463_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_d0463_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_72c5e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_72c5e_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_72c5e_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_72c5e_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_72c5e_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_72c5e_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_72c5e_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_72c5e_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_72c5e_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_72c5e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_d0463_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d0463_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_d0463_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_d0463_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_d0463_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_d0463_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_d0463_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_d0463_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_d0463_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_d0463_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb414e3fd90>"
+       "<pandas.io.formats.style.Styler at 0x7ff6ce711670>"
       ]
      },
      "execution_count": 20,
@@ -1643,191 +1619,191 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_a23fa_row0_col0, #T_a23fa_row1_col1, #T_a23fa_row2_col2, #T_a23fa_row3_col3, #T_a23fa_row4_col4, #T_a23fa_row5_col5, #T_a23fa_row6_col6, #T_a23fa_row7_col7, #T_a23fa_row8_col8 {\n",
+       "#T_36c4a_row0_col0, #T_36c4a_row1_col1, #T_36c4a_row2_col2, #T_36c4a_row3_col3, #T_36c4a_row4_col4, #T_36c4a_row5_col5, #T_36c4a_row6_col6, #T_36c4a_row7_col7, #T_36c4a_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row0_col1 {\n",
+       "#T_36c4a_row0_col1 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row0_col2 {\n",
+       "#T_36c4a_row0_col2 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row0_col3 {\n",
+       "#T_36c4a_row0_col3 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row0_col4, #T_a23fa_row0_col5, #T_a23fa_row1_col4, #T_a23fa_row4_col5, #T_a23fa_row8_col7 {\n",
+       "#T_36c4a_row0_col4, #T_36c4a_row0_col5, #T_36c4a_row1_col4, #T_36c4a_row4_col5, #T_36c4a_row8_col7 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row0_col6 {\n",
+       "#T_36c4a_row0_col6 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row0_col7 {\n",
+       "#T_36c4a_row0_col7 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row0_col8, #T_a23fa_row8_col1 {\n",
+       "#T_36c4a_row0_col8, #T_36c4a_row8_col1 {\n",
        "  background-color: #58ab58;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row1_col0 {\n",
+       "#T_36c4a_row1_col0 {\n",
        "  background-color: #d3e7d3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row1_col2 {\n",
+       "#T_36c4a_row1_col2 {\n",
        "  background-color: #078407;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row1_col3 {\n",
+       "#T_36c4a_row1_col3 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row1_col5, #T_a23fa_row1_col8, #T_a23fa_row7_col4, #T_a23fa_row8_col4 {\n",
+       "#T_36c4a_row1_col5, #T_36c4a_row1_col8, #T_36c4a_row7_col4, #T_36c4a_row8_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row1_col6 {\n",
+       "#T_36c4a_row1_col6 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row1_col7 {\n",
+       "#T_36c4a_row1_col7 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row2_col0, #T_a23fa_row2_col1, #T_a23fa_row2_col3, #T_a23fa_row2_col4, #T_a23fa_row2_col5, #T_a23fa_row2_col6, #T_a23fa_row2_col7, #T_a23fa_row2_col8, #T_a23fa_row7_col2 {\n",
+       "#T_36c4a_row2_col0, #T_36c4a_row2_col1, #T_36c4a_row2_col3, #T_36c4a_row2_col4, #T_36c4a_row2_col5, #T_36c4a_row2_col6, #T_36c4a_row2_col7, #T_36c4a_row2_col8, #T_36c4a_row7_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col0, #T_a23fa_row7_col3 {\n",
+       "#T_36c4a_row3_col0, #T_36c4a_row7_col3 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col1, #T_a23fa_row6_col7 {\n",
+       "#T_36c4a_row3_col1, #T_36c4a_row6_col7 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col2, #T_a23fa_row5_col2 {\n",
+       "#T_36c4a_row3_col2, #T_36c4a_row5_col2 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col4 {\n",
+       "#T_36c4a_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col5, #T_a23fa_row4_col0 {\n",
+       "#T_36c4a_row3_col5, #T_36c4a_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col6, #T_a23fa_row4_col3, #T_a23fa_row5_col7 {\n",
+       "#T_36c4a_row3_col6, #T_36c4a_row4_col3, #T_36c4a_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row3_col7 {\n",
+       "#T_36c4a_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row3_col8 {\n",
+       "#T_36c4a_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row4_col1 {\n",
+       "#T_36c4a_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row4_col2 {\n",
+       "#T_36c4a_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row4_col6, #T_a23fa_row4_col7, #T_a23fa_row7_col5 {\n",
+       "#T_36c4a_row4_col6, #T_36c4a_row4_col7, #T_36c4a_row7_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row4_col8, #T_a23fa_row5_col1 {\n",
+       "#T_36c4a_row4_col8, #T_36c4a_row5_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row5_col0, #T_a23fa_row8_col6 {\n",
+       "#T_36c4a_row5_col0, #T_36c4a_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row5_col3, #T_a23fa_row8_col3 {\n",
+       "#T_36c4a_row5_col3, #T_36c4a_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row5_col4 {\n",
+       "#T_36c4a_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row5_col6, #T_a23fa_row6_col4 {\n",
+       "#T_36c4a_row5_col6, #T_36c4a_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row5_col8 {\n",
+       "#T_36c4a_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a23fa_row6_col0 {\n",
+       "#T_36c4a_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row6_col1 {\n",
+       "#T_36c4a_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row6_col2 {\n",
+       "#T_36c4a_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row6_col3 {\n",
+       "#T_36c4a_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row6_col5 {\n",
+       "#T_36c4a_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row6_col8 {\n",
+       "#T_36c4a_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row7_col0 {\n",
+       "#T_36c4a_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row7_col1 {\n",
+       "#T_36c4a_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row7_col6 {\n",
+       "#T_36c4a_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row7_col8 {\n",
+       "#T_36c4a_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row8_col0 {\n",
+       "#T_36c4a_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row8_col2 {\n",
+       "#T_36c4a_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a23fa_row8_col5 {\n",
+       "#T_36c4a_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_a23fa_\">\n",
+       "<table id=\"T_36c4a_\">\n",
        "  <thead>\n",
        "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
        "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
        "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
        "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
@@ -1838,133 +1814,121 @@
        "      <th class=\"col_heading level0 col7\" >MET</th>\n",
        "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >kinase_klifs</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "      <th class=\"blank col7\" >&nbsp;</th>\n",
-       "      <th class=\"blank col8\" >&nbsp;</th>\n",
-       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_a23fa_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_a23fa_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_a23fa_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_a23fa_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_a23fa_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_a23fa_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_a23fa_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_a23fa_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "      <th id=\"T_36c4a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_36c4a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_36c4a_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_36c4a_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_36c4a_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_36c4a_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_36c4a_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_36c4a_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_36c4a_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_a23fa_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_a23fa_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_a23fa_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_a23fa_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_a23fa_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_a23fa_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_a23fa_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_a23fa_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "      <th id=\"T_36c4a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_36c4a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_36c4a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_36c4a_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_36c4a_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_36c4a_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_36c4a_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_36c4a_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_36c4a_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_a23fa_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_a23fa_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_a23fa_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_a23fa_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_a23fa_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_a23fa_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_a23fa_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_a23fa_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "      <th id=\"T_36c4a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_36c4a_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_36c4a_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_36c4a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_36c4a_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_36c4a_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_36c4a_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_36c4a_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_36c4a_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_a23fa_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_a23fa_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_a23fa_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_a23fa_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_a23fa_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_a23fa_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_a23fa_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_a23fa_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "      <th id=\"T_36c4a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_36c4a_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_36c4a_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_36c4a_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_36c4a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_36c4a_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_36c4a_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_36c4a_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_36c4a_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_a23fa_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_a23fa_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_a23fa_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_a23fa_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_a23fa_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_a23fa_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_a23fa_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_a23fa_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "      <th id=\"T_36c4a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_36c4a_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_36c4a_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_36c4a_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_36c4a_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_36c4a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_36c4a_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_36c4a_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_36c4a_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_a23fa_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_a23fa_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_a23fa_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_a23fa_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_a23fa_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_a23fa_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_a23fa_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_a23fa_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "      <th id=\"T_36c4a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_36c4a_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_36c4a_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_36c4a_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_36c4a_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_36c4a_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_36c4a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_36c4a_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_36c4a_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_a23fa_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_a23fa_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_a23fa_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_a23fa_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_a23fa_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_a23fa_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_a23fa_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_a23fa_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "      <th id=\"T_36c4a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_36c4a_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_36c4a_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_36c4a_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_36c4a_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_36c4a_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_36c4a_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_36c4a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_36c4a_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_a23fa_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_a23fa_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_a23fa_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_a23fa_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_a23fa_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_a23fa_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_a23fa_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_a23fa_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_a23fa_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "      <th id=\"T_36c4a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_36c4a_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_36c4a_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_36c4a_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_36c4a_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_36c4a_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_36c4a_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_36c4a_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_36c4a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_36c4a_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a23fa_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_a23fa_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_a23fa_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_a23fa_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_a23fa_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_a23fa_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_a23fa_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_a23fa_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_a23fa_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_a23fa_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_36c4a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_36c4a_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_36c4a_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_36c4a_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_36c4a_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_36c4a_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_36c4a_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_36c4a_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_36c4a_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_36c4a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb41520fbb0>"
+       "<pandas.io.formats.style.Styler at 0x7ff6cea4fdc0>"
       ]
      },
      "execution_count": 24,
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
index dbba4d8f..1e333212 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
-EGFR,0.0,0.4430823117338003,0.5806451612903225,0.8837209302325582,0.7615384615384615,0.7741935483870968,0.6842105263157895,0.6517857142857143,0.9285714285714286
-ErbB2,0.4430823117338003,0.0,0.75,0.8461538461538461,0.8235294117647058,0.9642857142857143,0.875,0.6022099447513812,1.0
-BRAF,0.5806451612903225,0.75,0.0,0.9333333333333333,0.4883720930232558,0.8888888888888888,0.3555555555555555,0.25187969924812026,0.8333333333333334
-CDK2,0.8837209302325582,0.8461538461538461,0.9333333333333333,0.0,0.8333333333333334,0.9130434782608696,0.9166666666666666,0.39830508474576276,0.7333333333333334
-LCK,0.7615384615384615,0.8235294117647058,0.4883720930232558,0.8333333333333334,0.0,0.7121212121212122,0.5106382978723405,0.5673076923076923,1.0
-MET,0.7741935483870968,0.9642857142857143,0.8888888888888888,0.9130434782608696,0.7121212121212122,0.0,0.9523809523809523,0.4606413994169096,1.0
-p38a,0.6842105263157895,0.875,0.3555555555555555,0.9166666666666666,0.5106382978723405,0.9523809523809523,0.0,0.5,1.0
-KDR,0.6517857142857143,0.6022099447513812,0.25187969924812026,0.39830508474576276,0.5673076923076923,0.4606413994169096,0.5,0.0,0.8192090395480226
-p110a,0.9285714285714286,1.0,0.8333333333333334,0.7333333333333334,1.0,1.0,1.0,0.8192090395480226,0.0
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.0,0.4341880341880342,0.9273743016759777,0.6494960806270997,0.5423728813559322,0.875,0.753968253968254,0.7717391304347826,0.6538461538461539
+ErbB2,0.4341880341880342,0.0,1.0,0.6,0.75,0.8518518518518519,0.8484848484848485,0.9642857142857143,0.875
+p110a,0.9273743016759777,1.0,0.0,0.8160919540229885,0.6666666666666667,0.6666666666666667,1.0,1.0,1.0
+KDR,0.6494960806270997,0.6,0.8160919540229885,0.0,0.24045801526717558,0.3826086956521739,0.5665859564164648,0.45882352941176474,0.48360655737704916
+BRAF,0.5423728813559322,0.75,0.6666666666666667,0.24045801526717558,0.0,0.9230769230769231,0.44999999999999996,0.8846153846153846,0.29268292682926833
+CDK2,0.875,0.8518518518518519,0.6666666666666667,0.3826086956521739,0.9230769230769231,0.0,0.8888888888888888,0.9090909090909091,0.8888888888888888
+LCK,0.753968253968254,0.8484848484848485,1.0,0.5665859564164648,0.44999999999999996,0.8888888888888888,0.0,0.7301587301587302,0.5
+MET,0.7717391304347826,0.9642857142857143,1.0,0.45882352941176474,0.8846153846153846,0.9090909090909091,0.7301587301587302,0.0,0.95
+p38a,0.6538461538461539,0.875,1.0,0.48360655737704916,0.29268292682926833,0.8888888888888888,0.5,0.95,0.0
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
index ba4d62d2..c92c959a 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
-EGFR,0.6089623445220651,0.5569176882661997,0.41935483870967744,0.11627906976744186,0.23846153846153847,0.22580645161290322,0.3157894736842105,0.3482142857142857,0.07142857142857142
-ErbB2,0.5569176882661997,0.6050767414403778,0.25,0.15384615384615385,0.17647058823529413,0.03571428571428571,0.125,0.39779005524861877,0.0
-BRAF,0.41935483870967744,0.25,0.8115154807170016,0.06666666666666667,0.5116279069767442,0.1111111111111111,0.6444444444444445,0.7481203007518797,0.16666666666666666
-CDK2,0.11627906976744186,0.15384615384615385,0.06666666666666667,0.5441860465116279,0.16666666666666666,0.08695652173913043,0.08333333333333333,0.6016949152542372,0.26666666666666666
-LCK,0.23846153846153847,0.17647058823529413,0.5116279069767442,0.16666666666666666,0.605074821080026,0.2878787878787879,0.48936170212765956,0.4326923076923077,0.0
-MET,0.22580645161290322,0.03571428571428571,0.1111111111111111,0.08695652173913043,0.2878787878787879,0.7922722438851472,0.047619047619047616,0.5393586005830904,0.0
-p38a,0.3157894736842105,0.125,0.6444444444444445,0.08333333333333333,0.48936170212765956,0.047619047619047616,0.7647547797173733,0.5,0.0
-KDR,0.3482142857142857,0.39779005524861877,0.7481203007518797,0.6016949152542372,0.4326923076923077,0.5393586005830904,0.5,0.6967471143756558,0.1807909604519774
-p110a,0.07142857142857142,0.0,0.16666666666666666,0.26666666666666666,0.0,0.0,0.0,0.1807909604519774,0.6395215090867264
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.6093880972338642,0.5658119658119658,0.07262569832402235,0.3505039193729003,0.4576271186440678,0.125,0.24603174603174602,0.22826086956521738,0.34615384615384615
+ErbB2,0.5658119658119658,0.6064705882352941,0.0,0.4,0.25,0.14814814814814814,0.15151515151515152,0.03571428571428571,0.125
+p110a,0.07262569832402235,0.0,0.6435237878442978,0.1839080459770115,0.3333333333333333,0.3333333333333333,0.0,0.0,0.0
+KDR,0.3505039193729003,0.4,0.1839080459770115,0.6972909305064782,0.7595419847328244,0.6173913043478261,0.43341404358353514,0.5411764705882353,0.5163934426229508
+BRAF,0.4576271186440678,0.25,0.3333333333333333,0.7595419847328244,0.8112798264642083,0.07692307692307693,0.55,0.11538461538461539,0.7073170731707317
+CDK2,0.125,0.14814814814814814,0.3333333333333333,0.6173913043478261,0.07692307692307693,0.5433333333333333,0.1111111111111111,0.09090909090909091,0.1111111111111111
+LCK,0.24603174603174602,0.15151515151515152,0.0,0.43341404358353514,0.55,0.1111111111111111,0.5993589743589743,0.2698412698412698,0.5
+MET,0.22826086956521738,0.03571428571428571,0.0,0.5411764705882353,0.11538461538461539,0.09090909090909091,0.2698412698412698,0.7937853107344632,0.05
+p38a,0.34615384615384615,0.125,0.0,0.5163934426229508,0.7073170731707317,0.1111111111111111,0.5,0.05,0.7638163321418752
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 9915a25a..3d54cbf6 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -618,7 +618,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pIC50', 'pKi', 'pKd'}\n",
+      "Activities: {'pKi', 'pKd', 'pIC50'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -1488,46 +1488,46 @@
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@@ -1538,64 +1538,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_925af_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_925af_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_925af_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_925af_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
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+       "      <td id=\"T_a754e_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_925af_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_925af_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_925af_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_925af_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_925af_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_925af_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_925af_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_925af_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_925af_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_925af_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_925af_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
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+       "      <td id=\"T_a754e_row5_col1\" class=\"data row5 col1\" >815</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_925af_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_925af_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_925af_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
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        "    <tr>\n",
-       "      <th id=\"T_925af_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_925af_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_925af_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_925af_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_a754e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_a754e_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_a754e_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
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-       "      <th id=\"T_925af_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_925af_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
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-       "      <td id=\"T_925af_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
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+       "      <td id=\"T_a754e_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
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        "}\n",
-       "#T_99488_row1_col6 {\n",
+       "#T_06b9c_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row1_col7 {\n",
+       "#T_06b9c_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row1_col8 {\n",
+       "#T_06b9c_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row2_col0, #T_99488_row2_col3, #T_99488_row2_col6, #T_99488_row2_col7, #T_99488_row2_col8, #T_99488_row4_col5, #T_99488_row5_col4, #T_99488_row6_col2, #T_99488_row7_col1, #T_99488_row7_col2, #T_99488_row8_col2 {\n",
+       "#T_06b9c_row2_col0, #T_06b9c_row2_col3, #T_06b9c_row2_col6, #T_06b9c_row2_col7, #T_06b9c_row2_col8, #T_06b9c_row4_col5, #T_06b9c_row5_col4, #T_06b9c_row6_col2, #T_06b9c_row7_col1, #T_06b9c_row7_col2, #T_06b9c_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row2_col4 {\n",
+       "#T_06b9c_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row2_col5 {\n",
+       "#T_06b9c_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row3_col1 {\n",
+       "#T_06b9c_row3_col1 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row3_col3 {\n",
+       "#T_06b9c_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row3_col6 {\n",
+       "#T_06b9c_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row3_col7 {\n",
+       "#T_06b9c_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row3_col8 {\n",
+       "#T_06b9c_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row4_col0 {\n",
+       "#T_06b9c_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row4_col6 {\n",
+       "#T_06b9c_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row4_col7, #T_99488_row5_col8 {\n",
+       "#T_06b9c_row4_col7, #T_06b9c_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row5_col0 {\n",
+       "#T_06b9c_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row5_col1 {\n",
+       "#T_06b9c_row5_col1 {\n",
        "  background-color: #bddcbd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row5_col3 {\n",
+       "#T_06b9c_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row5_col5 {\n",
+       "#T_06b9c_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row5_col6 {\n",
+       "#T_06b9c_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row5_col7 {\n",
+       "#T_06b9c_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col0 {\n",
+       "#T_06b9c_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col1 {\n",
+       "#T_06b9c_row6_col1 {\n",
        "  background-color: #bcdcbc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col3 {\n",
+       "#T_06b9c_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col4 {\n",
+       "#T_06b9c_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row6_col5, #T_99488_row7_col8, #T_99488_row8_col5, #T_99488_row8_col7 {\n",
+       "#T_06b9c_row6_col5, #T_06b9c_row7_col8, #T_06b9c_row8_col5, #T_06b9c_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col7 {\n",
+       "#T_06b9c_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row6_col8 {\n",
+       "#T_06b9c_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row7_col3 {\n",
+       "#T_06b9c_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row7_col4 {\n",
+       "#T_06b9c_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row7_col5 {\n",
+       "#T_06b9c_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row7_col6 {\n",
+       "#T_06b9c_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row8_col0 {\n",
+       "#T_06b9c_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row8_col1 {\n",
+       "#T_06b9c_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_99488_row8_col3 {\n",
+       "#T_06b9c_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row8_col4 {\n",
+       "#T_06b9c_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_99488_row8_col6 {\n",
+       "#T_06b9c_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_99488_\">\n",
+       "<table id=\"T_06b9c_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2400,118 +2400,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_99488_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_99488_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_99488_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_99488_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_99488_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_99488_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_99488_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_99488_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_99488_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_99488_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_99488_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_99488_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_99488_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_99488_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_99488_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_99488_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_99488_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_99488_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_99488_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_99488_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_99488_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_99488_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_99488_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_99488_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_99488_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_99488_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_99488_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_99488_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_99488_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_99488_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_99488_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_99488_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_99488_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_99488_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_99488_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_99488_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_99488_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_99488_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_99488_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_99488_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_99488_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_99488_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_99488_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_99488_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_99488_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_99488_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_99488_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_99488_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_99488_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_99488_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_99488_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_99488_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_99488_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_99488_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_99488_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_99488_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_99488_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_99488_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_99488_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_99488_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_99488_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_99488_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_99488_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_99488_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_99488_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_99488_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_99488_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_99488_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_99488_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_99488_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_99488_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_99488_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_99488_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_99488_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_99488_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_99488_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_99488_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_99488_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_99488_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_99488_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_99488_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_99488_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_99488_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_06b9c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_06b9c_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_06b9c_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_06b9c_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_06b9c_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_06b9c_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_06b9c_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_06b9c_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_06b9c_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_06b9c_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_06b9c_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_06b9c_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_06b9c_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_06b9c_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_06b9c_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_06b9c_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_06b9c_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_06b9c_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_06b9c_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_06b9c_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_06b9c_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_06b9c_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_06b9c_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_06b9c_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_06b9c_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_06b9c_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_06b9c_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_06b9c_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_06b9c_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_06b9c_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_06b9c_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_06b9c_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_06b9c_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_06b9c_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_06b9c_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_06b9c_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_06b9c_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_06b9c_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_06b9c_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_06b9c_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_06b9c_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_06b9c_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_06b9c_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_06b9c_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_06b9c_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_06b9c_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_06b9c_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_06b9c_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_06b9c_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_06b9c_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_06b9c_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_06b9c_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_06b9c_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_06b9c_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_06b9c_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_06b9c_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_06b9c_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_06b9c_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_06b9c_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_06b9c_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_06b9c_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_06b9c_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_06b9c_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_06b9c_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_06b9c_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_06b9c_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_06b9c_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_06b9c_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_06b9c_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_06b9c_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_06b9c_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_06b9c_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_06b9c_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_06b9c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_06b9c_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_06b9c_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_06b9c_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_06b9c_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_06b9c_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_06b9c_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_06b9c_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_06b9c_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_06b9c_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f889cd059a0>"
+       "<pandas.io.formats.style.Styler at 0x7f9cf84fd0d0>"
       ]
      },
      "execution_count": 30,
@@ -2547,212 +2547,212 @@
      "data": {
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        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row0_col1 {\n",
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        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row0_col2 {\n",
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        "  color: #000000;\n",
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-       "#T_40d7b_row0_col3, #T_40d7b_row7_col0 {\n",
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-       "#T_40d7b_row0_col4, #T_40d7b_row3_col0, #T_40d7b_row4_col2, #T_40d7b_row5_col2 {\n",
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-       "#T_40d7b_row0_col6, #T_40d7b_row4_col1 {\n",
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-       "#T_40d7b_row0_col7, #T_40d7b_row3_col2 {\n",
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-       "#T_40d7b_row0_col8 {\n",
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        "  background-color: #80bf80;\n",
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-       "#T_40d7b_row1_col0 {\n",
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-       "#T_40d7b_row1_col2, #T_40d7b_row2_col0, #T_40d7b_row2_col1, #T_40d7b_row2_col3, #T_40d7b_row2_col6, #T_40d7b_row2_col7, #T_40d7b_row2_col8, #T_40d7b_row4_col5, #T_40d7b_row5_col4, #T_40d7b_row6_col2, #T_40d7b_row7_col2, #T_40d7b_row8_col2 {\n",
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        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col3 {\n",
+       "#T_f289a_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col4 {\n",
+       "#T_f289a_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col5 {\n",
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        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col6 {\n",
+       "#T_f289a_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col7 {\n",
+       "#T_f289a_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row1_col8 {\n",
+       "#T_f289a_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row2_col4 {\n",
+       "#T_f289a_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row2_col5 {\n",
+       "#T_f289a_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row3_col1 {\n",
+       "#T_f289a_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row3_col3 {\n",
+       "#T_f289a_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row3_col4, #T_40d7b_row4_col8 {\n",
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        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row3_col6 {\n",
+       "#T_f289a_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row3_col7 {\n",
+       "#T_f289a_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row3_col8 {\n",
+       "#T_f289a_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row4_col0 {\n",
+       "#T_f289a_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row4_col6 {\n",
+       "#T_f289a_row4_col6 {\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row4_col7, #T_40d7b_row5_col8 {\n",
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        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row5_col0 {\n",
+       "#T_f289a_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row5_col1 {\n",
+       "#T_f289a_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row5_col3 {\n",
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        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row5_col5 {\n",
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        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row5_col6 {\n",
+       "#T_f289a_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row5_col7 {\n",
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        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row6_col0 {\n",
+       "#T_f289a_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
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-       "#T_40d7b_row6_col1 {\n",
+       "#T_f289a_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row6_col3 {\n",
+       "#T_f289a_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row6_col4 {\n",
+       "#T_f289a_row6_col4 {\n",
        "  background-color: #54a954;\n",
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-       "#T_40d7b_row6_col5, #T_40d7b_row7_col8, #T_40d7b_row8_col5, #T_40d7b_row8_col7 {\n",
+       "#T_f289a_row6_col5, #T_f289a_row7_col8, #T_f289a_row8_col5, #T_f289a_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row6_col7 {\n",
+       "#T_f289a_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row6_col8 {\n",
+       "#T_f289a_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row7_col1 {\n",
+       "#T_f289a_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row7_col3 {\n",
+       "#T_f289a_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row7_col4 {\n",
+       "#T_f289a_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row7_col5 {\n",
+       "#T_f289a_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row7_col6 {\n",
+       "#T_f289a_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row8_col0 {\n",
+       "#T_f289a_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row8_col1 {\n",
+       "#T_f289a_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_40d7b_row8_col3 {\n",
+       "#T_f289a_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row8_col4 {\n",
+       "#T_f289a_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_40d7b_row8_col6 {\n",
+       "#T_f289a_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_40d7b_\">\n",
+       "<table id=\"T_f289a_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2769,118 +2769,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_40d7b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_40d7b_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_40d7b_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_40d7b_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_40d7b_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_40d7b_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_40d7b_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_40d7b_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_40d7b_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_40d7b_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_40d7b_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_40d7b_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_40d7b_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_40d7b_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_40d7b_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_40d7b_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_40d7b_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_40d7b_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_40d7b_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_40d7b_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_40d7b_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_40d7b_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_40d7b_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_40d7b_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_40d7b_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_40d7b_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_40d7b_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_40d7b_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_40d7b_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_40d7b_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_40d7b_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_40d7b_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_40d7b_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_40d7b_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_40d7b_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_40d7b_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_40d7b_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_40d7b_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_40d7b_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_40d7b_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_40d7b_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_40d7b_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_40d7b_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_40d7b_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_40d7b_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_40d7b_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_40d7b_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_40d7b_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_40d7b_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_40d7b_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_40d7b_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_40d7b_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_40d7b_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_40d7b_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_40d7b_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_40d7b_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_40d7b_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_40d7b_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_40d7b_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_40d7b_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_40d7b_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_40d7b_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_40d7b_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_40d7b_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_40d7b_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_40d7b_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_40d7b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_40d7b_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_40d7b_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_40d7b_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_40d7b_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_40d7b_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_40d7b_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_40d7b_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_40d7b_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_40d7b_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_f289a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_f289a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_f289a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_f289a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_f289a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_f289a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_f289a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_f289a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_f289a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_f289a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_f289a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_f289a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_f289a_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_f289a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_f289a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_f289a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_f289a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_f289a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_f289a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_f289a_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_f289a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_f289a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_f289a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_f289a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_f289a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_f289a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_f289a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_f289a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_f289a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_f289a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_f289a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_f289a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_f289a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_f289a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_f289a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_f289a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_f289a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_f289a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_f289a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_f289a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_f289a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_f289a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_f289a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_f289a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_f289a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_f289a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_f289a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_f289a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_f289a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_f289a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_f289a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_f289a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_f289a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_f289a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_f289a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_f289a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_f289a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_f289a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_f289a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_f289a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_f289a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_f289a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_f289a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_f289a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_f289a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_f289a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_f289a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f289a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_f289a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_f289a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_f289a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_f289a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_f289a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_f289a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_f289a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_f289a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_f289a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f889cd05a90>"
+       "<pandas.io.formats.style.Styler at 0x7f9cf8505940>"
       ]
      },
      "execution_count": 31,
@@ -2980,204 +2980,204 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_48848_row0_col0, #T_48848_row1_col1, #T_48848_row2_col2, #T_48848_row3_col3, #T_48848_row4_col4, #T_48848_row5_col5, #T_48848_row6_col6, #T_48848_row7_col7, #T_48848_row8_col8 {\n",
+       "#T_8b8a8_row0_col0, #T_8b8a8_row1_col1, #T_8b8a8_row2_col2, #T_8b8a8_row3_col3, #T_8b8a8_row4_col4, #T_8b8a8_row5_col5, #T_8b8a8_row6_col6, #T_8b8a8_row7_col7, #T_8b8a8_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row0_col1 {\n",
+       "#T_8b8a8_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row0_col2 {\n",
+       "#T_8b8a8_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col3 {\n",
+       "#T_8b8a8_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col4 {\n",
+       "#T_8b8a8_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col5 {\n",
+       "#T_8b8a8_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col6 {\n",
+       "#T_8b8a8_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col7 {\n",
+       "#T_8b8a8_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row0_col8 {\n",
+       "#T_8b8a8_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col0 {\n",
+       "#T_8b8a8_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row1_col2, #T_48848_row2_col0, #T_48848_row2_col1, #T_48848_row2_col3, #T_48848_row2_col6, #T_48848_row2_col7, #T_48848_row2_col8, #T_48848_row4_col5, #T_48848_row5_col4, #T_48848_row6_col2, #T_48848_row7_col2, #T_48848_row8_col2 {\n",
+       "#T_8b8a8_row1_col2, #T_8b8a8_row2_col0, #T_8b8a8_row2_col1, #T_8b8a8_row2_col3, #T_8b8a8_row2_col6, #T_8b8a8_row2_col7, #T_8b8a8_row2_col8, #T_8b8a8_row4_col5, #T_8b8a8_row5_col4, #T_8b8a8_row6_col2, #T_8b8a8_row7_col2, #T_8b8a8_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col3 {\n",
+       "#T_8b8a8_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col4, #T_48848_row3_col2, #T_48848_row6_col0 {\n",
+       "#T_8b8a8_row1_col4, #T_8b8a8_row3_col2, #T_8b8a8_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col5 {\n",
+       "#T_8b8a8_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col6, #T_48848_row6_col1 {\n",
+       "#T_8b8a8_row1_col6, #T_8b8a8_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col7, #T_48848_row6_col5, #T_48848_row7_col1, #T_48848_row8_col5 {\n",
+       "#T_8b8a8_row1_col7, #T_8b8a8_row6_col5, #T_8b8a8_row7_col1, #T_8b8a8_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row1_col8, #T_48848_row8_col1 {\n",
+       "#T_8b8a8_row1_col8, #T_8b8a8_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row2_col4, #T_48848_row2_col5 {\n",
+       "#T_8b8a8_row2_col4, #T_8b8a8_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row3_col0 {\n",
+       "#T_8b8a8_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row3_col1 {\n",
+       "#T_8b8a8_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row3_col4 {\n",
+       "#T_8b8a8_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row3_col5 {\n",
+       "#T_8b8a8_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row3_col6 {\n",
+       "#T_8b8a8_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row3_col7 {\n",
+       "#T_8b8a8_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row3_col8 {\n",
+       "#T_8b8a8_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row4_col0 {\n",
+       "#T_8b8a8_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row4_col1 {\n",
+       "#T_8b8a8_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row4_col2, #T_48848_row5_col2 {\n",
+       "#T_8b8a8_row4_col2, #T_8b8a8_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row4_col3 {\n",
+       "#T_8b8a8_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row4_col6 {\n",
+       "#T_8b8a8_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row4_col7 {\n",
+       "#T_8b8a8_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row4_col8 {\n",
+       "#T_8b8a8_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row5_col0 {\n",
+       "#T_8b8a8_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row5_col1 {\n",
+       "#T_8b8a8_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row5_col3 {\n",
+       "#T_8b8a8_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row5_col6, #T_48848_row5_col8 {\n",
+       "#T_8b8a8_row5_col6, #T_8b8a8_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row5_col7 {\n",
+       "#T_8b8a8_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row6_col3 {\n",
+       "#T_8b8a8_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row6_col4 {\n",
+       "#T_8b8a8_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_48848_row6_col7, #T_48848_row7_col6 {\n",
+       "#T_8b8a8_row6_col7, #T_8b8a8_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row6_col8, #T_48848_row8_col6 {\n",
+       "#T_8b8a8_row6_col8, #T_8b8a8_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row7_col0 {\n",
+       "#T_8b8a8_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row7_col3 {\n",
+       "#T_8b8a8_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row7_col4 {\n",
+       "#T_8b8a8_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row7_col5 {\n",
+       "#T_8b8a8_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row7_col8, #T_48848_row8_col7 {\n",
+       "#T_8b8a8_row7_col8, #T_8b8a8_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row8_col0 {\n",
+       "#T_8b8a8_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row8_col3 {\n",
+       "#T_8b8a8_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_48848_row8_col4 {\n",
+       "#T_8b8a8_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_48848_\">\n",
+       "<table id=\"T_8b8a8_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3194,118 +3194,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_48848_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_48848_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_48848_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_48848_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_48848_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_48848_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_48848_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_48848_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_48848_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_48848_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_48848_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_48848_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_48848_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_48848_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_48848_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_48848_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_48848_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_48848_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_48848_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_48848_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_48848_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_48848_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_48848_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_48848_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_48848_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_48848_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_48848_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_48848_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_48848_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_48848_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_48848_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_48848_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_48848_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_48848_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_48848_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_48848_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_48848_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_48848_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_48848_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_48848_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_48848_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_48848_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_48848_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_48848_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_48848_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_48848_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_48848_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_48848_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_48848_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_48848_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_48848_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_48848_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_48848_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_48848_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_48848_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_48848_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_48848_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_48848_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_48848_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_48848_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_48848_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_48848_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_48848_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_48848_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_48848_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_48848_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_48848_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_48848_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_48848_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_48848_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_48848_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_48848_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_48848_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_48848_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_48848_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_48848_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_48848_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_48848_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_48848_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_48848_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_48848_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_48848_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_48848_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_8b8a8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_8b8a8_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_8b8a8_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_8b8a8_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_8b8a8_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_8b8a8_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_8b8a8_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_8b8a8_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_8b8a8_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_8b8a8_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_8b8a8_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_8b8a8_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_8b8a8_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_8b8a8_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_8b8a8_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_8b8a8_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_8b8a8_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_8b8a8_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_8b8a8_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_8b8a8_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_8b8a8_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_8b8a8_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_8b8a8_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_8b8a8_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_8b8a8_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_8b8a8_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_8b8a8_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_8b8a8_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_8b8a8_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_8b8a8_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_8b8a8_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_8b8a8_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_8b8a8_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_8b8a8_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_8b8a8_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_8b8a8_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_8b8a8_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_8b8a8_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_8b8a8_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_8b8a8_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_8b8a8_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_8b8a8_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_8b8a8_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_8b8a8_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_8b8a8_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_8b8a8_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_8b8a8_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_8b8a8_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_8b8a8_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_8b8a8_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_8b8a8_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_8b8a8_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_8b8a8_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_8b8a8_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_8b8a8_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_8b8a8_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_8b8a8_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_8b8a8_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_8b8a8_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_8b8a8_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8b8a8_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_8b8a8_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_8b8a8_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_8b8a8_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_8b8a8_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_8b8a8_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_8b8a8_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_8b8a8_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_8b8a8_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_8b8a8_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f889cf93970>"
+       "<pandas.io.formats.style.Styler at 0x7f9cf87c8250>"
       ]
      },
      "execution_count": 36,
diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 41a79e76..4f9826a7 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -159,8 +159,8 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
-    "    #\"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv\",\n",
-    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_identity_matrix_sequence.csv\",\n",
+    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv\",\n",
+    "    # \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_identity_matrix_sequence.csv\",\n",
     "    #\"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv\",\n",
     "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
     "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
@@ -215,27 +215,29 @@
     {
      "data": {
       "text/plain": [
-       "{'sequence':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       " EGFR   0.000000  0.105882  0.623529  0.682353  0.552941  0.541176  0.611765   \n",
-       " ErbB2  0.105882  0.000000  0.600000  0.670588  0.576471  0.529412  0.600000   \n",
-       " BRAF   0.623529  0.600000  0.000000  0.670588  0.611765  0.623529  0.623529   \n",
-       " CDK2   0.682353  0.670588  0.670588  0.000000  0.623529  0.635294  0.529412   \n",
-       " LCK    0.552941  0.576471  0.611765  0.623529  0.000000  0.600000  0.611765   \n",
-       " MET    0.541176  0.529412  0.623529  0.635294  0.600000  0.000000  0.635294   \n",
-       " p38a   0.611765  0.600000  0.623529  0.529412  0.611765  0.635294  0.000000   \n",
-       " KDR    0.529412  0.564706  0.600000  0.658824  0.564706  0.529412  0.611765   \n",
-       " p110a  0.882353  0.882353  0.847059  0.894118  0.858824  0.894118  0.858824   \n",
+       "{'sequence':                   EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
+       " kinase_klifs                                                               \n",
+       " EGFR          0.000000  0.059037  0.572938  0.283953  0.344972  0.351553   \n",
+       " ErbB2         0.059037  0.000000  0.586362  0.297882  0.345400  0.369692   \n",
+       " p110a         0.572938  0.586362  0.000000  0.577295  0.563541  0.576006   \n",
+       " KDR           0.283953  0.297882  0.577295  0.000000  0.328732  0.346621   \n",
+       " BRAF          0.344972  0.345400  0.563541  0.328732  0.000000  0.353245   \n",
+       " CDK2          0.351553  0.369692  0.576006  0.346621  0.353245  0.000000   \n",
+       " LCK           0.288679  0.314925  0.548664  0.312352  0.327067  0.318747   \n",
+       " MET           0.288742  0.302033  0.606301  0.286194  0.361842  0.343975   \n",
+       " p38a          0.355356  0.364827  0.568806  0.346556  0.362088  0.276907   \n",
        " \n",
-       "             KDR     p110a  \n",
-       " EGFR   0.529412  0.882353  \n",
-       " ErbB2  0.564706  0.882353  \n",
-       " BRAF   0.600000  0.847059  \n",
-       " CDK2   0.658824  0.894118  \n",
-       " LCK    0.564706  0.858824  \n",
-       " MET    0.529412  0.894118  \n",
-       " p38a   0.611765  0.858824  \n",
-       " KDR    0.000000  0.847059  \n",
-       " p110a  0.847059  0.000000  ,\n",
+       "                    LCK       MET      p38a  \n",
+       " kinase_klifs                                \n",
+       " EGFR          0.288679  0.288742  0.355356  \n",
+       " ErbB2         0.314925  0.302033  0.364827  \n",
+       " p110a         0.548664  0.606301  0.568806  \n",
+       " KDR           0.312352  0.286194  0.346556  \n",
+       " BRAF          0.327067  0.361842  0.362088  \n",
+       " CDK2          0.318747  0.343975  0.276907  \n",
+       " LCK           0.000000  0.309121  0.337419  \n",
+       " MET           0.309121  0.000000  0.370645  \n",
+       " p38a          0.337419  0.370645  0.000000  ,\n",
        " 'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
        " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
        " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
@@ -278,27 +280,27 @@
        " MET    0.428571  0.352941  \n",
        " p110a  0.000000  0.473684  \n",
        " p38a   0.473684  0.000000  ,\n",
-       " 'ligand-profile':            EGFR     ErbB2      BRAF      CDK2       LCK       MET      p38a  \\\n",
-       " EGFR   0.000000  0.443082  0.580645  0.883721  0.761538  0.774194  0.684211   \n",
-       " ErbB2  0.443082  0.000000  0.750000  0.846154  0.823529  0.964286  0.875000   \n",
-       " BRAF   0.580645  0.750000  0.000000  0.933333  0.488372  0.888889  0.355556   \n",
-       " CDK2   0.883721  0.846154  0.933333  0.000000  0.833333  0.913043  0.916667   \n",
-       " LCK    0.761538  0.823529  0.488372  0.833333  0.000000  0.712121  0.510638   \n",
-       " MET    0.774194  0.964286  0.888889  0.913043  0.712121  0.000000  0.952381   \n",
-       " p38a   0.684211  0.875000  0.355556  0.916667  0.510638  0.952381  0.000000   \n",
-       " KDR    0.651786  0.602210  0.251880  0.398305  0.567308  0.460641  0.500000   \n",
-       " p110a  0.928571  1.000000  0.833333  0.733333  1.000000  1.000000  1.000000   \n",
+       " 'ligand-profile':            EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       " EGFR   0.000000  0.434188  0.927374  0.649496  0.542373  0.875000  0.753968   \n",
+       " ErbB2  0.434188  0.000000  1.000000  0.600000  0.750000  0.851852  0.848485   \n",
+       " p110a  0.927374  1.000000  0.000000  0.816092  0.666667  0.666667  1.000000   \n",
+       " KDR    0.649496  0.600000  0.816092  0.000000  0.240458  0.382609  0.566586   \n",
+       " BRAF   0.542373  0.750000  0.666667  0.240458  0.000000  0.923077  0.450000   \n",
+       " CDK2   0.875000  0.851852  0.666667  0.382609  0.923077  0.000000  0.888889   \n",
+       " LCK    0.753968  0.848485  1.000000  0.566586  0.450000  0.888889  0.000000   \n",
+       " MET    0.771739  0.964286  1.000000  0.458824  0.884615  0.909091  0.730159   \n",
+       " p38a   0.653846  0.875000  1.000000  0.483607  0.292683  0.888889  0.500000   \n",
        " \n",
-       "             KDR     p110a  \n",
-       " EGFR   0.651786  0.928571  \n",
-       " ErbB2  0.602210  1.000000  \n",
-       " BRAF   0.251880  0.833333  \n",
-       " CDK2   0.398305  0.733333  \n",
-       " LCK    0.567308  1.000000  \n",
-       " MET    0.460641  1.000000  \n",
-       " p38a   0.500000  1.000000  \n",
-       " KDR    0.000000  0.819209  \n",
-       " p110a  0.819209  0.000000  }"
+       "             MET      p38a  \n",
+       " EGFR   0.771739  0.653846  \n",
+       " ErbB2  0.964286  0.875000  \n",
+       " p110a  1.000000  1.000000  \n",
+       " KDR    0.458824  0.483607  \n",
+       " BRAF   0.884615  0.292683  \n",
+       " CDK2   0.909091  0.888889  \n",
+       " LCK    0.730159  0.500000  \n",
+       " MET    0.000000  0.950000  \n",
+       " p38a   0.950000  0.000000  }"
       ]
      },
      "execution_count": 6,
@@ -364,7 +366,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -409,12 +411,12 @@
     {
      "data": {
       "text/plain": [
-       "array([0.105882, 0.623529, 0.682353, 0.552941, 0.541176, 0.611765,\n",
-       "       0.529412, 0.882353, 0.6     , 0.670588, 0.576471, 0.529412,\n",
-       "       0.6     , 0.564706, 0.882353, 0.670588, 0.611765, 0.623529,\n",
-       "       0.623529, 0.6     , 0.847059, 0.623529, 0.635294, 0.529412,\n",
-       "       0.658824, 0.894118, 0.6     , 0.611765, 0.564706, 0.858824,\n",
-       "       0.635294, 0.529412, 0.894118, 0.611765, 0.858824, 0.847059])"
+       "array([0.059037, 0.572938, 0.283953, 0.344972, 0.351553, 0.288679,\n",
+       "       0.288742, 0.355356, 0.586362, 0.297882, 0.3454  , 0.369692,\n",
+       "       0.314925, 0.302033, 0.364827, 0.577295, 0.563541, 0.576006,\n",
+       "       0.548664, 0.606301, 0.568806, 0.328732, 0.346621, 0.312352,\n",
+       "       0.286194, 0.346556, 0.353245, 0.327067, 0.361842, 0.362088,\n",
+       "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
      "execution_count": 10,
@@ -474,7 +476,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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uNDfQ+20GbAOMfdJqT+D0qvpxVf2MZtbytCSLkuwD/Dnwu8DDqmohcCcw0HRoQwwQSRotr0+yc5KHA38JnJVk/yRPSbJ5kgU0N8h/Any3PeYy4Mgk2yXZAvhDYGVVrQbmA2uBHwEPSfJWYMFUFGqASNJoORP4InBdu70DWAh8jGbm8H2aT2AdWFX3tsccT3Nz/b9oguIg4LC27QKay1nfo7kkdi+/fJmsM2+iS9Jouayq3tW375PtNq6qup3mk1fjta0DXt1uY94z2SLBGYgkqSMDRJLUiZewJGlEVNWSYdcwEc5AJEmdOAPR9Fu1Ak47eNhVrN+qq2DxHsOuQpp1DBBNr90PH3YFkqaJAaLptdfSZhtlozw7kkaY90AkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASNKISfLyJMuTrEnywySfT7J3krcnuS/JT9vte0lOSbJjz7H7Jrm55/WWSc5OclGSBUmOSnJ5kruS3JzkPUk6fSfQAJGkEZLkz4CTgL8GHkmzrO2pwCFtl7Oqaj7wcJpFoxYDl/eGSM+5tqJZH30h8PyquotmKdzjaNZEfzrwPJoFqSbMAJGkEZFkO+D/Aa+vqrOr6u6quq+qPltVb+zt2+6/GjiCZhXCN/Sdaxvgs8AWwMFVdXd73Pur6sKq+nlV3QJ8FHhml3p9lIkEo//AR20qngFsDXx60AOqal2Sc4EDenZvRbOM7Z3AS6rqZxs4xbOAqzvUaoBIPvBRI2R7YHVVrZ3gcStpLmmNmU8TRi/bUHgkWQrsBfz+RAsFA0SaHQ981NxwdDbW43ZgUZKHTDBEdgJ+3PN6NfDHwEeSrKmqC/oPSHIo8G5gv6paPYHf9QveA5Gk0fEt4F7g0EEPSLIZ8CLgwt79VXU28BpgWZLn9B1zIPAh4EVVtaJrsc5AJGlEVNWdSd4K/EOStcAXgfuA/YDnAP891jfJFsCvAW+n+STWieOc72NJtgTOTfKCqrooyXNpbpwfVlWXTqZeZyCSNEKq6kTgz4A303y66ibgWOCctssRSdYAdwCfobns9RtVtXI95/tnmk9onZfkacBbgO2A89vvmaxJ8vkutToDkaQRU1UfpZkl9LuYZsaxoWO/Buzct+9DNJesoJnJTAlnIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkacQl2TLJsiTXJ6kk+/a1PyfJV5PcmeT6cY5f0rb/d5Jrkuw3FXUZIJI0O3wT+D1g1ThtdwP/BLxxPcd+DPh3YHvg/9Csk/6IyRY0lBUJv/PDuzjig98axq+WpJHVzh4+CLwS2JFmGdtjqupe4KS2z7r+49q1zS8db2aR5HHArwPPr6p7gE8lOQ54MfCBJI+hWa1wT6CAC4DXV9UdG6t3xmcghzx5J3bbccFM/1pJmi1eARwAPAZ4HM3a6JPxJOC6qvppz74r2/0AAd4FPAp4IrALG1k2d8yMz0Be/vRdefnTd53pXytJQ/eJ1w3U7ZSqugkgyTuBk5lciMwD7uzbdyewE0BVXQtc2+7/UZITgbcNcuKhXMKSJK3XTT0/30AzM5iMNUD/ZZ8FwE8BkuwAvA/YB5hPc2XqJ4Oc2JvokjRadun5eVdg5STPdzXwq0nm9+zbs90PzeWrAvaoqgU0N+ozyIkNEEkaLa9PsnOShwN/CZwFkGSrJFu3fbZMsnWStG2btW1bNC+zdZItAarqe8AVwNva/YcBewCfas81n2aWckeSnVj/J7kexACRpNFyJvBF4Lp2e0e7/z+Be2juXVzQ/vwrbduz2tfn08xa7mnPMealwF40l6beDRxeVT9q2/4vzae07gTOA84etFDvgUjSaLmsqt7Vv7OqlqzvgKr6Ghu47FRV1wP7rqftauA3+nb/7cbLdAYiSerIAJEkdeIlLEkaERu6TDWKnIFIkjoxQCRJnaSqJn5Q8iOab0guAlZPdVEjZC6Pby6PDeb2+Bzb7PX4qpq/8W6zQ6d7IFX1CIAky6tqr6ktaXTM5fHN5bHB3B6fY5u9kiwfdg1TyUtYkqRODBBJUieTDZD/PyVVjK65PL65PDaY2+NzbLPXnBpfp5vokiR5CUuS1IkBIknqZKAASXJgkv9Mcm2SN62nz75JrkhydZKvT22Z02djY2vHdWc7tiuSvHUYdXY1yHvX9ntqknVJDp/J+iZjgPfukCRXte/b8iR7D6POrgYY3yva8V2V5OIkew6jzi4GGNsTknwryc+SHD+MGidjgPElyfva9quS/Pow6py0qtrgBmwOfB/4VWBLmsXYd+vrsxD4DrBr+3qHjZ13FLYBx7Yv8Llh1zpd4+vp9680awkcPuy6p/C9m8cD9/n2AK4Zdt1TPL7fAh7W/vwC4NvDrnsKx7YD8FTgncDxw655GsZ3EPB5mkew/+Zsee/6t0FmIE8Drq2q66rq58DHgUP6+rwcOLuqbgSoqtsGOO8oGGRss9mg4/sjmtXJZsv7BgOMrarWVPtfK7AtzbKds8Ug47u4qsbWrr4E2HmGa+xqkLHdVlWXAfcNo8BJGuS/u0OAj1TjEmBhkh1nutDJGiRAduKXF3m/ud3X63HAw5J8LcnlSY6cqgKn2SBjA3hGkiuTfD7Jk2amtCmx0fG1S1geBnxgBuuaCgO9d0kOS3INzUprR89QbVNh0H83x7ya5v9oZ4OJjm22GWR8c+LPYJBHmYy3ylX//8k9hGZFq+cBDwW+leSSatbiHWWDjO3fgF+pqjVJDgLOAR473YVNkUHGdxLw51W1rl1eebYYZGxU1aeBTyd5FvBXwH7TXdgUGWh8AEmeQxMgs+Uez8Bjm6UGGd+c+DMYJEBuBnbpeb0zsHKcPqur6m7g7iTfAPYERj1ANjq2qrqr5+fzk5yaZFFVzYYHvg3y3u0FfLwNj0XAQUnWVtU5M1Jhd4OM7Req6htJHjPH3juS7AF8GHhBVd0+Q7VN1oTeu1lo0L8zZ/+fwQA3hB5Cs7D7o3nghtCT+vo8EfhK23cb4D+A/zXsGzxTNLbFPHAj9mnAjWOvR30bZHx9/U9n9txEH+S9+7We9+7XgVvm0nsH7ApcC/zWsOud6rH19H07s+8m+iDv3cH88k30S4ddd5dtozOQqlqb5FjgAppPF/xTVV2d5HVt+weq6rtJvgBcBdwPfLiq/mNj5x62QcYGHA4ck2QtcA/w0mr/DRh1A45vVhpwbC8GjkxyH817d8Qce+/eCmwPnNrOINfWLHiS7SBjS7IYWA4sAO5PchzNJ5nuWt95R8WA7935NJ/Euhb4b2DpsOqdDB9lIknqxG+iS5I6MUAkSZ0YIJKkTgwQSVInBogkqRMDREPVPgF47CnOVyb5sySbtW17JXnfBo5dkuTlM1etpF5+jFdDlWRNVc1rf94BOBO4qKreNsCx+9J8yeyF01qkpHE5A9HIqOYpzq8Fjm3XS9g3yecAkjy7Z02Wf08yH3g3sE+770/bGcmFSf6t3X6rPXbf9kGfy5Jck+Sjab95166DcnE7+7k0yfwkmyd5b5LL2rUa/mBYfybSKBvkWVjSjKmq69pLWDv0NR0PvL6qLkoyD7gXeBM9M5Ak2wD7V9W9SR4LfIzmWV8ATwGeRPO8oYuAZya5FDiL5hvqlyVZQPON9VcDd1bVU5NsBVyU5ItV9YPpHLs02xggGkXjPan0IuDEJB+lWXvm5nGeHrwFcEqSJwPraJYZGHNpVd0MkOQKYAlwJ/DDatadYOwxGUmeD+yRB1Zn3I7mCcwGiNTDANFISfKrNH/530bzkE4AqurdSc6jeX7QJUnGeyz7nwK30jwJejOaWcqYn/X8vI7m3/0w/iO0A/xRVV0wiaFIc573QDQykjyCZmGrU/ofepjkMVW1oqr+huYhe08AfgrM7+m2Hc2M4n7glTQPstuQa4BHJXlq+zvmJ3kIzUPwjkmyRbv/cUm2nfwIpbnFGYiG7aHtJaUtgLXAGcCJ4/Q7rl04aR3wHZpHYd8PrE1yJc2j6E8FPpXkJcBXgbs39Iur6udJjgBOTvJQmvsf+9Gsr7EE+Lf2ZvuPgEMnNUppDvJjvJKkTryEJUnqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKmT/wHP3t2wAgH2UQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -750,19 +752,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "46.564710000000005\n",
+      "27.09695\n",
       "sequence\n",
       "1751.566954\n",
       "kissim\n",
       "22.258792\n",
       "ifp\n",
-      "53.27453800000001\n",
+      "52.39304\n",
       "ligand-profile\n"
      ]
     },
     {
      "data": {
-      "image/png": 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b1y0lrbb7HiolHYCXX1+7tLTmtg0oJZ1BA98oJR2Al14fXFpar7V19MqZ2g0a/HppafU2b6w8lCc/tWUpaa3+o5tKSQfgie+UkyeAtsHqeKMa9HutnHQA+r9S3rWxKPEy25wFg0pLq/9Lve/632ujhvLQsZuXkta6n7u1lHQA2rZ5Z2lpvWv7x0tJ57J/lhfDyz8bpaW1zPRlSkvr65/Yq7S0NKu82GsWr686lMePKKccrPnt8s4vq95SXhmY/sbypaSz3tCq7zzvkoOXvbu0tG5adqXS0vrq5I+WltYmA3tfvJSl9515zczMzMzMeiE33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQuoufEmqU3SnYXp2MVsd52kcVWWHyLpmbzvPZImSRqS131J0r2S7pb0d0lrdv0nmTWV/pLOk/RILuNXSFpP0lxJd0i6T9J/JB3cvkOOldPz56Uk/UHS7yUNlXS5pPtzDP2kcT/LrHySVna8mC3eY185hukn/RxgjOtiZn1TZ+68zY2IsYVpkROhpI7Gfp+Y990IeB3YNy+/AxgXEZsAk4ATOpEvs6YUEQDrANdFxNoRMQY4DlgJeCQiNouIDYH9gC9KOrS4vyQBvwYGAJ8GAjgxIjYANgPeJ2mXHvtBZvV3EY4Xs8XSgAGs9qUvA9zruphZ39TtbpOSpkr6lqR/AfvkxR+XdJOk/0l6T5V9+gNDgecBIuKfEfFqXn0LsHp382XWaNf/ex5ARMSv25dFxJ3AtOJ2EfEo8CXg6IokTgVWAA6KiAUR8WpE/DPv8zrwXxwr1nssA7zheDHrPNfFzPqOzjTeBld0m9y3sG5eRGwVEefl+aERsSXwWeD3he32lXQnMB1YHri0yvd8CriyWgYkHSZpsqTJr7e9Wm0Ts6Zx7/1vANRaUP8LbFCYPwB4F7BfRMyv3FjSCGAP4O/VEivGyvxXX+lMts0aZTBwe43b1i1e2l5+uVOZNutJ8cYbld0mG1oXa3vF5xezntadbpMTC+smVmx7LkBE3AAMzydOyLfqgZWBKcBXijtJ+jgwDvhZtQxExJkRMS4ixg3sN6QTWTdreqqY/y+wJrC4q6XnAr/IdyEWUYyV/kOGlp5ZswarW7z0Gzas9MyalaVKt8mG1sX6DfX5xaynlTXaZOWll1jSfKSHgS4F3t++TNL2wDeAD0XEayXly6xhNlx/AECtVxk2A+4rzN8PfAyYKGmjim3PBB6KiFO6m0ezJjKXdPesFo4Xs0W5LmbWB9TrVQH7AkjaCngxIl6sss1WwCN5u82A35AOFk/XKU9mPWqbrQZBGkfhM+3LJL2bdIeAwrLRwInAacXlEXETcARwuaRRedsfAMsCX6hj1s0a4SVgaceLWWlcFzPrhfp3YtvBuY90u6siouoQtcDzkm4ChgOfLCzfNx9ElgKeBA7Jy38GDAMuSAOG8UREfKgTeTNrOrksPwLskIdzngdMJVUk15Z0BzCIVGk9LSLGV6YREZdJWhG4Ko+U9w3SXYb/5vRPj4jf1v/XmPWIDwOnOF7Mqqt85g3Xxcz6nJobbxFRdejZiBhdMb/NYrabAExYzLrta82HWYt5IyI+VmX54MXtUBkruZLaXlGtfNbHrNeIiBmk7o+VHC9mwFo/OxGAx4758r0R8eZ73FwXM+s76tVt0szMzMzMzErkxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gI6M9pkU4lRC3jj9HmlpPXy62uXkg7A8F0eKS0tbh5eSjLLDi7n7wQwcljla2R6nxnze9erbRb0g9eHV77up2ue+M6WpaQDMOo7N5WW1oyvlpOvtgGlJAPAUvPLS2tBifl6acFix/7otDdGtJWWVtNYAEu9Ws51zbZt3llKOgD9rvtvaWldfNkWpaRTZgViwCvlHKMAntuovIBZfvjzpaUVU8p7ofVjpaXUPbF08Ppa5dQxVr1lmVLSAZjx3pdKS+t9U8up1+056YulpAOw8V5PlpbWd089qLS0Vppe3jnhtE3W7Hijmj1cYlqN5ztvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBXS58SapTdKdku6S9F9JW+bloyXNzevulfRHSQMK+/WX9KykH1ekd52kB/J+d0rau+s/y6x5FGKlfTo2L+8v6UeSHiqs+8YS9hstaRtJL0q6Q9L9kk5s3C8zK5/jxax2rouZ9T3dGSxqbkSMBZC0E/Bj4AN53SMRMVZSP+Ba4GPAn/O6HYEHgI9JOi4iikNMHRgRk7uRJ7Nm9GasVPgBsDKwcUTMk7QM8OUl7SdpNHBjROwuaTBwh6SLIuLf9cm6WY9zvJjVznUxsz6mrG6Tw4FFxtONiDbgP8BqhcX7A6cCTwDvLen7zVqKpCHAZ4D/i4h5ABHxUkR8p9Y0ImIucCcLx5dZr+N4MauJ62JmfUB37rwNlnQnMAhYBdi2cgNJg4DNgc/n+cHAdsDhwAjSwePmwi5/ljQ3f94uIp7rRv7MmkV7rLT7MXAf8ERELOllNMX9HouIDxdXSloOWBe4ocS8mjWa48Wsdq6LmfUx3bnzNjcixkbEBsDOwB8lKa9bOx9MniOdcO/Oy3cH/hkRrwJ/AT6cb+e3OzCnObbawULSYZImS5r8+otzK1ebNau5hXI9NiImVm4g6dD8fME0SWtU2a9YEd1a0t3ALOCyiJhVJb03Y2XBK73/xerWqzQ0XtocL9ZaGloXa5vjeDHraaV0m4yIm4GRwIp50SO5D/Y6wHslfSgv3x/YXtJU4HZgBeCDnfieMyNiXESMG7js4DKybtYoDwOj8nM7RMT4HDMvAv2WtCPpGZ5NgI2BIyWNrdygGCtLDR1abs7Nel6PxUs/x4u1qEbUxfoNd7yY9bRSGm+SNiCdQBe6QhMRM4Fjga9LGg5sBYyKiNERMRr4HOkgYtan5CuevwNOz11ayFc+B3YijQdJXcq+VpdMmjUJx4tZx1wXM+sbutN4G9w+lCwwETg4PxRb6WJgCKmv9T8i4rXCukuAD0lauhv5MGt2b8ZKnn6Sl38DmAn8T9IdwI3AH4AZnUj718D7Ja1VbpbNGsbxYlY718XM+pguD1gSEVW7qkTEVOAdhfkANl3MtrN56/b+Nl3Ni1kzW0KsvEG6GnrsYtYPq7LsOuC6wvxcPHqe9SKOF7PauS5m1veU9aoAMzMzMzMzqyM33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQvo8miTjbZ0v/m8fZlnS0lrbtuAUtIB4ObhpSX11BZzSklnwN+XLSUdgAFLVRuBuGsenLFSaWmNG/14aWmtNWCRQeta21LQNnRBKUm1DVYp6QDM+OqWpaW16gk3lZLOQ394ZynpAFDen4qY29F7qGv30WHlHFcAjh3+emlpNYuBQ95gjbGdefvA4r1r+/KOSxdftkVpaY3+5s2lpNPvn6uWkg7AsAGvdbxRjaa9NKK0tG7e9C+lpbX+7UeWllazGDX0OU7a8pxS0pr+xvKlpAPwvqmPlJbWl0aXE3ufv+fKUtIBGLHUq6Wl9Zsvn1paWp864/OlpXXj8+uUlhb8vcS0Gs933szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQvoUuNN0sqSzpP0iKR7JV0haT1JcyXdIek+Sf+RdHBhn0MknZ4/LyXpD5J+L2mopMsl3S/pHkk/KevHmTXKgNUe5p3bPwEwRtKdko6ttp2k6ySNq7L8EEnP5H3vkTRJ0pC87ks57u6W9HdJa9b315jVz6MHfJMnjz0dHCtmneK6mFnf1OnGmyQBFwHXRcTaETEGOA5YCXgkIjaLiA2B/YAvSjq0yv6/BgYAnwYCODEiNgA2A94naZfu/CizRhs8SPz3b6MA7o2IsRGxyIlQUkdDCE7M+24EvA7sm5ffAYyLiE2AScAJJWbdrEdp4ABW/8lR4Fgx6yzXxcz6oK7cefsg8EZE/Lp9QUTcCUwrbhQRjwJfAo6u2P9UYAXgoIhYEBGvRsQ/8z6vA/8FVu9CvsyanqSpkr4l6V/APnnxxyXdJOl/kt5TZZ/+wFDgeYCI+GdEtI8TfAuOF+uFHCtmS7QMrouZ9Uldaby9A7i9xm3/C2xQmD8AeBewX0TMr9xY0ghgD3rbCxmsz5k7Lyq7Te5bWD0vIraKiPPy/NCI2BL4LPD7wnb7SroTmA4sD1xa5as+BZT38hizHhavv1HZbdKxYtaxwbguZtYn1XvAksrX1P4XWBNY3BXTc4Ff5CtFiyYmHSZpsqTJ854v74WeZmWr0m1yYmH1xIrNzwWIiBuA4fnECbkrGLAyMAX4SnEnSR8HxgE/q/z+Yqy0vfxyCb/IrD6qdJvs0VjJ69+MlzdeKO/lt2ZNom51sRdnt5WeWTNbsq403u4hXbGpxWbAfYX5+4GPARMlbVSx7ZnAQxFxyuISi4gzI2JcRIwbtNzSnciyWVN5pWI+ljQfEUG6k/D+9mWStge+AXwoIha5klGMlX7DhpWTa7OeV/dYyfu9GS8DRgzpfq7N6m8uTVAXW3b5jh5HNbOydaXx9g9gaUmfaV8g6d2kqzgUlo0GTgROKy6PiJuAI4DLJY3K2/4AWBb4QhfyY9bq9gWQtBXwYkS8WGWbrYBH8nabAb8hVUaf7rFcmjWeY8UseQnXxcz6pP6d3SEiQtKHgVPykM7zgKmkYF9b0h3AINKB5bSIGF8ljcskrQhclUcz+gbpStB/0wBInB4Rv+3aTzJrvMpn3oCrIqLqEOjA85JuAoYDnyws3zdXUpcCngQOyct/BgwDLsjx8kREfKj0H2HWAyqfecOxYlYr18XM+qBON94AImIG6ZZ7pcFL2GcCMKEwPx5oP5hU9sc2a2lvTF8HgH6rPHxvRLz5bqqIGF3cLiK2qbZ/ZbxUrNu+nFyaNd7bz/k+AI/uf7xjxawTXBcz65vqPWCJmZmZmZmZlcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gK6NNpkM3jptUHc8Pg6paQ1aOAbpaQDsOzgeaWlNeDvy5aSzlLbTSslHYDnr1y7tLRWX/H50tL631OrlJbWzDVeLi2tprGgnEHE+r1W3mBkbQNKS4qH/vDOUtJZ9+D/lpIOwGM/3qK0tBYMrHw3dde1xYLS0ooZg0pLq1nMb1uKZ18eWkpal/1zy1LSgXJP1v3+uWop6bR9cEYp6QC8vM5apaX19LeHl5bWPa/PLS2tdX79eGlpPVxaSt3z/PyhXPxcre8KX7L1hs4qJR2APSd9sbS0Pn/PlaWkc9lGy5WSDsDV1+1eWlr33rVmxxvVKDZ8rbS0zn/730tLq7e9St533szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQuoS+NNUtURHyQdJOl/ku6RdK+kY/LyCZL2zp+Xl3SHpEPrkTezZlCMEUm7SnpI0ihJ35E0XdKdedmFksYUtr1O0gOS7pJ0m6SxDfkBZj3I8WLWea6LmfVOPXbnTdIuwBeAHSNiI+CdwIsV2ywLXA2cGRHjeypvZo0iaTvgNGDniHgiLz45IsZGxLrAROAfklYs7HZgRGwK/Ar4Wc/m2KxxHC9m3eO6mFnr68luk18HjomIGQARMS8iziqsHwZcCZwTEWf0YL7MGkLS1sBZwG4R8Ui1bSJiInANcECV1TcDq9Uvh2bNw/FiVgrXxcxaXE823t4B3L6E9ScB/4qIk3soP2aNtDRwCbBXRNzfwbb/BTaosnxn4OKS82XWjBwvZuVwXcysxTXTgCX/APaU9LbFbSDpMEmTJU1um/NKD2bNrHRvADcBn6ph28o3Y/9Z0pPA10hdyBbdoRgrLztWrOX1XLzMebV7OTVrbZ2qi817YV4PZs3MoGcbb/cA71rC+vOAM4ArJC1TbYOIODMixkXEuH7Dh9Yjj2Y9ZQHwMeDdko7rYNvNgPsK8wcCawHnAL+stsNCsTLMsWItr+fiZfiQMvJr1qxKrYsNGjGoHnk0syXoycbbj4ETJK0MIGlpSUcXN4iIU4C/AxdJGtiDeTPrcRHxKrA7cKCkqncUJH0U2BE4t2LfN4DjgfdK2rDeeTVrNMeLWSlcFzNrcfVqvA2R9GRh+lJEXEG66vk3SfeQ+lz3r9wxIr4GTAPOltRM3TrNShcRs0nP4hwvac+8+IvtQ58DHwe2jYhnquw7F/g5cEyPZdisgRwvZp3iuphZL7RIwJYhIqoGeh5ydpFhZyPikIp5v1fEerWIGFb4PI3UrQvSoAzfWcJ+21TM/7wO2TNrKo4Xs85zXcysd/LVFDMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLqMmBJT2mbX07b86XXB5eSDsDIYeW9EHnAUm2lpPP8lWuXkg7A8F0eKS2tfv9ctbS05s1drrS0Vuk/rOONWomAflFKUv1fKe96z1LzS0tq0dcyd9FjP96inISAtb5+c2lplZmvJ+aX9xLqtmXKOUY1kwXzl+Ll2eW86235Z8uJO4ABr5SX1rABr5WSzsvrrNXxRjVqe/ix0tIa+PCWpaV10IiDS0uL3VYoL63flJdUdyylYHC/10tJ6+Bl7y4lHYCN93qytLRGLFXOMfPq63YvJR2AN7aZWVpa1z1xQWlp7XD2V0pLyxbPd97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBZTaeJMUks4uzPeX9Iyky/L8IXn+zsK0aeHzbEmP5c9/KzNvZs1K0suLWX6QpP9JukfSvZKOycsnSNo7f15e0h2SDu3JPJs1gmPFrGOui5n1bmW/KuAV4B2SBkfEXGAHYHrFNhMj4qiKZWMhnWiByyJiUsn5MmspknYBvgDsGBEzJA0CPlGxzbLA1cCZETG+53Np1niOFbNFuC5m1ovVo9vklcBu+fP+wLl1+A6z3u7rwDERMQMgIuZFxFmF9cNIsXZORJzRiAyaNQnHitmiXBcz66Xq0Xg7D9gvX/3cBLi1Yv2+Fbfqy3tDtlnv8Q7g9iWsPwn4V0Sc3EP5MWtWjhWzRbkuZtZLld54i4i7gdGkKz1XVNlkYkSMLUxza01b0mGSJkua3DbnlZJybNaS/gHsKelt1VYuFCsvV31MyKyvWGKsQGW8+Nxira+n6mJzn59XUo7NrFb1Gm3yr8CJlHybPiLOjIhxETGu3/ChZSZt1mzuAd61hPXnAWcAV0hapnLlQrEybFi98mjWDLoVK1AZLz63WK9R97rY4OUGlZm0mdWgXo233wPfi4gpdUrfrLf7MXCCpJUBJC0t6ejiBhFxCvB34CJJA3s+i2ZNwbFiVp3rYma9UF0abxHxZEScupjVlf2st6xHHsxayBBJTxamL0XEFcAvgb9Juof0TM8io8NGxNeAacDZkvzeRuvtHCtmNXJdzKx3KvVVARGxSP+siLgOuC5/ngBMWML+h5SZH7NWEBFVK5J5SPNFhjWvjJOI8HurrE9wrJh1zHUxs97NVx/NzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLKHXAkp40oF8bq67wYilpvdbWr5R0yvbgjJVKSWf1FZ8vJR2Afv9ctbS02j44o7S0Vr+mvNG/Z87vZS+1XgBLzVMpSVUfLqJrFgwoL62YW04MLxgYpaQD8NiPtygtrbW+fnNpaQ04sLSkYEE55aqZ9BuwgBEjyzkGLDO96mvluuS5jcoLmGkvjSglnae/PbyUdAAGPlzeYIejvntTaWmtdHN5v/G2HZYuLS1+U15S3TF3/gDunr1aKWndtGw5dR6A7556UGlp/ebLixuws3PuvWvNUtIBuO6JC0pL6zOjtiotrfknl3cOtcXznTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAaU33iT9TtJdku6WNEnSsLx8WUmX5nX3SDq07O82ayWSQtLZhfn+kp6RdFmePyTP31mYNi18ni3psfz5b437JWb15Vgx6zzXx8x6p3q8KuCLETEHQNJJwFHAT4DPAfdGxB6SVgQekPTniHi9DnkwawWvAO+QNDgi5gI7ANMrtpkYEUdVLBsLIGkCcFlETKp3Rs0azLFi1nmuj5n1Ql2+8yZptKT7Jf2hcFVnSOFAIWAw0P7ShwCWycuHAbOB+XnbiyXdnq8AHdatX2TWWq4Edsuf9wfObWBezJqZY8WsCtfHzPqW7nabXB84MyI2AeYAnwWQNB6YBWwAnJa3PR3YEJgBTAE+HxEL8rpPRsS7gHHA0ZJWqPZlkg6TNFnS5DdefLWbWTdrCucB+0kaBGwC3Fqxft+KrmCDa0m0GCttr7xSdp7NGqEusQILx8v8Fx0v1pJ6rD7muphZY3W38TYtIv6dP/8J2AogIg4FVgXuA/bN63cC7szLxwKnSxqe1x0t6S7gFmANYN1qXxYRZ0bEuIgYN2DZId3MulnjRcTdwGjSnYQrqmwyMSLGFqa5Nab7Zqz0Gzq0xBybNUa9YiWn/Wa89F/W8WItqcfqY66LmTVWdxtvsbj5iGgDJgIfzYsOBS6M5GHgMWADSdsA2wNbRMSmwB3AoG7my6yV/BU4EXcDM+uIY8WsOtfHzPqI7jbeRknaIn/eH/iXpHXgzT7WewD35/VPANvldSuRbvE/CiwLPB8Rr0raAHhvN/Nk1mp+D3wvIqY0OiNmTc6xYlad62NmfUR3R5u8DzhY0m+Ah4AzgGvz7XcBdwFH5m2/D0yQNCWv+1pEPCvpKuAISXcDD5Bu1Zv1GRHxJHDqYlbvK2mrwvxnI+KmHsiWWdNxrJgtlutjZn1EdxtvCyLiiIpl76u2YUTMAHassvw1YJdu5sOs5UTEsCrLrgOuy58nABOWsP8hdcmYWZNxrJh1yPUxsz6i9Jd0m5mZmZmZWfm6fOctIqYC7ygvK2ZmZmbWGa6PmfUtvvNmZmZmZmbWAtx4MzMzMzMzawHdHbCkYV6f348nn1mulLQGDX69lHTKNm7046Wk87+nViklHYB5c8v5mwOsfs3A0tJaeseppaU1r5w/e9NYa7mn+f1HfllKWnMWlPfKn5cWDC4trY8Om1NKOm2xoJR0AJ6Y/2ppaQ04sLSk+NSorTreqEaXTV3cwI+dt0lpKXXPKoNe5PgNq70DvPO+/om9SkkHYPnhz5eW1s2b/qWUdO55veb3oHfooBEHl5bWSjcP73ijGj21RTnHFoDD77mxtLS+XFpK3dMWYs68pUtJ66uTP9rxRjVaaXpbaWl96ozPl5JObPhaKekA7HD2V0pLa/7Jla8I7Lp1vljeAKU/3X6R98N3w8MlptV4vvNmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLaDUxpuklwufd5X0kKRRkr4jabqkO/OyCyWNKWx7naQHJN0l6TZJY8vMl1mjSTpK0sOSQtLIwvINJN0s6TVJx1Tss3OOi4clHdvzuTZrDMeLWde5LmbWu9Xlzpuk7YDTgJ0j4om8+OSIGBsR6wITgX9IWrGw24ERsSnwK+Bn9ciXWQP9G9geqHwRwWzgaODE4kJJ/YBfArsAY4D9iydZs17O8WLWTa6LmfVOpTfeJG0NnAXsFhGPVNsmIiYC1wAHVFl9M7Ba2fky6wlPTpvPjh98hq988QV22/FZgLdLGhIRd0TE1MrtI+LpiLgNeKNi1XuAhyPi0Yh4HTgP2BNA0mfyVdG7JP1F0pD6/iqz+pg+bT4f2vYpvvGl2QBjJE1yvJh1n+tiZr1X2Y23pYFLgL0i4v4Otv0vsEGV5TsDF1fbQdJhkiZLmtz20ivdyqhZvTz6SBv7HTCEy68ZCbAA+GwXklkNmFaYf5K3TqQXRsS789XR+4BPVe5cjJUXnivvZaVmZZv6yHz23n8owL3AHBocL3Nmz+/C15s1lR6ri81/8dVuZdTMOq/sxtsbwE1UOTlWoYr5P0t6Evga6Tb/IiLizIgYFxHj+i0ztHs5NauTVVZdine9e2D77HPAVl1IpjI+ACL/+w5JN0qaAhwIbLTIhoVYGbFCvy58vVnPWHnVfmz27qXbZ/9Eg+Nl+PL9u/D1Zk2lx+pi/Zf1jWyznlZ2420B8DHg3ZKO62DbzUhXQdsdCKwFnEN6dsGsJWnRamRU2awjTwJrFOZXB2bkzxOAoyJiY+C7wKAupG/WFBwvZqVzXcysFyv9mbeIeBXYHThQUtWrPpI+CuwInFux7xvA8cB7JW1Ydt7MesKM6Qv47+2vt88uD/yrC8ncBqwraS1JA4H9gL/mdcsAMyUNIJ1ozVrWzOlt3HX7a+2z++N4Mes218XMeq+6jDYZEbNJ/aWPl7RnXvzF9uFpgY8D20bEM1X2nQv8HDimcp1ZK1h7nX5cNGlu+4Al/YEzJB2du6KsDtwt6bcAklbOy79EipcnJQ2PiPnAUcDVpKui50fEPfkrvgncClwLdPQ8g1lTe/s6/fnrX16FNErk8jhezErhuphZ71Rq5/6IGFb4PI106x3Sg7PfWcJ+21TM/7zMfJn1pKWWEt//8bIArDNq1iP5Cugv8rSQiJhFqqAuIiKuAK6osvwM4Iwy82zWKFoKvvmj5bjgz6/eGxEfzYsdL2Zd5LqYWe9WlztvZmZmZmZmVi433sxKtPoa/bnybyMbnQ2zlrDaGv256NqVGp0NMzOzluHGm5mZmZmZWQtw483MzMzMzKwFtOzbSDda5ln+vc2Zjc7GImbMf63jjWq01oBhHW9Ug5lrvFxKOgCr9C8nTwAz55eXr3mPl5YUR6zZlXcEL86kEtPqmsdefBsHXPnZUtLq/1J513veGNFWWlrHDn+9441qEDPKewVY2zLl/T4WVHsHdddcNvXU0tL60ugtSkurGWIF4MlXRvDVWz/a8YY10KzyylNMGVpaWuvffmQp6azz6xIPvLutUFpSt+2wdMcb1ejwe24sLa0rNxpRWlrNYvTg55iwyR9KSWuTgeXFy2mbrFlaWjc+v04p6Zz/9r+Xkk4z++n265aW1j82Lu+Y19v4zpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1gC433iQdJelhSSFpZGH5BpJulvSapGMq9tlZ0gN5v2O7k3Gz3kDS7yTdJeluSZMkDcvLl5V0aV53j6RDG51Xs0ZzvJgtzHUxs76nO3fe/g1sD1SOFTwbOBo4sbhQUj/gl8AuwBhgf0ljuvH9Zr3BFyNi04jYBHgCOCov/xxwb0RsCmwD/FzSwAbl0axZOF7MFua6mFkf02HjTdJoSfdL+kPhaueQiLgjIqZWbh8RT0fEbcAbFaveAzwcEY9GxOvAecCe+Ts+I+m2fNX0L5KGdP+nmTWPJcTRnLxewGAg8i4BLJOXDyOdiOfnbS+WdHu+w3BYA36OWV05XswWNjde4aa4mnviNm6JawHe7rqYWd9U65239YEz89XOOUBX3vi7GjCtMP9kXgZwYUS8O181vQ/4VLUEJB0mabKkyc8+V+ILcM16RtU4kjQemAVsAJyWtz0d2BCYAUwBPh8RC/K6T0bEu4BxwNGSFnm7bTFW2l4u72XoZj2oMfEy55V6/iazLnuVl1iNtXivdgBYQBPUxV6YvaDaJmZWR7U23qZFxL/z5z8BW3Xhu1RlWftV03dIulHSFOBAYKNqCUTEmRExLiLGjVyhXxeyYNZQVeMoIg4FViWdLPfN63cC7szLxwKnSxqe1x0t6S7gFmANYN3KLyrGSr9hw+rza8zqqzHxMnxofX6NWTctzWBGvPVY23M0QV1sxPIe986sp9UaddHBfC2eJJ04261OukoKMAE4KiI2Br4LDOpC+mbNbrFxFBFtwETgo3nRoaSroBERDwOPARtI2ob0fMMW+eroHTherHdyvJgVaNF2l+tiZn1QrY23UZK2yJ/3B/7Vhe+6DVhX0lr5QfL9gL/mdcsAMyUNIF3tMeuNFokjSevAm8/w7AHcn9c/AWyX161E6kL2KLAs8HxEvCppA+C9PZh/s57keDErmMervBDPtc8uj+tiZn1SrY23+4CDJd1NOmCcIeloSU+SrtrcLem3AJJWzsu/BBwv6UlJwyNiPmlksKtzeudHxD05/W8CtwLX8tbJ2Ky3WSSOgD/kLipTgFWA7+Vtvw9smdf9HfhaRDwLXAX0z2l8n9QVzKw3cryYFQxlGWbyePuAJf1xXcysT+pf43YLIuKIimW/yNNCImIW6SCyiIi4AriiyvIzSCdms96sWhy9r9qGETED2LHK8tdIQzyb9XaOF7OFiA31TgD+FpMeiYhXcV3MrM/xk6ZmZmZmZmYtoMM7b/n9Ie+of1bMei/HkVntHC9mCxusoWyx6M1lM+uDfOfNzMzMzMysBbjxZmZmZmZm1gIU0ZXXhDSepGeAxzvYbCTwbElf6bR6Pp3ekNaaEbFiSd/ZJTXGCrT+37on02rGPLV6Wg2PFWj5eGnGPDmt+qTVV+Ollf/PnFb5abVMXaxMLdt4q4WkyRExzmn1bFrNmKdmTqtZNOvfpxnTasY89ZW0mkUz/n2aMU9Oq7FpNQuXcadVj7R6Y6zUwt0mzczMzMzMWoAbb2ZmZmZmZi2gtzfeznRaDUmrGfPUzGk1i2b9+zRjWs2Yp76SVrNoxr9PM+bJaTU2rWbhMu606pFWb4yVDvXqZ97MzMzMzMx6i95+583MzMzMzKxXcOPN6krSxo3Og1lf5Ngzq53jxXo7l/Heo0813iSp0XmoRtI7JTXFUKeS3idph/y5W38vSTsB50naoJTMWY9qpnLZrlljuAyOvdbmeOlZjpfW1azlspliuMzyndNwGe9F+lTjDRhYZmKS1pa0kqQuv/hP0s7An4DXSsjPeyR9WtJ7O5unwsHh68DbAKIbD0TmA8VpwOci4n5JTVPWJA2SNKAO6TblCakrmqVcVtFUMVxGWWql2OtNZbxMZcVLybECjpda0nO89LymKpc5jaY455VdvnOaTVkfq1ddLKfdq2OvKf4De4KkHYGzJP1A0qYlpLczcCHwY+B8Set1MU8/AT4bEVMkjehGfnYD/gC8G/ga8KG8vNYCvHT+dx7dr4DslPPyMPCUpIERsaA7aeZ0lf9dVtKgLqbR/v92lqRDupunYt4iIiRtJemA/G+pJ6ie0mTlsjJfTRPDJZalpo+9nHa3y3gZMdxsyoqXMmOlkC/Hy5Lz5HjpYc1WLgt5apZzXmnlO39v6WW8metiOe1eUx9brIjo9ROwC3A78HHgYuAUYEA30tsSeBB4H7As8A3g+3mdakxjGHAD8Is8vwZwN/CeLuRnPeBeYPM8/zHgVmBYjftvDHw8fz4V+Gj+vDSwVP68VI1pjQWmAtsCXwFOBz4A9Ovm/2H7yKh75f/DS4DtgUGdSGMn4L+5HBwI3AG8v8RytkdO//icv+OAIT1Rxkv8DU1TLivSaqoYLqsstULsVXxHl8t4GTHcbFNZ8VJmrOT9HS8dp+V46eGp2cpl3q5pznlllu+8bellvIxyWdbxoIPvaPn62BJ/X6MzUPcfCG8H5pCuqAAsB/wT+GDFdrWcgES6W/lx4MDC8o8AF3Qhb1sB1wLHAjcD/9eFNNYBhgD7Af0Ly/8KbFRjGh/OhXsP0pWQA6ps02GwA1sAmwLvyPODgB+Sbte/vzsHjJzeDsBtwErAn4GHgP2BpWvYdy3gEeCQwrIvV5aDbuRtzfw3XDUfsP8HnAV8Bxhc73Je5tQs5bKwT1PFcJllqVViL6fZ7TLenRhu1qm78VJmrOT9HC8dp+N46eGp2cplRXpNcc4rq3zn7epWxrtTLss8HizhO3pNfWyxv7HRGajrj0tXYkaQWtznApvl5RcCFwC/BPYFRtaY3tL53/7AKoXlqwNXFeYXe5UFGFox/z7gOuDPhWW1XjFaJx/8hpOvXrUHJPA3YJv8eWNgxSr7bwUcmn/PR4HxpKs0DwIXka5GnQucA5zIEq74ADsC04Bxeb79KtFA4Af5gLFV8aBW4//fGoX5z+YD0p7Av4GjSF0BPg0sW0N6vwD+CKyf568AJpMOrl8GtuhE3tbMB4WNSAfs/sAGwHtIV5HWAT6ZDxo/WdLfrhmmZiqXVcrACJoohssoS80ee2WVcUqO4WaZyoqXMmPF8eJ4adapGcslTXTOK7N816OMl10uKbEulvfvVfWxmn5zozNQtx+WrlxcDgwlXR34AvAX0gOpN5Bu2/4iL/sXHVwxyMFwAXAMMLZi3VrA/fnzoblQLtIVgHRr+XLg8DzfP/+7ZQ78w4FlOvEbVwOuz7+xPa2B+d9zgfVzcF1decAgdV94MH9n+4F0N+Aa4OfA3nmbfYD/A96+hHzsRroK8748vzIworB+IPBd0gFpyxp/2xhSd4PrgXMLy5cjHcw2yvN/yXleaTHp9KdwACcduM4Hfgf8Hfgg6WrXNaQrM1XTqUhzA2B63v4GUl/79+d1uwM/KZSZM8hXvpp1aqZyWZFOU8VwWWWJJo+9sso4JcVws01lxktZseJ4cbw069Rs5bLsGO5uHJdZvutRxssql9ShLlZW7LXi1PAM1OVHpYPBTcBuhWWDSA+PzuKt/sjtVyOWWEiAnUn9tI/IB5yjKtYvB0witexvZTG3yElXBp4hXVE5jXQQWz6vey/ptv2X6KBfbj4QjCZdybqBKldPSLfHryF1Adi4Yt07Sbe5F+ljTLr69ft8wOiwDzOwCnAncEqeXxl4ANiv4m88kNT3eOUa0lwVuAX4WJ7/D7B2Yf1v8+/binTlbvMllIM/5LT+COyVl38FuA/YpLDtsI7+7oVtDweOK/wtj8oHjq2AdYE24IT8/1xaV4A6xktTlMsq/3dNE8NllSWaPPbKKuOUFMPNOJURL2XGiuPF8dKsU7OVy8J2TXHOK7N816OMl1UuqVNdrIzYa9Wp4Rko/QelPr4LyLddSbdLTyC1+kcCXwQmAlsV9lnsrfFcGF4A9sjz+5CuQBwE7FPY7nHSLdkxFfuLtx7wHA58k3QA+ijp6sfj+UCyArAh6bbxiCXkZ1ngR8CvSLf5J1K4slT4rt8AbwDrVEnjQ8CpFdv3K6zfE7gs/9b+i/v7kG+Pkw6UPycd7G4gX80qbDewk/+H65L6UQ/L81NI3SrOAkYB25GuGv2PwkmhIo2dgHtIV3K2zH/3PwBfyet/AJyd/387dQuddPXr74X5NfMB4+d5flw+oHyg0fGwhN/QdOWysG2zxXBpZYkmj72yyjglxHAzTWXGS5mx4nhxvDTr1ITlsunOeWWV73qV8TLKJXWsi5URe606NTwDdflR6db7ucCKpD7MXyysW5H0/oxzqO3hyg8AE4DPkPrT/pd0peZ40i3jz+WDwpnk/rsdpPcD4OL8eQzweg6Ee/KBo8ORl0j9eL+dg+ZB0kO2++cDyM45z2uwmNvrpIdNzyc98PvmCEb5d+yQDxK7AasuIQ87ka6AbZ3nP5Hnz6vYbk9gmyUddKqkvUoO7rNIV8/+QOqWcB5wZmG71Rez//uAGe15y8v6kx5m/iOwU152JunKVocHs8r85/2+UZjfmNQlYstq2zf71AzlsiKtpojhsstSM8demWWcbsZws0/djZcyY8Xx4nhp1qlZyuVi0mv4Oa+M8l3nMt50dbFqsdSd2GvVqeEZKO2HpH6vmxfmryZd9TkszxevZowk3yLvqHDkINqedLXhKd66WjAsH0R+WJl+IY0P5oPLwRT6ZpOuZPya9IDnbnnZu4ENlpCfVUjD0K6Zg3vdXOCnkR4Y/UVO93pS/+G1Kn9LYX6zHIgb5/mlCuu+Sb5F3sHf5/9I7yG5Ftg5L/sEqU/xJ/L8PqRb9uvWkN5a+e/0AdKwuBvng8zFwOjCdv8BNlxCOgOAI0lXwd5f8dv6kR5WHV9Y9rYa8rY2qf/8CoV0ts1l4uuF7X5KRTeOZpyapVxWSaupYriMstQKsVdWGaekGG62qax4KTNWHC+Ol2admq1c5mVNcc6rR/kuu4yXVS6pQ12srNjrDVN/eoH8sr/vAbdIei0i7oyInSRdRrotfWZEtEkaEBFvRMSzHaSnyP/7+d+/SXoeWCavHxYRL0taHlgnv/zvjYo0diIVzkuBXYG3S3qMNFTuP0i36feLiH/lfN22hPzsRuojviB/TxvpFvR3gadJo+v8OCKeaX9bfUQU89MPmF/4/XdI+itwlaRdSH2u50r6BOmq0R5L+vtk55KG/p0GHJ7TPju/u/E9+fevA3w4Ih5aUkKSNiD1X7+D9GDuuhHx27xuK2BDSU+SDpIDgJcXk84HSX/rH5EOOh8hXd37S/6btEm6FviSpP4RMT8inu4gb2vlfN0KDJF0fv4730L6u+4r6UrSFalDSF0wmlaTlctiWk0VwyWWpaaOvfxbu13Gy4rhZlNWvJQZKzk9x4vjpek0W7nM2zTTOa8e5RtKKuPNXBfL6faq+li3NLr12N2JVECmsJihRUnDtF7TifRW4q2hXo8gHYh+SLrqsxXpxYlfAg4jPXw5pkoam5CCu33Em/eQHlgdnedXAe4Hds/zS+pO0X47/IOkkYxWJgXXg8BgUhD9iHTlZ1SV/UeSHtRsfxh36cK640jdGq4kjfhzH0t4H0n+XZvkz0uRrmz8ljSKz6XArnndp4EbqWFUn/y3uIG3rg59PudrOOmh2r3y/+Ef8//zXlXSaL8y91HghPx5WVKf+pPJL7rMy48iXWmrtdvAtjk/e+b/+/8jjxZFuv0/iPTukOPa/z+bdWqmctmsMVxmWaLJY6+sMk4JMdyMU1nxUmasOF4cL806NVO5rCgHTXHOK7N816OMl1EuqWNdrIzY601TwzPQ5YynAB5GuiW7c8W6k4GTC/O3kfs2d5DeSqRb3R/OheM/hQJ7YS4Ym5OGqX1yccGV83UhC78v5CpSv+j/I731fv8c5IsdRYjU/aB44Cl2N/hT/g6R3rdxPIsfonUP0gFquTxfPGi8Pf+mbVhCxYH0EO8C4AnS6EfvzsHyS9JBbT/SA7575e2H1/j/OKL4/0e6ovIv0kO+E0gHyU2ArYF3t/9fVaTRfoD/NPDbwvLhhYPG+4EDgbsX9/+2mPwNJr/UkXRAaj9gVB2lqTJvzTQ1YblsuhguuyzRxLFXVhmnhBhuxqmMeCkrVhwvjpdGx0MrlctCWs12zut2+a5XGS+jXFLHulgZsdebpoZnoNs/IBWq9xTmP0m6ynAjCz9QuWYH6bRfMfgY6crMBNIt5vb1FwLn589bUHhhYWGbN/tvk65U/CkfXE7IgXAsqd/1A6Tb3IsdppXUF3pdUj/j8e0HC956OeU6OZ/tVx2WOJQs6Rb4I5UHDdJVpEWuWC0mjW3zAeN7pP7Y5wA/Az6e1x9KOoAPrTG9/hXz2wPfa///Ih1QD+ggjfarWcuSDowTK9Yvl4P7UuDRWn4rFS/2LB4ASAfKXwAH5LLynUbHQA2/p2nLZd5mAk0Qw/UoS3m/Zoy9Uso4JcRws01lxUs9YsXx4nhp1qlZymWhbDblOa+M8l12GS+jXFK/40Gvqo+VNTU8A13KNLyLNORo/1wQvlpYt0Xh83XAO2tIr/gg5aaklz/eSuonvWxePpjUX7fqaDikLgP/IY0c1P7g7LAcUC8V98uFe7GjRpFGF7qX9IDpWqSD18WF9cppXAOs1om/W+VB47OkB3U77KpTSGM70pC6K5CGX72e9HDuQFI/9M5exXxfPshsRb6iUlh3AnBkDWl8iHQb/wBSf/TVSVeR2h9oXTvntZZRDt8BPAccvIQyshXpqtYc8vtTmnVq1nLZjDFcdlmqSLdpYq/sMl5GDDfLVFa8lBkr4XhxvDTp1IzlsqwYzuubti5WZhkvq1yWfTwoO/Z609TwDHQ6w6nQ304eMYh0VeRO4JCK7T5CusU+shNpvx+4Nn/eDfgnacSetUkvTLyrWjCQhoT9F+n2/ljSOyvab+22X/U5h9qGnv1ADuLiaE3DSP2ML+atq1KHkEYWWrYLf7+7SX3F76Mw8lIn0mjv297+7o8ljopWZf/23/DenJcJ+ff9qf3/K/+/3k1hiNkO0tyRdBVqbj543ZHz+E/S1adhNaQxnDQE7iTSAfvAxeR7C2A+b41O1ZS35pu1XNKEMVx2WVrC72507JVSxusRw42eyoqXMmOlUG4cL46XppqasVyWFcN5+6avi3W3jNejXJZ1PCgr9nrr1PAMdCqzKTD/DeyY50eS3m6/R/7P/Ube5vAc3Eu8LUu6avTe/HnfXLgOLKz/KOmW/zWkW+yLPPAJLJ8L6ofz/HuAmaQXN56Zlw0kXRk4u4bf+CXg8/lz8YWPQ0kj6IwHPk66srRxF/+Ou+U8b9qN/4td8wFn+cKyzjx4+h7SMLqb5/nRpPeufJ90K/xROvnAKakv9pxcLvqRXrS5Uq0HM1I/+vYDwHbAY+3lgYWv9GwL7Nn+m5vxYNGs5bIZY7geZWkJ6TY09sos4/WI4UZNZcZLWbGS93G8OF6abmrGcllmDOdtW6IuVkIZb7q6WE6j19TH6jE1PAOd+I9sD8y98vw6OZi3yvObkK6o/DoHVUcj67RfNWofrWc06erC7yu2+zBwBbk/82LS2o10dWFT0hWY75JezHgr+SWJOeCX9CLR9qsIpwE/KC4rbLM+cAnwKp3oG72Y7xtSwv/JnqQXZS7V2YAhvYCyDTguz/fPB6DTgLeRX67YhTztCvyPJTz4v5j9qr0bZsd84GoffWl9ClcPm/1A0WzlspljuMyyVEO6DYm9sst4vWK4UVN346XMWMnbOl7C8dJsUzOXy+7GcPv/Yf63ZepiXS3j9SyX3TkelB17vXFqeAY6+R+6Wy6cm+TAPCYvb38zfXvQLfEt7Sx61ehtpPdzrA7cAxxfsX0tXe52zge0Y4v7ka5orNCJ37gtqYvBu9p/W+H3HZkPbKWeHLv5f9KlLjF53z1J3RL2z/PvzwfxEd3M0575AL5UJ/dr7/P9Qd562Hln0pXEE0gnlM0a/Tfv5G9qqnLZzDFcZlnqof/bTv+msst4vWK4gX/TbsdLmcdwx0up/7eOl/L+lk1bLsuI4bxPS9XFav379GS57M7xoOzY621TwzPQhf/QhQKTt0b+2QXYPn9e0rs6Kq8arU3qI71dnl+L1G/7J13I2w6k0YtG5PlDgZuAZTqRxlDSeypOaD9o5OX75iBY7MO1rTiRulnMIfWFHk9J3UZqPYgVTjLFPt8TSH21V87rjiuWmVabmq1cNnMMd6csNetU7zJerxhu4N+rW/FS9jHc8dLj//+Ol9p+R9OWy+7GcN7HdbFy0q35eFDv2OtNU8Mz0MXCsAPpXRkj8vwhpH7HtT7bVHnV6Mt5efvBZzSpG0DND9gW0t6FdKv4s6QXHtb88s9CGqsB3yKNHHQiqf/x/V1JqxUm0ghF/wW+lOd79PY3S+7zPRq4mvxyyZ7MV8m/sanKZTPHcG+c6l3GGx3Ddfh7dSteyj6GO156/P/f8VLb72jactndGM5puC7Ww+Wy3rHXW6aGZ6Ab/8G7kEawOTIHd2df9re4q0a7A2PoRrePnMbrnc1TRRqDSUOgfof00O96jf6b1/n/c0fSyzY/0oDvXlyf7zNJD82Oystb8gRb+J1NVS6bOYZ729QTZbyRMVynv1m34qXsY7jjpUf/7x0vtf+Opi2XzXbOa4Wp0eWyJ2KvN0wNz0A3/5O7e3Jd3FWjTr2TZjFpl/IQal+a8v9Ht//2XfzuPVm0z/dtdKKPfCtMzVYumzmGe9vUE2W8kTFcp7+Z46WPTo6XTv2Opi2XzRbDrTA1ulz2lfpYd6b2/qUtS9KQiHi1G/vvQurT/CvSiwWPiIh7ysqftQ5Je5D6Vl9JGkXqLxFxWWNz1fs5hnuOy3jrc7z0HMdL7VwurUyOvSVr+cZbGSTtDlxIGrnGB4s+TNKHSN0j/hQRJ0kSQDhQmppjuHYu4+Z4qZ3jpee4XFqRY2/x3HjLunvVyHoPSTsCvweOjogLG50fq41juHYu4+Z4qZ3jpee4XFqRY686N97MqpC0A/BIRDza6LyY1YPLuFntHC9mjeHYW5Qbb2ZmZmZmZi1gqUZnwMzMzMzMzDrmxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQtw483MzMzMzKwFuPFmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmfYakqZK2r7L8HknbdCPdKyUd3J28mbW69jhSMl7S85L+0+h8mVVqPxdIOk7Sbxvw/aMlhaT+PfR9K0m6QdJLkn5e/N09nRfrPv9HmVmfFxEbdXP/XcrKi1mrao8jSVsDOwCrR8Qrjc2V2eJFxI8anYcechjwLDA8IqLRmbHu8Z03MzMzK9OawFQ33MzqL9/p7qg+vyZwrxtuvYMbb01M0tckTc+3uR+QtJ2kpSQdK+kRSc9JOl/S8oV9PiHp8bzuG8VuYpImSPpBYdttJD1ZmF9V0l8kPSPpMUlHF9Z9J3/XH3N+7pE0rrB+DUkX5n2fk3R6Yd0nJd2Xu9BcLWnNev7dzGohaYNczveriJP3SJosaY6kpySdlJcPkvSnXL5fkHSbpJXyuuskfTp/PkTSvyWdnLd7VNKWefk0SU+7i6X1RjmODgd+C2wh6WVJ320/1+SuWs/m7Q5sdH7Nct3mT4X5gwp1qG9WOTfcnI/rMyWdLmlgYd+QdISkh3J955eSlNf1k3RiLv+PArt1kK/288hpkl6UdL+k7Qrrr5P0Q0n/Bl4F3p7PM7fl7W+TtGXedgJwMPDVHJPbV/7uiu9eVtLv8m+cLukHkvp19W9s5XPjrUlJWh84Cnh3RCwD7ARMBY4G9gI+AKwKPA/8Mu8zBjgD+ERetwKweo3ftxRwKXAXsBqwHfAFSTsVNvsQcB4wAvgrcHretx9wGfA4MDrvf15etxdwHPARYEXgRuDc2v8SZuWT9E7gGuD/IuK8itWnAqdGxHBgbeD8vPxgYFlgDVJsHQHMXcxXbA7cnbc7hxQP7wbWAT4OnC5pWGk/yKx5PEKKjZsjYlhEfDsvXxkYSTo/HAycmc9zZk0h16F+BRwIrEI63q9W2KQN+CKpHG9Bqid9tiKZ3UnH+k2Bj5HqbgCfyes2A8YBe9eQpc2BR/P3fRu4UIWL9aS63mHAMsBLwOXAL0jnnZOAyyWtEBGHAH8GTsgx+bcOvvcPwHzS+WozYEfg0zXk13qIG2/Nqw1YGhgjaUBETI2IR4DDgW9ExJMR8RrwHWBvpQdN9wYui4gb8rpvAgtq/L53AytGxPci4vWIeBQ4C9ivsM2/IuKKiGgDziYdnADeQ2osfiUiXomIeRHxr7zucODHEXFfRMwHfgSM9d03a6CtSRcfDo6Iy6qsfwNYR9LIiHg5Im4pLF8BWCci2iLi9oiYs5jveCwixudYmUhq8H0vIl6LiGuA10knRrO+5Js5Bq4nVTQ/1ugMmRXsDVwaEf+KiNeBbwFvdjPMx/xbImJ+REwFfkO6kF70k4h4ISKeAP4JjM3LPwacEhHTImI28OMa8vN03ueNiJgIPMDCd+wmRMQ9uW61I/BQRJyd83cucD+wR2f+ALk3yS7AF3J97mngZBauC1qDufHWpCLiYeALpMbZ05LOk7Qqqd/yRfm2/QvAfaSG3kqkBtS0QhqvAM/V+JVrAqu2p5vTPi6n225W4fOrwKDcaFwDeDwfQKqle2ohzdmAWPhqlllPOgK4KSL+uZj1nwLWA+7PXU92z8vPBq4GzpM0Q9IJkgYsJo2nCp/nAkRE5TLfebO+5PmKZ+AeJ52zzJpFZR3qVQp1KEnrSbpM0ixJc0gXo0dWpFFZT2o/zi+UNqn8t6e7de7O+LKkewrbTK94Rq0yZorprVpMs7B9Z+taawIDgJmFettvgLd1Mh2rIzfemlhEnBMRW5GCKYCfkoJ1l4gYUZgGRcR0YCapIQWApCGkOwXtXgGGFOZXLnyeRrpbUEx3mYjYtYasTgNGqfows9OAwyvSHRwRN9WQrlk9HEEqrydXWxkRD0XE/qST1U+BSZKG5quf342IMcCWpC4wB/VYrs1a23KShhbmRwEzGpUZsypmUnjURNJgFq5DnUG6m7Vu7lZ/HOlidK1pr1GYH9X+ISJuzN0Zh1WMfLxa+zNzhX2KMVNs2M0g1RWLRgHTa8xfu2nAa8DIQp1teHdHZLZyufHWpCStL2lbSUsD80hX6tuAXwM/bO92KGlFSXvm3SYBu0vaKj9E+z0W/j++E9hV0vKSVibd2Wv3H2CO0iApg/PDte+Q9O4asvsf0oHpJ5KGKg3s8L687tfA1yW1DyG9rKR9Ov0HMSvPS8DOwPsl/aRypaSPS1oxIhYAL+TFbZI+KGnj/IznHFI3yraeyrRZL/BdSQOVXiWwO3BBozNkVjAJ2CMP/DEQ+C4LN86WIR37X5a0AXBkJ9I+Hzha0uqSlgOOrWGft+V9BuR604bAFYvZ9gpgPUkHSOovaV9gDGk8gppFxEzS8+A/lzRcaZC8tSVVdg+1BnLjrXktDfyE9F6OWaQgPo40mMJfgWskvQTcQnqolYi4B/gcaYCEmaTBTJ4spHk2aUCSqaTgnNi+Ij+bswepf/Zj+Xt/S3pgd4kK+64DPJG/c9+87iLS3YvzcjeD/5H6U5s1TES8QHoP1S6Svl+xemfgHkkvk+Jtv4iYR7pTPYl08r4PuB6oOlqXmS1iFumcNIM0eMIREXF/Y7Nk9pZch/o/0gBTM0kX+p4m3YkCOAY4IC8/i0IdqgZnkbrd3wX8F7iwhn1uBdYl1cd+COwdEVUfhcnLdwe+TOrq+VVg94h4thN5bHcQMBC4lxSzk0gDuFiTkF/50LtJmgp8uobRhczMzEonaRvgTxFR0+jHZs0gjwj8Aqmb5GM9/N2HkOpuW/Xk91pr8J03MzMzM+vzJO0haUh+PvNEYAqpt5JZ03DjzczMzMwM9iR17Z1B6rK4X7iLmjUZd5s0MzMzMzNrAb7zZmZmZmZm1gLceDMzMzMzM2sB1V6q3BJGjhwZo0ePbnQ2zJbo9ttvfzYiVmxkHhwr1gqaIVbA8WKtwfFiVrtmiZeytGzjbfTo0UyePLnR2TBbIkmPNzoPjhVrBc0QK+B4sdbgeDGrXbPES1ncbdLMzMzMzKwFuPFmZmZmZmbWAtx4MzMzMzMzawFuvJmZmZmZmbUAN97MzMzMzMxagBtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNrIEkTZW0fWF+P0nPS/qApJD0cp6eknSZpB2q7D83bzNL0gRJw3r+l5g1hqQxkibnuHle0t8kjSmsX1rSr3MMzZZ0qaTVGplns3qrODe0T6fndatIOkvSjLz80Xzu2CCvH11x/nlZ0l153SGS2vKyOY38jWZlabW6mBtvZk1C0sHAL4HdgMfz4hERMQzYFLgWuEjSIRW77pG3GQtsBny9RzJs1hxmAHsDywMjgb8C5xXWfx7YAtgEWBV4ATitZ7No1hB7RMSwwnSUpBWAm4AhwNbAMsA7geuBHSr2H1HYd9PC8pvzOWdED/wGsx7VCnWx/vVK2Brjggcv4IpHr2h0NqyTJB0G/AjYKSImSxpdXB8Rs4BTJQ0AfirpjxGxoHIbSVeTDhxWT5PHw5RJjc5FnyJpKvAb4BPAKsDFwJER8QKpQYYkAW3AOoVd1wKujoin8jbnAScV0j0V+AiwLPDQu971rvr+EGt+vTu+vwjMAT5ROIe8AIzvbEIRsWDcuHElZs2aWV+oX7ZKXcx33nqZKx69ggdmP9DobFjnHAl8H9guIiZ3sO2FwNuA9StXSFod2AV4uPQc2sKmTIJZUxqdi77oQGAnYG1gPeD49hWSXgDmke6q/aiwz++A90laVdKQnMaVhfW3kU6yywPn1DHv1ip6d3xvD1xUWeHsCkn9SsiPtYg+UL9smbqY77z1Qusvvz7jd+70RTSrgwlMqGWzHYB/ArXUFmbkf5cvLLtYUgDDgH8A3+5EFq2rVt4YDr280bnoPT6pWrY6PSKmAUj6IamhdjxARIyQNBQ4mLe6ugA8CDwBTCfdlZsCHNW+MiL+VNj25+PGjTuxG7/Ceotmj+/a4uViSfML818hdS2e1b5A0oeAPwL9SN0hdyxs/2y6mQ3ADyKiPTbemy+WDO1a5q1VtWr9srfVxXznzazxjiDdRfitCmfKxWgfaGF2YdleEbEMsA2wAenkbNYbTSt8fpz0DNubIuIV4NfAHyW9LS8+AxgErECqbF5I4c6bpC9Luk/Si7lCatZb7BURIwrTWcBzpG7HAETEXyNiBKk75cCK/UcW9i1e1Lgl77NcnfNv1pNapi7mxptZ4z0NbEd6ePxXHWz74bz9In0XIuJ6YALgOwfWW61R+DyKt65+Fi1FGoyh/eS6KTAhImZHxGuku3XvkTRS0tbA14CPAcvlCqlZb/Z3YC9J3a7/RcTLJeTHrFm0TF3MjTezJhARM4BtgZ0lnVy5XtJKko4i3Yb/+hKeVzgF2EHS2Hrl1ayBPidpdUnLA8cBEyXtIGkzSf0kDScNRvI8cF/e5zbgIEnL5ofMPwvMiIhnSSPtzQeeAfpL+laP/yKznnUS6Y7Z2ZLWVrIMHujKrGXqYm68mTWJ/CzPtqRhz3+cF78g6RVSH+xdgX0i4vdLSOMZ0vML36xzds0a4RzgGuDRPP2ANFz5ucCLwCOkkSZ3joh5eZ9jSAOZPERqpO1KumoKcDWpC+WDpG6Y7fuY9QaXVryr7aJ80eK9pLL+L+Al4E7ShYwjG5dVs+bQCnUxD1hi1kARMbpi/jHe6hq2f2f3z8t8Arbe6raI+HHFsgvyVFVEPEcaYbLaujbgU3kCYNy4cT8tIZ9mDVXt3FBYN4NCma+yfipQ9ZmfiJgAtY3+YNYqWq0u5jtvZmZmZmZmLcCNNzMzMzMzsxbgbpNmZtb0ltQNzMzMrK/wnTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAX4mbde4oIHL+CKR6/g/tn3s8HyGzQ6O2a9y+TxMGXSW/Oz7oaVN2lcfsz6qspYrAfHtzWZ9jpeI7l+2Tx8562XuOLRK3hg9gONzoZZ7zRlEsya0uhcmJlj0fog1/GsyHfeepH1l1+/0Vkw671W3hgOvTx9Hr9bY/Ni1pcVY7EeHN/WhNZffn3G7zy+Yd9/6FWHNuy7bWG+82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFqAG29mZmZmZmYtwI03MzMzMzOzFuDGm5mZmZmZWQtw483MzMzMzKwFuPFm1kCSpkp6XdLIiuV3SgpJoyVNyNu8XJjukrR1Yf6VvH1xm1GN+l1mZcuxsn1hfj9Jz0v6QEXZf0rSZZJ2qLL/3LzNrBxXw3r+l5j1HEkHSJqcy/1MSVdK2krSdyS9IemlPD0o6XRJqxT23UbSk4X5gZIulPRvScMlHdyYX2VWrorzw/OSLpe0Rl5XrIPNlnStpA2qpHFd3nfpiuXV6nD7die/bryZNd5jwP7tM5I2BgZXbHNCRAwrTJtGxI3t88BGebsRhW2e6KH8m/WoXGn8JbAb8HhePCLHwqbAtcBFkg6p2HWPvM1YYDPg6z2SYbMGkPQl4BTgR8BKwCjgV8CeeZOJEbEMsDzwYWBl4PZiA66Q1tLAhcAIYMeImAMMqfNPMOtJ7eeHVYCngNMK607I61YDpgO/K+4oaTSwNRDAh6qkXVmHm9idjLrxZtZ4ZwMHFeYPBv7YoLyYNTVJhwE/B3aKiJsq10fErIg4FfgO8FNJi5znImIWcDWpEWfW60haFvge8LmIuDAiXomINyLi0oj4SnHbvPweYF/gGeDLFWkNAS4FBgC7RcQreb8zeuK3mPWkiJgHTALGVFk3FzifRc8dBwG3ABNIdbi66l/vL7Ce98DsBzj0qkMbnQ2r3S3AJyRtCDxIOoFuBfygobmyJZs1Bcbv1uhc9DVHkmJju4i4q4NtLwR+BqwP3FdcIWl1YBfgH/XIpPUCrR/fWwCDgItq3SEi2iRdAuxUWLw0cCXwIrBPRLxWai6tpfSF+mW+WLEvqW5WuW4oqafUwxWrDgJOAm4FbpG0UkQ8Va88uvHWy+z69l0bnQXrmva7b9cD95NuyxcdI+mowvwlEeHnDRpl470bnYO+agfgn8CUGradkf9dvrDsYkkBDCM13L5dbvasV+gd8b0C8GxEzO/kfjNYOGaWITUE93fDrW/rA/XLiyXNJ50fnmbhixjtdbDhpK767V2PkbQVsCZwfkQ8K+kR4ADg5Cr7A8yPiIXGOegsN956mX3W24d91tun0dmwbAITat30bOAGYC2qd5k8MSKOLylb1l3jDk2TleeTqmWrI4BvAr+V9KmIiCVsu1r+d3Zh2V4R8TdJHwDOAUYCL3Qht9abtUJ8dxwvzwEjJfXvZANuNRaOmWeBo4E/Sno5Iq7uXEatt2jl+mWNdbH280M/UuPsekntXSdPjIjj80BwV5F6dNyd1x0MXBMRz+b5c/KyYuOt1Dqcn3kzawIR8Thp4JJdSd29zGxRTwPbkR4M/1UH2344b/9A5YqIuJ70bMKJJefPrFncDMwD9qp1h/x86B7AjcXlEXEh8BlgkqQPlphHs6YTEW25zLeRuukX1z0BfB44VdJgSYOBjwEfyKMYzwK+CGwqadN65dGNN7Pm8Slg2/aHwc1sURExA9gW2FnSyZXrJa2Uu6d8G/h6RCxYTFKnADtIGluvvJo1SkS8CHwL+KWkvSQNkTRA0i6STihum5dvCJxLGnHypCrpnQscBVwi6X15v23r/kPMepiSPYHlqHheGiAiriV1Lz6MdHGkjTS4ydg8bUi6AHJQ5b5lcbdJsyYREY8sYfVXJX2hMD+vu32mzVpVREzLFccbSJVNgBckCXgFmEwaXOGqJaTxjKQ/krphfrTeeTbraRFxkqSngOOBPwMvAbcDPwR2BPaVtBcgUmX0WuBd+QJJtfT+IGkgcLmkHUmxY9ZbXCqpjTTc/+PAwRFxTzqtLOJnpIscDwLjK1/NJOl04BeSvlaPjLrxZtZAETF6Mcvnk06oAIfkaUnpTC1sb9brVMZKRDwGrJFn919khw72z8uOLCNvZs0qIv5MarhVuon0Oo0l7XsdsHrFsrOAs/LsB8eNG7ek507NWsLi6mJ53SFVlk0EFvuutog4n/RKAeig/tYVbryZmZlZeSaPhymT6pP2rLth5U3qk7ZZHVzw4AVc8egV3Urj/tn3s8HyG5SUI2t1fubNzMzMyjNlUnpPmplxxaNX8MDsRcZNMusy33kzMzOzcq28MRx6efnptvaLs62PWn/59Rm/8/gu79/bX4xtneM7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAV4wJIWs7ghZz2MrJmZmZlZ7+Y7by3GQ86amZmZmfVNvvPWgqoNOethZM3MzMzMejffeTMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WbWIiQdIulfjc6HmZmZmTWGG29mDSRpqqS5kl4uTKd3Ma3rJM3Labwo6QZJG5edZ7NGyLHyuqSRFcvvlBSSRkuakLcpxtNdkrYuzL+Sty9uM6pRv8usHhwvZt0naYykyZKez9PfJI0prF9a0q8lPSVptqRLJa1W73y58WbWeHtExLDCdFTlBpL615jWURExDFgBuA44u8R8mjXaY8D+7TP54sTgim1OqIinTSPixvZ5YKO83YjCNk/0UP7NepLjxax7ZgB7A8sDI4G/AucV1n8e2ALYBFgVeAE4rd6ZqrVCaNbrXfDgBVzx6BWNzgaQukgCnwH+AxwM/Ap4OK3SacBBwEzgcxHx98r9I2K+pPOAY3ss033Z5PEwZVKjc9EXnE0q++0nx4OBPwI/aFiOrOfNmgLjd2t0LlqB46UXeWD2Axx61aGNzkavJGkq8BvgE8AqwMXAkRHxAqlBhiQBbcA6hV3XAq6OiKfyNucBJxXSPRX4CLAs8BDwhYi4sbv59Z03s+yKR6/ggdkPNDobRZsDjwJvA35YsWwk8G3gQknLV+4oaSBwIHBLz2S1j5syKVUord5uAYZL2lBSP2Bf4E8NzpP1pI33hpXdG7xGjpdeYte378r6y6/f6Gz0dgcCOwFrA+sBx7evkPQCMI90IeRHhX1+B7xP0qqShuQ0riysvw0YS7pzdw5wgaRB3c2o77yZFay//PqM33l8aelNYEItm10saX5h/ivAG8CMiGi/Yjo/XfThaeCUiAhgoqQvA7vxVvfIX0g6ERgCzCVd8flbd3+H1WDljeHQyxudi9b1SdW6ZfvdhOuB+4HpFeuPkVTsenxJRBzc/QxaUxh3aJr6OsdLn7LPevuwz3r7NDobLavGutjpETENQNIPSQ214wEiYoSkoaS7148X9nkQeIIUV23AFODNeIqI4sWSn0s6HlgfuKurvwXceDNrBntFxEINrNxtclqVbafnhlu7x0n9rNsdHRG/lbQU8D5S/2yz3uRs4AZSd5U/Vll/YkQcX2W5WV/keDGrTbHOVVm3IiJekfRr4BlJG0bE08AZwCDSOAOvAF8l3XnbHCBfYP90TiuA4aSeU93ixptZ84oqy1aTpEIDbhRVGmgRsQC4UdLDwLg65tGsR0XE45IeA3YFPtXo/Jg1s74YL+fc+gSX3Fl5g7FxHpj9ATbb7PpGZ8M6tkbh8yjSYCWVliL1bFqN1BNqU+AbETEbII9J8L08yuuGwNeA7YB7ImKBpOeBmm+bL46feTNrLW8DjpY0QNI+pIND1VFWJG0BjKm2zqzFfQrYNiJeaXRGzFpAn4qXS+6czr0z5zQ6G9Z6Pidp9TyOwHGkR1N2kLSZpH6ShpMGI3keuC/vcxtwkKRlJQ0APkt65OVZYBlgPvAM0F/St0h33rrNd97MGu9SSW2F+WuBSxaz7a3AusCzwFPA3hHxXGH96ZJOyZ9nkfprn4RZLxIRjyxh9VclfaEwPy8iut1NxaxV9cV4GbPKcCYevkWjswHAoVed2egsWG3OAa4hdXG8hDQq626kZ99WJ40jcBuwc0TMy/scA/yCNJLkQOB/wIfzuqtJXSgfJHWpPJnqj8N0mhtvZg0UEaOXsHpCxbYTCssWeRdcRGxTLZFx48a58WYtb3GxEhHzeasbyiF5WlI6Uymh24pZM3O8mHXabRHx44plF+Spqnzx/MDFrGsj3fUudlc+obuZBHebNDMzMzMzawluvJmZmZmZmbUAd5s0MzMzM7M+qYNHWJqO77yZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3Az7z1Ig/MfoBDrzq00dloWffPvp8Nlt+g0dmwVjN5PMy6G1bepNE5MTMzs17OjbdeYte379roLJj1TVMmwWtzGp0LMzMz6wPceOsl9llvH/ZZb59GZ6Ol+a6lddnSwxudAzMzM+sD/MybmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAR6wpAmdc+sTXHLn9KrrHpj9ATbb7PoezpH1Zksqb5bN2pOJoy5pdC7MOq0h8e14MTOrG995a0KX3Dmde2d66HHrGS5vZr2X49vMrHfxnbcmNWaV4Uw8fItFlh961ZkNyI31dosrb5aN/0Gjc2DWZT0e344XM7O68Z03MzMzMzOzFuDGm1kDSZoqaa6klwvT6XndKpLOkjQjL39U0gRJG+T1oyVFxb535XWHSGqT9HIjf59ZWXKsbF9l+XBJp0h6IsfAw3l+ZLX9JO0n6XlJH+jJ/Js1A0ljJE3OMfC8pL9JGlNYv7SkX0t6StJsSZdKWq2ReTart1Y7v7jxZtZ4e0TEsMJ0lKQVgJuAIcDWwDLAO4HrgR0q9h9R2HfTwvKbI2JYj/wCswaQNBD4O7ARsDMwHNgSeA54T5XtDwZ+CewWER75yfqiGcDewPLASOCvwHmF9Z8HtgA2AVYFXgBO69ksmjVeM59f/MybWXP6IjAH+ERELMjLXgDGNyxHZs3nIGAU8MGIaL/L/DTw/coNJR0G/AjYKSIm91wWzXqepKnAb4BPAKsAFwNHRsQLpHMJkgS0AesUdl0LuDoinsrbnAecVEj3VOAjwLLAQ+9617vq+0PMGqdpzy9uvJkVPDD7AQ696tBGZwNge+CiQsPNmt2sKTB+t0bnoq/ZHriqcGJdnCOBrYDtIuKu+mfLrAOTx8OUSfX+lgOBnYBXgEuB4/OEpBeAYaQeWN8q7PM74FRJ7XfdDgSuLKy/Dfge8CLpLt076/kDeosmqltY7Zr2/OJuk2bZrm/flfWXX78RX32xpBcK02dI3VlmtW8g6UN53UuSrqnY/9nCvscUlr83n6Ct3oauCCtv3Ohc9EUrADNr2G4H4BZgSn2zY1ajKZPSBZ/6Oj0ipkXEbOCHwP7tKyJiBOnu2VHAHYV9HgSeAKaTen9sSGqste/3p4h4LiLmR8TP6/0DeoMG1i2se5r2/OI7b2bZPuvtwz7r7VNqmhOYUMtme0XE34oLJH2a1NUFgIj4KzAiL/94xf4jI2J+lXRviYitxo0bF53LtXXaMqvAoZc3Ohet7ZPqyl7PUYiTJTgC+CbwW0mfigjHhDXeyht3/bhRW7xMK3x+nPQM25si4hVJvwaekbRhRDwNnAEMIlVcXwG+SrrztjmApC8Dn85phbtNdqwedQvrnBrrYpWa9vziO29mzenvwF6SHKNmi/c3YCdJQzvY7mlgO9LgP7+qe67MmsMahc+jSIOVVFqKNDBW+4iSmwITImJ2RLxGGqzkPZJGStoa+BrwMWC5fPfOrLdq2vOLK4ZmzekkYDngbElrK1kGGNvYbJk11ABJg9on4GzS3YW/SNpA0lKSVpB0nKRdiztGxAxgW2BnSSc3IO9mPe1zklaXtDxwHDBR0g6SNpPUT9Jw0rnmeeC+vM9twEGSlpU0APgsMCMiniWNejwfeAboL+lbi3yjWetqmfOLG29mjXdpxbvaLsonyvcC84B/AS8Bd5JOnkc2LqtmDXUFMLcwfZP0UPn9wLWkZ3T+Q3pm9NbKnSNiGukEu7ekH/dQns0a5RzgGuDRPP0AGAGcSxpw5BHSSJM7R8S8vM8xpPPOQ6RG2q7Ah/O6q0ldKB8kdcNs38esN2iZ84ufeTNroIgYvYR1M4BPLWH9VKDqgw8RMQG61snbrBktKVaAL+Spw/0i4jEW7k5m1lvdFhGVlcgL8lRVRDxHGmGy2ro20jnpzfPSuHHjflpCPs0aqtXOL77zZmZmZmZm1gJ8583MzMzMmso5tz7BJXdO7/R+986cw5hVhtchR2bNwXfezMzMzHqRiBhd+QqaVnPJndO5d+acRmfDrOn4zpuZmZmZNZ0xqwxn4uFbdGqffX9zc51yY9YcfOfNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBfubNzKyzJo+HKZPS51l3w8qbNDY/ZtY1xVjuST5umFkX+c6bmVlnTZkEs6Y0Ohdm1l2OZTNrMb7zZmbWFStvDIdeDuN3a3ROzKw72mO5J/m4YWZd5DtvZmZmZmZmLcCNNzMzMzMzsxbgxpuZmZmZmVkLcOPNzMzMzMysBbjxZmZmZmZm1gLceDMzMzMzM2sBbryZmZmZmZm1AL/nrcHOufUJLrlz+kLL7p05hzGrDG9QjszMzMzMrJKkQ4BPR8RWjcqD77w12CV3TufemXManQ1rEElTJW1fZflwSadIekLSy5IezvMjq+0naT9Jz0v6QE/m36ynSTpA0uQcFzMlXSlpK0nfkfSGpJfy9KCk0yWtUth3G0lPFuYHSrpQ0r9zzB3cmF9lVq4lnSMkRY6flyU9JekySTtU2X9u3maWpAmShvX8LzGrv4ry3j6d3sW0rpM0L6fxoqQbJG1cWH+wpNslzZH0pKQTJHXqZpobb01gzCrDmXj4Fm9OvuvWt0kaCPwd2AjYGRgObAk8B7ynyvYHA78EdouI63swq2Y9StKXgFOAHwErAaOAXwF75k0mRsQywPLAh4GVgduLDbhCWksDFwIjgB0jYg4wpM4/wazHFc8RwON58YiIGAZsClwLXJTvKBTtkbcZC2wGfL1HMmzWGHtExLDCdFTlBp1oZB2VY2cF4Drg7MK6IcAXgJHA5sB2wDGdyai7TbagB2Y/wKFXHdrobFj9HESqlH4wIl7Oy54Gvl+5oaTDSBXZnSJics9l0Tpt8niYMqnRuWhZkpYFvgccGhEXFlZdClwq6TvtCyLiDeAeSfsC/wW+TOHkKGkIcDEQpIsec/N+Z4wbN+5Xdf4pZsmsKTB+t7p+ReU5QtLo4vqImAWcKmkA8FNJf4yIBZXbSLqa1IiznuJzRsPlCxqfAf4DHEy6WPhwWqXTSPW1mcDnIuLvlftHxHxJ5wHHFpadUdhkuqQ/Ax/sTL58563F7Pr2XVl/+fUbnQ2rr+2BqwoNt8U5ktSg284NtxYwZVKqrFlXbQEMAi6qdYeIaAMuAbYuLF4auBKYB3yoveFm1qM23htW3rjj7bqnM+eIC4G3AYtUMCStDuxCqrRaT/E5o1lsDjxKio8fViwbCXwbuFDS8pU75p5UBwK3LCH99wP3dCZDvvPWYvZZbx/2WW+fRmfDajSBCV3ZbQXg9hq22wH4J+Cje6tYeWM49PJG56I5fVIdbbEC8GxEzO9kyjNI3SjbLUNqCO4fEa91Mi2zcow7NE1d1XG8QOfOETPyv8VYuVhSAMOAf5AqqdaTfM4oR23xcrGk4vnlK8AbwIyIOC0vmy8JUm+oUyIigImSvkzqltzePfIXkk4kdZGcC3yk2hdKOhQYB3y6Mz/Hd97Mms9zwCLP6FRxBLAe8Fvlo4lZL/YcMLKzD3YDqwGzC/PPAvsBf5C0U1mZM2tCnTlHrJb/LcbKXvkZ0m2ADUh3Gcx6q70iYkRhOisvn1Zl2+m54dbucWDVwvzRETGC1Ftkd2CSpE2KCUjaC/gJsEtEPNuZjLrxZtZ8/gbsJGloB9s9TXrQdWtSP2yz3uxmUlfHvWrdQdJSwB7AjcXl+Zm5z5BOqJ161sCshXTmHPHhvP0DlSvyQFgTgBNLzp9ZK4gqy1aruCAyirfuXr+1Y8SCiLiR1OV4x/blknYGziINktLp3lNuvJk13gBJg9on0m33acBfJG0gaSlJK0g6TtKuxR0jYgawLbCzpJMbkHezHhERLwLfAn4paS9JQyQNkLSLpBOK2+blGwLnkkacPKlKeucCRwGXSHpf3m/buv8Qsx7U0TlC0kqSjiJ1ifx65WAlBacAO0gaW6+8mrWQtwFH53PNPsCGwBXVNpS0BTCG/FxbPs/8GfhoRPynK1/uZ97MGq8y4H9IGrTku6QhnJcDniINvHBr5c4RMS0fDG6QNC8iPJyz9UoRcZKkp4DjSSe/l0jPh/6QdFVz39wVRaSroNcC78oV2Grp/SE/UH65pB2Bb9b/V5j1rOI5gnQxA+CFfOfgFWAysE9EXLWENJ6R9EdSjHy03nk2a4BLJbUV5q8l1buquRVYl9QN/ylg74h4rrD+dEmn5M+zgOMj4so8/01gWeCKws27GyNil1oz6sabWQNFxOglrP5CnjrcLyIeA9YoKVtmTSsi/kxquFW6CfhOB/teB6xesewsUvcVgA+OGzeuWhcZ66weGAa/FLPuhpU36Xi7FtPBOWL/zu6flx1ZRt7Mmk0HdbEJFdtOKCxb5F1wEbFNB9/V7a76bryZmZlZeTbeu9E5MDPrtdx4MzMzs/J0dxj8ntQKdwfNzAo8YImZmZmZmVkL8J03MzOzJnfOrU9wyZ3TO73fvTPnMGaV4XXIkZmZNYLvvJmZmTW5S+6czr0z5zQ6G2Zm1mC+82ZmZtYCxqwynImHb9Gpffb9zc11yo2ZmTWCG29mZt1Vy7DovXRIcjOzZnPvzDmtf+Fi1p5MHLW414xZX+bGm5lZd3hYdDOzprHn2NUanQWzunLjzcysO2odFt1DkpuZ1d0Bm4/igM1HNTob3Tf+B43OgTUpD1hiZmZmZmbWAnznzczMzMxK1dXXW7Tzay7MqvOdNzMzMzMrlV9vYVYfvvNmZmZmZqXryust2rX8aJFmdeI7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WbWQJKmSpor6WVJz0u6XNIaed0ESa/ndbMlXStpgyppXJf3Xbpi+QRJr/fUbzGrp56IFUkv99TvMaunHC/bV1k+XNIpkp7I8fJwnh9ZbT9J++WY+UBP5t+sJ1WcX9qn0/O6VSSdJWlGXv5oPmdskNePlhQV+96V1x0iqS0vmyPpLkm7dze/bryZNd4eETEMWAV4CjitsO6EvG41YDrwu+KOkkYDWwMBfKhK2ifUI8NmDVLXWMn7m/VKkgYCfwc2AnYGhgNbAs8B76my/cHAL4HdIuL6HsyqWSPsERHDCtNRklYAbgKGkM4fywDvBK4HdqjYf0Rh300Ly2/O55YRwK+A8ySN6E5G3XgzaxIRMQ+YBIypsm4ucD4wtmLVQcAtwATg4Prm0Kw5OFbMuuQgYBTw4Yi4NyIWRMTTEfH9iLiiuKGkw4CfAztFxE2NyKxZE/giMAf4REQ8EskLETE+Ik7raOeiiFgAnA0MBdbtTqb8nrcmde/MOX7HSR8jaQiwL6mCWbluKLA/8HDFqoOAk4BbgVskrRQRT9U7r33SrCkwfrf8+W5YeZPupWFd5lixUvWduNweuCoiOuoefCSwFbBdRNxV/2zZYvWdstmstgcuyg2vbpHUDzgUeAN4vDtp+c5bE9pz7GqMWWV4o7NhPediSS+Qru7sAPyssO6YvO4l0sn0E+0rJG0FrAmcHxG3A48AB1SkfUz9st2HbLw3rLxx49OwusZK3t/6kr4VlysAM2vYbgfShZEp9c2OLVHfKpvN4GJJLxSmzwAjgVntG0j6UF73kqRrKvZ/trBvse713nxumQecCHw8Ip7uTkZ9560JHbD5KA7YfFSjs2ElOP+ImjbbKyL+lq/K7AlcL6m9O9iJEXG8pFHAVcD6wN153cHANRHxbJ4/Jy87uZD2icA3uvcrjHGHpqldV66EVqZhC/ukatmqrrESEcePGzcuuvtTrIW0alzWFi+VniM9L9qRI4BvAr+V9KmIcEw0QquWzWbUifNLcYGkT1OImYj4KzAiL/94xf4jI2J+lXRviYitJA0jPYu9Nalrf5f5zptZk4iItoi4EGgj3TkornsC+DxwqqTBkgYDHwM+IGmWpFmkvtmbStq0Mm2z3sSxYtYlfwN2yl2Ll+RpYDtSJfNXdc+VWfP6O7CXpG63l3J35c8Cn5C0WXfScuPNrEko2RNYDrivcn1EXAvMAA4D9iJVXMeQBmYYC2wI3Eh6tses13KsmNVkgKRB7RNpsIRpwF8kbSBpKUkrSDpO0q7FHSNiBrAtsLOkk6ukbdYXnEQ6z5wtae187lmGRQfEqklEPAf8FvhWdzLVK7tNnnPrE1xy5/RGZ6Mm986c4+fb7FJJbaQhzB8HDo6Ie6Sqt/l/RjqYPAiMz3cZ3pTfS/ILSV+rc57NGsGxYla7Kyrmf0gagOG7wLWkSulTwCWkgXwWEhHTJG0L3CBpXkR8vc75NWuk9vNLu2sj4sOS3gt8H/gX6VUBT+XPR3bxe04BHpG0SUTc3dHG1fTKxtsld053o8haQkSMXsK6Q6osmwhMXMI+5/NWX+pDAMaNG+dn3qzl9USsmPUWS4oX4At56nC/iHgMWKOkbJk1pQ7OLzOATy1h/VSg6hXEiJhAej1NcdmTwNKdz+VbemXjDWDMKsOZePgWjc5Gh/w6ADMzMzMzq4WfeTMzMzMzM2sBbryZmZmZmZm1ADfezMzMzMzMWoAbb2ZmZmZmZi3AjTczMzMzM7MW4MabmZmZmZlZC3DjzczMzMzMrAW48WZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA33szMzMzMzFpA/0ZnwMzMrBWcc+sTXHLn9IZ8970z5zBmleEN+W4zM2sevvNmZmZWg0vunM69M+c0OhtmZtaH+c6bmZlZjcasMpyJh2/R49+7729u7vHvNDOz5uM7b2ZmZmZmZi3AjTczMzMzM7MW4MabmZn1KpIOkfSvRufDzMysbG68mTUhSWMkTZb0fJ7+JmlMYf3Skn4t6SlJsyVdKmm1RubZrJ4kTZU0V9LLhen0LqZ1naR5OY0XJd0gaeOy82zWKBXx8rykyyWtkddNkPR6Xjdb0rWSNqiSxnV536Urlr+5f0/9HrN6yvHyuqSRFcvvlBSSRlfETft0l6StC/Ov5O2L24wqO79uvJk1pxnA3sDywEjgr8B5hfWfB7YANgFWBV4ATuvZLJr1uD0iYlhhOqpyA0m1DsR1VEQMA1YArgPOLjGfZs1gj1zGVwGeYuFzxAl53WrAdOB3xR0ljQa2BgL4UJW02/c36y0eA/Zvn8kX9AZXbHNCxTlo04i4sX0e2ChvN6KwzRNlZ9SjTfY2k8fDlEmNzoXVSNJU4DfAJ0gn2IuBIyPiBVKDDEkC2oB1CruuBVwdEU/lbc4DTiqkeyrwEWDZd73rXXX+FdZpjtPSSDoE+AzwH+Bg4FfAw2mVTgMOAmYCn4uIv1fuHxHzc/wc22OZ7mH3zpzj0Sr7sIiYJ2kScEqVdXMlnQ9cULHqIOAW4FZSXFWu7xEuu9aDziaV+/aLHAcDfwR+0LAcLYbvvPU2UybBrCmNzoV1zoHATsDawHrA8e0rJL0AzCMdTH5U2Od3wPskrSppSE7jysL624CxpDt31mwcp2XbHHgUeBvww4plI4FvAxdKWiQeJA0kxc8tPZPVnrXn2NX8cu8+Lp8j9qVKGZc0lHS34eGKVQcBf87TTpJWqnc+K7nsWg+7BRguaUNJ/Ugx86cG56kq33nrjVbeGA69vNG5MIBPqpatTo+IaQCSfkhqqB0PEBEj8sn1YODxwj4PAk+Quru0AVOAN7uQRcSbB5xx48Z17zdYfThOF1ZbrFwsaX5h/ivAG8CMiGi/Wjo/3azmaeCUiAhgoqQvA7vxVvfIX0g6ERgCzCXdqe51Dth8FAdsXvojF9Zg5x9R02bt8TKMFA87FdYdI+koYDjp3LJn+wpJWwFrAudHxLOSHgEOAE6u3L+ePTtcdq0sNcYLvHX37XrgflIdq6g9btpdEhEHdzuDneQ7b2aNN63w+XHSM2xviohXgF8Df5T0trz4DGAQ6XmdocCFFO68SfqypPskvVjPjJv1sL0iYkRhOisvn1Zl2+m54dauMraOjogRpDjaHZgkaZO65NqsMfbKZXxp0sW96yWtnNedmNeNJl28WL+w38HANRHxbJ4/Jy8rat/frDc5m3Sh4hBSl8lKJ1acg3q84QZuvJk1gzUKn0eRBiuptBTpDkH7iJKbAhMiYnZEvEa6W/ceSSMlbQ18DfgYsFz9sm3WNKLKstXy86LtqsZWRCyIiBtJ3cZ2rFP+zBomItoi4kJSL42tKtY9QRoA61RJgyUNJp07PiBplqRZwBeBTSVt2tN5N+tJEfE4aeCSXUkXxZuSG29mjfc5Savn53GOI3Xx2kHSZpL6SRpOGozkeeC+vM9twEGSlpU0APgsqevYs8AywHzgGdw12vqutwFHSxogaR9gQ+CKahtK2gIYA9zTg/kz6xFK9iRdzLuvcn1EXEu6sHEYsBepkTeG9Nz0WFLs3EjqTmbW230K2Db3empKbryZNd45wDWkwRUeJY1sNAI4F3gReIQ00uTOETEv73MMaSCTh0iNtF2BD+d1V5O6UD7Iws/JmbW6Syven3PREra9FVgXeJY0iMneEfFcYf3p7emQusocHxFXVknHrFVdmsv3HFIMHBwRi7tA8TPgq6QG3PiIeCIiZrVPwOnAgZ14FYdZS4qIRyJi8mJWf7XiHPTsYrarKwehWePdFhE/rlh2AUsYmjlXQg9czLo20pWjTwGMGzeuWpcys5YSEaOXsHpCxbYTCssWeRdcRGxTTq7MmtOS4iUiDqmybCIwcQn7nA+cn2cX2d+slS0uXiJiPtDe/f4QOij7ETG1sH3d+M6bmZmZmZlZC3DjzczMzMzMrAW426RZA3XQFczMzMzM7E2+82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmZmZtQA/89YE7p05h31/c3M5ic3ak4mjLiknLTNbvFlTYPxuXdz3blh5k3LzY2ZmZr2eG28NtufY1RqdBTPrrI33bnQOzMzMrA9y463BDth8FAdsPqq8BMf/oLy0zKy6cYemqau6esfOzMzM+jQ/82ZmZmZmZtYC3HgzMzMzMzNrAW68mZmZmdn/t3fvUVfVdR7H3x8FRAJExQuiRDo5aqWOYmSaYTmWmomTLgptyq62NDOz6Z7WZJnNOCyl2+jK26jZUrygstRK84YiJl4gNSNLlBJUwIfwht/5Y/+QzfHwsJ/nOefsvR8+r7X2Yp99ez77cL57n9++HTOrATfezMzMzMzMasCNNzMzMzMzsxpw483MzMzMzKwG3HgzMzMzMzOrATfezMzMzNZTkj4u6fayc5hZMW68mZVM0mRJsyV1SVooaYakfSWdKullSc+n7lFJUyWNys07QdKC3OtBkqZJukPScEkfK2etzFpL0uOSVqQ6WdVNTeNGSTpH0lNp+HxJ50vaKY0fKyka5r0/jfu4pJVp2LIy19GsVbqrl14s6xZJL6RlLJV0q6S3tTqzWdWk71SXp3oKSRMaxu8v6eZUF483mX9sGv8PSQ9LOqAVudx4MyuRpJOAKcD3ga2AMcBPgMPSJJdFxDBgM+BwYGvg3nwDLresjYBpwAjgwIhYBgxp8yqYddKhETE01x0vaXPgTrLP+ruAYcAewO+Af22Yf0Ru3t1yw2dGxFCy2jHrL15XL40TSBpQcFnHpxrZHLgFuKiFOc2q7HbgaOBvTcYtB34BfHkt814K3EdWN98ALpe0RV8DufFmVhJJmwDfBY6LiGkRsTwiXo6I6RGxxoYgDZ8LTAIWAV9qWNYQYDowEDgkIpan+X7aiXUxK9EXgWXARyPiT5FZEhHnRcTZPVlQRLzanohm1ZDONN8h6X8kPQucunqUzk5nEB6W9N5m80fEK8AvgV06FNms7dKZta9JmifpOUnnSRocES9FxJSIuB1Y2ThfRMyKiIuA+U2WuSPZgcRTImJFRFwBPAh8KI3fQdJvJT0jabGkiyWNKJK36BEXq5O/PQjnHVJ2Clu3vYHBwJVFZ4iIlZKuBt6XG7wRMANYChwZES+2NKW1h+u0VQ4ArmxFw0vShnvuuWcLIrXHvIXLmPTzmWXHsPobT9YA25LsgN+kNOxyYCTwb8A0SW+KiGfzM0oaBBwF3NXRxOtwyd1/5eo5T5Ydw+rtKLLvVsvJDoZ/M3W99RZgfkQ8nxt2fxoOIOAHwK3AcOAKsoMpJ65rwT7z1t+87QjY2pei18TmwOJ0JLMnniK7jHKVYWQNwQvccKsJ12lvXSVpSa77NNmXzdcuZ5H0wTTueUk3Nsy/ODfvybnh75C0BHih/avQO4ftPppdRg0vO4bVS7N6AXgqIs6OiFciYkUa9jQwJV3lcRnwCJA/unRWqpEu4HjgO51aiSKunvMk8xb6llXrk6kR8UQ6YHEa8JE+Lm8o2UH1vKVk39mIiMci4qaIeDEiFgFnAu8usmCfeetvxh2TdVYNn1B3Y58BRkoa0MMG3GggfzR0MXACcKGkroi4oedBraNcp6/Xfa2sMjEifp0fIOlTwGv3gEbENcCINPzohvlHrqXW7oqIfSUNBZ5vMr50k8ePYfL4MWXHsIr41bGFJmtWLx8Hnmgy7ZMREbnXfwG2yb0+ISLOlbQBsA9wjaR3V+lM9S6jhnPZZ/cuO4ZVUMF6yddF4+e/N7rIzqjlDSftYyRtCZzF6nu1NwCeK7Jgn3kzK89MsiP9E4vOkHachwK35YdHxDTg02Q3w+7fwoxmVfcbYGKqjT6JiK4W5DGrumgybLSk/BGUMWRXeaw5Y8SrEXEb8BhwYJvymZVhu1x/089/D80Ftpc0LDdstzQcsksmA9g1IoaTHWwsdBTTjTezkkTEUuDbwI8lTZQ0RNJASQdJOiM/bRq+M9mTi7YmO73euLxLyS5nuVrSPmm+97R9RczKdSawKXBRugFcaWe5e7mxzGplS+CEtK85EtgZuL7ZhJL2Jntgydxm481q6jhJ20raDPg6cBlkT/KWNDhNM0jS4FUHOiRtkMYNzF5qcLovlIh4FJgDnJKGHw7sSnZvG2Rn27qAJZJGs/YnVr6OG29mJYqIM4GTyG6KXUR22v544Ko0ySRJXcAS4BqySy33jIimR4Qi4gKyJ1FeJ+ntwLfamd+sw6Zrzd+tujIiFgPvIDuLfTvZJSlzyHaMnysvqlnpXlcv3Ux7N/BmssvwTwOOiIhncuOnrloO2c8EfDMiZrQvulnHXQLcSPbkyPnA99LwR4AVZLes3JD635jG7ZdeX092tm5FWsYqHwbGkV0OeTpZXS1K475D9jTKpcB1ZD/1VIjveTMrWURcDFzcZNSdrH6M89rmvQXYtmHYOcA56eX+48aNa3aJjFmtRMTYbsY9BXyym/GPs5bLUSLifOD8PoUzq5ju6oWGz3tDDbzut+AiYkJrUplV2j0R8YPGgevY99xCN5c6pn3PhLWMmws03jT63+uO6TNvZmZmZmZmteDGm5mZmZmZWQ34skkzMzMzM1svreMy48rxmTczMzMzM7MacOPNzMzMzMysBtx4MzMzMzMzqwHf82ZmZmZmLTdv4TIm/XxmR/7OLqOGt/3vmFWBG29mZmZm1lKH7T667Ahm/ZIbb2ZmZmbWUpPHj2Hy+DEd+VudOLtnVhW+583MzMzMzKwG3HgzMzMzMzOrATfezMzMzMzMasCNNzMzMzMzsxpw483MzMzMzKwG3HgzMzMzMzOrATfezMzMzMzMasCNNzMzMzMzsxpw482sgiQNknS5pMclhaQJDeP3l3SzpKWSHm8y/9g0/h8dimxWilbWiqSHOxTbrBSuF7O1kzRZ0mxJXZIWSpohaV9Jp0p6WdLzqXtU0lRJo3LzTpC0IPd6kKRpku6QNFzSxyTdK2mZpAWSzpA0oDc53Xgzq67bgaOBvzUZtxz4BfDltcx7KXAfsHl7oplVSqtq5RttSWdWLa4XswaSTgKmAN8HtgLGAD8BDkuTXBYRw4DNgMOBrYF78w243LI2AqYBI4ADI2IZMAQ4ERgJjAfeC5zcm6xuvJmVKB39/JqkeZKek3SepMER8VJETImI24GVjfNFxKyIuAiY32SZOwJ7AKdExIr2r4VZ+3WiViLiivaviVn7uV7MipO0CfBd4LiImBYRyyPi5YiYHhFrHMhIw+cCk4BFwJcaljUEmA4MBA6JiOVpvp9GxG2pBp8ELgb26U3eXp2uq4N5C5cx6eczy45hVsRRwPvIjnhOB76Zut56CzA/Ip4vOoPrxWrCtWJWXOn10kmuTeuDvYHBwJVFZ4iIlZKuJquxVTYCZgBLgSMj4sVuFrEfMLcXWfvnmbfDdh/NLqOGlx3DrKipEfFERDwLnAZ8pI/LG0q24SjE9WI14loxK67Ueukk16b10ebA4oh4pYfzPUV2GeUqw8gaghd013CTdAwwDvivngaFfnrmbfL4MUweP6bsGGb86thCkz2R6/8LsE0f/2wXUHgv5nqxKnCtmBVXh3rpJNemdadAvTwDjJQ0oIcNuNHAs7nXi4ETgAsldUXEDY0zSJoInA4cEBGLe/C3XtMvz7yZ1cx2uf4xZEdy+mIusL2kYX1cjlnVuFbMinO9mBUzE3gBmFh0BkkbAIcCt+WHR8Q04NPA5ZL2b5jn/cA5wKER8WBvw7rxZla+4yRtK2kz4OvAZZA9rUjS4DTNIEmDJSmN2yCNG5i91GBJgwAi4lFgDnBKbn6z/qCttSLp8E6vkFkbuV7MCoiIpcC3gR9LmihpiKSBkg6SdEZ+2jR8Z7Inr24NnNlkeZcCxwNXS9onzfcesoeUfCgiZvUlrxtvZuW7BLiR7Ole84HvpeGPACvITsvfkPrfmMbtl15fT3ZEdUVaxiofJrue+rk2ZzfrpHbXyuntjW/WUa4Xs4Ii4kzgJLKH+iwiu+z4eOCqNMkkSV3AEuAaskst94yIpme0I+ICsidRXifp7cC3gE2A69PvyHVJmtGbrIqI3sxXunHjxsXs2bPLjmHWLUn3RsS4bsY/DnwqIn7drgyuFauDKtQKuF6sHlwvZsWtq17qxmfezMzMzMzMasCNNzMzMzMzsxrolz8VYFYXETG27AxmdeBaMSvO9WLWf/nMm5mZmZmZWQ248WZmZmZmZlYDbryZmZmZmZnVgBtvZmZmZmZmNeDGm5mZmZmZWQ3U9ke6JS0C/tLixY4EFrd4mX3lTMVUMRPAP0fEsDIDNNRKVd+nRs7ZWnXIWXqtQNv2LWtTtf+XKuWpUhaoXp71oV6q9p5XLQ84U1GVqJdWqe1PBUTEFq1epqTZVfsFdmcqpoqZIMtVdoZ8rVT1fWrknK1Vh5xVqBVoz75lbar2/1KlPFXKAtXMU3YGaG+9VPE9r1IecKaiqlIvreLLJs3MzMzMzGrAjTczMzMzM7MacONtTf9bdoAmnKmYKmaC6uWqWp61cc7WqkPOOmRstaqtc5XyVCkLOE8ZqraOVcsDzlRUFTP1Wm0fWGJmZmZmZrY+8Zk3MzMzMzOzGlgvG2+S3i/pEUmPSfpqk/GbSJou6X5JcyUdU3amNM0ESXNSpt9VIVOabi9JKyUdUXYmSUdJeiB1d0rarQKZJOmsNP4BSXu0O1Pub28o6T5J16bXm0m6SdIf07+bdirLWvINljQrV2vfqWjO7STdLOkPKecXKprzF5KelvRQblilMjZTdFtTR1XbjhbYXh2WtlNzJM2WtG/JeTq6TS+QZydJMyW9KOnkdmYpmKe0/UsrFFg/fx8rmClN5+9j/bheXhMR61UHbAj8CdgeGATcD+zSMM3XgR+m/i2AZ4FBJWcaAcwDxqTXW5b9PuWm+y1wPXBE2ZmAdwKbpv6DgLsrkOlgYAYg4B3tztTwt08CLgGuTa/PAL6a+r+66nNeVpfek6GpfyBwd3qPqpZzFLBH6h8GPArsUsGc+wF7AA/lhlUqY5PMhbY1deyqth0tuL0ayupbKnYFHi45T8e26QXzbAnsBZwGnFz256fM/UuH1s/fxyq2HenB++TvY23q1sczb28HHouI+RHxEvBL4LCGaQIYJklkO7JngVdKzjQZmBYRfwWIiKfbmKdoJoDPA1cA7c5TKFNE3BkRz6WXdwHblp0pvb4wMncBIySNanMuJG0LHAKc25DlgtR/ATCx3Tm6k96TrvRyYOqC6uVcGBG/T/3PA38ARlO9nLeSba/yKpWxiaLbmjqq2na0yDa0K9K3HOANZPVYZp5ObtOL5Hk6Iu4BXm5jjsJ5KGn/0iL+Pta6TODvY/29Xl6zPjbeRgNP5F4vSMPypgI7A08BDwJfiIhXS860I7CppFsk3Svp39uYp1AmSaOBw4GftTlL4UwNPkl2hKWdimTqae5WmQL8B5D/7G4VEQsha5CQHUUulbJLO+eQ7XBuioi7qWDOVSSNBf6F7CxhZXPmVD1jWfXRCVXbjhZ6ryUdLulh4DrgE2XnyWn3Nr1qn8Uq719awd/HWpTJ38eA/l8vrxlQdoASqMmwxiOL7wPmAO8BdgBuknRbRCwrMdMAYE/gvcDGwExJd0XEoyVmmgJ8JSJWZgfF2q5IpmxCaX+yjUVb79egWKbCuVtF0geApyPiXkkT2vm3+ioiVgK7SxoBXCnprSVHWitJQ8mObJ4YEcs69Lnv7zpeHx1Ute1oofc6Iq4kq8X9gP8EDigzD3Rsm161z2Il9y8t5O9jrcs0BX8f6+/18pr1sfG2ANgu93pbsiM6eccAp6dLRx6T9GdgJ2BWiZkWAIsjYjmwXNKtwG5k996UlWkc8Mu0oRgJHCzplYi4qsRMSNqV7FLBgyLimTZl6UmmQrlbbB/gg5IOBgYDwyX9H/B3SaMiYmG6VKATl1cUEhFLJN0CvJ8K5pQ0kKzhdnFETEuDK5eziapnLKM+OqVq29EevdcRcaukHSSNjIjFZeXp4Da9ap/Fqu5fWsXfx1qXyd/H+n+9rNabG+Xq3JE1WOcDb2L1DY1vaZjmp8CpqX8r4ElgZMmZdgZ+k6YdAjwEvLXMTA3Tn0/7b5At8j6NAR4D3lmhz9MhrHmD7KxOZMv9/QmsfmDJj1jz4RVndDJLk2xbACNS/8bAbcAHKphTwIXAlIbhlcqZcoxlzQeWVC5jQ94ebWvq1FVtO1pwe/VPrH5gyR5p/6cS83Rsm96T/y/gVNr/wJLK7186sH7+Plax7UgP3id/H2vXupYdoJSVzp428yjZU2m+kYYdCxyb+rcBbiS7vvoh4OiyM6XXXyZ7wtFDZJdtlZ4pN23bNxYF/+/OBZ4ju8xiDjC7ApkE/DiNfxAY1+5MDfkmsLrxtnna6fwx/btZJ7M0ybYrcB/wQPpcf7uiOfclu7Tigdxn6+AK5rwUWEj2MIUFZJeqVCrjWnK/rob6S1e17WiB7dVXgLnpMz4T2LfkPB3dphfIs3WqrWXAktQ/vMQ8pe5fOrB+/j5WMFNu2rZvRwr+3/n7WJu6VUfXzMzMzMzMrMLWx6dNmpmZmZmZ1Y4bb2ZmZmZmZjXgxpuZmZmZmVkNuPFmZmZmZmZWA268mZmZmZmZ1YAbbzUlaaWkOZLmSrpf0kmSNkjjxkk6q5t5x0qa3Lm0ZuVxrZgV53oxK871YmXwTwXUlKSuiBia+rcELgHuiIhTCsw7gezHRT/Q1pBmFeBaMSvO9WJWnOvFyuAzb/1ARDwNfAY4XpkJkq4FkPTudFRojqT7JA0DTgfelYZ9MR39uU3S71P3zjTvBEm3SLpc0sOSLpakNG4vSXemI02zJA2TtKGkH0m6R9IDkj5b1nti1oxrxaw414tZca4X65iyfyXcXe86oKvJsOeArYAJwLVp2HRgn9Q/FBiQH5+GDwEGp/43A7NT/wRgKbAtWUN/JrAvMAiYD+yVphuelvsZ4Jtp2EbAbOBNZb9X7tbvzrXizl3xzvXizl3xzvXiroxuANafqMmwO4AzJV0MTIuIBemATd5AYKqk3YGVwI65cbMiYgGApDnAWLKNyMKIuAcgIpal8QcCu0o6Is27CdkG6M99XjOz1nKtmBXnejErzvVibeXGWz8haXuyYn8a2HnV8Ig4XdJ1wMHAXZIOaDL7F4G/A7uRHdV5ITfuxVz/SrLPjIBmN0sK+HxE3NCHVTFrK9eKWXGuF7PiXC/WCb7nrR+QtAXwM2BqRETDuB0i4sGI+CHZqfOdgOeBYbnJNiE7evMq8FFgw3X8yYeBbSTtlf7GMEkDgBuAz0kamIbvKOkNfV9Ds9ZwrZgV53oxK871Yp3iM2/1tXE6dT4QeAW4CDizyXQnStqf7EjNPGAG8CrwiqT7gfOBnwBXSDoSuBlY3t0fjoiXJE0Czpa0MbACOAA4l+xU/u/TzbSLgIl9WkuzvnOtmBXnejErzvViHeefCjAzMzMzM6sBXzZpZmZmZmZWA268mZmZmZmZ1YAbb2ZmZmZmZjXgxpuZmZmZmVkNuPFmZmZmZmZWA268mZmZmZmZ1YAbb2ZmZmZmZjXgxpuZmZmZmVkN/D9U5ZCUChNNMgAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 1008x720 with 8 Axes>"
       ]
@@ -807,7 +809,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },

From e48f7a2b13e31d702ed511acb3a023870c18d9b7 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 09:09:49 +0200
Subject: [PATCH 091/158] review of theory (minor adjustements)

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 92 ++++++++++++++-----
 1 file changed, 68 insertions(+), 24 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 5d73256b..7a82031d 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -8,8 +8,8 @@
     "\n",
     "Authors:\n",
     "\n",
-    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
    ]
   },
@@ -64,7 +64,10 @@
     "  - Davis et al. dataset: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - KIBA dataset: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
     "  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)\n",
-    "- Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) "
+    "- Kinase selection: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
+    "- Kinmap\n",
+    "    - http://www.kinhub.org/kinmap/\n",
+    "    - KinMap: a web-based tool for interactive navigation through human kinome data. [<i>BMC Bioinformatics</i> (2017), <b>18</b>, 16](https://doi.org/10.1186/s12859-016-1433-7)"
    ]
   },
   {
@@ -87,13 +90,13 @@
    "source": [
     "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating and thereby activating themselves or other proteins and are most frequently mutated proteins in tumors.\n",
     "\n",
-    "As of Sep 2021, 5782 X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and 67 FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
+    "As of September 2021, $5782$ X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and $67$ FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
     "\n",
     "Despite of decades of kinase research, there are still many open challenges:\n",
     "\n",
-    "- A large fraction of the kinome is un-/underexplored\n",
-    "- Many kinase inhibitors are promiscous binders causing off-target effects or enabling polypharmacology\n",
-    "- Occurence of drug resistances due to mutations"
+    "- A large fraction of the kinome is un-/underexplored.\n",
+    "- Many kinase inhibitors are promiscous binders causing off-target effects or enabling polypharmacology.\n",
+    "- There are occurences of drug resistances due to mutations."
    ]
   },
   {
@@ -102,14 +105,14 @@
    "source": [
     "#### The human kinome \n",
     "\n",
-    "Human kinome consists of over 500 protein kinases (number varies depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
+    "The human kinome consists of over $500$ protein kinases (this number may vary depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
     "\n",
-    "Manning et al. clustered the human protein kinases based on their sequence into [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762) into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as Manning kinome tree. The kinase resource KinMap enables mapping of kinase data onto that tree, e.g. the number of of X-ray structures per kinase as shown in Figure 1.\n",
+    "In [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning et al. clustered the human protein kinases based on their sequence into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
     "![Manning tree with number of structures per kinase (KinMap)](images/kinmap_n_structures_per_kinase.png)\n",
     "\n",
     "*Figure 1:* \n",
-    "Number of PDB structures per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "Number of PDB structures per kinase mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/).\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -123,7 +126,7 @@
    "source": [
     "#### Kinase structures and important motifs\n",
     "\n",
-    "Kinases sequences and structures are highly conserved. Important regions in the kinase pocket include (Figure 2):\n",
+    "Kinase sequences and structures are highly conserved. Important regions in the kinase pocket include (see Figure 2):\n",
     "\n",
     "- Hinge region: Forms key hydrogen bonds to ligands \n",
     "- DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state\n",
@@ -154,8 +157,7 @@
    "source": [
     "### Kinase resources\n",
     "\n",
-    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development. \n",
-    "[TODO cite Kooistra, Volkamer, ARMC V.50, Elsevier, 2017, 153-192]"
+    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development ([<i>Annual Reports in Medicinal Chemistry</i> 50 (2017): 197-236](https://doi.org/10.1016/bs.armc.2017.08.001))."
    ]
   },
   {
@@ -169,7 +171,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of 85 residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position 45 can be easily and quickly looked up in any of the over 10,000 monomeric kinase structures in KLIFS. \n",
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily and quickly looked up in any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
     "\n",
     "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
     "\n",
@@ -195,14 +197,13 @@
    "source": [
     "#### Bioactivity data\n",
     "\n",
-    "TODO - short !\n",
-    "* maybe an overview of data points per kinase on Chembl (from kinodata)\n",
-    "* other profiling data (as available in karaman, xxx)\n",
+    "[ChEMBL](https://www.ebi.ac.uk/chembl/) is a well-known bioactivity database, which releases updated versions every now and then.\n",
+    "In September 2021, there are over two million compounds and $14,000$ targets that are stored. In ChEMBL29, there are over $160,000$ measurements on kinases (see Figure 3).\n",
     "\n",
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
     "*Figure 3:* \n",
-    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -212,13 +213,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- Karaman et al. dataset: TODO\n",
+    "However, ChEMBL is not the only available database. Below is an non-exhaustive list of available kinase data sets.\n",
+    "\n",
+    "\n",
+    "- Karaman et al. dataset:\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Davis_profiling.js)\n",
-    "- Davis et al. dataset: TODO\n",
+    "- Davis et al. dataset:\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Karaman_profiling.js)\n",
-    "- KIBA dataset: TODO\n",
+    "- KIBA dataset:\n",
     "  - Paper: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
     "  - Data: [SI data (XLSX)](https://ndownloader.figstatic.com/files/3950161)\n",
     "- PKIS dataset: \n",
@@ -309,7 +313,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+    "_Note_: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
    ]
   },
   {
@@ -538,7 +542,17 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n",
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    }
+   ],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -575,7 +589,37 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'kinase.uniprot'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'kinase.uniprot'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_7586/2395927186.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mklifs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetup_remote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklifs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkinases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_kinases\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinases_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkinases_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"kinase.uniprot\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"0\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;31m# Some UniProt IDs have several names in KLIFS, keep only first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinases_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"kinase.uniprot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'kinase.uniprot'"
+     ]
+    }
+   ],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -623,7 +667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From feb91c0e9f5509d15500cced498f30ba9cd437c0 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 09:49:52 +0200
Subject: [PATCH 092/158] T023: Post-revolve conflicts

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 67 ++++---------------
 1 file changed, 12 insertions(+), 55 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 44f2209c..865a3ccf 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -67,10 +67,7 @@
     "  - Davis et al. dataset: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - KIBA dataset: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
     "  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)\n",
-    "- Kinase selection: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
-    "- Kinmap\n",
-    "    - http://www.kinhub.org/kinmap/\n",
-    "    - KinMap: a web-based tool for interactive navigation through human kinome data. [<i>BMC Bioinformatics</i> (2017), <b>18</b>, 16](https://doi.org/10.1186/s12859-016-1433-7)"
+    "- Kinase selection: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)"
    ]
   },
   {
@@ -110,12 +107,12 @@
     "\n",
     "The human kinome consists of over $500$ protein kinases (this number may vary depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
     "\n",
-    "In [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning et al. clustered the human protein kinases based on their sequence into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
+    "In [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning _et al._ clustered the human protein kinases based on their sequence into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
     "![Manning tree with number of structures per kinase (KinMap)](images/kinmap_n_structures_per_kinase.png)\n",
     "\n",
     "*Figure 1:* \n",
-    "Number of PDB structures per kinase mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/).\n",
+    "Number of PDB structures per kinase mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). Check the appendix on how to generate this KinMap tree.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -212,7 +209,7 @@
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
     "*Figure 3:* \n",
-    "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap.\n",
+    "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. Check the appendix on how to generate this KinMap tree.\n",
     "<!---\n",
     "We are using KLIFS kinase names; some are not recognized by KinMap and were simply dropped!\n",
     "--->"
@@ -225,13 +222,13 @@
     "However, ChEMBL is not the only available database. Below is an non-exhaustive list of available kinase data sets.\n",
     "\n",
     "\n",
-    "- Karaman et al. dataset:\n",
+    "- Karaman _et al._ dataset\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Davis_profiling.js)\n",
-    "- Davis et al. dataset:\n",
+    "- Davis _et al._ dataset\n",
     "  - Paper: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)\n",
     "  - Data: [KinMap data (JSON)](http://kinhub.org/js/Karaman_profiling.js)\n",
-    "- KIBA dataset:\n",
+    "- KIBA dataset\n",
     "  - Paper: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)\n",
     "  - Data: [SI data (XLSX)](https://ndownloader.figstatic.com/files/3950161)\n",
     "- PKIS dataset\n",
@@ -562,17 +559,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n",
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -609,37 +596,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'kinase.uniprot'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'kinase.uniprot'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_7586/2395927186.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mklifs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msetup_remote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklifs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkinases\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_kinases\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinases_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkinases_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"kinase.uniprot\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"0\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;31m# Some UniProt IDs have several names in KLIFS, keep only first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mkinases_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkinases_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"kinase.uniprot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfirst\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'kinase.uniprot'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -699,7 +656,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "73a1e0c53d74415ab788e27e8822fea4",
+       "model_id": "73f5c6c754c54d38bfa3b9e5cbeb85f1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -711,7 +668,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e2c15064772547ee8f358ec9b3972436",
+       "model_id": "878d385221ed48a986af40273022b64e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -757,7 +714,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From dcc164b6e8dc231b10c3a77fdc9181c0a45e2d2f Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 10:15:52 +0200
Subject: [PATCH 093/158] minor edits in practical.

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 27 ++++++++++++-------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 865a3ccf..2dc39a8e 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -202,9 +202,9 @@
     "In September 2021, there are over two million compounds and $14,000$ targets that are stored. In ChEMBL29, there are over $160,000$ measurements on kinases (see Figure 3).\n",
     "\n",
     "- `kinodata` GitHub repository: https://github.com/openkinome/kinodata\n",
-    "- `kinodata` ChEMBL 29 release: https://github.com/openkinome/kinodata/releases/tag/v0.3 (`activities-chembl29_v0.3.zip`)\n",
+    "- `kinodata` ChEMBL29 release: https://github.com/openkinome/kinodata/releases/tag/v0.3 (`activities-chembl29_v0.3.zip`)\n",
     "\n",
-    "As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spend on certain kinases (Figure 3).\n",
+    "As with other data types, the coverage of bioactivity data is highly unbalanced among the human kinases, depending on how much research is spent on certain kinases (Figure 3).\n",
     "\n",
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
@@ -244,7 +244,7 @@
     "\n",
     "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also other targets (off-targets). This promiscuous binding can cause mild to severe side effects.\n",
     "\n",
-    "Predicting such side effects is a non-trivial task because some off-targets are not obvious: For example, the EGFR inhibitor Erlotinib shows side effects in the sequentially highly similar TK kinase group, however also shows the off-targets GAK, LOK, and SLK in more remote kinase groups (Figure 4). \n",
+    "Predicting such side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib produces side effects in the highly sequentially-similar TK kinase group. However, it also affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
     "\n",
     "![Erlotinib profiling data from Karaman dataset (KinMap)](images/kinmap_erlotinib_karaman.png)\n",
     "\n",
@@ -504,9 +504,9 @@
     "### KinMap data\n",
     "\n",
     "There are some KinMap trees shown in this notebook. Please find in the appendix the code to generate the KinMap CSV files to be uploaded to KinMap:\n",
-    "http://www.kinhub.org/kinmap\n",
+    "http://www.kinhub.org/kinmap.\n",
     "\n",
-    "Note: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 xxx.svg xxx.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
+    "_Note_: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 xxx.svg xxx.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
    ]
   },
   {
@@ -517,7 +517,7 @@
    "source": [
     "def format_for_kinmap(kinase_names, kinase_values, size_min=10, size_max=50):\n",
     "    \"\"\"\n",
-    "    Take kinase names and some associated values and generate a KinMap data file\n",
+    "    Given kinase names and some associated values, generates a KinMap data file\n",
     "    that will display values as circles of size [`size_min`, `size_max`].\n",
     "\n",
     "    Parameters\n",
@@ -525,7 +525,7 @@
     "    kinase_names : list of str\n",
     "        Kinase names.\n",
     "    kinase_values : list of float\n",
-    "        Some associated values.\n",
+    "        Some associated values, such as the number of bioactivites.\n",
     "    size_min : int\n",
     "        Minimum circle size on KinMap tree (minimum input value will be scaled to `size_min`).\n",
     "    size_max : int\n",
@@ -592,6 +592,13 @@
     "Generate data for number of ChEMBL bioactivities per kinase in the KinMap format to be mapped onto the kinome tree."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Note_: The cell below takes a few seconds to execute."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -656,7 +663,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "73f5c6c754c54d38bfa3b9e5cbeb85f1",
+       "model_id": "d078365468114954b9f1be5827716dcf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -668,7 +675,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "878d385221ed48a986af40273022b64e",
+       "model_id": "f1165449daf74d59a36a30892bd018dc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -714,7 +721,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 7d61be234fc7bb938a47af3ae2ed4df66889727e Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 10:20:15 +0200
Subject: [PATCH 094/158] T027: DS review text/code

---
 .../talktorial.ipynb                          | 1273 ++++++++---------
 1 file changed, 636 insertions(+), 637 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 3d54cbf6..93cfc200 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# T027 · Kinase similarity: ligand-profile\n",
+    "# T027 · Kinase similarity: Ligand profile\n",
     "\n",
     "Authors:\n",
     "\n",
@@ -30,7 +30,7 @@
     "\n",
     "* Kinase dataset\n",
     "* Bioactivity data\n",
-    "* Kinase similarity descriptor: ligand-profile\n",
+    "* Kinase similarity descriptor: Ligand profile\n",
     "    * Kinase similarity\n",
     "    * Kinase promiscuity\n",
     "* From similarity matrix to distance matrix"
@@ -42,9 +42,9 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
+    "* Define the kinases of interest\n",
     "* Retrieve the data\n",
     "* Preprocess the data\n",
-    "    * Kinases of interest\n",
     "    * Hit or non-hit\n",
     "* Kinase promiscuity\n",
     "* Kinase similarity\n",
@@ -61,8 +61,12 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* [ChEMBL](https://www.ebi.ac.uk/chembl/) database\n",
-    "* Kinmap: http://www.kinhub.org/kinmap/"
+    "* ChEMBL database\n",
+    "  * Website: https://www.ebi.ac.uk/chembl/\n",
+    "  * Paper: [<i>Nucleic Acid Research</i> (2017), <b>45(D1)</b>, D945-D954](https://doi.org/10.1093/nar/gkw1074) \n",
+    "* KinMap\n",
+    "  * Website: http://www.kinhub.org/kinmap/\n",
+    "  * Paper: [<i>BMC Bioinformatics</i> (2017), <b>18(1)</b>, 16](https://doi.org/10.1186/s12859-016-1433-7) "
    ]
   },
   {
@@ -97,8 +101,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human-kinases. Luckily, a curated version of ChEMBL29 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
-    "For more details on querying the ChEMBL database, please refer to [Talktorial T001](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb)."
+    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human kinases. Luckily, a curated version of ChEMBL29 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
+    "For more details on querying the ChEMBL database, please refer to __Talktorial T001__."
    ]
   },
   {
@@ -111,14 +115,14 @@
     "\n",
     "\n",
     "*Figure 3:* \n",
-    "Number of ChEMBL 29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. The figure is generated in __Talktorial T023__."
+    "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. The figure is generated in __Talktorial T023__."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: ligand-profile"
+    "### Kinase similarity descriptor: Ligand profile"
    ]
   },
   {
@@ -161,7 +165,7 @@
    "source": [
     "### From similarity matrix to distance matrix\n",
     "\n",
-    "As discussed in __Talktorial T024__, we use convert the similarity matrix to a distance matrix."
+    "As discussed in __Talktorial T024__, we convert the similarity matrix to a distance matrix."
    ]
   },
   {
@@ -178,10 +182,11 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
+    "from collections import Counter\n",
+    "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import seaborn as sns\n",
-    "from collections import Counter"
+    "import seaborn as sns"
    ]
   },
   {
@@ -364,7 +369,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We retrieve a pre-curated version of a kinase subset of ChEMBL v.29 freely available at Openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
+    "We retrieve a pre-curated version of a kinase subset of ChEMBL29 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
    ]
   },
   {
@@ -376,7 +381,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Current shape of data: (190634, 17)\n"
+      "Current shape of data: (190634, 16)\n"
      ]
     },
     {
@@ -400,7 +405,6 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
        "      <th>activities.activity_id</th>\n",
        "      <th>assays.chembl_id</th>\n",
        "      <th>target_dictionary.chembl_id</th>\n",
@@ -422,7 +426,6 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>96698</td>\n",
        "      <td>16291323</td>\n",
        "      <td>CHEMBL3705523</td>\n",
        "      <td>CHEMBL2973</td>\n",
@@ -442,7 +445,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>94326</td>\n",
        "      <td>16264754</td>\n",
        "      <td>CHEMBL3705523</td>\n",
        "      <td>CHEMBL2973</td>\n",
@@ -462,7 +464,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>98119</td>\n",
        "      <td>16306943</td>\n",
        "      <td>CHEMBL3705523</td>\n",
        "      <td>CHEMBL2973</td>\n",
@@ -482,7 +483,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>101161</td>\n",
        "      <td>16340050</td>\n",
        "      <td>CHEMBL3705523</td>\n",
        "      <td>CHEMBL2973</td>\n",
@@ -502,7 +502,6 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>96324</td>\n",
        "      <td>16287186</td>\n",
        "      <td>CHEMBL3705523</td>\n",
        "      <td>CHEMBL2973</td>\n",
@@ -525,33 +524,33 @@
        "</div>"
       ],
       "text/plain": [
-       "   Unnamed: 0  activities.activity_id assays.chembl_id  \\\n",
-       "0       96698                16291323    CHEMBL3705523   \n",
-       "1       94326                16264754    CHEMBL3705523   \n",
-       "2       98119                16306943    CHEMBL3705523   \n",
-       "3      101161                16340050    CHEMBL3705523   \n",
-       "4       96324                16287186    CHEMBL3705523   \n",
+       "   activities.activity_id assays.chembl_id target_dictionary.chembl_id  \\\n",
+       "0                16291323    CHEMBL3705523                  CHEMBL2973   \n",
+       "1                16264754    CHEMBL3705523                  CHEMBL2973   \n",
+       "2                16306943    CHEMBL3705523                  CHEMBL2973   \n",
+       "3                16340050    CHEMBL3705523                  CHEMBL2973   \n",
+       "4                16287186    CHEMBL3705523                  CHEMBL2973   \n",
        "\n",
-       "  target_dictionary.chembl_id molecule_dictionary.chembl_id  \\\n",
-       "0                  CHEMBL2973                 CHEMBL3666724   \n",
-       "1                  CHEMBL2973                 CHEMBL3666728   \n",
-       "2                  CHEMBL2973                 CHEMBL1968705   \n",
-       "3                  CHEMBL2973                 CHEMBL1997433   \n",
-       "4                  CHEMBL2973                 CHEMBL3666721   \n",
+       "  molecule_dictionary.chembl_id  molecule_dictionary.max_phase  \\\n",
+       "0                 CHEMBL3666724                              0   \n",
+       "1                 CHEMBL3666728                              0   \n",
+       "2                 CHEMBL1968705                              0   \n",
+       "3                 CHEMBL1997433                              0   \n",
+       "4                 CHEMBL3666721                              0   \n",
        "\n",
-       "   molecule_dictionary.max_phase activities.standard_type  \\\n",
-       "0                              0                    pIC50   \n",
-       "1                              0                    pIC50   \n",
-       "2                              0                    pIC50   \n",
-       "3                              0                    pIC50   \n",
-       "4                              0                    pIC50   \n",
+       "  activities.standard_type  activities.standard_value  \\\n",
+       "0                    pIC50                  14.096910   \n",
+       "1                    pIC50                  14.000000   \n",
+       "2                    pIC50                  14.000000   \n",
+       "3                    pIC50                  13.958607   \n",
+       "4                    pIC50                  13.920819   \n",
        "\n",
-       "   activities.standard_value activities.standard_units  \\\n",
-       "0                  14.096910                        nM   \n",
-       "1                  14.000000                        nM   \n",
-       "2                  14.000000                        nM   \n",
-       "3                  13.958607                        nM   \n",
-       "4                  13.920819                        nM   \n",
+       "  activities.standard_units  \\\n",
+       "0                        nM   \n",
+       "1                        nM   \n",
+       "2                        nM   \n",
+       "3                        nM   \n",
+       "4                        nM   \n",
        "\n",
        "                compound_structures.canonical_smiles  \\\n",
        "0  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...   \n",
@@ -590,7 +589,8 @@
    "source": [
     "path = \"https://github.com/openkinome/kinodata/releases/download/\\\n",
     "v0.3/activities-chembl29_v0.3.zip\"\n",
-    "data = pd.read_csv(path, index_col=None)\n",
+    "# Load data and reset index so that it starts from 0\n",
+    "data = pd.read_csv(path, index_col=0).reset_index(drop=True)\n",
     "print(f\"Current shape of data: {data.shape}\")\n",
     "data.head()"
    ]
@@ -618,7 +618,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKi', 'pKd', 'pIC50'}\n",
+      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -652,7 +652,7 @@
     {
      "data": {
       "text/plain": [
-       "Index(['Unnamed: 0', 'activities.activity_id', 'assays.chembl_id',\n",
+       "Index(['activities.activity_id', 'assays.chembl_id',\n",
        "       'target_dictionary.chembl_id', 'molecule_dictionary.chembl_id',\n",
        "       'molecule_dictionary.max_phase', 'activities.standard_type',\n",
        "       'activities.standard_value', 'activities.standard_units',\n",
@@ -676,7 +676,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The dataframe contains many columns that won't be necessary for the rest of the notebook which are therefore removed. \n",
+    "The DataFrame contains many columns that won't be necessary for the rest of the notebook which are therefore removed. \n",
     "Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the UniProt ID of the kinase. These columns are renamed for readability."
    ]
   },
@@ -831,7 +831,7 @@
     }
    ],
    "source": [
-    "data = data[data[\"UniprotID\"].isin(kinase_selection_df.uniprot_id)]\n",
+    "data = data[data[\"UniprotID\"].isin(kinase_selection_df[\"uniprot_id\"])]\n",
     "print(f\"Current shape of data: {data.shape}\")"
    ]
   },
@@ -923,7 +923,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at EGFR data (which corresponds to the first row in the kinase selection dataframe):"
+    "Let's look at EGFR data (which corresponds to the first row in the kinase selection DataFrame):"
    ]
   },
   {
@@ -932,7 +932,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "EGFR_data = data[data[\"UniprotID\"] == kinase_selection_df.uniprot_id[0]]"
+    "egfr_data = data[data[\"UniprotID\"] == kinase_selection_df[\"uniprot_id\"][0]]"
    ]
   },
   {
@@ -963,7 +963,7 @@
     }
    ],
    "source": [
-    "measured_compounds = Counter(EGFR_data[\"smiles\"])\n",
+    "measured_compounds = Counter(egfr_data[\"smiles\"])\n",
     "measured_compounds.most_common()[0:5]"
    ]
   },
@@ -971,7 +971,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., highest pIC50 value."
+    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value."
    ]
   },
   {
@@ -1071,7 +1071,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a pIC50 cut-off of $6.3$, similarly to the [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a pIC50 cut-off of $6.3$, similarly to the cutoff used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
    ]
   },
   {
@@ -1102,8 +1102,9 @@
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    1 if the pIC50 value is above the threshold, which indicates activity.\n",
-    "    0 otherwise.\n",
+    "    int\n",
+    "        1 if the pIC50 value is above the threshold, which indicates activity.\n",
+    "        0 otherwise.\n",
     "    \"\"\"\n",
     "    if pic50_value >= threshold:\n",
     "        return 1\n",
@@ -1243,7 +1244,7 @@
     "\n",
     "1. the total number of measured compounds against the given kinase,\n",
     "2. the number of active compounds against the kinase, and\n",
-    "3. the fraction of active compounds, i.e. the ratio of active compounds over the total number of measured compounds."
+    "3. the fraction of active compounds, i.e., the ratio of active compounds over the total number of measured compounds."
    ]
   },
   {
@@ -1252,28 +1253,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def kinase_to_activity_numbers(uniprot_id, activity_dataframe):\n",
+    "def kinase_to_activity_numbers(uniprot_id, activity_df):\n",
     "    \"\"\"\n",
     "    Retrieve the three values for a given kinase.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
     "    uniprot_id : str\n",
-    "        The uniprot id of the kinase of interest, e.g. \"P00533\" for \"EGFR\".\n",
-    "\n",
-    "    activity_dataframe :  pd.DataFrame\n",
+    "        The UniProt ID of the kinase of interest, e.g. \"P00533\" for \"EGFR\".\n",
+    "    activity_df : pd.DataFrame\n",
     "        The dataframe with activity values for kinases.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
     "    tuple : (int, int, float)\n",
     "        The three metrics:\n",
-    "        1. The total number of measured compounds against the kinase\n",
+    "        1. The total number of measured compounds against the kinase.\n",
     "        2. The number of active compounds against the kinase.\n",
     "        3. The fraction of active compounds against the kinase.\n",
     "    \"\"\"\n",
-    "    kinase_data = activity_dataframe[activity_dataframe[\"UniprotID\"]\n",
-    "                                     == uniprot_id]\n",
+    "    kinase_data = activity_df[activity_df[\"UniprotID\"] == uniprot_id]\n",
     "    total_measured_compounds = len(kinase_data)\n",
     "    active_compounds = len(kinase_data[kinase_data[\"activity_binary\"] == 1])\n",
     "    if total_measured_compounds > 0:\n",
@@ -1288,7 +1287,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see what information we get for EGFR (which corresponds to the first row in the dataframe):"
+    "Let's see what information we get for EGFR (which corresponds to the first row in the DataFrame):"
    ]
   },
   {
@@ -1300,22 +1299,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Total number of measured compounds: \t5965 \n",
-      "Number of active compounds: \t\t3635 \n",
-      "Fraction of active compounds: \t\t0.61 \n",
+      "Total number of measured compounds:     5965 \n",
+      "Number of active compounds:             3635 \n",
+      "Fraction of active compounds:           0.61 \n",
       "\n"
      ]
     }
    ],
    "source": [
-    "uniprot_id = kinase_selection_df.uniprot_id[0]\n",
-    "EGFR_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
-    "print(f\"Total number of measured compounds: \\t\"\n",
-    "      f\"{EGFR_metrics[0]} \\n\"\n",
-    "      f\"Number of active compounds: \\t\\t\"\n",
-    "      f\"{EGFR_metrics[1]} \\n\"\n",
-    "      f\"Fraction of active compounds: \\t\\t\"\n",
-    "      f\"{EGFR_metrics[2]:.2f} \\n\")"
+    "uniprot_id = kinase_selection_df[\"uniprot_id\"][0]\n",
+    "egfr_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
+    "print(f\"{'Total number of measured compounds:' : <40}\"\n",
+    "      f\"{egfr_metrics[0]} \\n\"\n",
+    "      f\"{'Number of active compounds:' : <40}\"\n",
+    "      f\"{egfr_metrics[1]} \\n\"\n",
+    "      f\"{'Fraction of active compounds:' : <40}\"\n",
+    "      f\"{egfr_metrics[2]:.2f} \\n\")"
    ]
   },
   {
@@ -1331,30 +1330,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def promiscuity_table(kinase_selection, activity_dataframe):\n",
+    "def promiscuity_table(kinase_selection, activity_df):\n",
     "    \"\"\"\n",
     "    Create a table with all three values for all kinases.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    kinase_selection :  pd.DataFrame\n",
-    "        The dataframe for the chosen kinases.\n",
-    "    activity_dataframe :  pd.DataFrame\n",
-    "        The dataframe with activity values for kinases.\n",
+    "    kinase_selection : pd.DataFrame\n",
+    "        The DataFrame for the chosen kinases.\n",
+    "    activity_df : pd.DataFrame\n",
+    "        The DataFrame with activity values for kinases.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
     "    promiscuity_table : pd.DataFrame\n",
-    "        A dataframe with the kinases as rows and values as columns.\n",
+    "        A DataFrame with the kinases as rows and values as columns.\n",
     "    \"\"\"\n",
-    "    promiscuity_table = pd.DataFrame(index=kinase_selection.kinase_klifs,\n",
+    "    promiscuity_table = pd.DataFrame(index=kinase_selection[\"kinase_klifs\"],\n",
     "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
     "    promiscuity_table.index.name = None\n",
     "    promiscuity_table.columns.name = None\n",
     "\n",
-    "    for name, uniprot_id in zip(kinase_selection.kinase_klifs,\n",
-    "                                kinase_selection.uniprot_id):\n",
-    "        values = kinase_to_activity_numbers(uniprot_id, activity_dataframe)\n",
+    "    for name, uniprot_id in zip(kinase_selection[\"kinase_klifs\"],\n",
+    "                                kinase_selection[\"uniprot_id\"]):\n",
+    "        values = kinase_to_activity_numbers(uniprot_id, activity_df)\n",
     "        promiscuity_table.loc[name] = values\n",
     "    return promiscuity_table"
    ]
@@ -1488,46 +1487,46 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_a754e_row0_col2 {\n",
+       "#T_13971_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a754e_row1_col2 {\n",
+       "#T_13971_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a754e_row2_col2 {\n",
+       "#T_13971_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a754e_row3_col2 {\n",
+       "#T_13971_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a754e_row4_col2 {\n",
+       "#T_13971_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_a754e_row5_col2 {\n",
+       "#T_13971_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_a754e_row6_col2 {\n",
+       "#T_13971_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a754e_row7_col2 {\n",
+       "#T_13971_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a754e_row8_col2 {\n",
+       "#T_13971_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_a754e_\">\n",
+       "<table id=\"T_13971_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1538,64 +1537,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_a754e_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_a754e_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_a754e_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_13971_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_13971_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_13971_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_13971_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_a754e_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_a754e_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_a754e_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_13971_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_13971_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_13971_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_13971_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_a754e_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_a754e_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_a754e_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_13971_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_13971_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_13971_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_13971_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_a754e_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_a754e_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_a754e_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_13971_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_13971_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_13971_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_13971_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_a754e_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_a754e_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_a754e_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_13971_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_13971_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_13971_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_13971_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_a754e_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_a754e_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_a754e_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_13971_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_13971_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_13971_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_13971_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_a754e_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_a754e_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_a754e_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_13971_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_13971_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_13971_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_13971_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_a754e_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_a754e_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_a754e_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_13971_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_13971_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_13971_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_13971_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_a754e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_a754e_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_a754e_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_a754e_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_13971_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_13971_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_13971_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_13971_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cf82163a0>"
+       "<pandas.io.formats.style.Styler at 0x7f894d7f4100>"
       ]
      },
      "execution_count": 24,
@@ -1622,14 +1621,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kinase similarity"
+    "### Kinase similarity"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now investigate how we can use the similarity measure discussed in the theory to compare kinases. "
+    "We now investigate how we can use the similarity measure discussed in the _Theory_ part to compare kinases. "
    ]
   },
   {
@@ -1638,24 +1637,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def similarity_ligand_profile(uniprot_id1, uniprot_id2, activity_dataframe):\n",
+    "def similarity_ligand_profile(uniprot_id1, uniprot_id2, activity_df):\n",
     "    \"\"\"\n",
-    "    Compute the similarity between two kinases using ligand profiling.\n",
+    "    Compute the similarity between two kinases using ligand profiling data.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
     "    uniprot_id1 : str\n",
-    "        Uniprot id of first kinase of interest.\n",
+    "        UniProt ID of first kinase of interest.\n",
     "    uniprot_id2 : str\n",
-    "        Uniprot id of second kinase of interest.\n",
-    "    activity_dataframe :  pd.DataFrame\n",
-    "        The dataframe with activity values for kinases.\n",
+    "        UniProt ID of second kinase of interest.\n",
+    "    activity_df :  pd.DataFrame\n",
+    "        The DataFrame with activity values for kinases.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
     "    tuple : (int, int, float)\n",
     "        The three metrics:\n",
-    "        1. The total number of measured compounds against both kinases\n",
+    "        1. The total number of measured compounds against both kinases.\n",
     "        2. The number of active compounds against both kinases.\n",
     "        3. The metric for kinase similarity,\n",
     "                i.e. number of active compounds on both kinases\n",
@@ -1663,10 +1662,10 @@
     "    \"\"\"\n",
     "    if uniprot_id1 == uniprot_id2:\n",
     "        return kinase_to_activity_numbers(uniprot_id1,\n",
-    "                                          activity_dataframe)\n",
+    "                                          activity_df)\n",
     "    else:\n",
     "        # Data for the two kinases only\n",
-    "        reduced_data = activity_dataframe[activity_dataframe\n",
+    "        reduced_data = activity_df[activity_df\n",
     "                                          [\"UniprotID\"].isin([uniprot_id1,\n",
     "                                                              uniprot_id2])]\n",
     "\n",
@@ -1698,7 +1697,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at the values and similarity between EGFR and MET (which corresponds to the first and 7th row in the dataframe)"
+    "Let's look at the values and similarity between EGFR and MET (which corresponds to the first and 7th row in the DataFrame)."
    ]
   },
   {
@@ -1712,33 +1711,33 @@
      "text": [
       "Values for EGFR and MET: \n",
       "\n",
-      "Total number of measured compounds: \t92 \n",
-      "Number of active compounds: \t\t21 \n",
+      "Total number of measured compounds:               92 \n",
+      "Number of active compounds:                       21 \n",
       "Fraction of active compounds or \n",
-      "ligand profile similarity between EGFR and MET: \t\t0.23 \n",
+      "ligand profile similarity between EGFR and MET:   0.23 \n",
       "\n"
      ]
     }
    ],
    "source": [
-    "similarity_EGFR_MET = similarity_ligand_profile(kinase_selection_df.uniprot_id[0],\n",
-    "                                                kinase_selection_df.uniprot_id[7],\n",
+    "similarity_egfr_met = similarity_ligand_profile(kinase_selection_df[\"uniprot_id\"][0],\n",
+    "                                                kinase_selection_df[\"uniprot_id\"][7],\n",
     "                                                data)\n",
     "print(f\"Values for EGFR and MET: \\n\\n\"\n",
-    "      f\"Total number of measured compounds: \\t\"\n",
-    "      f\"{similarity_EGFR_MET[0]} \\n\"\n",
-    "      f\"Number of active compounds: \\t\\t\"\n",
-    "      f\"{similarity_EGFR_MET[1]} \\n\"\n",
+    "      f\"{'Total number of measured compounds:' : <50}\"\n",
+    "      f\"{similarity_egfr_met[0]} \\n\"\n",
+    "      f\"{'Number of active compounds:' : <50}\"\n",
+    "      f\"{similarity_egfr_met[1]} \\n\"\n",
     "      f\"Fraction of active compounds or \\n\"\n",
-    "      f\"ligand profile similarity between EGFR and MET: \\t\\t\"\n",
-    "      f\"{similarity_EGFR_MET[2]:.2f} \\n\")"
+    "      f\"{'ligand profile similarity between EGFR and MET:' : <50}\"\n",
+    "      f\"{similarity_egfr_met[2]:.2f} \\n\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as kinase matrix"
+    "#### Visualize similarity as kinase matrix"
    ]
   },
   {
@@ -1948,7 +1947,7 @@
     "        total, actives, _ = similarity_ligand_profile(uniprot_id1,\n",
     "                                                      uniprot_id2,\n",
     "                                                      data)\n",
-    "        integer_ratio = f\"{str(actives)}/{str(total)}\"\n",
+    "        integer_ratio = f\"{actives}/{total}\"\n",
     "        kinase_counts_matrix[klifs_name1][klifs_name2] = integer_ratio\n",
     "kinase_counts_matrix"
    ]
@@ -2182,208 +2181,208 @@
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        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row3_col6 {\n",
+       "#T_a4349_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row3_col7 {\n",
+       "#T_a4349_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row3_col8 {\n",
+       "#T_a4349_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row4_col0 {\n",
+       "#T_a4349_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row4_col6 {\n",
+       "#T_a4349_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row4_col7, #T_06b9c_row5_col8 {\n",
+       "#T_a4349_row4_col7, #T_a4349_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row5_col0 {\n",
+       "#T_a4349_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row5_col1 {\n",
+       "#T_a4349_row5_col1 {\n",
        "  background-color: #bddcbd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row5_col3 {\n",
+       "#T_a4349_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row5_col5 {\n",
+       "#T_a4349_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row5_col6 {\n",
+       "#T_a4349_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row5_col7 {\n",
+       "#T_a4349_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col0 {\n",
+       "#T_a4349_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col1 {\n",
+       "#T_a4349_row6_col1 {\n",
        "  background-color: #bcdcbc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col3 {\n",
+       "#T_a4349_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col4 {\n",
+       "#T_a4349_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row6_col5, #T_06b9c_row7_col8, #T_06b9c_row8_col5, #T_06b9c_row8_col7 {\n",
+       "#T_a4349_row6_col5, #T_a4349_row7_col8, #T_a4349_row8_col5, #T_a4349_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col7 {\n",
+       "#T_a4349_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row6_col8 {\n",
+       "#T_a4349_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row7_col3 {\n",
+       "#T_a4349_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row7_col4 {\n",
+       "#T_a4349_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row7_col5 {\n",
+       "#T_a4349_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row7_col6 {\n",
+       "#T_a4349_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row8_col0 {\n",
+       "#T_a4349_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row8_col1 {\n",
+       "#T_a4349_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_06b9c_row8_col3 {\n",
+       "#T_a4349_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row8_col4 {\n",
+       "#T_a4349_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_06b9c_row8_col6 {\n",
+       "#T_a4349_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_06b9c_\">\n",
+       "<table id=\"T_a4349_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2400,118 +2399,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_06b9c_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_06b9c_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_06b9c_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_06b9c_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_06b9c_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_06b9c_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_06b9c_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_06b9c_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_06b9c_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_06b9c_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_06b9c_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_06b9c_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_06b9c_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_06b9c_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_06b9c_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_06b9c_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_06b9c_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_06b9c_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_06b9c_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_06b9c_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_06b9c_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_06b9c_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_06b9c_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_06b9c_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_06b9c_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_06b9c_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_06b9c_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_06b9c_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_06b9c_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_06b9c_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_06b9c_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_06b9c_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_06b9c_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_06b9c_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_06b9c_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_06b9c_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_06b9c_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_06b9c_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_06b9c_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_06b9c_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_06b9c_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_06b9c_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_06b9c_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_06b9c_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_06b9c_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_06b9c_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_06b9c_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_06b9c_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_06b9c_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_06b9c_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_06b9c_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_06b9c_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_06b9c_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_06b9c_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_06b9c_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_06b9c_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_06b9c_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_06b9c_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_06b9c_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_06b9c_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_06b9c_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_06b9c_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_06b9c_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_06b9c_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_06b9c_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_06b9c_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_06b9c_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_06b9c_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_06b9c_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_06b9c_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_06b9c_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_06b9c_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_06b9c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_06b9c_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_06b9c_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_06b9c_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_06b9c_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_06b9c_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_06b9c_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_06b9c_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_06b9c_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_06b9c_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_a4349_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_a4349_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_a4349_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_a4349_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_a4349_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_a4349_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_a4349_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_a4349_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_a4349_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_a4349_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_a4349_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_a4349_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_a4349_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_a4349_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_a4349_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_a4349_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_a4349_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_a4349_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_a4349_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_a4349_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_a4349_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_a4349_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_a4349_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_a4349_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_a4349_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_a4349_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_a4349_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_a4349_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_a4349_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_a4349_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_a4349_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_a4349_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_a4349_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_a4349_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_a4349_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_a4349_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_a4349_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_a4349_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_a4349_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_a4349_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_a4349_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_a4349_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_a4349_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_a4349_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_a4349_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_a4349_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_a4349_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_a4349_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_a4349_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_a4349_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_a4349_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_a4349_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_a4349_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_a4349_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_a4349_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_a4349_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_a4349_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_a4349_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_a4349_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_a4349_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_a4349_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_a4349_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_a4349_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_a4349_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_a4349_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_a4349_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_a4349_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_a4349_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_a4349_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_a4349_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_a4349_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_a4349_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_a4349_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4349_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_a4349_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_a4349_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_a4349_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_a4349_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_a4349_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_a4349_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_a4349_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_a4349_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_a4349_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cf84fd0d0>"
+       "<pandas.io.formats.style.Styler at 0x7f894d7f4c40>"
       ]
      },
      "execution_count": 30,
@@ -2547,212 +2546,212 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_f289a_row0_col0, #T_f289a_row1_col1, #T_f289a_row2_col2, #T_f289a_row3_col5, #T_f289a_row4_col3, #T_f289a_row4_col4, #T_f289a_row6_col6, #T_f289a_row7_col7, #T_f289a_row8_col8 {\n",
+       "#T_d4411_row0_col0, #T_d4411_row1_col1, #T_d4411_row2_col2, #T_d4411_row3_col5, #T_d4411_row4_col3, #T_d4411_row4_col4, #T_d4411_row6_col6, #T_d4411_row7_col7, #T_d4411_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row0_col1 {\n",
+       "#T_d4411_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row0_col2 {\n",
+       "#T_d4411_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row0_col3, #T_f289a_row7_col0 {\n",
+       "#T_d4411_row0_col3, #T_d4411_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row0_col4, #T_f289a_row3_col0, #T_f289a_row4_col2, #T_f289a_row5_col2 {\n",
+       "#T_d4411_row0_col4, #T_d4411_row3_col0, #T_d4411_row4_col2, #T_d4411_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row0_col5 {\n",
+       "#T_d4411_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row0_col6, #T_f289a_row4_col1 {\n",
+       "#T_d4411_row0_col6, #T_d4411_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row0_col7, #T_f289a_row3_col2 {\n",
+       "#T_d4411_row0_col7, #T_d4411_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row0_col8 {\n",
+       "#T_d4411_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col0 {\n",
+       "#T_d4411_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row1_col2, #T_f289a_row2_col0, #T_f289a_row2_col1, #T_f289a_row2_col3, #T_f289a_row2_col6, #T_f289a_row2_col7, #T_f289a_row2_col8, #T_f289a_row4_col5, #T_f289a_row5_col4, #T_f289a_row6_col2, #T_f289a_row7_col2, #T_f289a_row8_col2 {\n",
+       "#T_d4411_row1_col2, #T_d4411_row2_col0, #T_d4411_row2_col1, #T_d4411_row2_col3, #T_d4411_row2_col6, #T_d4411_row2_col7, #T_d4411_row2_col8, #T_d4411_row4_col5, #T_d4411_row5_col4, #T_d4411_row6_col2, #T_d4411_row7_col2, #T_d4411_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col3 {\n",
+       "#T_d4411_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col4 {\n",
+       "#T_d4411_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col5 {\n",
+       "#T_d4411_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col6 {\n",
+       "#T_d4411_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col7 {\n",
+       "#T_d4411_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row1_col8 {\n",
+       "#T_d4411_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row2_col4 {\n",
+       "#T_d4411_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row2_col5 {\n",
+       "#T_d4411_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row3_col1 {\n",
+       "#T_d4411_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row3_col3 {\n",
+       "#T_d4411_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row3_col4, #T_f289a_row4_col8 {\n",
+       "#T_d4411_row3_col4, #T_d4411_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row3_col6 {\n",
+       "#T_d4411_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row3_col7 {\n",
+       "#T_d4411_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row3_col8 {\n",
+       "#T_d4411_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row4_col0 {\n",
+       "#T_d4411_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row4_col6 {\n",
+       "#T_d4411_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row4_col7, #T_f289a_row5_col8 {\n",
+       "#T_d4411_row4_col7, #T_d4411_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row5_col0 {\n",
+       "#T_d4411_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row5_col1 {\n",
+       "#T_d4411_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row5_col3 {\n",
+       "#T_d4411_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row5_col5 {\n",
+       "#T_d4411_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row5_col6 {\n",
+       "#T_d4411_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row5_col7 {\n",
+       "#T_d4411_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col0 {\n",
+       "#T_d4411_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col1 {\n",
+       "#T_d4411_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col3 {\n",
+       "#T_d4411_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col4 {\n",
+       "#T_d4411_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row6_col5, #T_f289a_row7_col8, #T_f289a_row8_col5, #T_f289a_row8_col7 {\n",
+       "#T_d4411_row6_col5, #T_d4411_row7_col8, #T_d4411_row8_col5, #T_d4411_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col7 {\n",
+       "#T_d4411_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row6_col8 {\n",
+       "#T_d4411_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row7_col1 {\n",
+       "#T_d4411_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row7_col3 {\n",
+       "#T_d4411_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row7_col4 {\n",
+       "#T_d4411_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row7_col5 {\n",
+       "#T_d4411_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row7_col6 {\n",
+       "#T_d4411_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row8_col0 {\n",
+       "#T_d4411_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row8_col1 {\n",
+       "#T_d4411_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_f289a_row8_col3 {\n",
+       "#T_d4411_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row8_col4 {\n",
+       "#T_d4411_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_f289a_row8_col6 {\n",
+       "#T_d4411_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_f289a_\">\n",
+       "<table id=\"T_d4411_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2769,118 +2768,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_f289a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_f289a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_f289a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_f289a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_f289a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_f289a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_f289a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_f289a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_f289a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_f289a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_f289a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_f289a_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_f289a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_f289a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_f289a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_f289a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_f289a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_f289a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_f289a_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_f289a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_f289a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_f289a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_f289a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_f289a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_f289a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_f289a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_f289a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_f289a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_f289a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_f289a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_f289a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_f289a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_f289a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_f289a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_f289a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_f289a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_f289a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_f289a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_f289a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_f289a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_f289a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_f289a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_f289a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_f289a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_f289a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_f289a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_f289a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_f289a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_f289a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_f289a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_f289a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_f289a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_f289a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_f289a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_f289a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_f289a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_f289a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_f289a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_f289a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_f289a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_f289a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_f289a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_f289a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_f289a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_f289a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_f289a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f289a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_f289a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_f289a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_f289a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_f289a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_f289a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_f289a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_f289a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_f289a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_f289a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_d4411_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d4411_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_d4411_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_d4411_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_d4411_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_d4411_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_d4411_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_d4411_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_d4411_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_d4411_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d4411_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_d4411_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_d4411_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_d4411_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_d4411_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_d4411_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_d4411_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_d4411_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_d4411_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_d4411_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_d4411_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_d4411_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_d4411_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_d4411_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_d4411_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_d4411_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_d4411_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_d4411_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_d4411_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_d4411_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_d4411_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_d4411_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_d4411_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_d4411_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_d4411_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_d4411_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_d4411_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_d4411_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_d4411_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_d4411_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_d4411_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_d4411_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_d4411_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_d4411_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_d4411_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_d4411_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_d4411_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_d4411_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_d4411_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_d4411_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_d4411_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_d4411_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_d4411_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_d4411_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_d4411_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_d4411_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_d4411_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_d4411_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_d4411_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_d4411_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_d4411_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_d4411_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_d4411_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_d4411_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_d4411_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_d4411_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_d4411_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d4411_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d4411_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_d4411_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_d4411_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_d4411_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_d4411_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_d4411_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_d4411_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_d4411_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_d4411_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cf8505940>"
+       "<pandas.io.formats.style.Styler at 0x7f894d7f4760>"
       ]
      },
      "execution_count": 31,
@@ -2900,7 +2899,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase similarity matrix"
+    "#### Save kinase similarity matrix"
    ]
   },
   {
@@ -2917,7 +2916,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kinase distance matrix"
+    "### Kinase distance matrix"
    ]
   },
   {
@@ -2980,204 +2979,204 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_8b8a8_row0_col0, #T_8b8a8_row1_col1, #T_8b8a8_row2_col2, #T_8b8a8_row3_col3, #T_8b8a8_row4_col4, #T_8b8a8_row5_col5, #T_8b8a8_row6_col6, #T_8b8a8_row7_col7, #T_8b8a8_row8_col8 {\n",
+       "#T_3e2d2_row0_col0, #T_3e2d2_row1_col1, #T_3e2d2_row2_col2, #T_3e2d2_row3_col3, #T_3e2d2_row4_col4, #T_3e2d2_row5_col5, #T_3e2d2_row6_col6, #T_3e2d2_row7_col7, #T_3e2d2_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row0_col1 {\n",
+       "#T_3e2d2_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row0_col2 {\n",
+       "#T_3e2d2_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col3 {\n",
+       "#T_3e2d2_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col4 {\n",
+       "#T_3e2d2_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col5 {\n",
+       "#T_3e2d2_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col6 {\n",
+       "#T_3e2d2_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col7 {\n",
+       "#T_3e2d2_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row0_col8 {\n",
+       "#T_3e2d2_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col0 {\n",
+       "#T_3e2d2_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row1_col2, #T_8b8a8_row2_col0, #T_8b8a8_row2_col1, #T_8b8a8_row2_col3, #T_8b8a8_row2_col6, #T_8b8a8_row2_col7, #T_8b8a8_row2_col8, #T_8b8a8_row4_col5, #T_8b8a8_row5_col4, #T_8b8a8_row6_col2, #T_8b8a8_row7_col2, #T_8b8a8_row8_col2 {\n",
+       "#T_3e2d2_row1_col2, #T_3e2d2_row2_col0, #T_3e2d2_row2_col1, #T_3e2d2_row2_col3, #T_3e2d2_row2_col6, #T_3e2d2_row2_col7, #T_3e2d2_row2_col8, #T_3e2d2_row4_col5, #T_3e2d2_row5_col4, #T_3e2d2_row6_col2, #T_3e2d2_row7_col2, #T_3e2d2_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col3 {\n",
+       "#T_3e2d2_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col4, #T_8b8a8_row3_col2, #T_8b8a8_row6_col0 {\n",
+       "#T_3e2d2_row1_col4, #T_3e2d2_row3_col2, #T_3e2d2_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col5 {\n",
+       "#T_3e2d2_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col6, #T_8b8a8_row6_col1 {\n",
+       "#T_3e2d2_row1_col6, #T_3e2d2_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col7, #T_8b8a8_row6_col5, #T_8b8a8_row7_col1, #T_8b8a8_row8_col5 {\n",
+       "#T_3e2d2_row1_col7, #T_3e2d2_row6_col5, #T_3e2d2_row7_col1, #T_3e2d2_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row1_col8, #T_8b8a8_row8_col1 {\n",
+       "#T_3e2d2_row1_col8, #T_3e2d2_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row2_col4, #T_8b8a8_row2_col5 {\n",
+       "#T_3e2d2_row2_col4, #T_3e2d2_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row3_col0 {\n",
+       "#T_3e2d2_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row3_col1 {\n",
+       "#T_3e2d2_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row3_col4 {\n",
+       "#T_3e2d2_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row3_col5 {\n",
+       "#T_3e2d2_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row3_col6 {\n",
+       "#T_3e2d2_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row3_col7 {\n",
+       "#T_3e2d2_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row3_col8 {\n",
+       "#T_3e2d2_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row4_col0 {\n",
+       "#T_3e2d2_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row4_col1 {\n",
+       "#T_3e2d2_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row4_col2, #T_8b8a8_row5_col2 {\n",
+       "#T_3e2d2_row4_col2, #T_3e2d2_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row4_col3 {\n",
+       "#T_3e2d2_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row4_col6 {\n",
+       "#T_3e2d2_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row4_col7 {\n",
+       "#T_3e2d2_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row4_col8 {\n",
+       "#T_3e2d2_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row5_col0 {\n",
+       "#T_3e2d2_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row5_col1 {\n",
+       "#T_3e2d2_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row5_col3 {\n",
+       "#T_3e2d2_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row5_col6, #T_8b8a8_row5_col8 {\n",
+       "#T_3e2d2_row5_col6, #T_3e2d2_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row5_col7 {\n",
+       "#T_3e2d2_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row6_col3 {\n",
+       "#T_3e2d2_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row6_col4 {\n",
+       "#T_3e2d2_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8b8a8_row6_col7, #T_8b8a8_row7_col6 {\n",
+       "#T_3e2d2_row6_col7, #T_3e2d2_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row6_col8, #T_8b8a8_row8_col6 {\n",
+       "#T_3e2d2_row6_col8, #T_3e2d2_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row7_col0 {\n",
+       "#T_3e2d2_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row7_col3 {\n",
+       "#T_3e2d2_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row7_col4 {\n",
+       "#T_3e2d2_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row7_col5 {\n",
+       "#T_3e2d2_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row7_col8, #T_8b8a8_row8_col7 {\n",
+       "#T_3e2d2_row7_col8, #T_3e2d2_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row8_col0 {\n",
+       "#T_3e2d2_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row8_col3 {\n",
+       "#T_3e2d2_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8b8a8_row8_col4 {\n",
+       "#T_3e2d2_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_8b8a8_\">\n",
+       "<table id=\"T_3e2d2_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3194,118 +3193,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_8b8a8_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_8b8a8_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_8b8a8_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_8b8a8_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_8b8a8_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_8b8a8_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_8b8a8_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_8b8a8_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_8b8a8_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_8b8a8_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_8b8a8_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_8b8a8_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_8b8a8_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_8b8a8_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_8b8a8_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_8b8a8_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_8b8a8_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_8b8a8_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_8b8a8_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_8b8a8_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_8b8a8_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_8b8a8_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_8b8a8_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_8b8a8_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_8b8a8_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_8b8a8_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_8b8a8_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_8b8a8_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_8b8a8_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_8b8a8_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_8b8a8_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_8b8a8_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_8b8a8_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_8b8a8_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_8b8a8_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_8b8a8_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_8b8a8_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_8b8a8_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_8b8a8_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_8b8a8_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_8b8a8_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_8b8a8_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_8b8a8_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_8b8a8_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_8b8a8_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_8b8a8_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_8b8a8_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_8b8a8_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_8b8a8_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_8b8a8_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_8b8a8_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_8b8a8_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_8b8a8_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_8b8a8_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_8b8a8_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_8b8a8_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_8b8a8_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_8b8a8_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_8b8a8_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_8b8a8_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_8b8a8_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_8b8a8_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_8b8a8_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_8b8a8_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_8b8a8_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_8b8a8_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_8b8a8_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_8b8a8_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_8b8a8_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_3e2d2_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_3e2d2_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_3e2d2_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_3e2d2_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_3e2d2_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_3e2d2_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_3e2d2_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_3e2d2_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_3e2d2_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_3e2d2_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_3e2d2_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_3e2d2_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_3e2d2_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_3e2d2_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_3e2d2_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_3e2d2_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_3e2d2_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_3e2d2_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_3e2d2_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_3e2d2_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_3e2d2_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_3e2d2_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_3e2d2_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_3e2d2_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_3e2d2_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_3e2d2_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_3e2d2_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_3e2d2_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_3e2d2_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_3e2d2_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_3e2d2_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_3e2d2_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_3e2d2_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_3e2d2_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_3e2d2_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_3e2d2_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_3e2d2_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_3e2d2_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_3e2d2_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_3e2d2_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_3e2d2_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_3e2d2_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_3e2d2_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_3e2d2_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_3e2d2_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_3e2d2_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_3e2d2_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_3e2d2_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_3e2d2_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_3e2d2_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_3e2d2_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_3e2d2_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_3e2d2_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_3e2d2_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_3e2d2_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_3e2d2_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_3e2d2_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_3e2d2_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_3e2d2_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_3e2d2_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3e2d2_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_3e2d2_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_3e2d2_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_3e2d2_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_3e2d2_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_3e2d2_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_3e2d2_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_3e2d2_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_3e2d2_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_3e2d2_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cf87c8250>"
+       "<pandas.io.formats.style.Styler at 0x7f894d7f4a90>"
       ]
      },
      "execution_count": 36,
@@ -3323,7 +3322,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase distance matrix"
+    "#### Save kinase distance matrix"
    ]
   },
   {
@@ -3344,7 +3343,7 @@
     "\n",
     "In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
-    "The kinase distance matrix above will be reloaded in Talktorial T028, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
+    "The kinase distance matrix above will be reloaded in __Talktorial T028__, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
   },
   {
@@ -3375,7 +3374,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From 149135cf2191314e2619ae07f55532ef1e5eea02 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 10:23:46 +0200
Subject: [PATCH 095/158] :pencil2: fix typos.

---
 .../talktorials/T023_what_is_a_kinase/talktorial.ipynb    | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 2dc39a8e..0c4d9bbc 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -95,8 +95,8 @@
     "Despite of decades of kinase research, there are still many open challenges:\n",
     "\n",
     "- A large fraction of the kinome is un-/underexplored.\n",
-    "- Many kinase inhibitors are promiscous binders causing off-target effects or enabling polypharmacology.\n",
-    "- There are occurences of drug resistances due to mutations."
+    "- Many kinase inhibitors are promiscuous binders causing off-target effects or enabling polypharmacology.\n",
+    "- There are occurrences of drug resistances due to mutations."
    ]
   },
   {
@@ -663,7 +663,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d078365468114954b9f1be5827716dcf",
+       "model_id": "96fdf93d03174ccfaab19641cdee6f65",
        "version_major": 2,
        "version_minor": 0
       },
@@ -675,7 +675,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f1165449daf74d59a36a30892bd018dc",
+       "model_id": "2524da53259f468ab5069c83e7492d81",
        "version_major": 2,
        "version_minor": 0
       },

From dbd94e859168fdc5cebcc23da9abbd8e5f0a2a2d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 10:29:20 +0200
Subject: [PATCH 096/158] T027: Satisfy black-nb

---
 .../talktorial.ipynb                          | 164 +++++++++---------
 1 file changed, 83 insertions(+), 81 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 93cfc200..5c95dbb9 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -353,8 +353,11 @@
     }
    ],
    "source": [
-    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/\\\n",
-    "data/kinase_selection.csv\")\n",
+    "kinase_selection_df = pd.read_csv(\n",
+    "    HERE\n",
+    "    / \"../T023_what_is_a_kinase/\\\n",
+    "data/kinase_selection.csv\"\n",
+    ")\n",
     "kinase_selection_df"
    ]
   },
@@ -624,8 +627,10 @@
     }
    ],
    "source": [
-    "print(f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
-    "      f\"Units: {set(data['activities.standard_units'])}\")"
+    "print(\n",
+    "    f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
+    "    f\"Units: {set(data['activities.standard_units'])}\"\n",
+    ")"
    ]
   },
   {
@@ -686,11 +691,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = data[[\"compound_structures.canonical_smiles\",\n",
-    "             \"activities.standard_value\",\n",
-    "             \"UniprotID\"]]\n",
-    "data = data.rename(columns={\"compound_structures.canonical_smiles\": \"smiles\",\n",
-    "                            \"activities.standard_value\": \"pIC50\"})"
+    "data = data[[\"compound_structures.canonical_smiles\", \"activities.standard_value\", \"UniprotID\"]]\n",
+    "data = data.rename(\n",
+    "    columns={\n",
+    "        \"compound_structures.canonical_smiles\": \"smiles\",\n",
+    "        \"activities.standard_value\": \"pIC50\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
@@ -1055,8 +1062,7 @@
     }
    ],
    "source": [
-    "data = data.groupby([\"UniprotID\",\n",
-    "                     \"smiles\"])[\"pIC50\"].max().reset_index()\n",
+    "data = data.groupby([\"UniprotID\", \"smiles\"])[\"pIC50\"].max().reset_index()\n",
     "data.head()"
    ]
   },
@@ -1118,8 +1124,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data[\"activity_binary\"] = data[\"pIC50\"].apply(binarize_pic50,\n",
-    "                                                       args=(cutoff, ))"
+    "data[\"activity_binary\"] = data[\"pIC50\"].apply(binarize_pic50, args=(cutoff,))"
    ]
   },
   {
@@ -1276,7 +1281,7 @@
     "    total_measured_compounds = len(kinase_data)\n",
     "    active_compounds = len(kinase_data[kinase_data[\"activity_binary\"] == 1])\n",
     "    if total_measured_compounds > 0:\n",
-    "        fraction = active_compounds/total_measured_compounds\n",
+    "        fraction = active_compounds / total_measured_compounds\n",
     "    else:\n",
     "        print(\"No compounds were measured for this kinase.\")\n",
     "        fraction = np.nan\n",
@@ -1309,12 +1314,14 @@
    "source": [
     "uniprot_id = kinase_selection_df[\"uniprot_id\"][0]\n",
     "egfr_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
-    "print(f\"{'Total number of measured compounds:' : <40}\"\n",
-    "      f\"{egfr_metrics[0]} \\n\"\n",
-    "      f\"{'Number of active compounds:' : <40}\"\n",
-    "      f\"{egfr_metrics[1]} \\n\"\n",
-    "      f\"{'Fraction of active compounds:' : <40}\"\n",
-    "      f\"{egfr_metrics[2]:.2f} \\n\")"
+    "print(\n",
+    "    f\"{'Total number of measured compounds:' : <40}\"\n",
+    "    f\"{egfr_metrics[0]} \\n\"\n",
+    "    f\"{'Number of active compounds:' : <40}\"\n",
+    "    f\"{egfr_metrics[1]} \\n\"\n",
+    "    f\"{'Fraction of active compounds:' : <40}\"\n",
+    "    f\"{egfr_metrics[2]:.2f} \\n\"\n",
+    ")"
    ]
   },
   {
@@ -1346,13 +1353,13 @@
     "    promiscuity_table : pd.DataFrame\n",
     "        A DataFrame with the kinases as rows and values as columns.\n",
     "    \"\"\"\n",
-    "    promiscuity_table = pd.DataFrame(index=kinase_selection[\"kinase_klifs\"],\n",
-    "                                     columns=[\"total\", \"actives\", \"fraction\"])\n",
+    "    promiscuity_table = pd.DataFrame(\n",
+    "        index=kinase_selection[\"kinase_klifs\"], columns=[\"total\", \"actives\", \"fraction\"]\n",
+    "    )\n",
     "    promiscuity_table.index.name = None\n",
     "    promiscuity_table.columns.name = None\n",
     "\n",
-    "    for name, uniprot_id in zip(kinase_selection[\"kinase_klifs\"],\n",
-    "                                kinase_selection[\"uniprot_id\"]):\n",
+    "    for name, uniprot_id in zip(kinase_selection[\"kinase_klifs\"], kinase_selection[\"uniprot_id\"]):\n",
     "        values = kinase_to_activity_numbers(uniprot_id, activity_df)\n",
     "        promiscuity_table.loc[name] = values\n",
     "    return promiscuity_table"
@@ -1603,11 +1610,11 @@
     }
    ],
    "source": [
-    "kinase_promiscuity_df.style.\\\n",
-    "    format(\"{:.3f}\", subset=[\"fraction\"]).\\\n",
-    "    background_gradient(cmap='Purples', subset=[\"fraction\"]).\\\n",
-    "    highlight_min(color=\"yellow\", axis=None, subset=[\"fraction\"]).\\\n",
-    "    highlight_max(color=\"red\", subset=[\"fraction\"])"
+    "kinase_promiscuity_df.style.format(\"{:.3f}\", subset=[\"fraction\"]).background_gradient(\n",
+    "    cmap=\"Purples\", subset=[\"fraction\"]\n",
+    ").highlight_min(color=\"yellow\", axis=None, subset=[\"fraction\"]).highlight_max(\n",
+    "    color=\"red\", subset=[\"fraction\"]\n",
+    ")"
    ]
   },
   {
@@ -1661,13 +1668,10 @@
     "                over number of measured compounds on both kinases.\n",
     "    \"\"\"\n",
     "    if uniprot_id1 == uniprot_id2:\n",
-    "        return kinase_to_activity_numbers(uniprot_id1,\n",
-    "                                          activity_df)\n",
+    "        return kinase_to_activity_numbers(uniprot_id1, activity_df)\n",
     "    else:\n",
     "        # Data for the two kinases only\n",
-    "        reduced_data = activity_df[activity_df\n",
-    "                                          [\"UniprotID\"].isin([uniprot_id1,\n",
-    "                                                              uniprot_id2])]\n",
+    "        reduced_data = activity_df[activity_df[\"UniprotID\"].isin([uniprot_id1, uniprot_id2])]\n",
     "\n",
     "        # Look at active compounds only\n",
     "        active_entries = reduced_data[reduced_data[\"activity_binary\"] == 1]\n",
@@ -1682,11 +1686,12 @@
     "        measured_compounds_on_both = compounds[compounds == 2].shape[0]\n",
     "\n",
     "        if measured_compounds_on_both > 0:\n",
-    "            fraction = (active_compounds_on_both /\n",
-    "                        measured_compounds_on_both)\n",
+    "            fraction = active_compounds_on_both / measured_compounds_on_both\n",
     "        else:\n",
-    "            print(f\"No compounds were measured on both kinases, \"\n",
-    "                  f\"namely {uniprot_id1} and {uniprot_id2}.\")\n",
+    "            print(\n",
+    "                f\"No compounds were measured on both kinases, \"\n",
+    "                f\"namely {uniprot_id1} and {uniprot_id2}.\"\n",
+    "            )\n",
     "            fraction = np.nan\n",
     "            measured_compounds_on_both = np.nan\n",
     "            active_compounds_on_both = np.nan\n",
@@ -1720,17 +1725,19 @@
     }
    ],
    "source": [
-    "similarity_egfr_met = similarity_ligand_profile(kinase_selection_df[\"uniprot_id\"][0],\n",
-    "                                                kinase_selection_df[\"uniprot_id\"][7],\n",
-    "                                                data)\n",
-    "print(f\"Values for EGFR and MET: \\n\\n\"\n",
-    "      f\"{'Total number of measured compounds:' : <50}\"\n",
-    "      f\"{similarity_egfr_met[0]} \\n\"\n",
-    "      f\"{'Number of active compounds:' : <50}\"\n",
-    "      f\"{similarity_egfr_met[1]} \\n\"\n",
-    "      f\"Fraction of active compounds or \\n\"\n",
-    "      f\"{'ligand profile similarity between EGFR and MET:' : <50}\"\n",
-    "      f\"{similarity_egfr_met[2]:.2f} \\n\")"
+    "similarity_egfr_met = similarity_ligand_profile(\n",
+    "    kinase_selection_df[\"uniprot_id\"][0], kinase_selection_df[\"uniprot_id\"][7], data\n",
+    ")\n",
+    "print(\n",
+    "    f\"Values for EGFR and MET: \\n\\n\"\n",
+    "    f\"{'Total number of measured compounds:' : <50}\"\n",
+    "    f\"{similarity_egfr_met[0]} \\n\"\n",
+    "    f\"{'Number of active compounds:' : <50}\"\n",
+    "    f\"{similarity_egfr_met[1]} \\n\"\n",
+    "    f\"Fraction of active compounds or \\n\"\n",
+    "    f\"{'ligand profile similarity between EGFR and MET:' : <50}\"\n",
+    "    f\"{similarity_egfr_met[2]:.2f} \\n\"\n",
+    ")"
    ]
   },
   {
@@ -1935,18 +1942,19 @@
     }
    ],
    "source": [
-    "kinase_counts_matrix = pd.DataFrame(index=kinase_selection_df.kinase_klifs,\n",
-    "                                        columns=kinase_selection_df.kinase_klifs)\n",
+    "kinase_counts_matrix = pd.DataFrame(\n",
+    "    index=kinase_selection_df.kinase_klifs, columns=kinase_selection_df.kinase_klifs\n",
+    ")\n",
     "kinase_counts_matrix.index.name = None\n",
     "kinase_counts_matrix.columns.name = None\n",
     "\n",
     "for i, (uniprot_id1, klifs_name1) in enumerate(\n",
-    "    zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)):\n",
+    "    zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)\n",
+    "):\n",
     "    for j, (uniprot_id2, klifs_name2) in enumerate(\n",
-    "        zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)):\n",
-    "        total, actives, _ = similarity_ligand_profile(uniprot_id1,\n",
-    "                                                      uniprot_id2,\n",
-    "                                                      data)\n",
+    "        zip(kinase_selection_df.uniprot_id, kinase_selection_df.kinase_klifs)\n",
+    "    ):\n",
+    "        total, actives, _ = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)\n",
     "        integer_ratio = f\"{actives}/{total}\"\n",
     "        kinase_counts_matrix[klifs_name1][klifs_name2] = integer_ratio\n",
     "kinase_counts_matrix"
@@ -1974,14 +1982,12 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix = np.zeros((len(kinase_selection_df),\n",
-    "                                     len(kinase_selection_df)))\n",
+    "kinase_similarity_matrix = np.zeros((len(kinase_selection_df), len(kinase_selection_df)))\n",
     "for i, uniprot_id1 in enumerate(kinase_selection_df.uniprot_id):\n",
     "    for j, uniprot_id2 in enumerate(kinase_selection_df.uniprot_id):\n",
-    "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(\n",
-    "            uniprot_id1,\n",
-    "            uniprot_id2,\n",
-    "            data)[2]"
+    "        kinase_similarity_matrix[i, j] = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)[\n",
+    "            2\n",
+    "        ]"
    ]
   },
   {
@@ -2164,9 +2170,11 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                        index=kinase_selection_df.kinase_klifs,\n",
-    "                                        columns=kinase_selection_df.kinase_klifs)\n",
+    "kinase_similarity_matrix_df = pd.DataFrame(\n",
+    "    data=kinase_similarity_matrix,\n",
+    "    index=kinase_selection_df.kinase_klifs,\n",
+    "    columns=kinase_selection_df.kinase_klifs,\n",
+    ")\n",
     "kinase_similarity_matrix_df.index.name = None\n",
     "kinase_similarity_matrix_df.columns.name = None\n",
     "kinase_similarity_matrix_df"
@@ -2521,9 +2529,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_similarity_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -2890,9 +2896,7 @@
    "source": [
     "kinase_similarity_matrix_df = kinase_similarity_matrix_df.fillna(0)\n",
     "\n",
-    "kinase_similarity_matrix_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_similarity_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -2908,8 +2912,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_similarity_matrix_df.to_csv(DATA /\n",
-    "                                \"kinase_similarity_matrix_ligand_profile.csv\")"
+    "kinase_similarity_matrix_df.to_csv(DATA / \"kinase_similarity_matrix_ligand_profile.csv\")"
    ]
   },
   {
@@ -2940,9 +2943,11 @@
     }
    ],
    "source": [
-    "print(f\"The values of the similarity matrix lie between: \"\n",
-    "      f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
-    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")"
+    "print(\n",
+    "    f\"The values of the similarity matrix lie between: \"\n",
+    "    f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
+    "    f\" and {kinase_similarity_matrix_df.max().max():.2f}\"\n",
+    ")"
    ]
   },
   {
@@ -3313,9 +3318,7 @@
     }
    ],
    "source": [
-    "kinase_distance_matrix_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -3331,8 +3334,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix_df.to_csv(DATA /\n",
-    "                              \"kinase_distance_matrix_ligand_profile.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_ligand_profile.csv\")"
    ]
   },
   {

From c333d490bfbbd1278e557c1e74c20a414ba144d6 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 10:32:52 +0200
Subject: [PATCH 097/158] change ** to __ for bold markdown.

---
 .../talktorials/T023_what_is_a_kinase/talktorial.ipynb          | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 0c4d9bbc..32b1644b 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -336,7 +336,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "_Note_: You can run the kinase similarity **Talktorials T024-T028** with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+    "_Note_: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
    ]
   },
   {

From 1e4f3af2606b1f28d3726012b789663fcdc6fb16 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 10:49:59 +0200
Subject: [PATCH 098/158] Minor edits in theory.

---
 .../talktorial.ipynb                              | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 7af60c5a..1c1f8afa 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -10,8 +10,8 @@
     "\n",
     "Authors:\n",
     "\n",
-    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
    ]
   },
@@ -45,6 +45,9 @@
     "\n",
     "* Define the kinases of interest\n",
     "* Retrieve and preprocess data\n",
+    "    * Set up a remote KLIFS session\n",
+    "    * Fetch all structures describing these kinases\n",
+    "    * Filter structures\n",
     "* Show kinase coverage\n",
     "* Load KiSSim fingerprints\n",
     "* Compare structures\n",
@@ -106,14 +109,14 @@
     "\n",
     "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
     "\n",
-    "We here use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment of 85 pocket residues (see more details in __Talktorial T023__)."
+    "Here we use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment of $85$ pocket residues (see more details in __Talktorial T023__)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KiSSim fingerprint encodes each of the 85 residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 1). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase. See more details on structural motifs in kinases in __Talktorial T023__.\n",
+    "The KiSSim fingerprint encodes each of the $85$ residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 1). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase. See more details on structural motifs in kinases in __Talktorial T023__.\n",
     "\n",
     "![KiSSim fingerprint](https://raw.githubusercontent.com/volkamerlab/kissim/main/docs/_static/kissim_toc.png)\n",
     "\n",
@@ -128,8 +131,8 @@
    "source": [
     "### Fetching KLIFS data with `opencadd.databases.klifs`\n",
     "\n",
-    "`opencadd` is a Python library for structural cheminformatics developed by the Volkamer lab at the Charité in Berlin. \n",
-    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS IFP as `pandas` DataFrame.\n",
+    "`opencadd` is a Python library for structural cheminformatics developed by the [Volkamer lab](https://volkamerlab.org/) at the Charité in Berlin. \n",
+    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS interaction fingerprint (IFP) as a `pandas` DataFrame.\n",
     "\n",
     "- GitHub repository: https://github.com/volkamerlab/opencadd\n",
     "- Documentation: https://opencadd.readthedocs.io\n",
@@ -1858,7 +1861,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 35da64c66bd27377156ea9c981a72b4a96a179f7 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 11:26:38 +0200
Subject: [PATCH 099/158] minor changes in practical.

---
 .../talktorial.ipynb                          | 335 +++++++++---------
 1 file changed, 169 insertions(+), 166 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 1c1f8afa..560cd24a 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -328,7 +328,8 @@
    ],
    "source": [
     "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
-    "kinase_selection_df"
+    "kinase_selection_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -398,7 +399,8 @@
     "# Get all available structures for these kinases\n",
     "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
-    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -441,7 +443,8 @@
     }
    ],
    "source": [
-    "structures_df.columns"
+    "structures_df.columns\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -671,7 +674,8 @@
     }
    ],
    "source": [
-    "structures_df.head()"
+    "structures_df.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -712,7 +716,8 @@
     "    & (structures_df[\"structure.qualityscore\"] >= 6)\n",
     "]\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
-    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -737,14 +742,15 @@
    ],
    "source": [
     "structure_klifs_ids = structures_df[\"structure.klifs_id\"].to_list()\n",
-    "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please skip running the cell below (or change cell type from \"Code\" > \"Raw\")."
+    "To make it easier for us to maintain the talktorials, we will now load a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please skip the cell below (or change cell type from \"Code\" > \"Raw\")."
    ]
   },
   {
@@ -827,7 +833,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -844,7 +850,8 @@
     "ax.set_xlabel(\"Number of structures\")\n",
     "ax.set_ylabel(\"Kinase name\")\n",
     "for i, value in enumerate(n_structures_per_kinase):\n",
-    "    ax.text(value, i, str(value), va=\"center\")"
+    "    ax.text(value, i, str(value), va=\"center\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1138,7 +1145,8 @@
     }
    ],
    "source": [
-    "kissim_fingerprints_df.head()"
+    "kissim_fingerprints_df.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1147,15 +1155,13 @@
    "source": [
     "### Compare structures\n",
     "\n",
-    "Let's compare the KiSSim fingerprints pairwise. We use `sklearn`'s `nan_euclidean_distances` method, which calculates the Euclidean distance between all pairwise vectors of a matrix. \n",
+    "Let's make a pairwise comparison of the KiSSim fingerprints. We use `sklearn`'s `nan_euclidean_distances` method, which calculates the Euclidean distance between all pairwise vectors. \n",
     "\n",
     "If two vectors have NaN values (which can happen if residues are not resolved in a structure), the following procedure is applied:\n",
     "\n",
     ">  When calculating the distance between a pair of samples, this formulation ignores feature coordinates with a missing value in either sample and scales up the weight of the remaining coordinates.\n",
     "\n",
-    "Please find more information in their documentation:\n",
-    "\n",
-    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html"
+    "Please find more information in the [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html)."
    ]
   },
   {
@@ -1275,7 +1281,8 @@
     ")\n",
     "print(f\"Structure distance matrix size: {structure_distance_matrix_df.shape}\")\n",
     "print(\"Show matrix subset:\")\n",
-    "structure_distance_matrix_df.iloc[:5, :5]"
+    "structure_distance_matrix_df.iloc[:5, :5]\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1407,7 +1414,8 @@
     "kinase_distance_matrix_df.index = kinase_names\n",
     "kinase_distance_matrix_df.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
-    "kinase_distance_matrix_df.iloc[:5, :5]"
+    "kinase_distance_matrix_df.iloc[:5, :5]\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1444,14 +1452,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Structure matrix of shape (1611, 1611) reduced to kinase matrix of shape (9, 9).\n"
+      "Structure matrix of shape (1611, 1611)reduced to kinase matrix of shape (9, 9).\n"
      ]
     }
    ],
    "source": [
     "print(\n",
-    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
-    ")"
+    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape}\"\n",
+    "    f\"reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1463,188 +1473,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_9d518_row0_col0, #T_9d518_row1_col1, #T_9d518_row2_col2, #T_9d518_row3_col3, #T_9d518_row4_col4, #T_9d518_row5_col5, #T_9d518_row6_col6, #T_9d518_row7_col7, #T_9d518_row8_col8 {\n",
+       "#T_dc0f4_row0_col0, #T_dc0f4_row1_col1, #T_dc0f4_row2_col2, #T_dc0f4_row3_col3, #T_dc0f4_row4_col4, #T_dc0f4_row5_col5, #T_dc0f4_row6_col6, #T_dc0f4_row7_col7, #T_dc0f4_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row0_col1, #T_9d518_row5_col4 {\n",
+       "#T_dc0f4_row0_col1, #T_dc0f4_row5_col4 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row0_col2, #T_9d518_row4_col1, #T_9d518_row6_col0 {\n",
+       "#T_dc0f4_row0_col2, #T_dc0f4_row4_col1, #T_dc0f4_row6_col0 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row0_col3, #T_9d518_row2_col0, #T_9d518_row4_col6 {\n",
+       "#T_dc0f4_row0_col3, #T_dc0f4_row2_col0, #T_dc0f4_row4_col6 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row0_col4, #T_9d518_row0_col6 {\n",
+       "#T_dc0f4_row0_col4, #T_dc0f4_row0_col6 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row0_col5 {\n",
+       "#T_dc0f4_row0_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row0_col7 {\n",
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        "  background-color: #148a14;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row0_col8 {\n",
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        "  background-color: #66b266;\n",
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        "}\n",
-       "#T_9d518_row1_col0, #T_9d518_row3_col2, #T_9d518_row5_col2 {\n",
+       "#T_dc0f4_row1_col0, #T_dc0f4_row3_col2, #T_dc0f4_row5_col2 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row1_col2 {\n",
+       "#T_dc0f4_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row1_col3 {\n",
+       "#T_dc0f4_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row1_col4 {\n",
+       "#T_dc0f4_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row1_col5 {\n",
+       "#T_dc0f4_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row1_col6 {\n",
+       "#T_dc0f4_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row1_col7 {\n",
+       "#T_dc0f4_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row1_col8 {\n",
+       "#T_dc0f4_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row2_col1, #T_9d518_row4_col5, #T_9d518_row6_col5, #T_9d518_row8_col4 {\n",
+       "#T_dc0f4_row2_col1, #T_dc0f4_row4_col5, #T_dc0f4_row6_col5, #T_dc0f4_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row2_col3 {\n",
+       "#T_dc0f4_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row2_col4 {\n",
+       "#T_dc0f4_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row2_col5 {\n",
+       "#T_dc0f4_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row2_col6 {\n",
+       "#T_dc0f4_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row2_col7 {\n",
+       "#T_dc0f4_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row2_col8, #T_9d518_row4_col3, #T_9d518_row8_col0, #T_9d518_row8_col5 {\n",
+       "#T_dc0f4_row2_col8, #T_dc0f4_row4_col3, #T_dc0f4_row8_col0, #T_dc0f4_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col0 {\n",
+       "#T_dc0f4_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col1, #T_9d518_row3_col6, #T_9d518_row5_col0, #T_9d518_row6_col8 {\n",
+       "#T_dc0f4_row3_col1, #T_dc0f4_row3_col6, #T_dc0f4_row5_col0, #T_dc0f4_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col4, #T_9d518_row5_col3 {\n",
+       "#T_dc0f4_row3_col4, #T_dc0f4_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col5 {\n",
+       "#T_dc0f4_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col7, #T_9d518_row7_col0, #T_9d518_row7_col1, #T_9d518_row7_col2, #T_9d518_row7_col3, #T_9d518_row7_col4, #T_9d518_row7_col5, #T_9d518_row7_col6, #T_9d518_row7_col8 {\n",
+       "#T_dc0f4_row3_col7, #T_dc0f4_row7_col0, #T_dc0f4_row7_col1, #T_dc0f4_row7_col2, #T_dc0f4_row7_col3, #T_dc0f4_row7_col4, #T_dc0f4_row7_col5, #T_dc0f4_row7_col6, #T_dc0f4_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row3_col8 {\n",
+       "#T_dc0f4_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row4_col0 {\n",
+       "#T_dc0f4_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row4_col2 {\n",
+       "#T_dc0f4_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row4_col7 {\n",
+       "#T_dc0f4_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row4_col8 {\n",
+       "#T_dc0f4_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row5_col1 {\n",
+       "#T_dc0f4_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row5_col6, #T_9d518_row6_col2 {\n",
+       "#T_dc0f4_row5_col6, #T_dc0f4_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_9d518_row5_col7 {\n",
+       "#T_dc0f4_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row5_col8 {\n",
+       "#T_dc0f4_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row6_col1, #T_9d518_row6_col4 {\n",
+       "#T_dc0f4_row6_col1, #T_dc0f4_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row6_col3 {\n",
+       "#T_dc0f4_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row6_col7 {\n",
+       "#T_dc0f4_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row8_col1 {\n",
+       "#T_dc0f4_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row8_col2 {\n",
+       "#T_dc0f4_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row8_col3 {\n",
+       "#T_dc0f4_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row8_col6 {\n",
+       "#T_dc0f4_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_9d518_row8_col7 {\n",
+       "#T_dc0f4_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_9d518_\">\n",
+       "<table id=\"T_dc0f4_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1661,118 +1671,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_9d518_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_9d518_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_9d518_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_9d518_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_9d518_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_9d518_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_9d518_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_9d518_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_dc0f4_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_dc0f4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_dc0f4_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_dc0f4_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_dc0f4_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_dc0f4_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_dc0f4_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_dc0f4_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_dc0f4_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_9d518_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_9d518_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_9d518_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_9d518_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_9d518_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_9d518_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_9d518_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_9d518_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_dc0f4_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_dc0f4_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_dc0f4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_dc0f4_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_dc0f4_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_dc0f4_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_dc0f4_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_dc0f4_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_dc0f4_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_9d518_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_9d518_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_9d518_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_9d518_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_9d518_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_9d518_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_9d518_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_9d518_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_dc0f4_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_dc0f4_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_dc0f4_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_dc0f4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_dc0f4_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_dc0f4_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_dc0f4_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_dc0f4_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_dc0f4_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_9d518_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_9d518_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_9d518_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_9d518_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_9d518_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_9d518_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_9d518_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_9d518_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_dc0f4_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_dc0f4_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_dc0f4_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_dc0f4_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_dc0f4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_dc0f4_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_dc0f4_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_dc0f4_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_dc0f4_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_9d518_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_9d518_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_9d518_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_9d518_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_9d518_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_9d518_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_9d518_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_9d518_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_dc0f4_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_dc0f4_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_dc0f4_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_dc0f4_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_dc0f4_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_dc0f4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_dc0f4_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_dc0f4_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_dc0f4_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_9d518_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_9d518_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_9d518_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_9d518_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_9d518_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_9d518_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_9d518_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_9d518_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_dc0f4_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_dc0f4_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_dc0f4_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_dc0f4_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_dc0f4_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_dc0f4_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_dc0f4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_dc0f4_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_dc0f4_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_9d518_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_9d518_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_9d518_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_9d518_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_9d518_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_9d518_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_9d518_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_9d518_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_dc0f4_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_dc0f4_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_dc0f4_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_dc0f4_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_dc0f4_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_dc0f4_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_dc0f4_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_dc0f4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_dc0f4_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_9d518_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_9d518_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_9d518_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_9d518_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_9d518_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_9d518_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_9d518_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_9d518_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_9d518_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_dc0f4_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_dc0f4_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_dc0f4_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_dc0f4_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_dc0f4_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_dc0f4_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_dc0f4_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_dc0f4_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_dc0f4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_dc0f4_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_9d518_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_9d518_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_9d518_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_9d518_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_9d518_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_9d518_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_9d518_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_9d518_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_9d518_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_9d518_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_dc0f4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_dc0f4_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_dc0f4_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_dc0f4_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_dc0f4_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_dc0f4_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_dc0f4_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_dc0f4_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_dc0f4_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_dc0f4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f264522df70>"
+       "<pandas.io.formats.style.Styler at 0x7fe619bd8fd0>"
       ]
      },
      "execution_count": 21,
@@ -1816,10 +1826,10 @@
     "\n",
     "In this talktorial, we have assessed kinase similarity using the KiSSim fingerprints, which describe physicochemical and spatial properties of pockets in kinase structures. We have reduced a _structure_ distance matrix to a _kinase_ distance matrix by selecting for each _kinase pair_ the corresponding closest _structure pair_. \n",
     "\n",
-    "We have to keep two things in mind:\n",
+    "We have to keep two elements in mind:\n",
     "\n",
-    "- Pocket fingerprints like KiSSim encode the full pocket although some residues might be more important for ligand binding than others. As an alternative, one could instead use a subset of residues that have been shown to interact with co-crystallized ligands frequently.\n",
-    "- We only compare here the two closest structures per kinase pair, although we have &mdash; at least for kinases such as EGFR and CDK2 &mdash; much more structural data available. Aggregating multiple structures could be a strategy but has two disadvantages: \n",
+    "- Pocket fingerprints like KiSSim encode the full pocket although some residues might be more important for ligand binding than others. As an alternative, one could instead use a subset of residues that have been shown to frequently interact with co-crystallized ligands.\n",
+    "- We only compare here the two closest structures per kinase pair, although we have &mdash; at least for kinases such as EGFR and CDK2 &mdash; much more structural data available. Aggregating multiple structures is a reasonable strategy but has two disadvantages: \n",
     "  - Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
     "  - Aggregated kinase fingerprints may cause too much averaging, making pairwise kinase comparison noisier.\n",
     "\n",
@@ -1836,13 +1846,6 @@
     "2. Can you think of a reason why the side chain orientation and solvent exposure may be interesting features to consider in a fingerprint?\n",
     "2. Can you think of research questions for which you could make use of the structure distance matrix?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 98140d5609f0185dcc64957056b6ef52ecc88757 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 12:29:37 +0200
Subject: [PATCH 100/158] T024: DS review text/code; add nbval checks

---
 .../talktorial.ipynb                          | 1505 +++++++++++++----
 1 file changed, 1139 insertions(+), 366 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 02e1cc45..1fad10e3 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# T024 · Kinase similarity: sequence\n",
+    "# T024 · Kinase similarity: Sequence\n",
     "\n",
     "Authors:\n",
     "\n",
@@ -33,7 +33,7 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor: sequence\n",
+    "* Kinase similarity descriptor: Sequence\n",
     "    * Identity score\n",
     "    * Substitution score\n",
     "* From similarity matrix to distance matrix"
@@ -50,9 +50,11 @@
     "* Sequence similarity\n",
     "    * Identity score\n",
     "    * Substitution score\n",
-    "* Kinase comparison\n",
-    "* Visualize kinase similarity matrix\n",
-    "* Save kinase distance matrix"
+    "* Kinase similarity\n",
+    "  * Visualize similarity as kinase matrix\n",
+    "  * Save kinase similarity matrix\n",
+    "* Kinase distance matrix\n",
+    "  * Save kinase distance matrix"
    ]
   },
   {
@@ -65,13 +67,12 @@
     "* KLIFS\n",
     "  * KLIFS URL: https://klifs.net/\n",
     "  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)\n",
-    "* Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
+    "* Sequence-based kinase clustering: Manning _et al._ [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)\n",
     "* Substitution matrix: [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915)\n",
     "* Biotite\n",
-    "    * documentation: https://www.biotite-python.org/index.html\n",
+    "    * Documentation: https://www.biotite-python.org/index.html\n",
     "    * [Blosum matrix](https://www.biotite-python.org/examples/gallery/sequence/blosum_dendrogram.html?highlight=blosum)\n",
-    "* Sequence logo: http://www.cbs.dtu.dk/biotools/Seq2Logo/\n",
-    "    "
+    "* Sequence logo: http://www.cbs.dtu.dk/biotools/Seq2Logo/    "
    ]
   },
   {
@@ -101,7 +102,7 @@
    "source": [
     "### Kinase similarity descriptor: sequence\n",
     "\n",
-    "As mentioned in the previous talktorial, sequence is often used in kinase similarity (see the phylogenetic tree developed by Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
+    "As mentioned in the previous talktorial, sequence is often used to assess kinase similarity (see the phylogenetic tree developed by Manning _et al._ [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
    ]
   },
   {
@@ -115,13 +116,6 @@
     "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an $85$ residues sequence contains most relevant information?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Missing residues in KLIFS are noted using the symbol \"-\"."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -129,7 +123,7 @@
     "![sequence_logo](images/sequence_logo.png)\n",
     "\n",
     "_Figure 1:_\n",
-    "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion.\n",
+    "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion. For example, the sequence logo easily visualizes the conserved G-rich loop (position 4-9) and the DFG motif (position 81-83), see more details in __Talktorial T023__.\n",
     "\n",
     "**Note for reproducibility:**\n",
     "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to $85$ and _Page size_ to $1200 \\times 480$."
@@ -148,7 +142,7 @@
    "source": [
     "#### Identity score\n",
     "A simple way of assessing the similarity between two sequences is to use the so-called identity score.\n",
-    "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If there are, the entry is set to $1$, and $0$ otherwise.\n",
+    "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If they are, the entry is set to $1$, and $0$ otherwise.\n",
     "\n",
     "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
    ]
@@ -229,6 +223,7 @@
    "metadata": {},
    "source": [
     "The BLOSUM substitution matrix is symmetric, as shown in the practical part.\n",
+    "\n",
     "For convenience, we will translate and rescale the matrix using the following:\n",
     "\n",
     "$$\n",
@@ -295,11 +290,11 @@
    "outputs": [],
    "source": [
     "from pathlib import Path\n",
+    "import requests\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "import requests\n",
     "import matplotlib.pyplot as plt\n",
     "import biotite.sequence.align as align"
    ]
@@ -470,7 +465,8 @@
    "source": [
     "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/\\\n",
     "data/kinase_selection.csv\")\n",
-    "kinase_selection_df"
+    "kinase_selection_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -552,9 +548,10 @@
    ],
    "source": [
     "kinase_sequences = {}\n",
-    "for kinase in kinase_selection_df.kinase_klifs:\n",
+    "for kinase in kinase_selection_df[\"kinase_klifs\"]:\n",
     "    kinase_sequences[kinase] = klifs_pocket_sequence(kinase)\n",
-    "kinase_sequences"
+    "kinase_sequences\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -599,13 +596,13 @@
     "    Parameters\n",
     "    ----------\n",
     "    sequence1 : np.array\n",
-    "        An array of character describing the first sequence.\n",
-    "    sequence2 :\n",
-    "        An array of character describing the second sequence.\n",
+    "        An array of characters describing the first sequence.\n",
+    "    sequence2 : np.array\n",
+    "        An array of characters describing the second sequence.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    np.array :\n",
+    "    np.array\n",
     "        The bool array for each character.\n",
     "        1 if the elements are identical,\n",
     "        0 otherwise.\n",
@@ -671,7 +668,8 @@
    ],
    "source": [
     "substitution_matrix = align.SubstitutionMatrix.std_protein_matrix()\n",
-    "print(substitution_matrix)"
+    "print(substitution_matrix)\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -698,7 +696,15 @@
     }
    ],
    "source": [
-    "substitution_matrix.is_symmetric()"
+    "substitution_matrix.is_symmetric()\n",
+    "# NBVAL_CHECK_OUTPUT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's perform that translation-rescaling step we have discussed in the _Theory_ part of this talktorial."
    ]
   },
   {
@@ -707,35 +713,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def substitution_score(sequence1,\n",
-    "                       sequence2,\n",
-    "                       substitution_matrix=align.\n",
-    "                       SubstitutionMatrix.std_protein_matrix()):\n",
+    "def _translate_rescale_substitution_matrix(\n",
+    "    substitution_matrix=align.SubstitutionMatrix.std_protein_matrix()\n",
+    "):\n",
     "    \"\"\"\n",
-    "    Retrieve the match score given the substitution matrix.\n",
+    "    Translate and rescale substitution matrix.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    sequence1 : np.array\n",
-    "        An array of character describing the first sequence.\n",
-    "    sequence2 :\n",
-    "        An array of character describing the second sequence.\n",
-    "    substitution_matrix:\n",
-    "        A substituition matrix specific to amino acids.\n",
+    "    substitution_matrix : biotite.sequence.align.SubstitutionMatrix\n",
+    "        A substitution matrix specific to amino acids.\n",
     "        The default is align.SubstitutionMatrix.std_protein_matrix()\n",
     "        from biotite, which represents BLOSUM62.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
-    "    np.array :\n",
-    "        The vector of match score\n",
-    "        using the normalized substitution matrix.\n",
+    "    pd.DataFrame\n",
+    "        Translated and rescaled substitution matrix as DataFrame\n",
+    "        (index/columns contain letters).\n",
     "    \"\"\"\n",
     "    # Retrieve np.array from substitution matrix\n",
     "    score_matrix = substitution_matrix.score_matrix()\n",
     "\n",
     "    # Translation of substitution matrix\n",
-    "    translated_matrix = score_matrix-np.min(score_matrix)\n",
+    "    translated_matrix = score_matrix - np.min(score_matrix)\n",
     "\n",
     "    # Rescaling\n",
     "    normalized_score_matrix = np.zeros(score_matrix.shape)\n",
@@ -745,28 +746,792 @@
     "                                             np.sqrt(translated_matrix[i, i] *\n",
     "                                                     translated_matrix[j, j]))\n",
     "\n",
-    "    # Retrieve the letter (amino acid)\n",
-    "    letter_alphabet = substitution_matrix.get_alphabet1()\n",
+    "    # Create DataFrame from matrix with letters as index/column names\n",
+    "    normalized_score_matrix = pd.DataFrame(\n",
+    "        normalized_score_matrix,\n",
+    "        columns=substitution_matrix.get_alphabet1(),\n",
+    "        index=substitution_matrix.get_alphabet1()\n",
+    "    )\n",
+    "    \n",
+    "    # Check for symmetry\n",
+    "    symmetric = (normalized_score_matrix.values ==\n",
+    "                 normalized_score_matrix.values.transpose()).all()\n",
+    "    if not symmetric:\n",
+    "        raise ValueError(f\"Translated/rescaled matrix is not symmetric. \"\n",
+    "                         f\"Raise issue on GitHub.\")\n",
+    "    \n",
+    "    return normalized_score_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the translated and rescaled version of the substitution matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_16d35_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >A</th>\n",
+       "      <th class=\"col_heading level0 col1\" >C</th>\n",
+       "      <th class=\"col_heading level0 col2\" >D</th>\n",
+       "      <th class=\"col_heading level0 col3\" >E</th>\n",
+       "      <th class=\"col_heading level0 col4\" >F</th>\n",
+       "      <th class=\"col_heading level0 col5\" >G</th>\n",
+       "      <th class=\"col_heading level0 col6\" >H</th>\n",
+       "      <th class=\"col_heading level0 col7\" >I</th>\n",
+       "      <th class=\"col_heading level0 col8\" >K</th>\n",
+       "      <th class=\"col_heading level0 col9\" >L</th>\n",
+       "      <th class=\"col_heading level0 col10\" >M</th>\n",
+       "      <th class=\"col_heading level0 col11\" >N</th>\n",
+       "      <th class=\"col_heading level0 col12\" >P</th>\n",
+       "      <th class=\"col_heading level0 col13\" >Q</th>\n",
+       "      <th class=\"col_heading level0 col14\" >R</th>\n",
+       "      <th class=\"col_heading level0 col15\" >S</th>\n",
+       "      <th class=\"col_heading level0 col16\" >T</th>\n",
+       "      <th class=\"col_heading level0 col17\" >V</th>\n",
+       "      <th class=\"col_heading level0 col18\" >W</th>\n",
+       "      <th class=\"col_heading level0 col19\" >Y</th>\n",
+       "      <th class=\"col_heading level0 col20\" >B</th>\n",
+       "      <th class=\"col_heading level0 col21\" >Z</th>\n",
+       "      <th class=\"col_heading level0 col22\" >X</th>\n",
+       "      <th class=\"col_heading level0 col23\" >*</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_16d35_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_16d35_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_16d35_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_16d35_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_16d35_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_16d35_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_16d35_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_16d35_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_16d35_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_16d35_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_16d35_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_16d35_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_16d35_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_16d35_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_16d35_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_16d35_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_16d35_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_16d35_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_16d35_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_16d35_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_16d35_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_16d35_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_16d35_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_16d35_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_16d35_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_16d35_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_16d35_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_16d35_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_16d35_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_16d35_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_16d35_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_16d35_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_16d35_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_16d35_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_16d35_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_16d35_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_16d35_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_16d35_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_16d35_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_16d35_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_16d35_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_16d35_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_16d35_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_16d35_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_16d35_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_16d35_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_16d35_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_16d35_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_16d35_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_16d35_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_16d35_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_16d35_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_16d35_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_16d35_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_16d35_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_16d35_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_16d35_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_16d35_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_16d35_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_16d35_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_16d35_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_16d35_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_16d35_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_16d35_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_16d35_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_16d35_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_16d35_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_16d35_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_16d35_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_16d35_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_16d35_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_16d35_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_16d35_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_16d35_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_16d35_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_16d35_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_16d35_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_16d35_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_16d35_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_16d35_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_16d35_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_16d35_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_16d35_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_16d35_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_16d35_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_16d35_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_16d35_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_16d35_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_16d35_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_16d35_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_16d35_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_16d35_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_16d35_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_16d35_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_16d35_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_16d35_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_16d35_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_16d35_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_16d35_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_16d35_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_16d35_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_16d35_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_16d35_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_16d35_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_16d35_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_16d35_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_16d35_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_16d35_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_16d35_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_16d35_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_16d35_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_16d35_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_16d35_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_16d35_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f9bd9066b20>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "_translate_rescale_substitution_matrix().style.format(\"{:.2f}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define a function that calculates the substitution scores between two sequences (will make use of the previously defined function)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def substitution_score(sequence1,\n",
+    "                       sequence2,\n",
+    "                       substitution_matrix=align.\n",
+    "                       SubstitutionMatrix.std_protein_matrix()):\n",
+    "    \"\"\"\n",
+    "    Retrieve the match score given the substitution matrix.\n",
     "\n",
-    "    # Map letter to index\n",
-    "    dict_letters = {}\n",
-    "    for i, letter in enumerate(letter_alphabet.get_symbols()):\n",
-    "        dict_letters[letter] = i\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    sequence1 : np.array\n",
+    "        An array of characters describing the first sequence.\n",
+    "    sequence2 : np.array\n",
+    "        An array of characters describing the second sequence.\n",
+    "    substitution_matrix : biotite.sequence.align.SubstitutionMatrix\n",
+    "        A substitution matrix specific to amino acids.\n",
+    "        The default is align.SubstitutionMatrix.std_protein_matrix()\n",
+    "        from biotite, which represents BLOSUM62.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    np.array\n",
+    "        The vector of match score\n",
+    "        using the normalized substitution matrix.\n",
+    "    \"\"\"\n",
+    "    substitution_matrix_df = _translate_rescale_substitution_matrix(\n",
+    "        substitution_matrix\n",
+    "    )\n",
     "\n",
     "    match_score_array = np.zeros(len(sequence1))\n",
     "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1,\n",
     "                                                             sequence2)):\n",
-    "        ind1 = dict_letters[character_seq1]\n",
-    "        ind2 = dict_letters[character_seq2]\n",
-    "        match_score_array[i] = normalized_score_matrix[ind1, ind2]\n",
+    "        match_score_array[i] = substitution_matrix_df.loc[character_seq1,\n",
+    "                                                          character_seq2]\n",
     "    return match_score_array"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "## Kinase similarity"
+    "### Kinase similarity"
    ]
   },
   {
@@ -778,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -838,7 +1603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -855,12 +1620,13 @@
    "source": [
     "print(\"The sequences are:\\n\")\n",
     "for key in ('EGFR', 'MET'):\n",
-    "    print(f\"{key:5s}: {kinase_sequences[key]}\")"
+    "    print(f\"{key:5s}: {kinase_sequences[key]}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -872,17 +1638,18 @@
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "egfr_met_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                              \"MET\",\n",
     "                                              \"identity\")\n",
     "\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{EGFR_MET_seq_similarity:.2f} using identity.\")"
+    "      f\"{egfr_met_seq_similarity:.2f} using identity.\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -894,11 +1661,12 @@
     }
    ],
    "source": [
-    "EGFR_MET_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "egfr_met_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                              \"MET\",\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{EGFR_MET_seq_similarity:.2f} using substitution.\")"
+    "      f\"{egfr_met_seq_similarity:.2f} using substitution.\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -910,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -922,15 +1690,16 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "egfr_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                          \"EGFR\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{EGFR_seq_similarity:.2f} using identity.\")"
+    "      f\"{egfr_seq_similarity:.2f} using identity.\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -942,11 +1711,12 @@
     }
    ],
    "source": [
-    "EGFR_seq_similarity = sequence_similarity(\"EGFR\",\n",
+    "egfr_seq_similarity = sequence_similarity(\"EGFR\",\n",
     "                                          \"EGFR\",\n",
     "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{EGFR_seq_similarity:.2f} using substitution.\")"
+    "      f\"{egfr_seq_similarity:.2f} using substitution.\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -960,7 +1730,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarity as kinase matrix"
+    "#### Visualize similarity as kinase matrix"
    ]
   },
   {
@@ -972,7 +1742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -988,7 +1758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1003,7 +1773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1175,7 +1945,7 @@
        "p38a   0.629355  1.000000  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1186,7 +1956,8 @@
     "                                           columns=kinase_selection_df.kinase_klifs)\n",
     "kinase_similarity_matrix_df.index.name = None\n",
     "kinase_similarity_matrix_df.columns.name = None\n",
-    "kinase_similarity_matrix_df"
+    "kinase_similarity_matrix_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1198,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1207,207 +1978,208 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "(kinase_similarity_matrix_df.values ==\n",
-    " kinase_similarity_matrix_df.values.transpose()).all()"
+    " kinase_similarity_matrix_df.values.transpose()).all()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
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+       "#T_c3b4d_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d0463_row7_col1 {\n",
+       "#T_c3b4d_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d0463_row7_col6 {\n",
+       "#T_c3b4d_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d0463_row7_col8 {\n",
+       "#T_c3b4d_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d0463_row8_col0 {\n",
+       "#T_c3b4d_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d0463_row8_col2 {\n",
+       "#T_c3b4d_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d0463_row8_col5 {\n",
+       "#T_c3b4d_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d0463_\">\n",
+       "<table id=\"T_c3b4d_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1424,121 +2196,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d0463_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_d0463_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_d0463_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_d0463_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_d0463_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_d0463_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_d0463_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_d0463_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "      <th id=\"T_c3b4d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_c3b4d_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_c3b4d_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_c3b4d_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_c3b4d_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_c3b4d_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_c3b4d_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_c3b4d_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_c3b4d_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d0463_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_d0463_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_d0463_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_d0463_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_d0463_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_d0463_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_d0463_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_d0463_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "      <th id=\"T_c3b4d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_c3b4d_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_c3b4d_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_c3b4d_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_c3b4d_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_c3b4d_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_c3b4d_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_c3b4d_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_c3b4d_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_d0463_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_d0463_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_d0463_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_d0463_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_d0463_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_d0463_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_d0463_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_d0463_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "      <th id=\"T_c3b4d_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_c3b4d_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_c3b4d_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_c3b4d_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_c3b4d_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_c3b4d_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_c3b4d_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_c3b4d_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_c3b4d_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_d0463_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_d0463_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_d0463_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_d0463_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_d0463_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_d0463_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_d0463_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_d0463_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "      <th id=\"T_c3b4d_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_c3b4d_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_c3b4d_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_c3b4d_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_c3b4d_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_c3b4d_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_c3b4d_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_c3b4d_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_c3b4d_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_d0463_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_d0463_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_d0463_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_d0463_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_d0463_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_d0463_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_d0463_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_d0463_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "      <th id=\"T_c3b4d_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_c3b4d_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_c3b4d_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_c3b4d_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_c3b4d_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_c3b4d_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_c3b4d_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_c3b4d_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_c3b4d_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_d0463_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_d0463_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_d0463_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_d0463_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_d0463_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_d0463_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_d0463_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_d0463_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "      <th id=\"T_c3b4d_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_c3b4d_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_c3b4d_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_c3b4d_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_c3b4d_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_c3b4d_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_c3b4d_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_c3b4d_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_c3b4d_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_d0463_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_d0463_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_d0463_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_d0463_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_d0463_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_d0463_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_d0463_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_d0463_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "      <th id=\"T_c3b4d_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_c3b4d_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_c3b4d_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_c3b4d_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_c3b4d_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_c3b4d_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_c3b4d_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_c3b4d_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_c3b4d_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_d0463_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_d0463_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_d0463_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_d0463_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_d0463_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_d0463_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_d0463_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_d0463_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_d0463_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "      <th id=\"T_c3b4d_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_c3b4d_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_c3b4d_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_c3b4d_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_c3b4d_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_c3b4d_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_c3b4d_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_c3b4d_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_c3b4d_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_c3b4d_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0463_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d0463_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_d0463_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_d0463_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_d0463_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_d0463_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_d0463_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_d0463_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_d0463_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_d0463_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_c3b4d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c3b4d_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_c3b4d_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_c3b4d_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_c3b4d_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_c3b4d_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_c3b4d_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_c3b4d_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_c3b4d_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_c3b4d_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff6ce711670>"
+       "<pandas.io.formats.style.Styler at 0x7f9bd866b4f0>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1555,12 +2327,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase similarity matrix"
+    "#### Save kinase similarity matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1572,7 +2344,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Kinase distance matrix"
+    "### Kinase distance matrix"
    ]
   },
   {
@@ -1584,7 +2356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1598,12 +2370,13 @@
    "source": [
     "print(f\"The values of the similarity matrix lie between: \"\n",
     "      f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
-    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")"
+    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1612,195 +2385,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
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        "  background-color: #73b873;\n",
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-       "#T_36c4a_row3_col2, #T_36c4a_row5_col2 {\n",
+       "#T_33171_row3_col2, #T_33171_row5_col2 {\n",
        "  background-color: #0b850b;\n",
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+       "#T_33171_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row3_col5, #T_36c4a_row4_col0 {\n",
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        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row3_col7 {\n",
+       "#T_33171_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_36c4a_row3_col8 {\n",
+       "#T_33171_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row4_col1 {\n",
+       "#T_33171_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row4_col2 {\n",
+       "#T_33171_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row4_col6, #T_36c4a_row4_col7, #T_36c4a_row7_col5 {\n",
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        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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+       "#T_33171_row5_col0, #T_33171_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row5_col4 {\n",
+       "#T_33171_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row5_col6, #T_36c4a_row6_col4 {\n",
+       "#T_33171_row5_col6, #T_33171_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row5_col8 {\n",
+       "#T_33171_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_36c4a_row6_col0 {\n",
+       "#T_33171_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row6_col1 {\n",
+       "#T_33171_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row6_col2 {\n",
+       "#T_33171_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row6_col3 {\n",
+       "#T_33171_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row6_col5 {\n",
+       "#T_33171_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row6_col8 {\n",
+       "#T_33171_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row7_col0 {\n",
+       "#T_33171_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row7_col1 {\n",
+       "#T_33171_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row7_col6 {\n",
+       "#T_33171_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row7_col8 {\n",
+       "#T_33171_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row8_col0 {\n",
+       "#T_33171_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row8_col2 {\n",
+       "#T_33171_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_36c4a_row8_col5 {\n",
+       "#T_33171_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_36c4a_\">\n",
+       "<table id=\"T_33171_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1817,121 +2590,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_36c4a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_36c4a_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_36c4a_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_36c4a_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_36c4a_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_36c4a_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_36c4a_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_36c4a_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "      <th id=\"T_33171_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_33171_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_33171_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_33171_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_33171_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_33171_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_33171_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_33171_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_33171_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_33171_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_36c4a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_36c4a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_36c4a_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_36c4a_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_36c4a_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_36c4a_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_36c4a_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_36c4a_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "      <th id=\"T_33171_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_33171_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_33171_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_33171_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_33171_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_33171_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_33171_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_33171_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_33171_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_33171_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_36c4a_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_36c4a_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_36c4a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_36c4a_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_36c4a_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_36c4a_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_36c4a_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_36c4a_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "      <th id=\"T_33171_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_33171_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_33171_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_33171_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_33171_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_33171_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_33171_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_33171_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_33171_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_33171_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_36c4a_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_36c4a_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_36c4a_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_36c4a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_36c4a_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_36c4a_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_36c4a_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_36c4a_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "      <th id=\"T_33171_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_33171_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_33171_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_33171_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_33171_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_33171_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_33171_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_33171_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_33171_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_33171_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_36c4a_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_36c4a_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_36c4a_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_36c4a_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_36c4a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_36c4a_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_36c4a_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_36c4a_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "      <th id=\"T_33171_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_33171_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_33171_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_33171_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_33171_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_33171_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_33171_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_33171_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_33171_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_33171_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_36c4a_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_36c4a_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_36c4a_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_36c4a_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_36c4a_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_36c4a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_36c4a_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_36c4a_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "      <th id=\"T_33171_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_33171_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_33171_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_33171_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_33171_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_33171_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_33171_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_33171_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_33171_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_33171_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_36c4a_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_36c4a_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_36c4a_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_36c4a_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_36c4a_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_36c4a_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_36c4a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_36c4a_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "      <th id=\"T_33171_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_33171_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_33171_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_33171_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_33171_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_33171_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_33171_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_33171_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_33171_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_33171_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_36c4a_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_36c4a_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_36c4a_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_36c4a_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_36c4a_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_36c4a_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_36c4a_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_36c4a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_36c4a_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "      <th id=\"T_33171_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_33171_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_33171_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_33171_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_33171_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_33171_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_33171_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_33171_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_33171_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_33171_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_36c4a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_36c4a_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_36c4a_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_36c4a_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_36c4a_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_36c4a_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_36c4a_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_36c4a_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_36c4a_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_36c4a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_33171_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_33171_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_33171_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_33171_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_33171_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_33171_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_33171_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_33171_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_33171_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_33171_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff6cea4fdc0>"
+       "<pandas.io.formats.style.Styler at 0x7f9bd8624f10>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1946,12 +2719,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Save kinase distance matrix"
+    "#### Save kinase distance matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1998,7 +2771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From b0e8306acebb0bb9eac05b1061514dcdaae3dc32 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 13:18:51 +0200
Subject: [PATCH 101/158] T024: Use sequences as input for `similarity_score`

---
 .../talktorial.ipynb                          | 1783 ++++++++---------
 1 file changed, 891 insertions(+), 892 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 1fad10e3..0859f821 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -780,7 +780,7 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_16d35_\">\n",
+       "<table id=\"T_08c84_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -812,658 +812,658 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
-       "      <td id=\"T_16d35_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_16d35_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_08c84_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_08c84_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_16d35_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_16d35_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_16d35_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_16d35_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_16d35_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_16d35_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_16d35_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_08c84_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_08c84_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_08c84_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_08c84_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_08c84_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_08c84_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_08c84_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_16d35_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_16d35_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_16d35_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_16d35_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_08c84_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_08c84_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_08c84_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_08c84_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_16d35_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_16d35_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_16d35_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_16d35_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_08c84_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_08c84_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_08c84_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_08c84_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_16d35_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_16d35_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_16d35_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_08c84_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_08c84_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_08c84_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_16d35_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_16d35_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_16d35_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_16d35_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_08c84_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_08c84_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_08c84_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_08c84_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_16d35_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_16d35_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_16d35_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_16d35_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_16d35_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_16d35_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_16d35_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_16d35_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_08c84_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_08c84_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_08c84_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_08c84_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_08c84_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_08c84_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_08c84_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_08c84_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_16d35_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_16d35_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_16d35_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_08c84_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_08c84_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_08c84_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_16d35_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_08c84_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_16d35_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_16d35_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_16d35_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_08c84_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_08c84_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_08c84_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_16d35_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_16d35_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_16d35_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_16d35_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_16d35_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_08c84_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_08c84_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_08c84_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_08c84_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_08c84_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_16d35_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_16d35_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_16d35_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_16d35_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_08c84_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_08c84_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_08c84_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_08c84_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_16d35_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_16d35_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_08c84_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_08c84_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_16d35_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_16d35_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_08c84_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_08c84_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_16d35_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_16d35_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_08c84_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_08c84_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_16d35_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_16d35_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_08c84_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_08c84_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_16d35_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_16d35_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_16d35_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_16d35_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_16d35_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_08c84_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_08c84_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_08c84_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_08c84_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_08c84_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_16d35_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_16d35_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_16d35_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_16d35_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_16d35_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_08c84_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_08c84_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_08c84_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_08c84_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_08c84_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_16d35_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_16d35_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_16d35_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_16d35_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_16d35_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_16d35_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_08c84_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_08c84_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_08c84_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_08c84_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_08c84_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_08c84_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_16d35_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_16d35_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_16d35_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_16d35_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_16d35_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_16d35_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_16d35_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_08c84_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_08c84_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_08c84_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_08c84_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_08c84_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_08c84_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_08c84_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_16d35_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_16d35_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_16d35_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_16d35_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_08c84_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_08c84_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_08c84_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_08c84_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_16d35_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_16d35_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_16d35_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_16d35_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_16d35_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_16d35_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_16d35_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_16d35_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_16d35_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_16d35_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_16d35_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_16d35_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_16d35_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_16d35_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_16d35_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_16d35_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_08c84_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_08c84_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_08c84_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_08c84_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_08c84_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_08c84_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_08c84_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_08c84_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_08c84_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_08c84_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_08c84_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_08c84_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_08c84_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_08c84_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_08c84_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_08c84_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_16d35_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_16d35_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_16d35_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_16d35_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_16d35_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_16d35_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_16d35_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_16d35_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_16d35_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_16d35_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_16d35_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_16d35_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "      <th id=\"T_08c84_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_08c84_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_08c84_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_08c84_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_08c84_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_08c84_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_08c84_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_08c84_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_08c84_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_08c84_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_08c84_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_08c84_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_08c84_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_16d35_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_16d35_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_16d35_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_08c84_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_08c84_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_08c84_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9bd9066b20>"
+       "<pandas.io.formats.style.Styler at 0x7fb6cee52eb0>"
       ]
      },
      "execution_count": 10,
@@ -1547,14 +1547,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sequence_similarity(klifs_kinase_name1,\n",
-    "                        klifs_kinase_name2,\n",
+    "def sequence_similarity(sequence_1,\n",
+    "                        sequence_2,\n",
     "                        type_=\"identity\"):\n",
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
-    "    klifs_kinase_name1, klifs_kinase_name2 : str\n",
-    "        The two KLIFS names of the kinases for comparison.\n",
+    "    sequence_1, sequence_2 : str\n",
+    "        The two sequence strings for comparison.\n",
     "\n",
     "    type_ : str\n",
     "        The type of metric to compute the similarity.\n",
@@ -1565,8 +1565,6 @@
     "    float :\n",
     "        The similarity between the pocket sequences of the two kinases.\n",
     "    \"\"\"\n",
-    "    sequence_1 = klifs_pocket_sequence(klifs_kinase_name1)\n",
-    "    sequence_2 = klifs_pocket_sequence(klifs_kinase_name2)\n",
     "\n",
     "    # Replace possible unavailable residue\n",
     "    # noted in KLIFS with \"-\"\n",
@@ -1638,8 +1636,8 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                              \"MET\",\n",
+    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                              kinase_sequences[\"MET\"],\n",
     "                                              \"identity\")\n",
     "\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
@@ -1661,8 +1659,8 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                              \"MET\",\n",
+    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                              kinase_sequences[\"MET\"],\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
     "      f\"{egfr_met_seq_similarity:.2f} using substitution.\")\n",
@@ -1690,8 +1688,9 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                          \"EGFR\")\n",
+    "egfr_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                          kinase_sequences[\"EGFR\"],\n",
+    "                                          type_=\"identity\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{egfr_seq_similarity:.2f} using identity.\")\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -1711,8 +1710,8 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(\"EGFR\",\n",
-    "                                          \"EGFR\",\n",
+    "egfr_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
+    "                                          kinase_sequences[\"EGFR\"],\n",
     "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{egfr_seq_similarity:.2f} using substitution.\")\n",
@@ -1766,8 +1765,8 @@
     "                                     len(kinase_selection_df.kinase_klifs)))\n",
     "for i, kinase_name1 in enumerate(kinase_selection_df.kinase_klifs):\n",
     "    for j, kinase_name2 in enumerate(kinase_selection_df.kinase_klifs):\n",
-    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_name1,\n",
-    "                                                             kinase_name2,\n",
+    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
+    "                                                             kinase_sequences[kinase_name2],\n",
     "                                                             similarity_measure)"
    ]
   },
@@ -1998,188 +1997,188 @@
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        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
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        "  background-color: #178b17;\n",
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-       "#T_c3b4d_row0_col2 {\n",
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        "  background-color: #deedde;\n",
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        "}\n",
-       "#T_c3b4d_row0_col3 {\n",
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        "  background-color: #92c892;\n",
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        "}\n",
-       "#T_c3b4d_row1_col0 {\n",
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        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row1_col2 {\n",
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        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row1_col3 {\n",
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        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row1_col6 {\n",
+       "#T_3d2f1_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row1_col7 {\n",
+       "#T_3d2f1_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
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-       "#T_c3b4d_row2_col0, #T_c3b4d_row2_col1, #T_c3b4d_row2_col3, #T_c3b4d_row2_col4, #T_c3b4d_row2_col5, #T_c3b4d_row2_col6, #T_c3b4d_row2_col7, #T_c3b4d_row2_col8, #T_c3b4d_row7_col2 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row3_col0, #T_c3b4d_row7_col3 {\n",
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        "  color: #f1f1f1;\n",
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-       "#T_c3b4d_row3_col1, #T_c3b4d_row6_col7 {\n",
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        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row3_col2, #T_c3b4d_row5_col2 {\n",
+       "#T_3d2f1_row3_col2, #T_3d2f1_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row3_col4 {\n",
+       "#T_3d2f1_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row3_col5, #T_c3b4d_row4_col0 {\n",
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        "}\n",
-       "#T_c3b4d_row3_col6, #T_c3b4d_row4_col3, #T_c3b4d_row5_col7 {\n",
+       "#T_3d2f1_row3_col6, #T_3d2f1_row4_col3, #T_3d2f1_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row3_col7 {\n",
+       "#T_3d2f1_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row4_col1 {\n",
+       "#T_3d2f1_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row4_col2 {\n",
+       "#T_3d2f1_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row4_col6, #T_c3b4d_row4_col7, #T_c3b4d_row7_col5 {\n",
+       "#T_3d2f1_row4_col6, #T_3d2f1_row4_col7, #T_3d2f1_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row4_col8 {\n",
+       "#T_3d2f1_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col0, #T_c3b4d_row8_col6 {\n",
+       "#T_3d2f1_row5_col0, #T_3d2f1_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col1 {\n",
+       "#T_3d2f1_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col3, #T_c3b4d_row8_col3 {\n",
+       "#T_3d2f1_row5_col3, #T_3d2f1_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col4 {\n",
+       "#T_3d2f1_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col6, #T_c3b4d_row6_col4 {\n",
+       "#T_3d2f1_row5_col6, #T_3d2f1_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row5_col8 {\n",
+       "#T_3d2f1_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row6_col0 {\n",
+       "#T_3d2f1_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row6_col1 {\n",
+       "#T_3d2f1_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row6_col2 {\n",
+       "#T_3d2f1_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row6_col3 {\n",
+       "#T_3d2f1_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row6_col5 {\n",
+       "#T_3d2f1_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row6_col8 {\n",
+       "#T_3d2f1_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row7_col0 {\n",
+       "#T_3d2f1_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c3b4d_row7_col1 {\n",
+       "#T_3d2f1_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row7_col6 {\n",
+       "#T_3d2f1_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row7_col8 {\n",
+       "#T_3d2f1_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row8_col0 {\n",
+       "#T_3d2f1_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row8_col2 {\n",
+       "#T_3d2f1_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c3b4d_row8_col5 {\n",
+       "#T_3d2f1_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c3b4d_\">\n",
+       "<table id=\"T_3d2f1_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2196,118 +2195,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_c3b4d_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_c3b4d_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_c3b4d_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_c3b4d_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_c3b4d_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_c3b4d_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_c3b4d_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_c3b4d_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "      <th id=\"T_3d2f1_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_3d2f1_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_3d2f1_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_3d2f1_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_3d2f1_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_3d2f1_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_3d2f1_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_3d2f1_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_3d2f1_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_c3b4d_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_c3b4d_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_c3b4d_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_c3b4d_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_c3b4d_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_c3b4d_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_c3b4d_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_c3b4d_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "      <th id=\"T_3d2f1_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_3d2f1_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_3d2f1_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_3d2f1_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_3d2f1_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_3d2f1_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_3d2f1_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_3d2f1_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_3d2f1_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_c3b4d_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_c3b4d_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_c3b4d_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_c3b4d_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_c3b4d_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_c3b4d_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_c3b4d_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_c3b4d_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "      <th id=\"T_3d2f1_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_3d2f1_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_3d2f1_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_3d2f1_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_3d2f1_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_3d2f1_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_3d2f1_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_3d2f1_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_3d2f1_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_c3b4d_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_c3b4d_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_c3b4d_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_c3b4d_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_c3b4d_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_c3b4d_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_c3b4d_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_c3b4d_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "      <th id=\"T_3d2f1_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_3d2f1_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_3d2f1_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_3d2f1_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_3d2f1_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_3d2f1_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_3d2f1_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_3d2f1_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_3d2f1_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_c3b4d_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_c3b4d_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_c3b4d_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_c3b4d_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_c3b4d_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_c3b4d_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_c3b4d_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_c3b4d_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "      <th id=\"T_3d2f1_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_3d2f1_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_3d2f1_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_3d2f1_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_3d2f1_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_3d2f1_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_3d2f1_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_3d2f1_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_3d2f1_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_c3b4d_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_c3b4d_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_c3b4d_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_c3b4d_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_c3b4d_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_c3b4d_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_c3b4d_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_c3b4d_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "      <th id=\"T_3d2f1_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_3d2f1_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_3d2f1_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_3d2f1_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_3d2f1_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_3d2f1_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_3d2f1_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_3d2f1_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_3d2f1_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_c3b4d_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_c3b4d_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_c3b4d_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_c3b4d_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_c3b4d_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_c3b4d_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_c3b4d_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_c3b4d_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "      <th id=\"T_3d2f1_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_3d2f1_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_3d2f1_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_3d2f1_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_3d2f1_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_3d2f1_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_3d2f1_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_3d2f1_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_3d2f1_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_c3b4d_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_c3b4d_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_c3b4d_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_c3b4d_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_c3b4d_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_c3b4d_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_c3b4d_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_c3b4d_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_c3b4d_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "      <th id=\"T_3d2f1_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_3d2f1_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_3d2f1_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_3d2f1_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_3d2f1_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_3d2f1_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_3d2f1_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_3d2f1_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_3d2f1_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_3d2f1_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c3b4d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c3b4d_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_c3b4d_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_c3b4d_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_c3b4d_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_c3b4d_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_c3b4d_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_c3b4d_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_c3b4d_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_c3b4d_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_3d2f1_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_3d2f1_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_3d2f1_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_3d2f1_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_3d2f1_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_3d2f1_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_3d2f1_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_3d2f1_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_3d2f1_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_3d2f1_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9bd866b4f0>"
+       "<pandas.io.formats.style.Styler at 0x7fb6ce479550>"
       ]
      },
      "execution_count": 22,
@@ -2392,188 +2391,188 @@
      "data": {
       "text/html": [
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        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_33171_row0_col1 {\n",
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        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_33171_row0_col2 {\n",
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        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row0_col3 {\n",
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        "  background-color: #5bad5b;\n",
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-       "#T_33171_row0_col6 {\n",
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        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
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-       "#T_33171_row0_col7 {\n",
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        "}\n",
-       "#T_33171_row0_col8, #T_33171_row8_col1 {\n",
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        "  background-color: #58ab58;\n",
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-       "#T_33171_row1_col0 {\n",
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        "  background-color: #d3e7d3;\n",
        "  color: #000000;\n",
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-       "#T_33171_row1_col2 {\n",
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        "  background-color: #078407;\n",
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-       "#T_33171_row1_col3 {\n",
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-       "#T_33171_row1_col5, #T_33171_row1_col8, #T_33171_row7_col4, #T_33171_row8_col4 {\n",
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        "  background-color: #54a954;\n",
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-       "#T_33171_row1_col6 {\n",
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        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
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-       "#T_33171_row1_col7 {\n",
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        "  background-color: #76ba76;\n",
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-       "#T_33171_row2_col0, #T_33171_row2_col1, #T_33171_row2_col3, #T_33171_row2_col4, #T_33171_row2_col5, #T_33171_row2_col6, #T_33171_row2_col7, #T_33171_row2_col8, #T_33171_row7_col2 {\n",
+       "#T_576f6_row2_col0, #T_576f6_row2_col1, #T_576f6_row2_col3, #T_576f6_row2_col4, #T_576f6_row2_col5, #T_576f6_row2_col6, #T_576f6_row2_col7, #T_576f6_row2_col8, #T_576f6_row7_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col0, #T_33171_row7_col3 {\n",
+       "#T_576f6_row3_col0, #T_576f6_row7_col3 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col1, #T_33171_row6_col7 {\n",
+       "#T_576f6_row3_col1, #T_576f6_row6_col7 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col2, #T_33171_row5_col2 {\n",
+       "#T_576f6_row3_col2, #T_576f6_row5_col2 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col4 {\n",
+       "#T_576f6_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col5, #T_33171_row4_col0 {\n",
+       "#T_576f6_row3_col5, #T_576f6_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col6, #T_33171_row4_col3, #T_33171_row5_col7 {\n",
+       "#T_576f6_row3_col6, #T_576f6_row4_col3, #T_576f6_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row3_col7 {\n",
+       "#T_576f6_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_33171_row3_col8 {\n",
+       "#T_576f6_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row4_col1 {\n",
+       "#T_576f6_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row4_col2 {\n",
+       "#T_576f6_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row4_col6, #T_33171_row4_col7, #T_33171_row7_col5 {\n",
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        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row4_col8, #T_33171_row5_col1 {\n",
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        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row5_col0, #T_33171_row8_col6 {\n",
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        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row5_col3, #T_33171_row8_col3 {\n",
+       "#T_576f6_row5_col3, #T_576f6_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row5_col4 {\n",
+       "#T_576f6_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row5_col6, #T_33171_row6_col4 {\n",
+       "#T_576f6_row5_col6, #T_576f6_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row5_col8 {\n",
+       "#T_576f6_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_33171_row6_col0 {\n",
+       "#T_576f6_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row6_col1 {\n",
+       "#T_576f6_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row6_col2 {\n",
+       "#T_576f6_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row6_col3 {\n",
+       "#T_576f6_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row6_col5 {\n",
+       "#T_576f6_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row6_col8 {\n",
+       "#T_576f6_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row7_col0 {\n",
+       "#T_576f6_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row7_col1 {\n",
+       "#T_576f6_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row7_col6 {\n",
+       "#T_576f6_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row7_col8 {\n",
+       "#T_576f6_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row8_col0 {\n",
+       "#T_576f6_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row8_col2 {\n",
+       "#T_576f6_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_33171_row8_col5 {\n",
+       "#T_576f6_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_33171_\">\n",
+       "<table id=\"T_576f6_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2590,118 +2589,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_33171_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_33171_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_33171_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_33171_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_33171_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_33171_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_33171_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_33171_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_33171_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "      <th id=\"T_576f6_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_576f6_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_576f6_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_576f6_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_576f6_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_576f6_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_576f6_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_576f6_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_576f6_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_33171_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_33171_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_33171_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_33171_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_33171_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_33171_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_33171_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_33171_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_33171_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "      <th id=\"T_576f6_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_576f6_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_576f6_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_576f6_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_576f6_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_576f6_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_576f6_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_576f6_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_576f6_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_33171_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_33171_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_33171_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_33171_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_33171_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_33171_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_33171_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_33171_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_33171_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "      <th id=\"T_576f6_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_576f6_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_576f6_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_576f6_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_576f6_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_576f6_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_576f6_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_576f6_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_576f6_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_33171_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_33171_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_33171_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_33171_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_33171_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_33171_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_33171_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_33171_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_33171_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "      <th id=\"T_576f6_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_576f6_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_576f6_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_576f6_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_576f6_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_576f6_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_576f6_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_576f6_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_576f6_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_33171_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_33171_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_33171_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_33171_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_33171_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_33171_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_33171_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_33171_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_33171_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "      <th id=\"T_576f6_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_576f6_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_576f6_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_576f6_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_576f6_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_576f6_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_576f6_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_576f6_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_576f6_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_33171_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_33171_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_33171_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_33171_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_33171_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_33171_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_33171_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_33171_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_33171_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "      <th id=\"T_576f6_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_576f6_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_576f6_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_576f6_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_576f6_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_576f6_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_576f6_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_576f6_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_576f6_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_33171_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_33171_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_33171_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_33171_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_33171_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_33171_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_33171_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_33171_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_33171_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "      <th id=\"T_576f6_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_576f6_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_576f6_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_576f6_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_576f6_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_576f6_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_576f6_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_576f6_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_576f6_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_33171_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_33171_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_33171_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_33171_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_33171_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_33171_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_33171_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_33171_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_33171_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "      <th id=\"T_576f6_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_576f6_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_576f6_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_576f6_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_576f6_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_576f6_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_576f6_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_576f6_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_576f6_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_576f6_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_33171_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_33171_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_33171_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_33171_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_33171_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_33171_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_33171_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_33171_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_33171_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_33171_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_576f6_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_576f6_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_576f6_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_576f6_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_576f6_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_576f6_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_576f6_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_576f6_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_576f6_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_576f6_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9bd8624f10>"
+       "<pandas.io.formats.style.Styler at 0x7fb6ce45a220>"
       ]
      },
      "execution_count": 26,

From 787d86ed51129a06d6dd3b5b7592f3537d0ca79e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 13:29:24 +0200
Subject: [PATCH 102/158] Minor edits in theory.

---
 .../talktorial.ipynb                          | 29 ++++++++++++-------
 1 file changed, 18 insertions(+), 11 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index e174f9ac..90756fba 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -10,8 +10,8 @@
     "\n",
     "Authors:\n",
     "\n",
-    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
+    "- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)\n",
     "- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)"
    ]
   },
@@ -21,7 +21,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "We will assess the similarity between a set of kinases based on detected protein-ligand interactions in available complex structures. The [KLIFS](https://klifs.net/) interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex, will be used for this exercise.  "
+    "We will assess the similarity between a set of kinases based on detected protein-ligand interactions in available complex structures. The [KLIFS](https://klifs.net/) interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex, will be used in this exercise.  "
    ]
   },
   {
@@ -43,8 +43,15 @@
     "\n",
     "* Define the kinases of interest\n",
     "* Retrieve and preprocess data\n",
+    "    * Set up a remote KLIFS session\n",
+    "    * Fetch all structures describing these kinases\n",
+    "    * Filter structures\n",
+    "    * Fetch the structures' IFPs (if available)\n",
+    "    * Merge structural and IFP data\n",
     "* Show kinase coverage\n",
     "* Compare structures\n",
+    "    * Prepare IFPs as `numpy` array\n",
+    "    * Calculate pairwise Jaccard distances\n",
     "* Map structure to kinase distance matrix\n",
     "* Save kinase distance matrix"
    ]
@@ -87,7 +94,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use the kinase selection as defined in **Talktorial T023**."
+    "We use the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
@@ -96,15 +103,15 @@
    "source": [
     "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
-    "Interaction fingerprints (IFPs) encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowledge about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the found similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once."
+    "Interaction fingerprints (IFPs) encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowledge about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the known similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between their co-crystallized ligands' binding modes. \n",
-    "We will make use of the KLIFS IFP: For each kinase structure that is co-crystallized with a ligand, all interactions between the 85 KLIFS pocket residues (see more details in **Talktorial T023**) and the co-crystallized ligand are described using the IFP by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e))."
+    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between the co-crystallized binding modes of the ligands. \n",
+    "We will make use of the KLIFS IFP: For each kinase structure that is co-crystallized with a ligand, all interactions between the $85$ KLIFS pocket residues (see more details in __Talktorial T023__) and the co-crystallized ligand are described using the IFP by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e))."
    ]
   },
   {
@@ -113,14 +120,14 @@
    "source": [
     "This is how the IFP is explained in the respective KLIFS publication [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w):\n",
     "\n",
-    "> For each amino acid in the catalytic cleft, seven types of protein−ligand interactions are determined. The presence of a certain type of interaction results in the type-setting ofa “1” in the bit-string; otherwise a “0” is used to indicate the absence of the interaction. The following seven types of interactions are summarized in one bit-string; bit 1 = hydrophobic contact (HYD); bit 2 = face to face aromatic interactions (F−F); bit 3 = face to edge aromatic interactions (F−E); bit 4 = protein H-bond donor (DON); bit 5 = protein H- bond acceptor (ACC); bit 6 = protein cationic interactions (ION+), and bit 7 = protein anionic interactions (ION−)."
+    "> For each amino acid in the catalytic cleft, seven types of protein−ligand interactions are determined. The presence of a certain type of interaction results in the type-setting of a “1” in the bit-string; otherwise a “0” is used to indicate the absence of the interaction. The following seven types of interactions are summarized in one bit-string; bit 1 = hydrophobic contact (HYD); bit 2 = face to face aromatic interactions (F−F); bit 3 = face to edge aromatic interactions (F−E); bit 4 = protein H-bond donor (DON); bit 5 = protein H- bond acceptor (ACC); bit 6 = protein cationic interactions (ION+), and bit 7 = protein anionic interactions (ION−)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This results in a $85 \\times 7 = 595$ bit vector. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do now in the practical part of this tutorial."
+    "This results in a $85 \\times 7 = 595$ bit vector. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will now do in the practical part of this tutorial."
    ]
   },
   {
@@ -140,8 +147,8 @@
    "source": [
     "### Fetching KLIFS data with `opencadd.databases.klifs`\n",
     "\n",
-    "`opencadd` is a Python library for structural cheminformatics developed by the Volkamer lab at the Charité in Berlin. \n",
-    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS IFP as `pandas` DataFrame.\n",
+    "`opencadd` is a Python library for structural cheminformatics developed by the [Volkamer lab](https://volkamerlab.org/) at the Charité in Berlin. \n",
+    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS IFP as a `pandas` DataFrame.\n",
     "\n",
     "- GitHub repository: https://github.com/volkamerlab/opencadd\n",
     "- Documentation: https://opencadd.readthedocs.io\n",
@@ -1841,7 +1848,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 24e24fe0e689c18f616f2e87320f81e13549e6c2 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 13:32:51 +0200
Subject: [PATCH 103/158] T024: Compare identity+subsitution (save only
 substitution)

---
 .../talktorial.ipynb                          | 2516 ++++++++++-------
 1 file changed, 1501 insertions(+), 1015 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 0859f821..de380b1a 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -757,8 +757,7 @@
     "    symmetric = (normalized_score_matrix.values ==\n",
     "                 normalized_score_matrix.values.transpose()).all()\n",
     "    if not symmetric:\n",
-    "        raise ValueError(f\"Translated/rescaled matrix is not symmetric. \"\n",
-    "                         f\"Raise issue on GitHub.\")\n",
+    "        raise ValueError(f\"Translated/rescaled matrix is not symmetric.\")\n",
     "    \n",
     "    return normalized_score_matrix"
    ]
@@ -780,7 +779,7 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_08c84_\">\n",
+       "<table id=\"T_d7ef5_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -812,658 +811,658 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
-       "      <td id=\"T_08c84_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_08c84_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_08c84_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_08c84_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_08c84_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_08c84_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_08c84_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_08c84_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_08c84_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_08c84_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_08c84_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_08c84_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_08c84_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_08c84_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_08c84_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_08c84_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_08c84_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_08c84_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_08c84_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_08c84_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_08c84_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_08c84_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_08c84_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_08c84_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_08c84_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_08c84_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_08c84_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_08c84_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_08c84_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_08c84_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_08c84_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_08c84_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_08c84_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_08c84_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_08c84_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_08c84_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_08c84_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_08c84_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_08c84_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_08c84_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_08c84_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_08c84_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_08c84_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_08c84_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_08c84_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_08c84_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_08c84_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_08c84_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_08c84_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_08c84_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_08c84_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_08c84_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_08c84_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_08c84_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_08c84_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_08c84_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_08c84_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_08c84_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_08c84_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_08c84_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_08c84_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_08c84_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_08c84_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_08c84_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_08c84_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_08c84_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_08c84_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_08c84_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_08c84_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_08c84_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_08c84_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_08c84_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_08c84_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_08c84_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_08c84_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_08c84_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_08c84_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_08c84_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_08c84_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_08c84_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_08c84_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_08c84_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_08c84_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_08c84_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_08c84_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_08c84_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_08c84_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_08c84_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_08c84_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_08c84_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_08c84_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_08c84_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_08c84_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_08c84_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_08c84_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_08c84_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_08c84_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_08c84_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_08c84_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_08c84_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_08c84_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_08c84_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_08c84_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_08c84_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_08c84_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_08c84_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_08c84_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_08c84_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_08c84_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_08c84_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_08c84_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_08c84_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_08c84_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_08c84_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_d7ef5_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_d7ef5_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_d7ef5_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_d7ef5_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_d7ef5_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_d7ef5_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_d7ef5_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_d7ef5_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_d7ef5_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_d7ef5_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_d7ef5_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_d7ef5_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_d7ef5_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_d7ef5_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_d7ef5_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_d7ef5_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_d7ef5_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_d7ef5_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_d7ef5_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_d7ef5_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_d7ef5_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_d7ef5_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_d7ef5_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_d7ef5_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_d7ef5_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_d7ef5_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_d7ef5_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_d7ef5_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_d7ef5_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_d7ef5_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_d7ef5_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_d7ef5_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_d7ef5_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_d7ef5_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_d7ef5_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_d7ef5_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_d7ef5_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_d7ef5_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_d7ef5_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_d7ef5_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_d7ef5_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_d7ef5_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_d7ef5_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_d7ef5_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_d7ef5_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_d7ef5_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_d7ef5_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_d7ef5_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_d7ef5_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_d7ef5_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_d7ef5_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_d7ef5_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_d7ef5_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_d7ef5_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_d7ef5_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_d7ef5_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_d7ef5_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_d7ef5_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_d7ef5_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_d7ef5_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_d7ef5_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_d7ef5_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_d7ef5_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_d7ef5_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_d7ef5_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_d7ef5_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_d7ef5_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_d7ef5_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_d7ef5_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_d7ef5_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_d7ef5_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_d7ef5_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_d7ef5_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_d7ef5_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_d7ef5_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_d7ef5_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_d7ef5_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_d7ef5_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_d7ef5_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_d7ef5_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_d7ef5_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_d7ef5_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_d7ef5_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_d7ef5_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_d7ef5_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_d7ef5_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_d7ef5_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_d7ef5_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_d7ef5_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_d7ef5_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_d7ef5_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_d7ef5_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_d7ef5_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_d7ef5_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_d7ef5_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_d7ef5_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_d7ef5_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_d7ef5_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_d7ef5_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_d7ef5_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_d7ef5_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_d7ef5_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_d7ef5_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_d7ef5_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_d7ef5_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_d7ef5_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_d7ef5_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_d7ef5_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_d7ef5_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_d7ef5_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_d7ef5_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_d7ef5_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d7ef5_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_d7ef5_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_d7ef5_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb6cee52eb0>"
+       "<pandas.io.formats.style.Styler at 0x7fa81ab42700>"
       ]
      },
      "execution_count": 10,
@@ -1732,47 +1731,550 @@
     "#### Visualize similarity as kinase matrix"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pairwise_kinase_similarities(kinase_names, similarity_measure):\n",
+    "    \"\"\"\n",
+    "    TODO\n",
+    "    \"\"\"\n",
+    "    # Initialize matrix with 0\n",
+    "    kinase_similarity_matrix = np.zeros((len(kinase_names),\n",
+    "                                         len(kinase_names)))\n",
+    "    # Iterate over matrix and fill with similarity values\n",
+    "    for i, kinase_name1 in enumerate(kinase_selection_df.kinase_klifs):\n",
+    "        for j, kinase_name2 in enumerate(kinase_selection_df.kinase_klifs):\n",
+    "            kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
+    "                                                                 kinase_sequences[kinase_name2],\n",
+    "                                                                 similarity_measure)\n",
+    "\n",
+    "    # Cast matrix to DataFrame\n",
+    "    kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
+    "                                               index=kinase_selection_df.kinase_klifs,\n",
+    "                                               columns=kinase_selection_df.kinase_klifs)\n",
+    "    kinase_similarity_matrix_df.index.name = None\n",
+    "    kinase_similarity_matrix_df.columns.name = None\n",
+    "\n",
+    "    # Check for symmetry\n",
+    "    symmetric = (kinase_similarity_matrix_df.values ==\n",
+    "                 kinase_similarity_matrix_df.values.transpose()).all()\n",
+    "    if not symmetric:\n",
+    "        raise ValueError(f\"Matrix is not symmetric.\")\n",
+    "    \n",
+    "    return kinase_similarity_matrix_df"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We visualize the similarity matrix using substitution:"
+    "We visualize the similarity matrices using identity and substitution:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "similarity_measure = \"substitution\""
+    "kinase_names = kinase_selection_df[\"kinase_klifs\"]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "_Note_: The cell below takes a few seconds to execute."
+    "##### Kinase similarity matrix: Identity"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.894118</td>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.317647</td>\n",
+       "      <td>0.447059</td>\n",
+       "      <td>0.458824</td>\n",
+       "      <td>0.388235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.894118</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.423529</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>0.117647</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.141176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.341176</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.388235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.376471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.317647</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.341176</td>\n",
+       "      <td>0.329412</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>0.470588</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.447059</td>\n",
+       "      <td>0.423529</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.435294</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.388235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.458824</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.105882</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.364706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.400000</td>\n",
+       "      <td>0.141176</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.376471</td>\n",
+       "      <td>0.470588</td>\n",
+       "      <td>0.388235</td>\n",
+       "      <td>0.364706</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       "EGFR   1.000000  0.894118  0.117647  0.470588  0.376471  0.317647  0.447059   \n",
+       "ErbB2  0.894118  1.000000  0.117647  0.435294  0.400000  0.329412  0.423529   \n",
+       "p110a  0.117647  0.117647  1.000000  0.152941  0.152941  0.105882  0.141176   \n",
+       "KDR    0.470588  0.435294  0.152941  1.000000  0.400000  0.341176  0.435294   \n",
+       "BRAF   0.376471  0.400000  0.152941  0.400000  1.000000  0.329412  0.388235   \n",
+       "CDK2   0.317647  0.329412  0.105882  0.341176  0.329412  1.000000  0.376471   \n",
+       "LCK    0.447059  0.423529  0.141176  0.435294  0.388235  0.376471  1.000000   \n",
+       "MET    0.458824  0.470588  0.105882  0.470588  0.376471  0.364706  0.400000   \n",
+       "p38a   0.388235  0.400000  0.141176  0.388235  0.376471  0.470588  0.388235   \n",
+       "\n",
+       "            MET      p38a  \n",
+       "EGFR   0.458824  0.388235  \n",
+       "ErbB2  0.470588  0.400000  \n",
+       "p110a  0.105882  0.141176  \n",
+       "KDR    0.470588  0.388235  \n",
+       "BRAF   0.376471  0.376471  \n",
+       "CDK2   0.364706  0.470588  \n",
+       "LCK    0.400000  0.388235  \n",
+       "MET    1.000000  0.364706  \n",
+       "p38a   0.364706  1.000000  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "kinase_similarity_matrix = np.zeros((len(kinase_selection_df.kinase_klifs),\n",
-    "                                     len(kinase_selection_df.kinase_klifs)))\n",
-    "for i, kinase_name1 in enumerate(kinase_selection_df.kinase_klifs):\n",
-    "    for j, kinase_name2 in enumerate(kinase_selection_df.kinase_klifs):\n",
-    "        kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
-    "                                                             kinase_sequences[kinase_name2],\n",
-    "                                                             similarity_measure)"
+    "kinase_similarity_matrix_identity_df = pairwise_kinase_similarities(kinase_names, similarity_measure=\"identity\")\n",
+    "kinase_similarity_matrix_identity_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
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+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_eeb94_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_eeb94_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_eeb94_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_eeb94_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_eeb94_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_eeb94_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_eeb94_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_eeb94_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_eeb94_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_eeb94_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_eeb94_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_eeb94_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_eeb94_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_eeb94_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_eeb94_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_eeb94_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_eeb94_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_eeb94_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_eeb94_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_eeb94_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_eeb94_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_eeb94_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_eeb94_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_eeb94_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_eeb94_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_eeb94_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_eeb94_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_eeb94_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_eeb94_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_eeb94_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_eeb94_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_eeb94_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_eeb94_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_eeb94_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_eeb94_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_eeb94_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_eeb94_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_eeb94_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_eeb94_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_eeb94_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_eeb94_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_eeb94_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_eeb94_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_eeb94_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_eeb94_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_eeb94_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_eeb94_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_eeb94_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_eeb94_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_eeb94_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_eeb94_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_eeb94_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_eeb94_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_eeb94_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_eeb94_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_eeb94_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_eeb94_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fa8886181f0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Show matrix with background gradient\n",
+    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
+    "kinase_similarity_matrix_identity_df.style.\\\n",
+    "    background_gradient(cmap=cm).\\\n",
+    "    format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Kinase similarity matrix: Substitution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1944,241 +2446,208 @@
        "p38a   0.629355  1.000000  "
       ]
      },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                           index=kinase_selection_df.kinase_klifs,\n",
-    "                                           columns=kinase_selection_df.kinase_klifs)\n",
-    "kinase_similarity_matrix_df.index.name = None\n",
-    "kinase_similarity_matrix_df.columns.name = None\n",
-    "kinase_similarity_matrix_df\n",
-    "# NBVAL_CHECK_OUTPUT"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's check that the matrix is symmetric:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(kinase_similarity_matrix_df.values ==\n",
-    " kinase_similarity_matrix_df.values.transpose()).all()\n",
+    "kinase_similarity_matrix_substitution_df = pairwise_kinase_similarities(kinase_names, similarity_measure=\"substitution\")\n",
+    "kinase_similarity_matrix_substitution_df\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_3d2f1_row0_col0, #T_3d2f1_row1_col1, #T_3d2f1_row2_col2, #T_3d2f1_row3_col3, #T_3d2f1_row4_col4, #T_3d2f1_row5_col5, #T_3d2f1_row6_col6, #T_3d2f1_row7_col7, #T_3d2f1_row8_col8 {\n",
+       "#T_1d417_row0_col0, #T_1d417_row1_col1, #T_1d417_row2_col2, #T_1d417_row3_col3, #T_1d417_row4_col4, #T_1d417_row5_col5, #T_1d417_row6_col6, #T_1d417_row7_col7, #T_1d417_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row0_col1 {\n",
+       "#T_1d417_row0_col1 {\n",
        "  background-color: #178b17;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row0_col2 {\n",
+       "#T_1d417_row0_col2 {\n",
        "  background-color: #deedde;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row0_col3 {\n",
+       "#T_1d417_row0_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row0_col4, #T_3d2f1_row0_col5, #T_3d2f1_row1_col4, #T_3d2f1_row3_col8, #T_3d2f1_row4_col5, #T_3d2f1_row8_col7 {\n",
+       "#T_1d417_row0_col4, #T_1d417_row0_col5, #T_1d417_row1_col4, #T_1d417_row3_col8, #T_1d417_row4_col5, #T_1d417_row8_col7 {\n",
        "  background-color: #8fc68f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row0_col6 {\n",
+       "#T_1d417_row0_col6 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row0_col7 {\n",
+       "#T_1d417_row0_col7 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row0_col8, #T_3d2f1_row8_col1 {\n",
+       "#T_1d417_row0_col8, #T_1d417_row8_col1 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row1_col0 {\n",
+       "#T_1d417_row1_col0 {\n",
        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row1_col2 {\n",
+       "#T_1d417_row1_col2 {\n",
        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row1_col3 {\n",
+       "#T_1d417_row1_col3 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row1_col5, #T_3d2f1_row1_col8, #T_3d2f1_row7_col4, #T_3d2f1_row8_col4 {\n",
+       "#T_1d417_row1_col5, #T_1d417_row1_col8, #T_1d417_row7_col4, #T_1d417_row8_col4 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row1_col6 {\n",
+       "#T_1d417_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row1_col7 {\n",
+       "#T_1d417_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row2_col0, #T_3d2f1_row2_col1, #T_3d2f1_row2_col3, #T_3d2f1_row2_col4, #T_3d2f1_row2_col5, #T_3d2f1_row2_col6, #T_3d2f1_row2_col7, #T_3d2f1_row2_col8, #T_3d2f1_row7_col2 {\n",
+       "#T_1d417_row2_col0, #T_1d417_row2_col1, #T_1d417_row2_col3, #T_1d417_row2_col4, #T_1d417_row2_col5, #T_1d417_row2_col6, #T_1d417_row2_col7, #T_1d417_row2_col8, #T_1d417_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row3_col0, #T_3d2f1_row7_col3 {\n",
+       "#T_1d417_row3_col0, #T_1d417_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row3_col1, #T_3d2f1_row6_col7 {\n",
+       "#T_1d417_row3_col1, #T_1d417_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row3_col2, #T_3d2f1_row5_col2 {\n",
+       "#T_1d417_row3_col2, #T_1d417_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row3_col4 {\n",
+       "#T_1d417_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row3_col5, #T_3d2f1_row4_col0 {\n",
+       "#T_1d417_row3_col5, #T_1d417_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row3_col6, #T_3d2f1_row4_col3, #T_3d2f1_row5_col7 {\n",
+       "#T_1d417_row3_col6, #T_1d417_row4_col3, #T_1d417_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row3_col7 {\n",
+       "#T_1d417_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row4_col1 {\n",
+       "#T_1d417_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row4_col2 {\n",
+       "#T_1d417_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row4_col6, #T_3d2f1_row4_col7, #T_3d2f1_row7_col5 {\n",
+       "#T_1d417_row4_col6, #T_1d417_row4_col7, #T_1d417_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row4_col8 {\n",
+       "#T_1d417_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col0, #T_3d2f1_row8_col6 {\n",
+       "#T_1d417_row5_col0, #T_1d417_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col1 {\n",
+       "#T_1d417_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col3, #T_3d2f1_row8_col3 {\n",
+       "#T_1d417_row5_col3, #T_1d417_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col4 {\n",
+       "#T_1d417_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col6, #T_3d2f1_row6_col4 {\n",
+       "#T_1d417_row5_col6, #T_1d417_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row5_col8 {\n",
+       "#T_1d417_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row6_col0 {\n",
+       "#T_1d417_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row6_col1 {\n",
+       "#T_1d417_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row6_col2 {\n",
+       "#T_1d417_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row6_col3 {\n",
+       "#T_1d417_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row6_col5 {\n",
+       "#T_1d417_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row6_col8 {\n",
+       "#T_1d417_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row7_col0 {\n",
+       "#T_1d417_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3d2f1_row7_col1 {\n",
+       "#T_1d417_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row7_col6 {\n",
+       "#T_1d417_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row7_col8 {\n",
+       "#T_1d417_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row8_col0 {\n",
+       "#T_1d417_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row8_col2 {\n",
+       "#T_1d417_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3d2f1_row8_col5 {\n",
+       "#T_1d417_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_3d2f1_\">\n",
+       "<table id=\"T_1d417_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2195,121 +2664,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_3d2f1_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_3d2f1_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_3d2f1_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_3d2f1_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_3d2f1_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_3d2f1_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_3d2f1_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_3d2f1_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_3d2f1_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_3d2f1_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_3d2f1_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_3d2f1_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_3d2f1_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_3d2f1_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_3d2f1_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_3d2f1_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_3d2f1_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_3d2f1_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_3d2f1_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_3d2f1_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_3d2f1_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_3d2f1_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_3d2f1_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_3d2f1_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_3d2f1_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_3d2f1_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_3d2f1_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_3d2f1_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_3d2f1_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_3d2f1_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_3d2f1_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_3d2f1_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_3d2f1_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_3d2f1_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_3d2f1_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_3d2f1_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_3d2f1_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_3d2f1_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_3d2f1_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_3d2f1_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_3d2f1_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_3d2f1_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_3d2f1_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_3d2f1_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_3d2f1_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_3d2f1_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_3d2f1_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_3d2f1_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_3d2f1_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_3d2f1_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_3d2f1_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_3d2f1_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_3d2f1_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_3d2f1_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_3d2f1_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_3d2f1_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_3d2f1_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_3d2f1_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_3d2f1_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_3d2f1_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_3d2f1_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_3d2f1_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_3d2f1_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_3d2f1_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_3d2f1_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3d2f1_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_3d2f1_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_3d2f1_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_3d2f1_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_3d2f1_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_3d2f1_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_3d2f1_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_3d2f1_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_3d2f1_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_3d2f1_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_1d417_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1d417_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_1d417_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_1d417_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_1d417_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_1d417_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_1d417_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_1d417_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_1d417_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1d417_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_1d417_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_1d417_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_1d417_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_1d417_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_1d417_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_1d417_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_1d417_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_1d417_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_1d417_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_1d417_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_1d417_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_1d417_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_1d417_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_1d417_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_1d417_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_1d417_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_1d417_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_1d417_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_1d417_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_1d417_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_1d417_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_1d417_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_1d417_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_1d417_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_1d417_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_1d417_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_1d417_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_1d417_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_1d417_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_1d417_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_1d417_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_1d417_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_1d417_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_1d417_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_1d417_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_1d417_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_1d417_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_1d417_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_1d417_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_1d417_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_1d417_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_1d417_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_1d417_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_1d417_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_1d417_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_1d417_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_1d417_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_1d417_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_1d417_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_1d417_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_1d417_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_1d417_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_1d417_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_1d417_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_1d417_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_1d417_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1d417_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_1d417_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_1d417_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_1d417_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_1d417_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_1d417_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_1d417_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_1d417_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_1d417_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_1d417_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb6ce479550>"
+       "<pandas.io.formats.style.Styler at 0x7fa81a1006a0>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2317,11 +2786,28 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix_df.style.\\\n",
+    "kinase_similarity_matrix_substitution_df.style.\\\n",
     "    background_gradient(cmap=cm).\\\n",
     "    format(\"{:.3f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For all downstream analysis, we will consider only the kinase similarity matrix calculated based on the substitution matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "similarity_measure = \"substitution\"\n",
+    "kinase_similarity_matrix_df = kinase_similarity_matrix_substitution_df"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2331,7 +2817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2355,7 +2841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2375,7 +2861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2384,195 +2870,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "#T_576f6_row3_col0, #T_576f6_row7_col3 {\n",
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-       "#T_576f6_row3_col1, #T_576f6_row6_col7 {\n",
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        "  background-color: #73b873;\n",
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-       "#T_576f6_row3_col4 {\n",
+       "#T_c000f_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row3_col5, #T_576f6_row4_col0 {\n",
+       "#T_c000f_row3_col5, #T_c000f_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row3_col6, #T_576f6_row4_col3, #T_576f6_row5_col7 {\n",
+       "#T_c000f_row3_col6, #T_c000f_row4_col3, #T_c000f_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row3_col7 {\n",
+       "#T_c000f_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_576f6_row3_col8 {\n",
+       "#T_c000f_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row4_col1 {\n",
+       "#T_c000f_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row4_col2 {\n",
+       "#T_c000f_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row4_col6, #T_576f6_row4_col7, #T_576f6_row7_col5 {\n",
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        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row4_col8, #T_576f6_row5_col1 {\n",
+       "#T_c000f_row4_col8, #T_c000f_row5_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row5_col0, #T_576f6_row8_col6 {\n",
+       "#T_c000f_row5_col0, #T_c000f_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row5_col3, #T_576f6_row8_col3 {\n",
+       "#T_c000f_row5_col3, #T_c000f_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row5_col4 {\n",
+       "#T_c000f_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row5_col6, #T_576f6_row6_col4 {\n",
+       "#T_c000f_row5_col6, #T_c000f_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row5_col8 {\n",
+       "#T_c000f_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_576f6_row6_col0 {\n",
+       "#T_c000f_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row6_col1 {\n",
+       "#T_c000f_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row6_col2 {\n",
+       "#T_c000f_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row6_col3 {\n",
+       "#T_c000f_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row6_col5 {\n",
+       "#T_c000f_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row6_col8 {\n",
+       "#T_c000f_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row7_col0 {\n",
+       "#T_c000f_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row7_col1 {\n",
+       "#T_c000f_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row7_col6 {\n",
+       "#T_c000f_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row7_col8 {\n",
+       "#T_c000f_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row8_col0 {\n",
+       "#T_c000f_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row8_col2 {\n",
+       "#T_c000f_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_576f6_row8_col5 {\n",
+       "#T_c000f_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_576f6_\">\n",
+       "<table id=\"T_c000f_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2589,121 +3075,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_576f6_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_576f6_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_576f6_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_576f6_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_576f6_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_576f6_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_576f6_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_576f6_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_576f6_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_576f6_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_576f6_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_576f6_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_576f6_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_576f6_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_576f6_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_576f6_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_576f6_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_576f6_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_576f6_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_576f6_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_576f6_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_576f6_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_576f6_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_576f6_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_576f6_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_576f6_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_576f6_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_576f6_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_576f6_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_576f6_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_576f6_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_576f6_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_576f6_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_576f6_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_576f6_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_576f6_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_576f6_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_576f6_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_576f6_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_576f6_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_576f6_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_576f6_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_576f6_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_576f6_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_576f6_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_576f6_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_576f6_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_576f6_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_576f6_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_576f6_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_576f6_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_576f6_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_576f6_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_576f6_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_576f6_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_576f6_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_576f6_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_576f6_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_576f6_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_576f6_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_576f6_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_576f6_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_576f6_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_576f6_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_576f6_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_576f6_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_576f6_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_576f6_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_576f6_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_576f6_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_576f6_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_576f6_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_576f6_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_576f6_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_576f6_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_c000f_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_c000f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_c000f_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_c000f_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_c000f_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_c000f_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_c000f_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_c000f_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_c000f_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_c000f_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_c000f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_c000f_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_c000f_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_c000f_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_c000f_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_c000f_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_c000f_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_c000f_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_c000f_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_c000f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_c000f_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_c000f_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_c000f_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_c000f_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_c000f_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_c000f_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_c000f_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_c000f_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_c000f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_c000f_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_c000f_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_c000f_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_c000f_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_c000f_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_c000f_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_c000f_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_c000f_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_c000f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_c000f_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_c000f_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_c000f_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_c000f_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_c000f_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_c000f_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_c000f_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_c000f_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_c000f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_c000f_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_c000f_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_c000f_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_c000f_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_c000f_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_c000f_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_c000f_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_c000f_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_c000f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_c000f_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_c000f_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_c000f_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_c000f_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_c000f_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_c000f_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_c000f_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_c000f_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_c000f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_c000f_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c000f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c000f_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_c000f_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_c000f_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_c000f_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_c000f_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_c000f_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_c000f_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_c000f_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_c000f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fb6ce45a220>"
+       "<pandas.io.formats.style.Styler at 0x7fa81a116730>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2723,7 +3209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [

From c02623a3801e9b2d5886f448c2a2759f4f4b83f4 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 13:45:33 +0200
Subject: [PATCH 104/158] Minor changes in the practical part.

---
 .../talktorial.ipynb                          | 842 +++---------------
 1 file changed, 111 insertions(+), 731 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 90756fba..cb7c8f2e 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -103,7 +103,7 @@
    "source": [
     "### Kinase similarity descriptor: KLIFS interaction fingerprints\n",
     "\n",
-    "Interaction fingerprints (IFPs) encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other (off-targets) than its designated protein (on-target), it is possible that this ligand will cause unintended side effects. Knowledge about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the known similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once."
+    "Interaction fingerprints (IFPs) encode the binding mode of a ligand in a binding site, i.e., the protein-ligand interactions that are present in a structurally resolved complex. If a ligand can form similar interaction patterns in proteins other than its designated protein (off- vs. on-target), it is possible that this ligand will cause unintended side effects. Knowledge about similarities between proteins can therefore help to avoid such off-target effects; or to exploit the known similarities for polypharmacology effects where one ligand could intentionally target multiple proteins at once."
    ]
   },
   {
@@ -167,15 +167,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pathlib import Path\n",
     "\n",
@@ -209,7 +201,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's load the kinase selection as defined in **Talktorial T023**."
+    "Let's load the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
@@ -353,7 +345,8 @@
    ],
    "source": [
     "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
-    "kinase_selection_df"
+    "kinase_selection_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -390,16 +383,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "klifs_session = setup_remote()"
    ]
@@ -420,7 +404,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of structures: 2486\n",
+      "Number of structures: 2484\n",
       "Kinases: CDK2 p38a EGFR ErbB2 MET LCK KDR BRAF p110a\n"
      ]
     }
@@ -432,7 +416,8 @@
     "# Get all available structures for these kinases\n",
     "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
-    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -473,7 +458,8 @@
     "    & (structures_df[\"structure.qualityscore\"] >= 6)\n",
     "]\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
-    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())"
+    "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -498,14 +484,15 @@
    ],
    "source": [
     "structure_klifs_ids = structures_df[\"structure.klifs_id\"].to_list()\n",
-    "print(f\"Number of structures: {len(structure_klifs_ids)}\")"
+    "print(f\"Number of structures: {len(structure_klifs_ids)}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To make it easier for us to maintain the talktorials, we will load now a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please uncomment the cell below."
+    "To make it easier for us to maintain the talktorials, we will now load a set of frozen structure KLIFS IDs (2021-08-23) and continue to work with those. If you would like to work with the latest KLIFS data, please uncomment the cell below."
    ]
   },
   {
@@ -625,7 +612,8 @@
    "source": [
     "ifps_df = klifs_session.interactions.by_structure_klifs_id(structure_klifs_ids)\n",
     "print(f\"Number of structures with IFPs: {len(ifps_df)}\")\n",
-    "ifps_df.head()"
+    "ifps_df.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -669,17 +657,16 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>structure.klifs_id</th>\n",
-       "      <th>interaction.fingerprint</th>\n",
+       "      <th>interaction.fingerprint_x</th>\n",
        "      <th>structure.pdb_id</th>\n",
        "      <th>structure.alternate_model</th>\n",
        "      <th>structure.chain</th>\n",
        "      <th>species.klifs</th>\n",
        "      <th>kinase.klifs_id</th>\n",
        "      <th>kinase.klifs_name</th>\n",
+       "      <th>kinase.names</th>\n",
        "      <th>kinase.family</th>\n",
-       "      <th>kinase.group</th>\n",
        "      <th>...</th>\n",
-       "      <th>structure.bp_ii_b_in</th>\n",
        "      <th>structure.bp_ii_out</th>\n",
        "      <th>structure.bp_ii_b</th>\n",
        "      <th>structure.bp_iii</th>\n",
@@ -689,6 +676,7 @@
        "      <th>structure.grich_angle</th>\n",
        "      <th>structure.grich_rotation</th>\n",
        "      <th>structure.filepath</th>\n",
+       "      <th>structure.curation_flag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -710,11 +698,11 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>21.684799</td>\n",
        "      <td>71.330002</td>\n",
        "      <td>42.087002</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -734,11 +722,11 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -758,11 +746,11 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>16.591600</td>\n",
        "      <td>53.096100</td>\n",
        "      <td>37.676601</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -782,11 +770,11 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>20.174601</td>\n",
        "      <td>65.518700</td>\n",
        "      <td>21.431101</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -806,19 +794,19 @@
        "      <td>False</td>\n",
        "      <td>False</td>\n",
        "      <td>False</td>\n",
-       "      <td>False</td>\n",
        "      <td>14.820200</td>\n",
        "      <td>48.159698</td>\n",
        "      <td>43.046001</td>\n",
        "      <td>&lt;NA&gt;</td>\n",
+       "      <td>False</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 42 columns</p>\n",
+       "<p>5 rows × 47 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   structure.klifs_id                            interaction.fingerprint  \\\n",
+       "   structure.klifs_id                          interaction.fingerprint_x  \\\n",
        "0                 775  0000000000000010000000000000000000000000000000...   \n",
        "1                 777  0000000000000010000001000000000000000000000000...   \n",
        "2                 778  0000000000000010000000000000000000000000000000...   \n",
@@ -832,35 +820,35 @@
        "3             2itu                         A               A         Human   \n",
        "4             3w33                         A               A         Human   \n",
        "\n",
-       "   kinase.klifs_id kinase.klifs_name kinase.family kinase.group  ...  \\\n",
-       "0              406              EGFR          <NA>         <NA>  ...   \n",
-       "1              406              EGFR          <NA>         <NA>  ...   \n",
-       "2              406              EGFR          <NA>         <NA>  ...   \n",
-       "3              406              EGFR          <NA>         <NA>  ...   \n",
-       "4              406              EGFR          <NA>         <NA>  ...   \n",
+       "   kinase.klifs_id kinase.klifs_name kinase.names kinase.family  ...  \\\n",
+       "0              406              EGFR         <NA>          <NA>  ...   \n",
+       "1              406              EGFR         <NA>          <NA>  ...   \n",
+       "2              406              EGFR         <NA>          <NA>  ...   \n",
+       "3              406              EGFR         <NA>          <NA>  ...   \n",
+       "4              406              EGFR         <NA>          <NA>  ...   \n",
        "\n",
-       "  structure.bp_ii_b_in structure.bp_ii_out structure.bp_ii_b structure.bp_iii  \\\n",
-       "0                False               False             False            False   \n",
-       "1                False               False             False            False   \n",
-       "2                False               False             False            False   \n",
-       "3                False               False             False            False   \n",
-       "4                False               False             False            False   \n",
+       "  structure.bp_ii_out structure.bp_ii_b structure.bp_iii structure.bp_iv  \\\n",
+       "0               False             False            False           False   \n",
+       "1               False             False            False           False   \n",
+       "2               False             False            False           False   \n",
+       "3               False             False            False           False   \n",
+       "4               False             False            False           False   \n",
        "\n",
-       "  structure.bp_iv structure.bp_v structure.grich_distance  \\\n",
-       "0           False          False                21.684799   \n",
-       "1           False          False                 0.000000   \n",
-       "2           False          False                16.591600   \n",
-       "3           False          False                20.174601   \n",
-       "4           False          False                14.820200   \n",
+       "   structure.bp_v  structure.grich_distance structure.grich_angle  \\\n",
+       "0           False                 21.684799             71.330002   \n",
+       "1           False                  0.000000              0.000000   \n",
+       "2           False                 16.591600             53.096100   \n",
+       "3           False                 20.174601             65.518700   \n",
+       "4           False                 14.820200             48.159698   \n",
        "\n",
-       "   structure.grich_angle  structure.grich_rotation  structure.filepath  \n",
-       "0              71.330002                 42.087002                <NA>  \n",
-       "1               0.000000                  0.000000                <NA>  \n",
-       "2              53.096100                 37.676601                <NA>  \n",
-       "3              65.518700                 21.431101                <NA>  \n",
-       "4              48.159698                 43.046001                <NA>  \n",
+       "  structure.grich_rotation structure.filepath structure.curation_flag  \n",
+       "0                42.087002               <NA>                   False  \n",
+       "1                 0.000000               <NA>                   False  \n",
+       "2                37.676601               <NA>                   False  \n",
+       "3                21.431101               <NA>                   False  \n",
+       "4                43.046001               <NA>                   False  \n",
        "\n",
-       "[5 rows x 42 columns]"
+       "[5 rows x 47 columns]"
       ]
      },
      "execution_count": 11,
@@ -871,7 +859,8 @@
    "source": [
     "structures_with_ifps_df = ifps_df.merge(structures_df, on=\"structure.klifs_id\", how=\"inner\")\n",
     "print(f\"Number of structures with IFPs: {len(structures_with_ifps_df)}\")\n",
-    "structures_with_ifps_df.head()"
+    "structures_with_ifps_df.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -932,7 +921,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -949,7 +938,8 @@
     "ax.set_xlabel(\"Number of structures\")\n",
     "ax.set_ylabel(\"Kinase name\")\n",
     "for i, value in enumerate(n_structures_per_kinase):\n",
-    "    ax.text(value, i, str(value), va=\"center\")"
+    "    ax.text(value, i, str(value), va=\"center\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -965,7 +955,7 @@
    "source": [
     "### Compare structures\n",
     "\n",
-    "We will compare the structures' IFP pairwise using the Tanimoto/Jaccard distance as implemented in `sklearn.metrics.pairwise_distances`, which uses under the hood the method `scipy.spatial.distance`."
+    "We will make a pairwise comparison of the structures' IFP using the Tanimoto/Jaccard distance as implemented in `sklearn.metrics.pairwise_distances`, which uses under the hood the method `scipy.spatial.distance`."
    ]
   },
   {
@@ -983,20 +973,25 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "structure.klifs_id  kinase.klifs_name\n",
-       "775                 EGFR                 0000000000000010000000000000000000000000000000...\n",
-       "777                 EGFR                 0000000000000010000001000000000000000000000000...\n",
-       "778                 EGFR                 0000000000000010000000000000000000000000000000...\n",
-       "779                 EGFR                 0000000000000010000001000000000000000000000000...\n",
-       "782                 EGFR                 0000000000000010000001000000000000000000000000...\n",
-       "Name: interaction.fingerprint, dtype: string"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "KeyError",
+     "evalue": "'interaction.fingerprint'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_9005/868816743.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"interaction.fingerprint\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m ]\n\u001b[1;32m      5\u001b[0m \u001b[0mifp_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'"
+     ]
     }
    ],
    "source": [
@@ -1004,63 +999,32 @@
     "ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n",
     "    \"interaction.fingerprint\"\n",
     "]\n",
-    "ifp_series.head()"
+    "ifp_series.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "structure.klifs_id  kinase.klifs_name\n",
-       "775                 EGFR                 [False, False, False, False, False, False, Fal...\n",
-       "777                 EGFR                 [False, False, False, False, False, False, Fal...\n",
-       "778                 EGFR                 [False, False, False, False, False, False, Fal...\n",
-       "779                 EGFR                 [False, False, False, False, False, False, Fal...\n",
-       "782                 EGFR                 [False, False, False, False, False, False, Fal...\n",
-       "Name: interaction.fingerprint, dtype: object"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Cast \"0\" and \"1\" to boolean False and True\n",
     "ifp_series = ifp_series.apply(lambda x: [True if i == \"1\" else False for i in x])\n",
-    "ifp_series.head()"
+    "ifp_series.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Convert to numpy array\n",
     "ifps_array = np.array(ifp_series.to_list())\n",
-    "ifps_array"
+    "ifps_array\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1076,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1085,113 +1049,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Structure distance matrix size: (1466, 1466)\n",
-      "Show matrix subset:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>structure.klifs_id</th>\n",
-       "      <th>775</th>\n",
-       "      <th>777</th>\n",
-       "      <th>778</th>\n",
-       "      <th>779</th>\n",
-       "      <th>782</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>structure.klifs_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>775</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.720000</td>\n",
-       "      <td>0.542857</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.483871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>777</th>\n",
-       "      <td>0.720000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.741935</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.703704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>778</th>\n",
-       "      <td>0.542857</td>\n",
-       "      <td>0.741935</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.696970</td>\n",
-       "      <td>0.411765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>779</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.696970</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.655172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>782</th>\n",
-       "      <td>0.483871</td>\n",
-       "      <td>0.703704</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.655172</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "structure.klifs_id       775       777       778       779       782\n",
-       "structure.klifs_id                                                  \n",
-       "775                 0.000000  0.720000  0.542857  0.500000  0.483871\n",
-       "777                 0.720000  0.000000  0.741935  0.500000  0.703704\n",
-       "778                 0.542857  0.741935  0.000000  0.696970  0.411765\n",
-       "779                 0.500000  0.500000  0.696970  0.000000  0.655172\n",
-       "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
     "structure_klifs_ids = ifp_series.index.get_level_values(0)\n",
@@ -1200,7 +1060,8 @@
     ")\n",
     "print(f\"Structure distance matrix size: {structure_distance_matrix_df.shape}\")\n",
     "print(\"Show matrix subset:\")\n",
-    "structure_distance_matrix_df.iloc[:5, :5]"
+    "structure_distance_matrix_df.iloc[:5, :5]\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1216,112 +1077,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Show matrix subset:\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>EGFR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kinase.klifs_name</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.720000</td>\n",
-       "      <td>0.542857</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.483871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.720000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.741935</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.703704</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.542857</td>\n",
-       "      <td>0.741935</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.696970</td>\n",
-       "      <td>0.411765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.500000</td>\n",
-       "      <td>0.696970</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.655172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>0.483871</td>\n",
-       "      <td>0.703704</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.655172</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR      EGFR\n",
-       "kinase.klifs_name                                                  \n",
-       "EGFR               0.000000  0.720000  0.542857  0.500000  0.483871\n",
-       "EGFR               0.720000  0.000000  0.741935  0.500000  0.703704\n",
-       "EGFR               0.542857  0.741935  0.000000  0.696970  0.411765\n",
-       "EGFR               0.500000  0.500000  0.696970  0.000000  0.655172\n",
-       "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Copy distance matrix to kinase matrix\n",
     "kinase_distance_matrix_df = structure_distance_matrix_df.copy()\n",
@@ -1330,34 +1088,22 @@
     "kinase_distance_matrix_df.index = kinase_names\n",
     "kinase_distance_matrix_df.columns = kinase_names\n",
     "print(\"Show matrix subset:\")\n",
-    "kinase_distance_matrix_df.iloc[:5, :5]"
+    "kinase_distance_matrix_df.iloc[:5, :5]\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A kinase pair can be represented by many different structure pairs which are associated with different distance values. For example, if we compare all EGFR structure to each other, the range of distance values is already quite high because we can observe quite different binding modes of the co-crystallized ligands."
+    "A kinase pair can be represented by many different structure pairs which are associated with different distance values. For example, if we compare all EGFR structure to each other, the range of distance values is already quite high because we can observe different binding modes of the co-crystallized ligands."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Select EGFR-EGFR structure pairs only\n",
     "D = kinase_distance_matrix_df.loc[\"EGFR\", \"EGFR\"]\n",
@@ -1365,7 +1111,8 @@
     "# = lower triangular matrix without the diagonal\n",
     "D_condensed = distance.squareform(D)\n",
     "# Plot pairwise distances\n",
-    "plt.boxplot(D_condensed);"
+    "plt.boxplot(D_condensed);\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1377,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1394,381 +1141,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Structure matrix of shape (1466, 1466) reduced to kinase matrix of shape (9, 9).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\n",
     "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
-    ")"
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-       "<table id=\"T_98eee_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_98eee_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_98eee_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_98eee_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_98eee_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_98eee_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_98eee_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_98eee_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_98eee_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_98eee_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_98eee_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_98eee_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_98eee_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_98eee_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_98eee_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_98eee_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_98eee_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_98eee_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_98eee_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_98eee_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_98eee_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_98eee_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_98eee_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_98eee_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_98eee_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_98eee_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_98eee_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_98eee_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_98eee_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_98eee_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_98eee_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_98eee_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_98eee_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_98eee_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_98eee_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_98eee_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_98eee_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_98eee_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_98eee_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_98eee_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_98eee_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_98eee_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_98eee_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_98eee_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_98eee_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_98eee_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_98eee_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_98eee_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_98eee_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_98eee_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_98eee_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_98eee_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_98eee_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_98eee_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_98eee_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_98eee_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_98eee_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_98eee_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_98eee_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_98eee_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_98eee_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_98eee_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_98eee_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_98eee_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_98eee_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_98eee_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_98eee_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_98eee_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_98eee_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_98eee_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_98eee_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_98eee_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_98eee_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_98eee_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_98eee_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_98eee_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa8cb0c0640>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
@@ -1787,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1805,12 +1192,12 @@
     "\n",
     "In this talktorial, we have used the KLIFS interaction fingerprints, which describe binding modes of co-crystallized ligands in kinase structures, to assess kinase similarity.\n",
     "\n",
-    "We have to keep two things in mind:\n",
+    "We have to keep two elements in mind:\n",
     "\n",
     "- Interaction fingerprints may miss important key interactions if the co-crystallized ligand(s) simply do not form the interaction but other ligands might.\n",
-    "- We only compare here the two closest binding modes per kinase pair, although we have &mdash; at least for kinases such as EGFR and CDK2 &mdash; much more data available on binding modes. As an alternative, one could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its drawbacks itself: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
+    "- We only compare here the two closest binding modes per kinase pair, although we have &mdash; for kinases such as EGFR and CDK2 &mdash; much more data available on binding modes. As an alternative, one could instead aggregate the information from multiple binding modes per kinase in a fingerprint that reports all interactions seen in any structure. However, this approach has its own drawbacks: Some kinases have much higher coverage than others, leading to an imbalance in information content.\n",
     "\n",
-    "The kinase distance matrix above will be reloaded in **Talktorial T028**, where we compare kinase similarities from different perspectives, including the interaction perspective we have talked about in this talktorial."
+    "The kinase distance matrix above will be reloaded in __Talktorial T028__, where we compare kinase similarities from different perspectives, including the interaction perspective we have talked about in this talktorial."
    ]
   },
   {
@@ -1823,13 +1210,6 @@
     "2. At one point, we are mapping structure pairs to kinase pairs. We use here the minimum structure distance. What other mapping strategies can you think of? What advantages and disadvantages could they have?\n",
     "3. What is the difference between the distances that we show in the heatmap and the dendrogram? "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From b9ccefdbe4b675b84bff7449613b60ef3bf27aa4 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 14:07:00 +0200
Subject: [PATCH 105/158] minor change

---
 .../talktorials/T026_kinase_similarity_ifp/talktorial.ipynb  | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index cb7c8f2e..40af31d9 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -987,7 +987,7 @@
       "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'",
       "\nThe above exception was the direct cause of the following exception:\n",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_9005/868816743.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"interaction.fingerprint\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m ]\n\u001b[1;32m      5\u001b[0m \u001b[0mifp_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_9098/868816743.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"interaction.fingerprint\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m ]\n\u001b[1;32m      5\u001b[0m \u001b[0mifp_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'"
@@ -1159,8 +1159,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
-    "# NBVAL_CHECK_OUTPUT"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {

From 67fa57158fd0b32b38756d536d08ee36b8e8d22d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 14:09:18 +0200
Subject: [PATCH 106/158] T024: Comment on id/subst results; simplify output
 file names

---
 .../data/kinase_distance_matrix_sequence.csv  |   20 +-
 ..._distance_substitution_matrix_sequence.csv |   10 -
 .../kinase_similarity_matrix_sequence.csv     |   20 +-
 ...imilarity_substitution_matrix_sequence.csv |   10 -
 .../talktorial.ipynb                          | 2209 +++++++++--------
 5 files changed, 1127 insertions(+), 1142 deletions(-)
 delete mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
 delete mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
index e972d41d..3acff1d6 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
-EGFR,0.0,0.10588235294117643,0.6235294117647059,0.6823529411764706,0.5529411764705883,0.5411764705882354,0.611764705882353,0.5294117647058824,0.8823529411764706
-ErbB2,0.10588235294117643,0.0,0.6,0.6705882352941177,0.5764705882352941,0.5294117647058824,0.6,0.5647058823529412,0.8823529411764706
-BRAF,0.6235294117647059,0.6,0.0,0.6705882352941177,0.611764705882353,0.6235294117647059,0.6235294117647059,0.6,0.8470588235294118
-CDK2,0.6823529411764706,0.6705882352941177,0.6705882352941177,0.0,0.6235294117647059,0.6352941176470588,0.5294117647058824,0.6588235294117647,0.8941176470588236
-LCK,0.5529411764705883,0.5764705882352941,0.611764705882353,0.6235294117647059,0.0,0.6,0.611764705882353,0.5647058823529412,0.8588235294117648
-MET,0.5411764705882354,0.5294117647058824,0.6235294117647059,0.6352941176470588,0.6,0.0,0.6352941176470588,0.5294117647058824,0.8941176470588236
-p38a,0.611764705882353,0.6,0.6235294117647059,0.5294117647058824,0.611764705882353,0.6352941176470588,0.0,0.611764705882353,0.8588235294117648
-KDR,0.5294117647058824,0.5647058823529412,0.6,0.6588235294117647,0.5647058823529412,0.5294117647058824,0.611764705882353,0.0,0.8470588235294118
-p110a,0.8823529411764706,0.8823529411764706,0.8470588235294118,0.8941176470588236,0.8588235294117648,0.8941176470588236,0.8588235294117648,0.8470588235294118,0.0
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.0,0.05903728309403988,0.5729382823021627,0.2839529624274032,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369
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+LCK,0.2886787123597768,0.31492453290530287,0.5486635309945707,0.31235243702199045,0.3270674806464644,0.31874681698629526,0.0,0.3091212110335312,0.33741879744441083
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+p38a,0.3553555545508369,0.3648271324857587,0.5688055661744779,0.34655582404619856,0.36208805042926506,0.2769069645096425,0.33741879744441083,0.3706450713064281,0.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
deleted file mode 100644
index 3acff1d6..00000000
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
-EGFR,0.0,0.05903728309403988,0.5729382823021627,0.2839529624274032,0.34497247960673305,0.3515532811454096,0.2886787123597768,0.28874208716346716,0.3553555545508369
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-KDR,0.2839529624274032,0.297882231158642,0.5772950670526018,0.0,0.3287322624812222,0.3466212151836624,0.31235243702199045,0.28619407705453626,0.34655582404619856
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diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
index ce6a35bf..59da49d6 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
@@ -1,10 +1,10 @@
-,EGFR,ErbB2,BRAF,CDK2,LCK,MET,p38a,KDR,p110a
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-BRAF,0.3764705882352941,0.4,1.0,0.32941176470588235,0.38823529411764707,0.3764705882352941,0.3764705882352941,0.4,0.15294117647058825
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-LCK,0.4470588235294118,0.4235294117647059,0.38823529411764707,0.3764705882352941,1.0,0.4,0.38823529411764707,0.43529411764705883,0.1411764705882353
-MET,0.4588235294117647,0.47058823529411764,0.3764705882352941,0.36470588235294116,0.4,1.0,0.36470588235294116,0.47058823529411764,0.10588235294117647
-p38a,0.38823529411764707,0.4,0.3764705882352941,0.47058823529411764,0.38823529411764707,0.36470588235294116,1.0,0.38823529411764707,0.1411764705882353
-KDR,0.47058823529411764,0.43529411764705883,0.4,0.3411764705882353,0.43529411764705883,0.47058823529411764,0.38823529411764707,1.0,0.15294117647058825
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+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,1.0,0.9409627169059601,0.42706171769783724,0.7160470375725968,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631
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+p110a,0.42706171769783724,0.413637652698922,1.0,0.4227049329473982,0.43645909185236703,0.4239941365369449,0.4513364690054293,0.3936988596505412,0.43119443382552214
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+p38a,0.6446444454491631,0.6351728675142413,0.43119443382552214,0.6534441759538014,0.6379119495707349,0.7230930354903575,0.6625812025555892,0.6293549286935719,1.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
deleted file mode 100644
index 59da49d6..00000000
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_substitution_matrix_sequence.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
-EGFR,1.0,0.9409627169059601,0.42706171769783724,0.7160470375725968,0.655027520393267,0.6484467188545904,0.7113212876402232,0.7112579128365328,0.6446444454491631
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-MET,0.7112579128365328,0.6979674361705003,0.3936988596505412,0.7138059229454637,0.6381580952374444,0.6560249367058824,0.6908787889664688,1.0,0.6293549286935719
-p38a,0.6446444454491631,0.6351728675142413,0.43119443382552214,0.6534441759538014,0.6379119495707349,0.7230930354903575,0.6625812025555892,0.6293549286935719,1.0
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index de380b1a..27a99237 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -779,7 +779,7 @@
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-       "      <td id=\"T_d7ef5_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_d7ef5_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_d7ef5_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_d7ef5_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_d7ef5_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_d7ef5_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_d7ef5_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_d7ef5_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_d7ef5_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_d7ef5_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_d7ef5_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_d7ef5_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_d7ef5_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_d7ef5_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_d7ef5_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_d7ef5_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_d7ef5_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_d7ef5_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_d7ef5_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_d7ef5_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_d7ef5_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_d7ef5_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_d7ef5_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_d7ef5_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_d7ef5_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_d7ef5_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_d7ef5_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_d7ef5_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_d7ef5_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_d7ef5_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_d7ef5_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_d7ef5_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_d7ef5_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_d7ef5_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_d7ef5_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_d7ef5_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_d7ef5_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_d7ef5_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_d7ef5_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_d7ef5_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_d7ef5_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_d7ef5_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_d7ef5_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_d7ef5_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_d7ef5_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_d7ef5_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_d7ef5_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_d7ef5_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_d7ef5_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_d7ef5_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_d7ef5_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_d7ef5_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_d7ef5_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_d7ef5_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_d7ef5_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_d7ef5_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_d7ef5_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_d7ef5_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_d7ef5_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_d7ef5_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_d7ef5_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_d7ef5_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_d7ef5_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_d7ef5_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_d7ef5_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_d7ef5_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_d7ef5_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_d7ef5_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_d7ef5_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_d7ef5_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_d7ef5_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_d7ef5_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_d7ef5_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_d7ef5_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_d7ef5_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_d7ef5_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_d7ef5_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_d7ef5_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_d7ef5_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_d7ef5_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_d7ef5_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_d7ef5_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_d7ef5_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_d7ef5_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_d7ef5_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_d7ef5_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_d7ef5_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_d7ef5_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_d7ef5_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_d7ef5_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_d7ef5_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_d7ef5_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_d7ef5_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_d7ef5_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_d7ef5_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_d7ef5_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_d7ef5_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_d7ef5_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_d7ef5_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_d7ef5_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_d7ef5_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_d7ef5_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_d7ef5_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_d7ef5_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_d7ef5_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_d7ef5_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_d7ef5_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_d7ef5_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_d7ef5_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_d7ef5_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_d7ef5_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_d7ef5_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_d7ef5_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_d7ef5_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d7ef5_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_d7ef5_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_d7ef5_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_2f87e_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_2f87e_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_2f87e_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_2f87e_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_2f87e_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_2f87e_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_2f87e_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_2f87e_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_2f87e_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_2f87e_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_2f87e_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_2f87e_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_2f87e_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_2f87e_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_2f87e_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_2f87e_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_2f87e_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_2f87e_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_2f87e_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_2f87e_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_2f87e_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_2f87e_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_2f87e_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_2f87e_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_2f87e_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_2f87e_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_2f87e_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_2f87e_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_2f87e_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_2f87e_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_2f87e_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_2f87e_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_2f87e_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_2f87e_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_2f87e_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_2f87e_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_2f87e_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_2f87e_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_2f87e_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_2f87e_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_2f87e_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_2f87e_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_2f87e_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_2f87e_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_2f87e_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_2f87e_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_2f87e_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_2f87e_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_2f87e_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_2f87e_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_2f87e_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_2f87e_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_2f87e_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_2f87e_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_2f87e_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_2f87e_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_2f87e_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_2f87e_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_2f87e_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_2f87e_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_2f87e_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_2f87e_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_2f87e_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_2f87e_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_2f87e_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_2f87e_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_2f87e_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_2f87e_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_2f87e_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_2f87e_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_2f87e_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_2f87e_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_2f87e_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_2f87e_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_2f87e_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_2f87e_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_2f87e_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_2f87e_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_2f87e_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_2f87e_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_2f87e_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_2f87e_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_2f87e_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_2f87e_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_2f87e_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_2f87e_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_2f87e_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_2f87e_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_2f87e_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_2f87e_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_2f87e_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_2f87e_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_2f87e_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_2f87e_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_2f87e_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_2f87e_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_2f87e_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_2f87e_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_2f87e_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_2f87e_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_2f87e_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_2f87e_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_2f87e_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_2f87e_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_2f87e_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_2f87e_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_2f87e_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_2f87e_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_2f87e_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_2f87e_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_2f87e_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_2f87e_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2f87e_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_2f87e_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_2f87e_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa81ab42700>"
+       "<pandas.io.formats.style.Styler at 0x7fc951ee7f70>"
       ]
      },
      "execution_count": 10,
@@ -1970,7 +1970,9 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix_identity_df = pairwise_kinase_similarities(kinase_names, similarity_measure=\"identity\")\n",
+    "kinase_similarity_matrix_identity_df = pairwise_kinase_similarities(\n",
+    "    kinase_names, similarity_measure=\"identity\"\n",
+    ")\n",
     "kinase_similarity_matrix_identity_df\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -1984,144 +1986,144 @@
      "data": {
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-       "#T_eeb94_row0_col0, #T_eeb94_row1_col1, #T_eeb94_row2_col2, #T_eeb94_row3_col3, #T_eeb94_row4_col4, #T_eeb94_row5_col5, #T_eeb94_row6_col6, #T_eeb94_row7_col7, #T_eeb94_row8_col8 {\n",
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-       "#T_eeb94_row1_col3, #T_eeb94_row6_col3 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row1_col6, #T_eeb94_row6_col7 {\n",
+       "#T_13e6e_row1_col6, #T_13e6e_row6_col7 {\n",
        "  background-color: #9dcd9d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row1_col7, #T_eeb94_row3_col7, #T_eeb94_row8_col5 {\n",
+       "#T_13e6e_row1_col7, #T_13e6e_row3_col7, #T_13e6e_row8_col5 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row1_col8, #T_eeb94_row4_col7, #T_eeb94_row6_col5, #T_eeb94_row7_col6 {\n",
+       "#T_13e6e_row1_col8, #T_13e6e_row4_col7, #T_13e6e_row6_col5, #T_13e6e_row7_col6 {\n",
        "  background-color: #a4d0a4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row2_col0, #T_eeb94_row2_col1, #T_eeb94_row2_col3, #T_eeb94_row2_col4, #T_eeb94_row2_col5, #T_eeb94_row2_col6, #T_eeb94_row2_col7, #T_eeb94_row2_col8, #T_eeb94_row5_col2, #T_eeb94_row7_col2 {\n",
+       "#T_13e6e_row2_col0, #T_13e6e_row2_col1, #T_13e6e_row2_col3, #T_13e6e_row2_col4, #T_13e6e_row2_col5, #T_13e6e_row2_col6, #T_13e6e_row2_col7, #T_13e6e_row2_col8, #T_13e6e_row5_col2, #T_13e6e_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row3_col0, #T_eeb94_row7_col1 {\n",
+       "#T_13e6e_row3_col0, #T_13e6e_row7_col1 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row3_col1 {\n",
+       "#T_13e6e_row3_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row3_col2, #T_eeb94_row4_col2 {\n",
+       "#T_13e6e_row3_col2, #T_13e6e_row4_col2 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row3_col6 {\n",
+       "#T_13e6e_row3_col6 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row4_col0 {\n",
+       "#T_13e6e_row4_col0 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row4_col1, #T_eeb94_row8_col1 {\n",
+       "#T_13e6e_row4_col1, #T_13e6e_row8_col1 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row4_col8, #T_eeb94_row5_col6 {\n",
+       "#T_13e6e_row4_col8, #T_13e6e_row5_col6 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row5_col0 {\n",
+       "#T_13e6e_row5_col0 {\n",
        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row5_col1 {\n",
+       "#T_13e6e_row5_col1 {\n",
        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row5_col3 {\n",
+       "#T_13e6e_row5_col3 {\n",
        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row5_col4 {\n",
+       "#T_13e6e_row5_col4 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row5_col8, #T_eeb94_row7_col0 {\n",
+       "#T_13e6e_row5_col8, #T_13e6e_row7_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row6_col0 {\n",
+       "#T_13e6e_row6_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row6_col1 {\n",
+       "#T_13e6e_row6_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row6_col2, #T_eeb94_row8_col2 {\n",
+       "#T_13e6e_row6_col2, #T_13e6e_row8_col2 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row6_col4, #T_eeb94_row8_col3 {\n",
+       "#T_13e6e_row6_col4, #T_13e6e_row8_col3 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row7_col8 {\n",
+       "#T_13e6e_row7_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_eeb94_row8_col0 {\n",
+       "#T_13e6e_row8_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_eeb94_\">\n",
+       "<table id=\"T_13e6e_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2138,118 +2140,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_eeb94_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_eeb94_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_eeb94_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_eeb94_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_eeb94_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_eeb94_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_eeb94_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_eeb94_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_eeb94_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_eeb94_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_eeb94_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_eeb94_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_eeb94_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_eeb94_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_eeb94_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_eeb94_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_eeb94_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_eeb94_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_eeb94_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_eeb94_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_eeb94_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_eeb94_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_eeb94_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_eeb94_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_eeb94_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_eeb94_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_eeb94_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_eeb94_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_eeb94_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_eeb94_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_eeb94_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_eeb94_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_eeb94_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_eeb94_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_eeb94_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_eeb94_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_eeb94_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_eeb94_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_eeb94_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_eeb94_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_eeb94_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_eeb94_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_eeb94_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_eeb94_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_eeb94_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_eeb94_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_eeb94_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_eeb94_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_eeb94_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_eeb94_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_eeb94_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_eeb94_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_eeb94_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_eeb94_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_eeb94_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_eeb94_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_eeb94_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_13e6e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_13e6e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_13e6e_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_13e6e_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_13e6e_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_13e6e_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_13e6e_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_13e6e_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_13e6e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_13e6e_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_13e6e_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_13e6e_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_13e6e_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_13e6e_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_13e6e_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_13e6e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_13e6e_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_13e6e_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_13e6e_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_13e6e_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_13e6e_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_13e6e_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_13e6e_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_13e6e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_13e6e_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_13e6e_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_13e6e_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_13e6e_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_13e6e_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_13e6e_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_13e6e_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_13e6e_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_13e6e_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_13e6e_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_13e6e_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_13e6e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_13e6e_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_13e6e_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_13e6e_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_13e6e_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_13e6e_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_13e6e_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_13e6e_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_13e6e_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_13e6e_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_13e6e_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_13e6e_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_13e6e_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13e6e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_13e6e_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_13e6e_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_13e6e_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_13e6e_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_13e6e_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_13e6e_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_13e6e_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_13e6e_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_13e6e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa8886181f0>"
+       "<pandas.io.formats.style.Styler at 0x7fc9514ab730>"
       ]
      },
      "execution_count": 21,
@@ -2452,7 +2454,9 @@
     }
    ],
    "source": [
-    "kinase_similarity_matrix_substitution_df = pairwise_kinase_similarities(kinase_names, similarity_measure=\"substitution\")\n",
+    "kinase_similarity_matrix_substitution_df = pairwise_kinase_similarities(\n",
+    "    kinase_names, similarity_measure=\"substitution\"\n",
+    ")\n",
     "kinase_similarity_matrix_substitution_df\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -2466,188 +2470,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_1d417_row0_col0, #T_1d417_row1_col1, #T_1d417_row2_col2, #T_1d417_row3_col3, #T_1d417_row4_col4, #T_1d417_row5_col5, #T_1d417_row6_col6, #T_1d417_row7_col7, #T_1d417_row8_col8 {\n",
+       "#T_e02c4_row0_col0, #T_e02c4_row1_col1, #T_e02c4_row2_col2, #T_e02c4_row3_col3, #T_e02c4_row4_col4, #T_e02c4_row5_col5, #T_e02c4_row6_col6, #T_e02c4_row7_col7, #T_e02c4_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row0_col1 {\n",
+       "#T_e02c4_row0_col1 {\n",
        "  background-color: #178b17;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row0_col2 {\n",
+       "#T_e02c4_row0_col2 {\n",
        "  background-color: #deedde;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row0_col3 {\n",
+       "#T_e02c4_row0_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row0_col4, #T_1d417_row0_col5, #T_1d417_row1_col4, #T_1d417_row3_col8, #T_1d417_row4_col5, #T_1d417_row8_col7 {\n",
+       "#T_e02c4_row0_col4, #T_e02c4_row0_col5, #T_e02c4_row1_col4, #T_e02c4_row3_col8, #T_e02c4_row4_col5, #T_e02c4_row8_col7 {\n",
        "  background-color: #8fc68f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row0_col6 {\n",
+       "#T_e02c4_row0_col6 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row0_col7 {\n",
+       "#T_e02c4_row0_col7 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row0_col8, #T_1d417_row8_col1 {\n",
+       "#T_e02c4_row0_col8, #T_e02c4_row8_col1 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row1_col0 {\n",
+       "#T_e02c4_row1_col0 {\n",
        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row1_col2 {\n",
+       "#T_e02c4_row1_col2 {\n",
        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row1_col3 {\n",
+       "#T_e02c4_row1_col3 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row1_col5, #T_1d417_row1_col8, #T_1d417_row7_col4, #T_1d417_row8_col4 {\n",
+       "#T_e02c4_row1_col5, #T_e02c4_row1_col8, #T_e02c4_row7_col4, #T_e02c4_row8_col4 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row1_col6 {\n",
+       "#T_e02c4_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row1_col7 {\n",
+       "#T_e02c4_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row2_col0, #T_1d417_row2_col1, #T_1d417_row2_col3, #T_1d417_row2_col4, #T_1d417_row2_col5, #T_1d417_row2_col6, #T_1d417_row2_col7, #T_1d417_row2_col8, #T_1d417_row7_col2 {\n",
+       "#T_e02c4_row2_col0, #T_e02c4_row2_col1, #T_e02c4_row2_col3, #T_e02c4_row2_col4, #T_e02c4_row2_col5, #T_e02c4_row2_col6, #T_e02c4_row2_col7, #T_e02c4_row2_col8, #T_e02c4_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row3_col0, #T_1d417_row7_col3 {\n",
+       "#T_e02c4_row3_col0, #T_e02c4_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row3_col1, #T_1d417_row6_col7 {\n",
+       "#T_e02c4_row3_col1, #T_e02c4_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row3_col2, #T_1d417_row5_col2 {\n",
+       "#T_e02c4_row3_col2, #T_e02c4_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row3_col4 {\n",
+       "#T_e02c4_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row3_col5, #T_1d417_row4_col0 {\n",
+       "#T_e02c4_row3_col5, #T_e02c4_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row3_col6, #T_1d417_row4_col3, #T_1d417_row5_col7 {\n",
+       "#T_e02c4_row3_col6, #T_e02c4_row4_col3, #T_e02c4_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row3_col7 {\n",
+       "#T_e02c4_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row4_col1 {\n",
+       "#T_e02c4_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row4_col2 {\n",
+       "#T_e02c4_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row4_col6, #T_1d417_row4_col7, #T_1d417_row7_col5 {\n",
+       "#T_e02c4_row4_col6, #T_e02c4_row4_col7, #T_e02c4_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row4_col8 {\n",
+       "#T_e02c4_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col0, #T_1d417_row8_col6 {\n",
+       "#T_e02c4_row5_col0, #T_e02c4_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col1 {\n",
+       "#T_e02c4_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col3, #T_1d417_row8_col3 {\n",
+       "#T_e02c4_row5_col3, #T_e02c4_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col4 {\n",
+       "#T_e02c4_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col6, #T_1d417_row6_col4 {\n",
+       "#T_e02c4_row5_col6, #T_e02c4_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row5_col8 {\n",
+       "#T_e02c4_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row6_col0 {\n",
+       "#T_e02c4_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row6_col1 {\n",
+       "#T_e02c4_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row6_col2 {\n",
+       "#T_e02c4_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row6_col3 {\n",
+       "#T_e02c4_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row6_col5 {\n",
+       "#T_e02c4_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row6_col8 {\n",
+       "#T_e02c4_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row7_col0 {\n",
+       "#T_e02c4_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_1d417_row7_col1 {\n",
+       "#T_e02c4_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row7_col6 {\n",
+       "#T_e02c4_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row7_col8 {\n",
+       "#T_e02c4_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row8_col0 {\n",
+       "#T_e02c4_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row8_col2 {\n",
+       "#T_e02c4_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_1d417_row8_col5 {\n",
+       "#T_e02c4_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_1d417_\">\n",
+       "<table id=\"T_e02c4_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2664,118 +2668,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_1d417_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_1d417_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_1d417_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_1d417_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_1d417_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_1d417_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_1d417_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_1d417_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_1d417_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_1d417_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_1d417_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_1d417_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_1d417_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_1d417_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_1d417_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_1d417_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_1d417_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_1d417_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_1d417_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_1d417_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_1d417_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_1d417_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_1d417_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_1d417_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_1d417_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_1d417_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_1d417_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_1d417_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_1d417_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_1d417_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_1d417_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_1d417_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_1d417_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_1d417_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_1d417_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_1d417_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_1d417_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_1d417_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_1d417_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_1d417_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_1d417_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_1d417_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_1d417_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_1d417_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_1d417_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_1d417_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_1d417_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_1d417_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_1d417_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_1d417_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_1d417_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_1d417_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_1d417_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_1d417_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_1d417_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_1d417_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_1d417_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_1d417_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_1d417_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_1d417_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_1d417_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_1d417_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_1d417_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_1d417_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_1d417_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_1d417_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_1d417_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_1d417_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_1d417_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_1d417_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_1d417_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_1d417_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_1d417_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_1d417_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_1d417_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_e02c4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_e02c4_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_e02c4_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_e02c4_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_e02c4_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_e02c4_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_e02c4_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_e02c4_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_e02c4_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_e02c4_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_e02c4_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_e02c4_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_e02c4_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_e02c4_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_e02c4_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_e02c4_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_e02c4_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_e02c4_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_e02c4_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_e02c4_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_e02c4_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_e02c4_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_e02c4_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_e02c4_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_e02c4_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_e02c4_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_e02c4_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_e02c4_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_e02c4_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_e02c4_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_e02c4_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_e02c4_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_e02c4_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_e02c4_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_e02c4_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_e02c4_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_e02c4_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_e02c4_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_e02c4_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_e02c4_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_e02c4_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_e02c4_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_e02c4_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_e02c4_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_e02c4_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_e02c4_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_e02c4_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_e02c4_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_e02c4_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_e02c4_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_e02c4_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_e02c4_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_e02c4_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_e02c4_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_e02c4_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_e02c4_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_e02c4_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_e02c4_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_e02c4_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_e02c4_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_e02c4_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_e02c4_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_e02c4_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_e02c4_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_e02c4_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_e02c4_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_e02c4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_e02c4_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_e02c4_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_e02c4_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_e02c4_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_e02c4_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_e02c4_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_e02c4_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_e02c4_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_e02c4_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa81a1006a0>"
+       "<pandas.io.formats.style.Styler at 0x7fc9514b5e20>"
       ]
      },
      "execution_count": 23,
@@ -2795,7 +2799,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For all downstream analysis, we will consider only the kinase similarity matrix calculated based on the substitution matrix."
+    "When we compare the matrices calculated based on the identity and substitution score, the overall pattern is similar, while the values are generally higher using the substitution score.\n",
+    "\n",
+    "_Note_: For all downstream analysis, we will consider only the kinase similarity matrix calculated based on the substitution matrix."
    ]
   },
   {
@@ -2804,7 +2810,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "similarity_measure = \"substitution\"\n",
     "kinase_similarity_matrix_df = kinase_similarity_matrix_substitution_df"
    ]
   },
@@ -2821,7 +2826,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_similarity_{similarity_measure}_matrix_sequence.csv\"\n",
+    "f_name = f\"kinase_similarity_matrix_sequence.csv\"\n",
     "kinase_similarity_matrix_df.to_csv(DATA / f_name)"
    ]
   },
@@ -2877,188 +2882,188 @@
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        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row2_col0, #T_c000f_row2_col1, #T_c000f_row2_col3, #T_c000f_row2_col4, #T_c000f_row2_col5, #T_c000f_row2_col6, #T_c000f_row2_col7, #T_c000f_row2_col8, #T_c000f_row7_col2 {\n",
+       "#T_71344_row2_col0, #T_71344_row2_col1, #T_71344_row2_col3, #T_71344_row2_col4, #T_71344_row2_col5, #T_71344_row2_col6, #T_71344_row2_col7, #T_71344_row2_col8, #T_71344_row7_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col0, #T_c000f_row7_col3 {\n",
+       "#T_71344_row3_col0, #T_71344_row7_col3 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col1, #T_c000f_row6_col7 {\n",
+       "#T_71344_row3_col1, #T_71344_row6_col7 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col2, #T_c000f_row5_col2 {\n",
+       "#T_71344_row3_col2, #T_71344_row5_col2 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col4 {\n",
+       "#T_71344_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col5, #T_c000f_row4_col0 {\n",
+       "#T_71344_row3_col5, #T_71344_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col6, #T_c000f_row4_col3, #T_c000f_row5_col7 {\n",
+       "#T_71344_row3_col6, #T_71344_row4_col3, #T_71344_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row3_col7 {\n",
+       "#T_71344_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c000f_row3_col8 {\n",
+       "#T_71344_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row4_col1 {\n",
+       "#T_71344_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row4_col2 {\n",
+       "#T_71344_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row4_col6, #T_c000f_row4_col7, #T_c000f_row7_col5 {\n",
+       "#T_71344_row4_col6, #T_71344_row4_col7, #T_71344_row7_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row4_col8, #T_c000f_row5_col1 {\n",
+       "#T_71344_row4_col8, #T_71344_row5_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row5_col0, #T_c000f_row8_col6 {\n",
+       "#T_71344_row5_col0, #T_71344_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row5_col3, #T_c000f_row8_col3 {\n",
+       "#T_71344_row5_col3, #T_71344_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row5_col4 {\n",
+       "#T_71344_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row5_col6, #T_c000f_row6_col4 {\n",
+       "#T_71344_row5_col6, #T_71344_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row5_col8 {\n",
+       "#T_71344_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c000f_row6_col0 {\n",
+       "#T_71344_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row6_col1 {\n",
+       "#T_71344_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row6_col2 {\n",
+       "#T_71344_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row6_col3 {\n",
+       "#T_71344_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row6_col5 {\n",
+       "#T_71344_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row6_col8 {\n",
+       "#T_71344_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row7_col0 {\n",
+       "#T_71344_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row7_col1 {\n",
+       "#T_71344_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row7_col6 {\n",
+       "#T_71344_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row7_col8 {\n",
+       "#T_71344_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row8_col0 {\n",
+       "#T_71344_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row8_col2 {\n",
+       "#T_71344_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c000f_row8_col5 {\n",
+       "#T_71344_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c000f_\">\n",
+       "<table id=\"T_71344_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3075,118 +3080,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_c000f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_c000f_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_c000f_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_c000f_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_c000f_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_c000f_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_c000f_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_c000f_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_c000f_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_c000f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_c000f_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_c000f_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_c000f_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_c000f_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_c000f_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_c000f_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_c000f_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_c000f_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_c000f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_c000f_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_c000f_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_c000f_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_c000f_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_c000f_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_c000f_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_c000f_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_c000f_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_c000f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_c000f_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_c000f_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_c000f_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_c000f_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_c000f_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_c000f_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_c000f_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_c000f_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_c000f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_c000f_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_c000f_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_c000f_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_c000f_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_c000f_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_c000f_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_c000f_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_c000f_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_c000f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_c000f_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_c000f_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_c000f_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_c000f_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_c000f_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_c000f_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_c000f_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_c000f_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_c000f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_c000f_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_c000f_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_c000f_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_c000f_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_c000f_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_c000f_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_c000f_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_c000f_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_c000f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_c000f_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c000f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c000f_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_c000f_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_c000f_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_c000f_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_c000f_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_c000f_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_c000f_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_c000f_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_c000f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_71344_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_71344_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_71344_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_71344_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_71344_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_71344_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_71344_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_71344_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_71344_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_71344_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_71344_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_71344_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_71344_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_71344_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_71344_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_71344_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_71344_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_71344_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_71344_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_71344_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_71344_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_71344_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_71344_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_71344_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_71344_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_71344_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_71344_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_71344_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_71344_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_71344_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_71344_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_71344_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_71344_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_71344_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_71344_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_71344_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_71344_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_71344_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_71344_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_71344_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_71344_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_71344_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_71344_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_71344_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_71344_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_71344_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_71344_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_71344_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_71344_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_71344_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_71344_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_71344_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_71344_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_71344_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_71344_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_71344_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_71344_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_71344_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_71344_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_71344_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_71344_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_71344_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_71344_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_71344_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_71344_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_71344_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_71344_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_71344_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_71344_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_71344_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_71344_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_71344_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_71344_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_71344_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_71344_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_71344_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_71344_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_71344_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_71344_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_71344_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_71344_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_71344_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_71344_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa81a116730>"
+       "<pandas.io.formats.style.Styler at 0x7fc951f1dc10>"
       ]
      },
      "execution_count": 28,
@@ -3213,7 +3218,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_distance_{similarity_measure}_matrix_sequence.csv\"\n",
+    "f_name = f\"kinase_distance_matrix_sequence.csv\"\n",
     "kinase_distance_matrix_df.to_csv(DATA / f_name)"
    ]
   },

From 242aae3d245f0862f2c605900730e64ae4f5360c Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 14:16:46 +0200
Subject: [PATCH 107/158] add NBVAL_CHECK_OUTPUT

---
 .../talktorials/T023_what_is_a_kinase/talktorial.ipynb     | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 32b1644b..47d5bf34 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -480,7 +480,8 @@
    ],
    "source": [
     "kinase_selection_df = pd.read_csv(DATA / \"kinase_selection.csv\")\n",
-    "kinase_selection_df"
+    "kinase_selection_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -663,7 +664,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96fdf93d03174ccfaab19641cdee6f65",
+       "model_id": "51787e3622e445b2a4e6a934f21be93c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -675,7 +676,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2524da53259f468ab5069c83e7492d81",
+       "model_id": "f3ee7ef0e80c4b55ae90db00bd2f1b04",
        "version_major": 2,
        "version_minor": 0
       },

From accb4dbb564123ebffa9b984c6da346f661778d6 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 14:23:31 +0200
Subject: [PATCH 108/158] Update csv name.

---
 .../data/kinase_distance_matrix.csv                    | 10 ++++++++++
 .../data/kinase_similarity_matrix.csv                  | 10 ++++++++++
 2 files changed, 20 insertions(+)
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix.csv
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix.csv

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix.csv
new file mode 100644
index 00000000..1e333212
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.0,0.4341880341880342,0.9273743016759777,0.6494960806270997,0.5423728813559322,0.875,0.753968253968254,0.7717391304347826,0.6538461538461539
+ErbB2,0.4341880341880342,0.0,1.0,0.6,0.75,0.8518518518518519,0.8484848484848485,0.9642857142857143,0.875
+p110a,0.9273743016759777,1.0,0.0,0.8160919540229885,0.6666666666666667,0.6666666666666667,1.0,1.0,1.0
+KDR,0.6494960806270997,0.6,0.8160919540229885,0.0,0.24045801526717558,0.3826086956521739,0.5665859564164648,0.45882352941176474,0.48360655737704916
+BRAF,0.5423728813559322,0.75,0.6666666666666667,0.24045801526717558,0.0,0.9230769230769231,0.44999999999999996,0.8846153846153846,0.29268292682926833
+CDK2,0.875,0.8518518518518519,0.6666666666666667,0.3826086956521739,0.9230769230769231,0.0,0.8888888888888888,0.9090909090909091,0.8888888888888888
+LCK,0.753968253968254,0.8484848484848485,1.0,0.5665859564164648,0.44999999999999996,0.8888888888888888,0.0,0.7301587301587302,0.5
+MET,0.7717391304347826,0.9642857142857143,1.0,0.45882352941176474,0.8846153846153846,0.9090909090909091,0.7301587301587302,0.0,0.95
+p38a,0.6538461538461539,0.875,1.0,0.48360655737704916,0.29268292682926833,0.8888888888888888,0.5,0.95,0.0
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix.csv
new file mode 100644
index 00000000..c92c959a
--- /dev/null
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix.csv
@@ -0,0 +1,10 @@
+,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
+EGFR,0.6093880972338642,0.5658119658119658,0.07262569832402235,0.3505039193729003,0.4576271186440678,0.125,0.24603174603174602,0.22826086956521738,0.34615384615384615
+ErbB2,0.5658119658119658,0.6064705882352941,0.0,0.4,0.25,0.14814814814814814,0.15151515151515152,0.03571428571428571,0.125
+p110a,0.07262569832402235,0.0,0.6435237878442978,0.1839080459770115,0.3333333333333333,0.3333333333333333,0.0,0.0,0.0
+KDR,0.3505039193729003,0.4,0.1839080459770115,0.6972909305064782,0.7595419847328244,0.6173913043478261,0.43341404358353514,0.5411764705882353,0.5163934426229508
+BRAF,0.4576271186440678,0.25,0.3333333333333333,0.7595419847328244,0.8112798264642083,0.07692307692307693,0.55,0.11538461538461539,0.7073170731707317
+CDK2,0.125,0.14814814814814814,0.3333333333333333,0.6173913043478261,0.07692307692307693,0.5433333333333333,0.1111111111111111,0.09090909090909091,0.1111111111111111
+LCK,0.24603174603174602,0.15151515151515152,0.0,0.43341404358353514,0.55,0.1111111111111111,0.5993589743589743,0.2698412698412698,0.5
+MET,0.22826086956521738,0.03571428571428571,0.0,0.5411764705882353,0.11538461538461539,0.09090909090909091,0.2698412698412698,0.7937853107344632,0.05
+p38a,0.34615384615384615,0.125,0.0,0.5163934426229508,0.7073170731707317,0.1111111111111111,0.5,0.05,0.7638163321418752

From 46135000fbaa7e528e5a4f14358b4c1998f5d31d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 14:23:36 +0200
Subject: [PATCH 109/158] T026: Fix df merge; rename output to
 "kinase_distance_matrix.csv"

---
 ...rix_ifp.csv => kinase_distance_matrix.csv} |   0
 .../talktorial.ipynb                          | 874 ++++++++++++++----
 2 files changed, 689 insertions(+), 185 deletions(-)
 rename teachopencadd/talktorials/T026_kinase_similarity_ifp/data/{kinase_distance_matrix_ifp.csv => kinase_distance_matrix.csv} (100%)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix.csv
similarity index 100%
rename from teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv
rename to teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix.csv
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 40af31d9..b52355d7 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -415,6 +415,8 @@
     "\n",
     "# Get all available structures for these kinases\n",
     "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
+    "# Keep only relevant columns\n",
+    "structures_df = structures_df[[\"structure.klifs_id\", \"kinase.klifs_name\", \"species.klifs\", \"structure.dfg\", \"structure.resolution\", \"structure.qualityscore\"]]\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
     "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -657,26 +659,12 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>structure.klifs_id</th>\n",
-       "      <th>interaction.fingerprint_x</th>\n",
-       "      <th>structure.pdb_id</th>\n",
-       "      <th>structure.alternate_model</th>\n",
-       "      <th>structure.chain</th>\n",
-       "      <th>species.klifs</th>\n",
-       "      <th>kinase.klifs_id</th>\n",
+       "      <th>interaction.fingerprint</th>\n",
        "      <th>kinase.klifs_name</th>\n",
-       "      <th>kinase.names</th>\n",
-       "      <th>kinase.family</th>\n",
-       "      <th>...</th>\n",
-       "      <th>structure.bp_ii_out</th>\n",
-       "      <th>structure.bp_ii_b</th>\n",
-       "      <th>structure.bp_iii</th>\n",
-       "      <th>structure.bp_iv</th>\n",
-       "      <th>structure.bp_v</th>\n",
-       "      <th>structure.grich_distance</th>\n",
-       "      <th>structure.grich_angle</th>\n",
-       "      <th>structure.grich_rotation</th>\n",
-       "      <th>structure.filepath</th>\n",
-       "      <th>structure.curation_flag</th>\n",
+       "      <th>species.klifs</th>\n",
+       "      <th>structure.dfg</th>\n",
+       "      <th>structure.resolution</th>\n",
+       "      <th>structure.qualityscore</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -684,171 +672,77 @@
        "      <th>0</th>\n",
        "      <td>775</td>\n",
        "      <td>0000000000000010000000000000000000000000000000...</td>\n",
-       "      <td>4r5s</td>\n",
-       "      <td>-</td>\n",
-       "      <td>A</td>\n",
-       "      <td>Human</td>\n",
-       "      <td>406</td>\n",
        "      <td>EGFR</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>21.684799</td>\n",
-       "      <td>71.330002</td>\n",
-       "      <td>42.087002</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>False</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>in</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>8.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>777</td>\n",
        "      <td>0000000000000010000001000000000000000000000000...</td>\n",
-       "      <td>3vjo</td>\n",
-       "      <td>-</td>\n",
-       "      <td>A</td>\n",
-       "      <td>Human</td>\n",
-       "      <td>406</td>\n",
        "      <td>EGFR</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>False</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>in</td>\n",
+       "      <td>2.64</td>\n",
+       "      <td>6.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>778</td>\n",
        "      <td>0000000000000010000000000000000000000000000000...</td>\n",
-       "      <td>3w2s</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>Human</td>\n",
-       "      <td>406</td>\n",
        "      <td>EGFR</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>16.591600</td>\n",
-       "      <td>53.096100</td>\n",
-       "      <td>37.676601</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>False</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>in</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>8.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>779</td>\n",
        "      <td>0000000000000010000001000000000000000000000000...</td>\n",
-       "      <td>2itu</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>Human</td>\n",
-       "      <td>406</td>\n",
        "      <td>EGFR</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>20.174601</td>\n",
-       "      <td>65.518700</td>\n",
-       "      <td>21.431101</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>False</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>in</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>8.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>782</td>\n",
        "      <td>0000000000000010000001000000000000000000000000...</td>\n",
-       "      <td>3w33</td>\n",
-       "      <td>A</td>\n",
-       "      <td>A</td>\n",
-       "      <td>Human</td>\n",
-       "      <td>406</td>\n",
        "      <td>EGFR</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>14.820200</td>\n",
-       "      <td>48.159698</td>\n",
-       "      <td>43.046001</td>\n",
-       "      <td>&lt;NA&gt;</td>\n",
-       "      <td>False</td>\n",
+       "      <td>Human</td>\n",
+       "      <td>in</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>8.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 47 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   structure.klifs_id                          interaction.fingerprint_x  \\\n",
+       "   structure.klifs_id                            interaction.fingerprint  \\\n",
        "0                 775  0000000000000010000000000000000000000000000000...   \n",
        "1                 777  0000000000000010000001000000000000000000000000...   \n",
        "2                 778  0000000000000010000000000000000000000000000000...   \n",
        "3                 779  0000000000000010000001000000000000000000000000...   \n",
        "4                 782  0000000000000010000001000000000000000000000000...   \n",
        "\n",
-       "  structure.pdb_id structure.alternate_model structure.chain species.klifs  \\\n",
-       "0             4r5s                         -               A         Human   \n",
-       "1             3vjo                         -               A         Human   \n",
-       "2             3w2s                         A               A         Human   \n",
-       "3             2itu                         A               A         Human   \n",
-       "4             3w33                         A               A         Human   \n",
+       "  kinase.klifs_name species.klifs structure.dfg  structure.resolution  \\\n",
+       "0              EGFR         Human            in                  3.00   \n",
+       "1              EGFR         Human            in                  2.64   \n",
+       "2              EGFR         Human            in                  1.90   \n",
+       "3              EGFR         Human            in                  2.80   \n",
+       "4              EGFR         Human            in                  1.70   \n",
        "\n",
-       "   kinase.klifs_id kinase.klifs_name kinase.names kinase.family  ...  \\\n",
-       "0              406              EGFR         <NA>          <NA>  ...   \n",
-       "1              406              EGFR         <NA>          <NA>  ...   \n",
-       "2              406              EGFR         <NA>          <NA>  ...   \n",
-       "3              406              EGFR         <NA>          <NA>  ...   \n",
-       "4              406              EGFR         <NA>          <NA>  ...   \n",
-       "\n",
-       "  structure.bp_ii_out structure.bp_ii_b structure.bp_iii structure.bp_iv  \\\n",
-       "0               False             False            False           False   \n",
-       "1               False             False            False           False   \n",
-       "2               False             False            False           False   \n",
-       "3               False             False            False           False   \n",
-       "4               False             False            False           False   \n",
-       "\n",
-       "   structure.bp_v  structure.grich_distance structure.grich_angle  \\\n",
-       "0           False                 21.684799             71.330002   \n",
-       "1           False                  0.000000              0.000000   \n",
-       "2           False                 16.591600             53.096100   \n",
-       "3           False                 20.174601             65.518700   \n",
-       "4           False                 14.820200             48.159698   \n",
-       "\n",
-       "  structure.grich_rotation structure.filepath structure.curation_flag  \n",
-       "0                42.087002               <NA>                   False  \n",
-       "1                 0.000000               <NA>                   False  \n",
-       "2                37.676601               <NA>                   False  \n",
-       "3                21.431101               <NA>                   False  \n",
-       "4                43.046001               <NA>                   False  \n",
-       "\n",
-       "[5 rows x 47 columns]"
+       "   structure.qualityscore  \n",
+       "0                     8.0  \n",
+       "1                     6.8  \n",
+       "2                     8.0  \n",
+       "3                     8.0  \n",
+       "4                     8.0  "
       ]
      },
      "execution_count": 11,
@@ -921,7 +815,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -973,25 +867,20 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'interaction.fingerprint'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/ipykernel_9098/868816743.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# This is the KLIFS format of the IFP (structure KLIFS ID and kinase name set as index)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m ifp_series = structures_with_ifps_df.set_index([\"structure.klifs_id\", \"kinase.klifs_name\"])[\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m\"interaction.fingerprint\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m ]\n\u001b[1;32m      5\u001b[0m \u001b[0mifp_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3453\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3454\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3455\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3457\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/miniconda3/envs/teachopencadd/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'interaction.fingerprint'"
-     ]
+     "data": {
+      "text/plain": [
+       "structure.klifs_id  kinase.klifs_name\n",
+       "775                 EGFR                 0000000000000010000000000000000000000000000000...\n",
+       "777                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "778                 EGFR                 0000000000000010000000000000000000000000000000...\n",
+       "779                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "782                 EGFR                 0000000000000010000001000000000000000000000000...\n",
+       "Name: interaction.fingerprint, dtype: string"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -1005,9 +894,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "structure.klifs_id  kinase.klifs_name\n",
+       "775                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "777                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "778                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "779                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "782                 EGFR                 [False, False, False, False, False, False, Fal...\n",
+       "Name: interaction.fingerprint, dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Cast \"0\" and \"1\" to boolean False and True\n",
     "ifp_series = ifp_series.apply(lambda x: [True if i == \"1\" else False for i in x])\n",
@@ -1017,9 +923,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Convert to numpy array\n",
     "ifps_array = np.array(ifp_series.to_list())\n",
@@ -1040,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1049,9 +972,113 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure distance matrix size: (1466, 1466)\n",
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th>775</th>\n",
+       "      <th>777</th>\n",
+       "      <th>778</th>\n",
+       "      <th>779</th>\n",
+       "      <th>782</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>structure.klifs_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>775</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.483871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.703704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>778</th>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.411765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>779</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.655172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>782</th>\n",
+       "      <td>0.483871</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.655172</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "structure.klifs_id       775       777       778       779       782\n",
+       "structure.klifs_id                                                  \n",
+       "775                 0.000000  0.720000  0.542857  0.500000  0.483871\n",
+       "777                 0.720000  0.000000  0.741935  0.500000  0.703704\n",
+       "778                 0.542857  0.741935  0.000000  0.696970  0.411765\n",
+       "779                 0.500000  0.500000  0.696970  0.000000  0.655172\n",
+       "782                 0.483871  0.703704  0.411765  0.655172  0.000000"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Create DataFrame with structure KLIFS IDs as index/columns\n",
     "structure_klifs_ids = ifp_series.index.get_level_values(0)\n",
@@ -1077,9 +1104,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Show matrix subset:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>EGFR</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kinase.klifs_name</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.483871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.720000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.703704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.542857</td>\n",
+       "      <td>0.741935</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.411765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.696970</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.655172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.483871</td>\n",
+       "      <td>0.703704</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.655172</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "kinase.klifs_name      EGFR      EGFR      EGFR      EGFR      EGFR\n",
+       "kinase.klifs_name                                                  \n",
+       "EGFR               0.000000  0.720000  0.542857  0.500000  0.483871\n",
+       "EGFR               0.720000  0.000000  0.741935  0.500000  0.703704\n",
+       "EGFR               0.542857  0.741935  0.000000  0.696970  0.411765\n",
+       "EGFR               0.500000  0.500000  0.696970  0.000000  0.655172\n",
+       "EGFR               0.483871  0.703704  0.411765  0.655172  0.000000"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Copy distance matrix to kinase matrix\n",
     "kinase_distance_matrix_df = structure_distance_matrix_df.copy()\n",
@@ -1101,9 +1231,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Select EGFR-EGFR structure pairs only\n",
     "D = kinase_distance_matrix_df.loc[\"EGFR\", \"EGFR\"]\n",
@@ -1124,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1141,9 +1284,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Structure matrix of shape (1466, 1466) reduced to kinase matrix of shape (9, 9).\n"
+     ]
+    }
+   ],
    "source": [
     "print(\n",
     "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
@@ -1153,9 +1304,362 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_8216f_row7_col1 {\n",
+       "  background-color: #359a35;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_8216f_row8_col1 {\n",
+       "  background-color: #5aac5a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_8216f_row8_col4 {\n",
+       "  background-color: #d8ead8;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_8216f_row8_col6 {\n",
+       "  background-color: #299429;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_8216f_row8_col7 {\n",
+       "  background-color: #289328;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_8216f_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col1\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col3\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col4\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col5\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col6\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col7\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_8216f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_8216f_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_8216f_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_8216f_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_8216f_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_8216f_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_8216f_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_8216f_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_8216f_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_8216f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_8216f_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_8216f_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_8216f_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_8216f_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_8216f_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_8216f_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_8216f_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_8216f_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_8216f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_8216f_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_8216f_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_8216f_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_8216f_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_8216f_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_8216f_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_8216f_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_8216f_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_8216f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_8216f_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_8216f_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_8216f_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_8216f_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_8216f_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_8216f_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_8216f_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_8216f_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_8216f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_8216f_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_8216f_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_8216f_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_8216f_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_8216f_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_8216f_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_8216f_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_8216f_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_8216f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_8216f_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_8216f_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_8216f_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_8216f_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_8216f_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_8216f_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_8216f_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_8216f_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_8216f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_8216f_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_8216f_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_8216f_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_8216f_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_8216f_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_8216f_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_8216f_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_8216f_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_8216f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_8216f_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_8216f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_8216f_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_8216f_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_8216f_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_8216f_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_8216f_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_8216f_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_8216f_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_8216f_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_8216f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f34d3ab2bb0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
@@ -1173,11 +1677,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_ifp.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix.csv\")"
    ]
   },
   {
@@ -1227,7 +1731,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From 9f9c8bf4e70ab855184b21d6fa94531b708b0154 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 14:23:48 +0200
Subject: [PATCH 110/158] Add # NBVAL_CHECK_OUTPUT

---
 .../kinase_distance_matrix_ligand_profile.csv |   10 -
 ...inase_similarity_matrix_ligand_profile.csv |   10 -
 .../talktorial.ipynb                          | 1211 +++++++++--------
 3 files changed, 609 insertions(+), 622 deletions(-)
 delete mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
 delete mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
deleted file mode 100644
index 1e333212..00000000
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
-EGFR,0.0,0.4341880341880342,0.9273743016759777,0.6494960806270997,0.5423728813559322,0.875,0.753968253968254,0.7717391304347826,0.6538461538461539
-ErbB2,0.4341880341880342,0.0,1.0,0.6,0.75,0.8518518518518519,0.8484848484848485,0.9642857142857143,0.875
-p110a,0.9273743016759777,1.0,0.0,0.8160919540229885,0.6666666666666667,0.6666666666666667,1.0,1.0,1.0
-KDR,0.6494960806270997,0.6,0.8160919540229885,0.0,0.24045801526717558,0.3826086956521739,0.5665859564164648,0.45882352941176474,0.48360655737704916
-BRAF,0.5423728813559322,0.75,0.6666666666666667,0.24045801526717558,0.0,0.9230769230769231,0.44999999999999996,0.8846153846153846,0.29268292682926833
-CDK2,0.875,0.8518518518518519,0.6666666666666667,0.3826086956521739,0.9230769230769231,0.0,0.8888888888888888,0.9090909090909091,0.8888888888888888
-LCK,0.753968253968254,0.8484848484848485,1.0,0.5665859564164648,0.44999999999999996,0.8888888888888888,0.0,0.7301587301587302,0.5
-MET,0.7717391304347826,0.9642857142857143,1.0,0.45882352941176474,0.8846153846153846,0.9090909090909091,0.7301587301587302,0.0,0.95
-p38a,0.6538461538461539,0.875,1.0,0.48360655737704916,0.29268292682926833,0.8888888888888888,0.5,0.95,0.0
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
deleted file mode 100644
index c92c959a..00000000
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_similarity_matrix_ligand_profile.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-,EGFR,ErbB2,p110a,KDR,BRAF,CDK2,LCK,MET,p38a
-EGFR,0.6093880972338642,0.5658119658119658,0.07262569832402235,0.3505039193729003,0.4576271186440678,0.125,0.24603174603174602,0.22826086956521738,0.34615384615384615
-ErbB2,0.5658119658119658,0.6064705882352941,0.0,0.4,0.25,0.14814814814814814,0.15151515151515152,0.03571428571428571,0.125
-p110a,0.07262569832402235,0.0,0.6435237878442978,0.1839080459770115,0.3333333333333333,0.3333333333333333,0.0,0.0,0.0
-KDR,0.3505039193729003,0.4,0.1839080459770115,0.6972909305064782,0.7595419847328244,0.6173913043478261,0.43341404358353514,0.5411764705882353,0.5163934426229508
-BRAF,0.4576271186440678,0.25,0.3333333333333333,0.7595419847328244,0.8112798264642083,0.07692307692307693,0.55,0.11538461538461539,0.7073170731707317
-CDK2,0.125,0.14814814814814814,0.3333333333333333,0.6173913043478261,0.07692307692307693,0.5433333333333333,0.1111111111111111,0.09090909090909091,0.1111111111111111
-LCK,0.24603174603174602,0.15151515151515152,0.0,0.43341404358353514,0.55,0.1111111111111111,0.5993589743589743,0.2698412698412698,0.5
-MET,0.22826086956521738,0.03571428571428571,0.0,0.5411764705882353,0.11538461538461539,0.09090909090909091,0.2698412698412698,0.7937853107344632,0.05
-p38a,0.34615384615384615,0.125,0.0,0.5163934426229508,0.7073170731707317,0.1111111111111111,0.5,0.05,0.7638163321418752
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 5c95dbb9..eff6ec3f 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -347,7 +347,7 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -358,7 +358,8 @@
     "    / \"../T023_what_is_a_kinase/\\\n",
     "data/kinase_selection.csv\"\n",
     ")\n",
-    "kinase_selection_df"
+    "kinase_selection_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -377,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -584,7 +585,7 @@
        "4  CHEMBL3639077     2014.0          NaN    O75116  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -595,7 +596,8 @@
     "# Load data and reset index so that it starts from 0\n",
     "data = pd.read_csv(path, index_col=0).reset_index(drop=True)\n",
     "print(f\"Current shape of data: {data.shape}\")\n",
-    "data.head()"
+    "data.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -614,14 +616,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
+      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -630,7 +632,8 @@
     "print(\n",
     "    f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
     "    f\"Units: {set(data['activities.standard_units'])}\"\n",
-    ")"
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -642,7 +645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -668,13 +671,14 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.columns"
+    "data.columns\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -687,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -702,7 +706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -782,14 +786,15 @@
        "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  13.920819    O75116"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
-    "data.head()"
+    "data.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -801,7 +806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -814,7 +819,8 @@
    ],
    "source": [
     "data = data.dropna()\n",
-    "print(f\"Current shape of data: {data.shape}\")"
+    "print(f\"Current shape of data: {data.shape}\")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -826,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -835,18 +841,7 @@
      "text": [
       "Current shape of data: (33427, 3)\n"
      ]
-    }
-   ],
-   "source": [
-    "data = data[data[\"UniprotID\"].isin(kinase_selection_df[\"uniprot_id\"])]\n",
-    "print(f\"Current shape of data: {data.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
       "text/html": [
@@ -917,13 +912,16 @@
        "141  Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1  11.096910    P00533"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data.head()"
+    "data = data[data[\"UniprotID\"].isin(kinase_selection_df[\"uniprot_id\"])]\n",
+    "print(f\"Current shape of data: {data.shape}\")\n",
+    "data.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -935,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -951,7 +949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -964,14 +962,15 @@
        " ('CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1', 8)]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "measured_compounds = Counter(egfr_data[\"smiles\"])\n",
-    "measured_compounds.most_common()[0:5]"
+    "measured_compounds.most_common()[0:5]\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -983,7 +982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1056,14 +1055,15 @@
        "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  8.420216"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "data = data.groupby([\"UniprotID\", \"smiles\"])[\"pIC50\"].max().reset_index()\n",
-    "data.head()"
+    "data.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1082,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1120,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1222,14 +1222,15 @@
        "4                1  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "print(f\"Current shape of data: {data.shape}\")\n",
-    "data.head()"
+    "data.head()\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1254,7 +1255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1297,7 +1298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1321,7 +1322,8 @@
     "    f\"{egfr_metrics[1]} \\n\"\n",
     "    f\"{'Fraction of active compounds:' : <40}\"\n",
     "    f\"{egfr_metrics[2]:.2f} \\n\"\n",
-    ")"
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1333,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1367,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1468,14 +1470,15 @@
        "p38a   3637    2778  0.763816"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "kinase_promiscuity_df = promiscuity_table(kinase_selection_df, data)\n",
-    "kinase_promiscuity_df"
+    "kinase_promiscuity_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1487,53 +1490,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_13971_row0_col2 {\n",
+       "#T_7ce86_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13971_row1_col2 {\n",
+       "#T_7ce86_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13971_row2_col2 {\n",
+       "#T_7ce86_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13971_row3_col2 {\n",
+       "#T_7ce86_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13971_row4_col2 {\n",
+       "#T_7ce86_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_13971_row5_col2 {\n",
+       "#T_7ce86_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_13971_row6_col2 {\n",
+       "#T_7ce86_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13971_row7_col2 {\n",
+       "#T_7ce86_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13971_row8_col2 {\n",
+       "#T_7ce86_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_13971_\">\n",
+       "<table id=\"T_7ce86_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1544,67 +1547,67 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_13971_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_13971_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_13971_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_7ce86_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_7ce86_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_7ce86_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_7ce86_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_13971_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_13971_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_13971_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_7ce86_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_7ce86_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_7ce86_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_7ce86_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_13971_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_13971_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_13971_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_7ce86_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_7ce86_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_7ce86_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_7ce86_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_13971_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_13971_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_13971_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_7ce86_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_7ce86_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_7ce86_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_7ce86_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_13971_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_13971_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_13971_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_7ce86_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_7ce86_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_7ce86_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_7ce86_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_13971_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_13971_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_13971_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_7ce86_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_7ce86_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_7ce86_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_7ce86_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_13971_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_13971_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_13971_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_7ce86_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_7ce86_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_7ce86_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_7ce86_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_13971_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_13971_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_13971_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_7ce86_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_7ce86_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_7ce86_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_7ce86_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_13971_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_13971_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_13971_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_13971_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_7ce86_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_7ce86_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_7ce86_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_7ce86_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f894d7f4100>"
+       "<pandas.io.formats.style.Styler at 0x7ff2aed8b5b0>"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1640,7 +1643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1707,7 +1710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1737,7 +1740,8 @@
     "    f\"Fraction of active compounds or \\n\"\n",
     "    f\"{'ligand profile similarity between EGFR and MET:' : <50}\"\n",
     "    f\"{similarity_egfr_met[2]:.2f} \\n\"\n",
-    ")"
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1756,7 +1760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1936,7 +1940,7 @@
        "p38a     69/138       1/20  2778/3637  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1957,7 +1961,8 @@
     "        total, actives, _ = similarity_ligand_profile(uniprot_id1, uniprot_id2, data)\n",
     "        integer_ratio = f\"{actives}/{total}\"\n",
     "        kinase_counts_matrix[klifs_name1][klifs_name2] = integer_ratio\n",
-    "kinase_counts_matrix"
+    "kinase_counts_matrix\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
@@ -1969,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1992,7 +1997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -2164,7 +2169,7 @@
        "p38a   0.050000  0.763816  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2177,220 +2182,221 @@
     ")\n",
     "kinase_similarity_matrix_df.index.name = None\n",
     "kinase_similarity_matrix_df.columns.name = None\n",
-    "kinase_similarity_matrix_df"
+    "kinase_similarity_matrix_df\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
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+       "#T_84296_row0_col0, #T_84296_row1_col1, #T_84296_row2_col2, #T_84296_row3_col5, #T_84296_row4_col3, #T_84296_row4_col4, #T_84296_row6_col6, #T_84296_row7_col7, #T_84296_row8_col8 {\n",
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        "}\n",
-       "#T_a4349_row0_col1, #T_a4349_row3_col4, #T_a4349_row4_col8 {\n",
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        "  background-color: #118811;\n",
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-       "#T_a4349_row0_col2 {\n",
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-       "#T_a4349_row1_col8 {\n",
+       "#T_84296_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row2_col0, #T_a4349_row2_col3, #T_a4349_row2_col6, #T_a4349_row2_col7, #T_a4349_row2_col8, #T_a4349_row4_col5, #T_a4349_row5_col4, #T_a4349_row6_col2, #T_a4349_row7_col1, #T_a4349_row7_col2, #T_a4349_row8_col2 {\n",
+       "#T_84296_row2_col0, #T_84296_row2_col3, #T_84296_row2_col6, #T_84296_row2_col7, #T_84296_row2_col8, #T_84296_row4_col5, #T_84296_row5_col4, #T_84296_row6_col2, #T_84296_row7_col1, #T_84296_row7_col2, #T_84296_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row2_col4 {\n",
+       "#T_84296_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row2_col5 {\n",
+       "#T_84296_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row3_col1 {\n",
+       "#T_84296_row3_col1 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row3_col3 {\n",
+       "#T_84296_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row3_col6 {\n",
+       "#T_84296_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row3_col7 {\n",
+       "#T_84296_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row3_col8 {\n",
+       "#T_84296_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row4_col0 {\n",
+       "#T_84296_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row4_col6 {\n",
+       "#T_84296_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row4_col7, #T_a4349_row5_col8 {\n",
+       "#T_84296_row4_col7, #T_84296_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row5_col0 {\n",
+       "#T_84296_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row5_col1 {\n",
+       "#T_84296_row5_col1 {\n",
        "  background-color: #bddcbd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row5_col3 {\n",
+       "#T_84296_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row5_col5 {\n",
+       "#T_84296_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row5_col6 {\n",
+       "#T_84296_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row5_col7 {\n",
+       "#T_84296_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col0 {\n",
+       "#T_84296_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col1 {\n",
+       "#T_84296_row6_col1 {\n",
        "  background-color: #bcdcbc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col3 {\n",
+       "#T_84296_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col4 {\n",
+       "#T_84296_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row6_col5, #T_a4349_row7_col8, #T_a4349_row8_col5, #T_a4349_row8_col7 {\n",
+       "#T_84296_row6_col5, #T_84296_row7_col8, #T_84296_row8_col5, #T_84296_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col7 {\n",
+       "#T_84296_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row6_col8 {\n",
+       "#T_84296_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row7_col3 {\n",
+       "#T_84296_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row7_col4 {\n",
+       "#T_84296_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row7_col5 {\n",
+       "#T_84296_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row7_col6 {\n",
+       "#T_84296_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row8_col0 {\n",
+       "#T_84296_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row8_col1 {\n",
+       "#T_84296_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a4349_row8_col3 {\n",
+       "#T_84296_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row8_col4 {\n",
+       "#T_84296_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a4349_row8_col6 {\n",
+       "#T_84296_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_a4349_\">\n",
+       "<table id=\"T_84296_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2407,121 +2413,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_a4349_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_a4349_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_a4349_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_a4349_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_a4349_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_a4349_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_a4349_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_a4349_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_a4349_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_a4349_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_a4349_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_a4349_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_a4349_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_a4349_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_a4349_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_a4349_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_a4349_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_a4349_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_a4349_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_a4349_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_a4349_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_a4349_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_a4349_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_a4349_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_a4349_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_a4349_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_a4349_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_a4349_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_a4349_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_a4349_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_a4349_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_a4349_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_a4349_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_a4349_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_a4349_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_a4349_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_a4349_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_a4349_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_a4349_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_a4349_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_a4349_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_a4349_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_a4349_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_a4349_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_a4349_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_a4349_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_a4349_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_a4349_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_a4349_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_a4349_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_a4349_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_a4349_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_a4349_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_a4349_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_a4349_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_a4349_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_a4349_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_a4349_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_a4349_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_a4349_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_a4349_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_a4349_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_a4349_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_a4349_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_a4349_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_a4349_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_a4349_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_a4349_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_a4349_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_a4349_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_a4349_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_a4349_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a4349_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_a4349_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_a4349_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_a4349_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_a4349_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_a4349_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_a4349_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_a4349_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_a4349_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_a4349_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_84296_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_84296_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_84296_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_84296_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_84296_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_84296_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_84296_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_84296_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_84296_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_84296_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_84296_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_84296_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_84296_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_84296_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_84296_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_84296_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_84296_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_84296_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_84296_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_84296_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_84296_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_84296_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_84296_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_84296_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_84296_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_84296_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_84296_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_84296_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_84296_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_84296_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_84296_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_84296_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_84296_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_84296_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_84296_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_84296_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_84296_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_84296_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_84296_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_84296_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_84296_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_84296_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_84296_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_84296_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_84296_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_84296_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_84296_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_84296_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_84296_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_84296_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_84296_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_84296_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_84296_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_84296_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_84296_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_84296_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_84296_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_84296_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_84296_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_84296_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_84296_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_84296_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_84296_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_84296_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_84296_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_84296_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_84296_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_84296_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_84296_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_84296_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_84296_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_84296_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_84296_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_84296_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_84296_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_84296_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_84296_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_84296_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_84296_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_84296_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_84296_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_84296_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_84296_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f894d7f4c40>"
+       "<pandas.io.formats.style.Styler at 0x7ff296608d60>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2545,219 +2551,219 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "<style type=\"text/css\">\n",
-       "#T_d4411_row0_col0, #T_d4411_row1_col1, #T_d4411_row2_col2, #T_d4411_row3_col5, #T_d4411_row4_col3, #T_d4411_row4_col4, #T_d4411_row6_col6, #T_d4411_row7_col7, #T_d4411_row8_col8 {\n",
+       "#T_ec019_row0_col0, #T_ec019_row1_col1, #T_ec019_row2_col2, #T_ec019_row3_col5, #T_ec019_row4_col3, #T_ec019_row4_col4, #T_ec019_row6_col6, #T_ec019_row7_col7, #T_ec019_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row0_col1 {\n",
+       "#T_ec019_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row0_col2 {\n",
+       "#T_ec019_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row0_col3, #T_d4411_row7_col0 {\n",
+       "#T_ec019_row0_col3, #T_ec019_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row0_col4, #T_d4411_row3_col0, #T_d4411_row4_col2, #T_d4411_row5_col2 {\n",
+       "#T_ec019_row0_col4, #T_ec019_row3_col0, #T_ec019_row4_col2, #T_ec019_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row0_col5 {\n",
+       "#T_ec019_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row0_col6, #T_d4411_row4_col1 {\n",
+       "#T_ec019_row0_col6, #T_ec019_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row0_col7, #T_d4411_row3_col2 {\n",
+       "#T_ec019_row0_col7, #T_ec019_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row0_col8 {\n",
+       "#T_ec019_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col0 {\n",
+       "#T_ec019_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row1_col2, #T_d4411_row2_col0, #T_d4411_row2_col1, #T_d4411_row2_col3, #T_d4411_row2_col6, #T_d4411_row2_col7, #T_d4411_row2_col8, #T_d4411_row4_col5, #T_d4411_row5_col4, #T_d4411_row6_col2, #T_d4411_row7_col2, #T_d4411_row8_col2 {\n",
+       "#T_ec019_row1_col2, #T_ec019_row2_col0, #T_ec019_row2_col1, #T_ec019_row2_col3, #T_ec019_row2_col6, #T_ec019_row2_col7, #T_ec019_row2_col8, #T_ec019_row4_col5, #T_ec019_row5_col4, #T_ec019_row6_col2, #T_ec019_row7_col2, #T_ec019_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col3 {\n",
+       "#T_ec019_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col4 {\n",
+       "#T_ec019_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col5 {\n",
+       "#T_ec019_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col6 {\n",
+       "#T_ec019_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col7 {\n",
+       "#T_ec019_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row1_col8 {\n",
+       "#T_ec019_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row2_col4 {\n",
+       "#T_ec019_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row2_col5 {\n",
+       "#T_ec019_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row3_col1 {\n",
+       "#T_ec019_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row3_col3 {\n",
+       "#T_ec019_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row3_col4, #T_d4411_row4_col8 {\n",
+       "#T_ec019_row3_col4, #T_ec019_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row3_col6 {\n",
+       "#T_ec019_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row3_col7 {\n",
+       "#T_ec019_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row3_col8 {\n",
+       "#T_ec019_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row4_col0 {\n",
+       "#T_ec019_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row4_col6 {\n",
+       "#T_ec019_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row4_col7, #T_d4411_row5_col8 {\n",
+       "#T_ec019_row4_col7, #T_ec019_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row5_col0 {\n",
+       "#T_ec019_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row5_col1 {\n",
+       "#T_ec019_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row5_col3 {\n",
+       "#T_ec019_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row5_col5 {\n",
+       "#T_ec019_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row5_col6 {\n",
+       "#T_ec019_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row5_col7 {\n",
+       "#T_ec019_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col0 {\n",
+       "#T_ec019_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col1 {\n",
+       "#T_ec019_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col3 {\n",
+       "#T_ec019_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col4 {\n",
+       "#T_ec019_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row6_col5, #T_d4411_row7_col8, #T_d4411_row8_col5, #T_d4411_row8_col7 {\n",
+       "#T_ec019_row6_col5, #T_ec019_row7_col8, #T_ec019_row8_col5, #T_ec019_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col7 {\n",
+       "#T_ec019_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row6_col8 {\n",
+       "#T_ec019_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row7_col1 {\n",
+       "#T_ec019_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row7_col3 {\n",
+       "#T_ec019_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row7_col4 {\n",
+       "#T_ec019_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row7_col5 {\n",
+       "#T_ec019_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row7_col6 {\n",
+       "#T_ec019_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row8_col0 {\n",
+       "#T_ec019_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row8_col1 {\n",
+       "#T_ec019_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d4411_row8_col3 {\n",
+       "#T_ec019_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row8_col4 {\n",
+       "#T_ec019_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d4411_row8_col6 {\n",
+       "#T_ec019_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d4411_\">\n",
+       "<table id=\"T_ec019_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2774,121 +2780,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d4411_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_d4411_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_d4411_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_d4411_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_d4411_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_d4411_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_d4411_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_d4411_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_d4411_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d4411_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_d4411_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_d4411_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_d4411_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_d4411_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_d4411_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_d4411_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_d4411_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_d4411_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_d4411_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_d4411_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_d4411_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_d4411_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_d4411_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_d4411_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_d4411_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_d4411_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_d4411_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_d4411_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_d4411_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_d4411_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_d4411_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_d4411_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_d4411_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_d4411_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_d4411_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_d4411_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_d4411_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_d4411_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_d4411_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_d4411_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_d4411_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_d4411_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_d4411_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_d4411_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_d4411_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_d4411_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_d4411_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_d4411_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_d4411_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_d4411_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_d4411_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_d4411_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_d4411_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_d4411_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_d4411_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_d4411_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_d4411_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_d4411_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_d4411_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_d4411_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_d4411_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_d4411_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_d4411_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_d4411_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_d4411_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_d4411_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d4411_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d4411_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_d4411_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_d4411_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_d4411_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_d4411_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_d4411_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_d4411_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_d4411_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_d4411_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_ec019_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_ec019_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_ec019_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_ec019_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_ec019_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_ec019_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_ec019_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_ec019_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_ec019_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_ec019_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_ec019_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_ec019_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_ec019_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_ec019_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_ec019_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_ec019_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_ec019_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_ec019_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_ec019_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_ec019_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_ec019_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_ec019_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_ec019_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_ec019_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_ec019_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_ec019_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_ec019_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_ec019_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_ec019_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_ec019_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_ec019_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_ec019_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_ec019_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_ec019_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_ec019_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_ec019_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_ec019_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_ec019_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_ec019_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_ec019_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_ec019_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_ec019_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_ec019_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_ec019_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_ec019_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_ec019_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_ec019_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_ec019_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_ec019_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_ec019_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_ec019_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_ec019_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_ec019_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_ec019_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_ec019_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_ec019_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_ec019_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_ec019_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_ec019_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_ec019_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_ec019_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_ec019_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_ec019_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_ec019_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_ec019_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_ec019_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_ec019_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ec019_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_ec019_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_ec019_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_ec019_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_ec019_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_ec019_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_ec019_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_ec019_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_ec019_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_ec019_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f894d7f4760>"
+       "<pandas.io.formats.style.Styler at 0x7ff296608dc0>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2908,11 +2914,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_similarity_matrix_df.to_csv(DATA / \"kinase_similarity_matrix_ligand_profile.csv\")"
+    "kinase_similarity_matrix_df.to_csv(DATA / \"kinase_similarity_matrix.csv\")"
    ]
   },
   {
@@ -2931,7 +2937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -2947,12 +2953,13 @@
     "    f\"The values of the similarity matrix lie between: \"\n",
     "    f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
     "    f\" and {kinase_similarity_matrix_df.max().max():.2f}\"\n",
-    ")"
+    ")\n",
+    "# NBVAL_CHECK_OUTPUT"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2968,7 +2975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2977,211 +2984,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_3e2d2_row0_col0, #T_3e2d2_row1_col1, #T_3e2d2_row2_col2, #T_3e2d2_row3_col3, #T_3e2d2_row4_col4, #T_3e2d2_row5_col5, #T_3e2d2_row6_col6, #T_3e2d2_row7_col7, #T_3e2d2_row8_col8 {\n",
+       "#T_a7172_row0_col0, #T_a7172_row1_col1, #T_a7172_row2_col2, #T_a7172_row3_col3, #T_a7172_row4_col4, #T_a7172_row5_col5, #T_a7172_row6_col6, #T_a7172_row7_col7, #T_a7172_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row0_col1 {\n",
+       "#T_a7172_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row0_col2 {\n",
+       "#T_a7172_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col3 {\n",
+       "#T_a7172_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col4 {\n",
+       "#T_a7172_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col5 {\n",
+       "#T_a7172_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col6 {\n",
+       "#T_a7172_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col7 {\n",
+       "#T_a7172_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row0_col8 {\n",
+       "#T_a7172_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col0 {\n",
+       "#T_a7172_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row1_col2, #T_3e2d2_row2_col0, #T_3e2d2_row2_col1, #T_3e2d2_row2_col3, #T_3e2d2_row2_col6, #T_3e2d2_row2_col7, #T_3e2d2_row2_col8, #T_3e2d2_row4_col5, #T_3e2d2_row5_col4, #T_3e2d2_row6_col2, #T_3e2d2_row7_col2, #T_3e2d2_row8_col2 {\n",
+       "#T_a7172_row1_col2, #T_a7172_row2_col0, #T_a7172_row2_col1, #T_a7172_row2_col3, #T_a7172_row2_col6, #T_a7172_row2_col7, #T_a7172_row2_col8, #T_a7172_row4_col5, #T_a7172_row5_col4, #T_a7172_row6_col2, #T_a7172_row7_col2, #T_a7172_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col3 {\n",
+       "#T_a7172_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col4, #T_3e2d2_row3_col2, #T_3e2d2_row6_col0 {\n",
+       "#T_a7172_row1_col4, #T_a7172_row3_col2, #T_a7172_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col5 {\n",
+       "#T_a7172_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col6, #T_3e2d2_row6_col1 {\n",
+       "#T_a7172_row1_col6, #T_a7172_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col7, #T_3e2d2_row6_col5, #T_3e2d2_row7_col1, #T_3e2d2_row8_col5 {\n",
+       "#T_a7172_row1_col7, #T_a7172_row6_col5, #T_a7172_row7_col1, #T_a7172_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row1_col8, #T_3e2d2_row8_col1 {\n",
+       "#T_a7172_row1_col8, #T_a7172_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row2_col4, #T_3e2d2_row2_col5 {\n",
+       "#T_a7172_row2_col4, #T_a7172_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row3_col0 {\n",
+       "#T_a7172_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row3_col1 {\n",
+       "#T_a7172_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row3_col4 {\n",
+       "#T_a7172_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row3_col5 {\n",
+       "#T_a7172_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row3_col6 {\n",
+       "#T_a7172_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row3_col7 {\n",
+       "#T_a7172_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row3_col8 {\n",
+       "#T_a7172_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row4_col0 {\n",
+       "#T_a7172_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row4_col1 {\n",
+       "#T_a7172_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row4_col2, #T_3e2d2_row5_col2 {\n",
+       "#T_a7172_row4_col2, #T_a7172_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row4_col3 {\n",
+       "#T_a7172_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row4_col6 {\n",
+       "#T_a7172_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row4_col7 {\n",
+       "#T_a7172_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row4_col8 {\n",
+       "#T_a7172_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row5_col0 {\n",
+       "#T_a7172_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row5_col1 {\n",
+       "#T_a7172_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row5_col3 {\n",
+       "#T_a7172_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row5_col6, #T_3e2d2_row5_col8 {\n",
+       "#T_a7172_row5_col6, #T_a7172_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row5_col7 {\n",
+       "#T_a7172_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row6_col3 {\n",
+       "#T_a7172_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row6_col4 {\n",
+       "#T_a7172_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3e2d2_row6_col7, #T_3e2d2_row7_col6 {\n",
+       "#T_a7172_row6_col7, #T_a7172_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row6_col8, #T_3e2d2_row8_col6 {\n",
+       "#T_a7172_row6_col8, #T_a7172_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row7_col0 {\n",
+       "#T_a7172_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row7_col3 {\n",
+       "#T_a7172_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row7_col4 {\n",
+       "#T_a7172_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row7_col5 {\n",
+       "#T_a7172_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row7_col8, #T_3e2d2_row8_col7 {\n",
+       "#T_a7172_row7_col8, #T_a7172_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row8_col0 {\n",
+       "#T_a7172_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row8_col3 {\n",
+       "#T_a7172_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3e2d2_row8_col4 {\n",
+       "#T_a7172_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_3e2d2_\">\n",
+       "<table id=\"T_a7172_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3198,121 +3205,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_3e2d2_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_3e2d2_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_3e2d2_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_3e2d2_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_3e2d2_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_3e2d2_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_3e2d2_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_3e2d2_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_3e2d2_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_3e2d2_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_3e2d2_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_3e2d2_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_3e2d2_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_3e2d2_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_3e2d2_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_3e2d2_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_3e2d2_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_3e2d2_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_3e2d2_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_3e2d2_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_3e2d2_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_3e2d2_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_3e2d2_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_3e2d2_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_3e2d2_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_3e2d2_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_3e2d2_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_3e2d2_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_3e2d2_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_3e2d2_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_3e2d2_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_3e2d2_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_3e2d2_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_3e2d2_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_3e2d2_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_3e2d2_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_3e2d2_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_3e2d2_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_3e2d2_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_3e2d2_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_3e2d2_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_3e2d2_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_3e2d2_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_3e2d2_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_3e2d2_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_3e2d2_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_3e2d2_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_3e2d2_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_3e2d2_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_3e2d2_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_3e2d2_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_3e2d2_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_3e2d2_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_3e2d2_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_3e2d2_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_3e2d2_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_3e2d2_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_3e2d2_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_3e2d2_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3e2d2_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_3e2d2_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_3e2d2_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_3e2d2_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_3e2d2_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_3e2d2_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_3e2d2_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_3e2d2_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_3e2d2_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_3e2d2_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_a7172_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_a7172_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_a7172_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_a7172_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_a7172_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_a7172_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_a7172_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_a7172_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_a7172_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_a7172_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_a7172_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_a7172_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_a7172_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_a7172_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_a7172_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_a7172_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_a7172_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_a7172_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_a7172_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_a7172_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_a7172_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_a7172_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_a7172_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_a7172_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_a7172_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_a7172_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_a7172_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_a7172_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_a7172_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_a7172_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_a7172_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_a7172_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_a7172_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_a7172_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_a7172_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_a7172_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_a7172_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_a7172_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_a7172_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_a7172_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_a7172_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_a7172_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_a7172_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_a7172_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_a7172_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_a7172_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_a7172_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_a7172_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_a7172_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_a7172_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_a7172_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_a7172_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_a7172_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_a7172_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_a7172_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_a7172_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_a7172_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_a7172_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_a7172_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_a7172_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7172_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_a7172_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_a7172_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_a7172_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_a7172_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_a7172_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_a7172_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_a7172_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_a7172_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_a7172_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f894d7f4a90>"
+       "<pandas.io.formats.style.Styler at 0x7ff296674970>"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3330,11 +3337,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_ligand_profile.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix.csv\")"
    ]
   },
   {
@@ -3376,7 +3383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 7b1723b3415433038f5a44be7ac6f46ca9bea01e Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 14:39:33 +0200
Subject: [PATCH 111/158] T025: Rename output to "kinase_distance_matrix.csv"

---
 ..._kissim.csv => kinase_distance_matrix.csv} |   0
 .../talktorial.ipynb                          | 280 +++++++++---------
 2 files changed, 140 insertions(+), 140 deletions(-)
 rename teachopencadd/talktorials/T025_kinase_similarity_kissim/data/{kinase_distance_matrix_kissim.csv => kinase_distance_matrix.csv} (100%)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix.csv
similarity index 100%
rename from teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv
rename to teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix.csv
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 560cd24a..f76f2757 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -833,7 +833,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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zBuUzY+ODwG+A3wObChVIRLwkqYLk6mR6PVXuiYgLt/PY44HxAG06dY3tDrKARo4cyciRIwH48Y9/TJcuXejWrRuPPPIIAK+//jrTppXW485mZrnySSjrIuLmgkeSmAJcDwwCPtNEbZaEZcuW0bFjR959913uv/9+nn322c1lmzZt4qqrruL8888vdphmZg3KJ6HcJOkK4BHg49rCiHihAPFMAFZGxDxJgwpw/JL1jW98g+XLl7PLLrswbtw4PvWpT3HTTTcxbtw4AE4++WRGjBhR5CjNzBqWT0LpDXwLOJZ/dHlFup6piFgM3NTA5tMkHZWz/t2IeCbrGIrlqaee+kTZRRddxEUXXVSEaMzMtl0+CWUYcGBErC9UEBHRrp6yWcCsdPkO4I5G9j+7IIGZmVne8kkoc4EOwLLChtI0enduT5XHzjIzy1w+CWUf4DVJz7PlPZSvFiwqMzNrdvJJKFcUPAozM2v2tppQIuKPTRGImZk1b/m8KX+4pOcl/V3Sekk1kjzsrZmZbSGfwSFvJXl7/Q1gN2BUWmZmZrZZPvdQiIg3JZVFRA0wUdJO8/6HmZllI5+EskZSa6Ba0rXAUsDzy5uZ2Rby6fL6VlrvQpJ5S/YFvlHIoMzMrPnJ5ymvv6SL64CfFDYcMzNrrraaUCR9EbgS2D+3fkQcWLiwzMysucnnHspvgEuAOUBNYcMxM7PmKp+EsjIiHi54JE1k3pKVVIxumomqFnrMMDNrQfJJKE9Iug64n8LPh2JmZs1UPgllYPq1f05ZQeZD2RktWLCA0047bfP622+/zU9/+lOWLFnC73//e1q3bs1BBx3ExIkT6dChQ/ECNTPbQYpo2inWJdUA8wCR3JO5MCKeSeeTfxVYALQGqoCREbEh3a8c+Ctwe0RcnnO8WUAnYG1adFVE/K6h9tt06hqdzrox47OqX90ur5qaGjp37syf//xnFixYwLHHHkt5eTmXXXYZANdcc02TxGVmtq0kzYmI/o3Vyec9lKytjYjKiOgLXA6Mydn2VkRUkswS2QU4NWfb8STJ5lRJqnPMM9JjVjaWTIrt8ccf56CDDmL//ffn+OOPp7w8uUA8/PDDWbx4cZGjMzPbMcVIKLn2BFbULUyHeJkNdM4pHk4yPfC7wOFNEl3GJk+ezPDhwz9RPmHCBIYMGVKEiMzMspPXWF4Z201SNbArSVfVJ+7FSNqV5N7NRen6bsBxwHdIZo8cDjybs8tdkmq7vI6LiOV1jncecB5A2Z57Z3gq+Vu/fj1TpkxhzJgxW5RfffXVlJeXc8YZZxQlLjOzrOQzfH1bSf9X0u3peldJQ3egzdour27ACcB/5XRhHZQmm+XAuxHxUlo+FHgiItYA9wHDJJXlHDO3y2uLZAIQEeMjon9E9C9r234HQt9+Dz/8MIceeij77LPP5rI777yTqVOnctddd/HJXjwzs+Ylny6viSSPCx+Rri8Grsqi8Yh4FtgLqL1sqL2HcjBwuKTaaYaHA1+WtJDkBcvPAF/KIoamcvfdd2/R3TVjxgyuueYapkyZQtu2bYsYmZlZNvJJKAdFxLXABoCIWEvyhNYOk9QNKCO5ItksIpYCo4HLJe0JHAXsFxEVEVEB/CtJkmkW1qxZw6OPPsrJJ5+8uezCCy9k9erVDB48mMrKSs4///wiRmhmtuPyuYeyPr2HEQCSDiLnBcftUHsPBZLEdFZE1NTT5fMgyRhiFwEzIyK3zYeAayW12YE4mkzbtm1ZvnzLnrg333yzSNGYmRVGPgnlCmAGsK+ku4AvAmdvb4MRUdZA+UKgV856AH0bqPs3/tFNNmh7YzEzs+zk9WKjpM+QPKor4LmI+KDQgRVK//79o6qqqthhmJk1K5m82JgOX78uIqaRPLL7Y0n7ZxOimZntLPK5KX8byTTAfYEfAn8B/qugUZmZWbOTT0LZmN7P+Bpwc0TcBOxR2LDMzKy5yeem/GpJlwNnAkenLxTuUtiwzMysucnnCuU0kseER0bEX0nG17quoFGZmVmzs9UrlDSJ/CJn/V18D8XMzOrI5ymvwyU9L+nvktZLqpG0simCMzOz5iOfLq9bSYY5eQPYDRgFjCtkUGZm1vzkNXx9RLwpqSydp2SipGcKHJeZmTUz+SSUNZJaA9WSrgWWArsXNiwzM2tu8uny+lZa70LgI2Bf4BuFDMrMzJqfvMby2pm06dQ1Op114w4fZ+HYf97xYMzMmol8xvLaapdXOpbXlcD+ufUj4sAdDdDMzHYe+XR5/YbkPZSjgC/kfCxVU1NDv379GDo0mRl57ty5HHHEEfTu3ZuTTjqJVatWFTlCM7PCyyehrIyIhyNiWUQsr/1sb4OSLpT0pqSQtFdOeTdJz0r6WNKldfY5QdKCdL/R29t2odx0001079598/qoUaMYO3Ys8+bNY9iwYVx3nQcWMLOdXz4J5QlJ10k6QtKhtZ8daPNp4Mskoxbn+hvwPeD63MJ07LBxwBCgBzBcUo8daD9TixcvZtq0aYwaNWpz2YIFCzj66KMBGDx4MPfdd1+xwjMzazL5PDY8MP2aezMmgGMb20lSBclMj38G+gGvA9+OiBfT7VvUj4hlwDJJde92DwDejIi30/0mk4x8/Iqkc4HzgNbAm8C3ImJNHueUmYsvvphrr72W1atXby7r1asXU6ZM4Wtf+xr33nsvixYtasqQzMyKYqtXKBHxpXo+jSaTHIcA4yOiD7AK+O52xNgZyP2NvDgtA7g/Ir4QEX2BV4GR9R1A0nmSqiRV1azJbtSYqVOn0rFjRw477LAtyidMmMC4ceM47LDDWL16Na1bt86sTTOzUtXgFYqkMyNikqTv17c9In5RX3kdiyLi6XR5EvV0aeVB9ZTVPuvcS9JVJDNJtgP+UN8BImI8MB6Sx4a3sf0GPf3000yZMoXp06ezbt06Vq1axZlnnsmkSZN45JFHAHj99deZNm1aVk2amZWsxq5Qat+G36OeT7s8j1/3l/f2/DJfTPIyZa0uwHvp8h3AhRHRG/gJsOt2HH+7jRkzhsWLF7Nw4UImT57Msccey6RJk1i2bBkAmzZt4qqrruL8889vyrDMzIqisXso0wAi4id1N0g6Kc/j7yfpiIh4lmSAyT9te4g8D3SVdACwBDgd+Jd02x7AUkm7AGek24vu7rvvZty4ZPzMk08+mREjRhQ5IjOzwmvwTXlJC4CvRMTCOuUjgP8TEQc1euDkpvx04EngSJLRir9FMlrxj4DPAsuA6RExStJngSpgT2AT8HegR0SsknQicCNQBkyIiKvTNi5Ij/UXYB6wR0Sc3VhcflPezGzb7eib8pcAj0o6MSLeSA94OcnVwTF5xrApIur299ycfraQTuTVpb6DRMR0kuRUt/w24LY8YzEzswJqMKFExHRJHwMPS/o6yZXFF4CjI2JFE8WXud6d21Plqwszs8w1+thwRDwOnA3MAg4Ejss3mUTEwojotaMBmplZ89DYY8OrSZ7KEtAGOI7kxUMBERF7Nk2IZmbWHDTW5bVHUwZiZmbNWz5jeZmZmW2VE4qZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZiZWSacUMzMLBNOKGZmlol8pgDeqcxbspKK0flPeOVRhc3M8uMrFDMzy4QTylasW7eOAQMG0LdvX3r27MkVV1wBwA9/+EO6detGnz59GDZsGB9++GFxAzUzK7KSSiiS/t5A+bclzZf0sqRXJF2alt8h6ZR0+dOSXkwnAMtMmzZtmDlzJnPnzqW6upoZM2bw3HPPMXjwYObPn89LL73E5z//ecaMGZNls2ZmzU5JJZT6SBoCXAwcHxE9gUOBlXXqtAf+AIyPiIkZt0+7du0A2LBhAxs2bEASxx9/POXlyS2oww8/nMWLF2fZrJlZs1PyCQW4HLg0It4DiIh1EXF7zvZ2wMPAf6czOGaupqaGyspKOnbsyODBgxk4cOAW2ydMmMCQIUMK0bSZWbPRHBJKL2BOI9t/AfwpIm5oqIKk8yRVSaqqWbOyoWoNKisro7q6msWLFzN79mzmz5+/edvVV19NeXk5Z5xxxjYf18xsZ9IcEsrWzAS+JqljQxUiYnxE9I+I/mVt2293Qx06dGDQoEHMmDEDgDvvvJOpU6dy1113kcw7ZmbWcjWHhPIycFgj2ycDtwHTJWU+Kdj777+/+QmutWvX8thjj9GtWzdmzJjBNddcw5QpU2jbtm3WzZqZNTvN4cXGMcC1koZGxF8ltQG+ExE311aIiBsldQIekHRiRKzPqvGlS5dy1llnUVNTw6ZNmzj11FMZOnQoBx98MB9//DGDBw8Gkhvzv/zlL7Nq1sys2Sm1hNJWUu7jUr+IiF9I2gd4rHY+e2BC3R0j4jJJE4HfShoeEZuyCKhPnz68+OKLnyh/8803szi8mdlOo6QSSkTU2wWXPgr8iceBI+LsOuuZvoNiZmb5K6mE0hR6d25PlcfnMjPLXHO4KW9mZs2AE4qZmWXCCcXMzDLhhGJmZplwQjEzs0w4oZiZWSacUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMuGEYmZmmWhxCWXekpVUjJ5W7DDMzHY6LS6h1PXhhx9yyimn0K1bN7p3786zzz5b7JDMzJqlkkgokv6es3yipDck7SfpSklLJFWnZfdL6pFTd5akBZLmSnpeUuW2tn3RRRdxwgkn8NprrzF37ly6d++e0VmZmbUsJZFQakk6DrgFOCEi3k2Lb4iIyojoCtwDzJS0d85uZ0REX+A/geu2pb1Vq1bx5JNPMnLkSABat25Nhw4ddvQ0zMxapJJJKJL+Cbgd+OeIeKu+OhFxD/AI8C/1bH4W6Lwtbb799tvsvffejBgxgn79+jFq1Cg++uijbQ3dzMwonYTSBngI+HpEvLaVui8A3eopPwF4cFsa3bhxIy+88AIXXHABL774Irvvvjtjx47dlkOYmVmqVBLKBuAZYGQedVVn/S5Ji4HLSLrLPrmDdJ6kKklVNWtWbi7v0qULXbp0YeDAgQCccsopvPDCC9sTv5lZi1cqCWUTcCrwBUk/3krdfsCrOetnAAcA/w2Mq2+HiBgfEf0jon9Z2/abyz/72c+y7777smDBAgAef/xxevToUd8hzMxsK8qLHUCtiFgjaSjwlKT/jYjf1K0j6RvA8cAP6uy7QdL/Ad6S1D0iXq27b0NuueUWzjjjDNavX8+BBx7IxIkTd/RUzMxapJJJKAAR8TdJJwBPSvogLb5E0pnA7sB84NiIeL+efddK+jlwKfl1nQFQWVlJVVVVBtGbmbVsJZFQIqJdzvIiki4sSG7UX9nIfoPqrP+8AOGZmVkeSuUeipmZNXMtLqH07tyehWP/udhhmJntdFpcQjEzs8JwQjEzs0w4oZiZWSacUMzMLBNOKGZmlgknFDMzy4QTipmZZcIJxczMMuGEYmZmmXBCMTOzTDihmJlZJlpcQpm3ZOXWK5mZ2TZrcQnFzMwKo8UnlJqaGvr168fQoUOLHYqZWbNW8IQiqUZSdc5ndAP1ZknqX0/52ZLeT/d9WdLvJLVNt31f0iuSXpL0uKT9tzW+m266ie7du2/7iZmZ2Raa4gplbURU5nzG1q0gqWwrx7gn3bcnsB44LS1/EegfEX2A3wHXbktgixcvZtq0aYwaNWpbdjMzs3oUrctL0kJJ/yHpT8A30+IzJT0jab6kAfXsU04yt/wKgIh4IiLWpJufA7psSwwXX3wx1157La1atfiePzOzHdYUv0l3q9PldVrOtnURcVRETE7Xd4+II4HvAhNy6p0mqRpYAnwa+H097YwEHq4vAEnnSaqSVFWzJnnKa+rUqXTs2JHDDjtsx87OzMyA4nR53ZOz7Z46de8GiIgngT0ldaitFxGVwGeBecAPc3eSdCbQH7iuvgAiYnxE9I+I/mVt2wPw9NNPM2XKFCoqKjj99NOZOXMmZ5555o6dqZlZC1bsvp6P6qxHY+sRESRXJ0fXlkn6MvDvwFcj4uN8Gx4zZgyLFy9m4cKFTJ48mWOPPZZJkyZtW/RmZrZZsRNKXacBSDoKWBkR9b2FeBTwVlqvH/ArkmSyrMmiNDOzTyhvgjZ2S+9/1JoREfU+OgyskPQMsCdwTk75aWmSaQUsBs5Oy68D2gH3SgJ4NyK+uq0BDho0iEGDBm3rbmZmlkNJL1LL0aZT1/h46RvFDsPMrFmRNCciPvGuYK5S6/IquN6d2xc7BDOznVKLSyhmZlYYTihmZpYJJxQzM8uEE4qZmWXCCcXMzDLR4h4blrQaWFDsOOrYC/ig2EHUoxTjKsWYoDTjKsWYoDTjKsWYoLTi2j8i9m6sQlO82FhqFmztWeqmJqmq1GKC0oyrFGOC0oyrFGOC0oyrFGOC0o2rIe7yMjOzTDihmJlZJlpiQhlf7ADqUYoxQWnGVYoxQWnGVYoxQWnGVYoxQenGVa8Wd1PezMwKoyVeoZiZWQE4oZiZWSZaTEKRdIKkBZLelNTQfCyFanuCpGWS5ueUfVrSo5LeSL9+Kmfb5WmcCyR9pUAx7SvpCUmvSnpZ0kXFjkvSrpJmS5qbxvSTYsdUJ74ySS9KmloqcUlaKGmepGpJVaUQl6QOkn4n6bX05+uIEojpkPR7VPtZJeniEojrkvRnfb6ku9P/A0X/udpuEbHTf4AyklkeDwRaA3OBHk3Y/tHAocD8nLJrgdHp8mjgmnS5RxpfG+CANO6yAsTUCTg0Xd4DeD1tu2hxAQLapcu7AH8GDi/29yonvu8D/w1MLYV/w7SthcBedcqK/bN1JzAqXW4NdCh2THXiKwP+Cuxf5J/3zsA7wG7p+v+QTB5YMt+rbT6nYgfQJCcJRwB/yFm/HLi8iWOoYMuEsgDolC53Innh8hOxAX8AjmiC+B4CBpdKXEBb4AVgYCnEBHQBHgeO5R8JpRTiWsgnE0rR4iKZbfUd0gd+SiGmemI8Hni62HGRJJRFwKdJXjKfmsZWMt+rbf20lC6v2n+4WovTsmLaJyKWAqRfO6blTR6rpAqgH8kVQVHjSruVqoFlwKMRUfSYUjcCPwI25ZSVQlwBPCJpjqTzSiCuA4H3gYlp9+CvJe1e5JjqOh24O10uWlwRsQS4HngXWAqsjIhHihnTjmopCUX1lJXq89JNGqukdsB9wMURsaqxqvWUZR5XRNRERCXJFcEASb2KHZOkocCyiJiT7y71lBXq3/CLEXEoMAT4V0lHN1K3KeIqJ+nevS0i+gEfkXTbFDOmfzQmtQa+Cty7tar1lGUaV3pv5Gsk3VefA3aXdGYxY9pRLSWhLAb2zVnvArxXpFhq/a+kTgDp12VpeZPFKmkXkmRyV0TcXypxAUTEh8As4IQSiOmLwFclLQQmA8dKmlQCcRER76VflwEPAAOKHNdiYHF6ZQnwO5IEU/TvVWoI8EJE/G+6Xsy4vgy8ExHvR8QG4H7gyCLHtENaSkJ5Hugq6YD0L5TTgSlFjmkKcFa6fBbJPYza8tMltZF0ANAVmJ1145IE/AZ4NSJ+UQpxSdpbUod0eTeS/3CvFTMmgIi4PCK6REQFyc/OzIg4s9hxSdpd0h61yyT97/OLGVdE/BVYJOmQtOg44JVixlTHcP7R3VXbfrHiehc4XFLb9P/jccCrRY5pxxT7Jk5TfYATSZ5kegv49yZu+26SPtINJH9ljAQ+Q3KT943066dz6v97GucCYEiBYjqK5HL5JaA6/ZxYzLiAPsCLaUzzgf9Iy4v6vaoT4yD+cVO+2P+GB5I89TMXeLn257oE4qoEqtJ/xweBTxU7prSdtsByoH1OWbG/Vz8h+aNpPvBbkie4iv692t6Ph14xM7NMtJQuLzMzKzAnFDMzy4QTipmZZcIJxczMMuGEYmZmmXBCsWZLUkj6ec76pZKuzOjYd0g6JYtjbaWdb6Yj8j6RZ/0fZ9h2B0nfzep4Zk4o1px9DJwsaa9iB5JLUtk2VB8JfDcivpRn/XoTihLb+v+5A7DNCWUbz89aECcUa842ksy5fUndDXWvMCT9Pf06SNIfJf2PpNcljZV0hpJ5WOZJOijnMF+W9FRab2i6f5mk6yQ9L+klSd/JOe4Tkv4bmFdPPMPT48+XdE1a9h8kL5j+UtJ1dep3kvSkkrk75kv6J0ljgd3SsrskVaRXN/9JMjLzvrXnmR7jFEl3pMv7SHpAyVwzcyUdCYwFDkqPd116DlNz9r9V0tnp8kJJ/yHpT8A3JR0v6VlJL0i6V8mYcKTfz1fS7831+f0z2s6ivNgBmO2gccBLkq7dhn36At2BvwFvA7+OiAFKJhn7N+DitF4FcAxwEPCEpIOBb5OMCvsFSW2ApyU9ktYfAPSKiHdyG5P0OeAa4DBgBcnowF+PiJ9KOha4NCKq6sT4LyRTLlydXhG0jYinJF0YyeCZtaNEHwKMiIjvpmUNnfPNwB8jYlh6vHYkgzb2yjneoK1839ZFxFHpFeH9wJcj4iNJlwHfl3QrMAzoFhFRO4yOtRxOKNasRcQqSf8FfA9Ym+duz0c6PLikt4DahDAPyO16+p+I2AS8IeltoBvJeFl9cq5+2pOMqbQemF03maS+AMyKiPfTNu8imXTtwcZiBCYoGcDzwYiobqDeXyLiucZONnUsSTIkImqAlcqZCTBP96RfDyeZ7OnpNIG1Bp4FVgHrgF9LmkYyv4e1IO7ysp3BjST3InbPKdtI+vOdDrzXOmfbxznLm3LWN7HlH1l1xyUKkiHE/y0iKtPPAZHMYQHJUO31afCyoSER8SRJ0lkC/FbStxuoWrfN3Jh33cZmN3/PGti/ti2RzFVT+z3oEREjI2IjyVXafcDXgRnb2L41c04o1uxFxN9Ipk8dmVO8kKSLCZI5J3bZjkN/U1Kr9L7KgSQD8v0BuCC9ckDS55WM9NuYPwPHSNor7W4aDvyxsR0k7U8yB8vtJKNCH5pu2lDbdgP+V1L39Ab9sJzyx4EL0mOXSdoTWE0y/XOtvwA9lIxm255k9Nv6PAd8Me0CRMlouZ9P76O0j4jpJN2GlY2do+18nFBsZ/FzIPdpr9tJfonPJplGuKGrh8YsIPnF/zBwfkSsA35NMhz7C5LmA79iK13Haffa5cATJCMDvxARDzW2D8moxtWSXgS+AdyUlo8nuWd0VwP7jSbpappJMsJ1rYuAL0maB8wBekbEcpJuq/mSrouIRSSJ+SXgLpKRn+s7n/dJ5j6/W9JLJAmmG0lympqW/ZF6HpawnZtHGzYzs0z4CsXMzDLhhGJmZplwQjEzs0w4oZiZWSacUMzMLBNOKGZmlgknFDMzy8T/B9e/fMZqETomAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1473,188 +1473,188 @@
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+       "#T_71223_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row1_col3 {\n",
+       "#T_71223_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row1_col4 {\n",
+       "#T_71223_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row1_col5 {\n",
+       "#T_71223_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row1_col6 {\n",
+       "#T_71223_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row1_col7 {\n",
+       "#T_71223_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row1_col8 {\n",
+       "#T_71223_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row2_col1, #T_dc0f4_row4_col5, #T_dc0f4_row6_col5, #T_dc0f4_row8_col4 {\n",
+       "#T_71223_row2_col1, #T_71223_row4_col5, #T_71223_row6_col5, #T_71223_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row2_col3 {\n",
+       "#T_71223_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row2_col4 {\n",
+       "#T_71223_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row2_col5 {\n",
+       "#T_71223_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row2_col6 {\n",
+       "#T_71223_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row2_col7 {\n",
+       "#T_71223_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row2_col8, #T_dc0f4_row4_col3, #T_dc0f4_row8_col0, #T_dc0f4_row8_col5 {\n",
+       "#T_71223_row2_col8, #T_71223_row4_col3, #T_71223_row8_col0, #T_71223_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col0 {\n",
+       "#T_71223_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col1, #T_dc0f4_row3_col6, #T_dc0f4_row5_col0, #T_dc0f4_row6_col8 {\n",
+       "#T_71223_row3_col1, #T_71223_row3_col6, #T_71223_row5_col0, #T_71223_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col4, #T_dc0f4_row5_col3 {\n",
+       "#T_71223_row3_col4, #T_71223_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col5 {\n",
+       "#T_71223_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col7, #T_dc0f4_row7_col0, #T_dc0f4_row7_col1, #T_dc0f4_row7_col2, #T_dc0f4_row7_col3, #T_dc0f4_row7_col4, #T_dc0f4_row7_col5, #T_dc0f4_row7_col6, #T_dc0f4_row7_col8 {\n",
+       "#T_71223_row3_col7, #T_71223_row7_col0, #T_71223_row7_col1, #T_71223_row7_col2, #T_71223_row7_col3, #T_71223_row7_col4, #T_71223_row7_col5, #T_71223_row7_col6, #T_71223_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row3_col8 {\n",
+       "#T_71223_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row4_col0 {\n",
+       "#T_71223_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row4_col2 {\n",
+       "#T_71223_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row4_col7 {\n",
+       "#T_71223_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row4_col8 {\n",
+       "#T_71223_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row5_col1 {\n",
+       "#T_71223_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row5_col6, #T_dc0f4_row6_col2 {\n",
+       "#T_71223_row5_col6, #T_71223_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_dc0f4_row5_col7 {\n",
+       "#T_71223_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row5_col8 {\n",
+       "#T_71223_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row6_col1, #T_dc0f4_row6_col4 {\n",
+       "#T_71223_row6_col1, #T_71223_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row6_col3 {\n",
+       "#T_71223_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row6_col7 {\n",
+       "#T_71223_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row8_col1 {\n",
+       "#T_71223_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row8_col2 {\n",
+       "#T_71223_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row8_col3 {\n",
+       "#T_71223_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row8_col6 {\n",
+       "#T_71223_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_dc0f4_row8_col7 {\n",
+       "#T_71223_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_dc0f4_\">\n",
+       "<table id=\"T_71223_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1671,118 +1671,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_dc0f4_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_dc0f4_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_dc0f4_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_dc0f4_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_dc0f4_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_dc0f4_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_dc0f4_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_dc0f4_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_71223_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_71223_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_71223_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_71223_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_71223_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_71223_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_71223_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_71223_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_71223_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_71223_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_dc0f4_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_dc0f4_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_dc0f4_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_dc0f4_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_dc0f4_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_dc0f4_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_dc0f4_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_dc0f4_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_71223_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_71223_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_71223_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_71223_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_71223_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_71223_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_71223_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_71223_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_71223_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_71223_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_dc0f4_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_dc0f4_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_dc0f4_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_dc0f4_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_dc0f4_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_dc0f4_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_dc0f4_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_dc0f4_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_71223_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_71223_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_71223_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_71223_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_71223_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_71223_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_71223_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_71223_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_71223_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_71223_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_dc0f4_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_dc0f4_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_dc0f4_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_dc0f4_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_dc0f4_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_dc0f4_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_dc0f4_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_dc0f4_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_71223_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_71223_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_71223_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_71223_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_71223_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_71223_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_71223_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_71223_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_71223_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_71223_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_dc0f4_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_dc0f4_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_dc0f4_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_dc0f4_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_dc0f4_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_dc0f4_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_dc0f4_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_dc0f4_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_71223_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_71223_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_71223_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_71223_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_71223_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_71223_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_71223_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_71223_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_71223_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_71223_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_dc0f4_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_dc0f4_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_dc0f4_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_dc0f4_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_dc0f4_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_dc0f4_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_dc0f4_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_dc0f4_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_71223_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_71223_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_71223_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_71223_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_71223_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_71223_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_71223_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_71223_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_71223_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_71223_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_dc0f4_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_dc0f4_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_dc0f4_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_dc0f4_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_dc0f4_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_dc0f4_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_dc0f4_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_dc0f4_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_71223_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_71223_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_71223_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_71223_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_71223_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_71223_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_71223_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_71223_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_71223_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_71223_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_dc0f4_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_dc0f4_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_dc0f4_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_dc0f4_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_dc0f4_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_dc0f4_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_dc0f4_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_dc0f4_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_dc0f4_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_71223_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_71223_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_71223_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_71223_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_71223_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_71223_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_71223_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_71223_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_71223_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_71223_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_dc0f4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_dc0f4_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_dc0f4_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_dc0f4_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_dc0f4_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_dc0f4_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_dc0f4_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_dc0f4_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_dc0f4_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_dc0f4_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_71223_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_71223_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_71223_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_71223_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_71223_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_71223_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_71223_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_71223_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_71223_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_71223_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe619bd8fd0>"
+       "<pandas.io.formats.style.Styler at 0x7fcdc9dc93a0>"
       ]
      },
      "execution_count": 21,
@@ -1812,7 +1812,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix_kissim.csv\")"
+    "kinase_distance_matrix_df.to_csv(DATA / \"kinase_distance_matrix.csv\")"
    ]
   },
   {
@@ -1864,7 +1864,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From 7e73bf5dec4ae7817a6b7e8d03fe664fa68e20c8 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 14:41:18 +0200
Subject: [PATCH 112/158] T024: Rename output to
 "kinase_distance/similarity_matrix.csv"

---
 ...istance_matrix_sequence.csv => kinase_distance_matrix.csv} | 0
 ...arity_matrix_sequence.csv => kinase_similarity_matrix.csv} | 0
 .../T024_kinase_similarity_sequence/talktorial.ipynb          | 4 ++--
 3 files changed, 2 insertions(+), 2 deletions(-)
 rename teachopencadd/talktorials/T024_kinase_similarity_sequence/data/{kinase_distance_matrix_sequence.csv => kinase_distance_matrix.csv} (100%)
 rename teachopencadd/talktorials/T024_kinase_similarity_sequence/data/{kinase_similarity_matrix_sequence.csv => kinase_similarity_matrix.csv} (100%)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix.csv
similarity index 100%
rename from teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv
rename to teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix.csv
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix.csv
similarity index 100%
rename from teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix_sequence.csv
rename to teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_similarity_matrix.csv
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 27a99237..b5bb231d 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -2826,7 +2826,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_similarity_matrix_sequence.csv\"\n",
+    "f_name = f\"kinase_similarity_matrix.csv\"\n",
     "kinase_similarity_matrix_df.to_csv(DATA / f_name)"
    ]
   },
@@ -3218,7 +3218,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_distance_matrix_sequence.csv\"\n",
+    "f_name = f\"kinase_distance_matrix.csv\"\n",
     "kinase_distance_matrix_df.to_csv(DATA / f_name)"
    ]
   },

From f08b18ce84867aebd8134bf9c06580d1fa755ec7 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 28 Sep 2021 14:55:21 +0200
Subject: [PATCH 113/158] :pencil2: fix typo

---
 .../talktorial.ipynb                          | 309 +++++++++---------
 1 file changed, 158 insertions(+), 151 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index b52355d7..dff58ad1 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -416,7 +416,12 @@
     "# Get all available structures for these kinases\n",
     "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
     "# Keep only relevant columns\n",
-    "structures_df = structures_df[[\"structure.klifs_id\", \"kinase.klifs_name\", \"species.klifs\", \"structure.dfg\", \"structure.resolution\", \"structure.qualityscore\"]]\n",
+    "structures_df = structures_df[[\"structure.klifs_id\",\n",
+    "                               \"kinase.klifs_name\",\n",
+    "                               \"species.klifs\",\n",
+    "                               \"structure.dfg\",\n",
+    "                               \"structure.resolution\",\n",
+    "                               \"structure.qualityscore\"]]\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
     "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -815,7 +820,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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cObJU8czMdkghhWZ9RPw88ySJu4EfA0OBfYp0zLKzatUqunTpwksvvcTtt9/Oo48+ysqVK+nWrRsAd9xxB/36+WnYZtY8FFJorpZ0KclNAO/XNUbEUxnkuR5YHRGLJA3NYP/Nwoknnsibb77Jbrvtxi9+8Qs+9KEPcfrpp5PL5ZBEVVUVv/71r0sd08ysIIUUmv7A6cCx/GPoLNLlJhURK0jucGvIaElH5S1/LSL+3NQZysHDDz/8gbabbrqpBEnMzHZdIYVmFNAzIjZkFSIiOjTQ9hDwUPr+RuDGRrY/I5NgZma2ywopNAuATsCqbKMUR//uHanxnGJmZkVTSKHpCjwr6Qm2vkbz+cxSmZlZi1FIobk08xRmZtZibbfQRMQfixHEzMxapkJmBjhc0hOS/i5pg6RaSe8WI5yZmTV/hTwmYDLJt/WXAnsAZ6VtZmZm21XINRoi4m+SKiKiFrhBUov8/oqZmTW9QgrN2vQ5NDlJV5A8J2bPbGOZmVlLUcjQ2elpv/NInhuzH3BilqHMzKzlKOSusxfTt+uB72Ubx8zMWprtFhpJnwIuAw7I7x8RPbOLZWZmLUUh12iuAy4AngRqs41jZmYtTSGFZnVE3Jt5kiJZ9PJqqibOyGz/yzyPmpnZVgopNHMkXQncTvbPozEzsxamkEIzJP05KK8tk+fRtBRVVVXstddeVFRUUFlZSU1NDaNHj2bJkiUAvPPOO3Tq1IlcLlfaoGZmRVDIXWefbsoDSqoFFgEiueZzXkT8WVIV8AywBGgL1AATImJjul0l8CpwbURckre/h4BuwLq06QcR8bumzLwz5syZQ+fOnbcs33rrrVveX3jhhXTs2LEUsczMiq6gmQGa2LqIqAaQ9FngcuCYdN1zEVEtqQK4HzgZmJauO46kCJ0s6TsREXn7HBsRNUVJv4sigt/+9rfMnj271FHMzIqikC9sZmlv4O36jelUN/OA7nnNY0ge8/wScHhR0u0kSRx33HEcdthhTJkyZat1Dz/8MF27dqVXr14lSmdmVlylOKPZQ1IO2J1kyOsD13ok7U5ybegb6fIewDDgKyRP+xwDPJq3yTRJdUNnwyLizXr7Owc4B6Bi74804Udp2COPPMK+++7LqlWrGD58OL179+boo48G4JZbbmHMmDGZZzAzKxeFPCagvaT/kHRtutxL0shdOOa6iKiOiN7ACOB/JCldd2BahN4EXoqIhWn7SGBORKwFfg+MSofX6oxN91ldv8gARMSUiBgUEYMq2md/bWTfffcFoEuXLowaNYp58+YBsGnTJm6//XZGjx6deQYzs3JRyNDZDSS3NR+RLq8AftAUB4+IR4HOQN1pxnPp9ZuPA4dLqntc9BjgM5KWkXxxdB+gSW9SaCpr1qzhvffe2/L+D3/4A/369QPggQceoHfv3vTo0aOUEc3MiqqQobMDI2K0pDEAEbEu7wxkl0jqDVSQnMG0r2uPiJWSJgKXpHeVHQXsFxHvp9udSVJ8HmiKHE3ptddeY9SoUUByBnPqqacyYsQIAKZPn+5hMzNrdQopNBvSayQBIOlA8r64uRPqrtFAcovzuIiobaB23Ukyx9o3gNl1RSZ1F3CFpHa7kCMTPXv2ZMGCBQ2uu/HGG4sbxsysDBRSaC4FZgH7SZoGfAo4Y2cPGBEV22hfBvTLWw5g4Db6vsU/htuG7mwWMzPLnrb+Oso2Okn7kNxSLOCxiHgj62BZGTRoUNTUNIuv3JiZlQ1JT0bEoO33/KBC7jr7FLA+ImaQ3Fr8HUkH7MzBzMys9SnkrrNfkjzOeSBwMfAi8D+ZpjIzsxajkEKzKb1e8gXg5xFxNbBXtrHMzKylKORmgPckXQKcBhydflFyt2xjmZlZS1HIGc1oktuZJ0TEqyTzj12ZaSozM2sxCnlMwKvAT/OWX8LXaMzMrECF3HV2uKQnJP1d0gZJtZJWFyOcmZk1f4UMnU0mme5lKbAHcBbwiyxDmZlZy1HQYwIi4m+SKtLnxNwg6c8Z5zIzsxaikEKzVlJbICfpCmAlsGe2sczMrKUoZOjs9LTfecAaYD/gxCxDmZlZy1HQXGctSbtuvaLbuKuaZF/LJn2uSfZjZlbudmWus+0OnaVznV0GHJDfPyJ67swBzcysdSlk6Ow6ku/RHAV8Mu9lqdraWg455BBGjkyecH3ZZZfRvXt3qqurqa6uZubMmSVOaGZWOoUUmtURcW9ErIqIN+teO3tASedJ+pukkNQ5r723pEclvS/ponrbjJC0JN1u4s4eOytXX301ffr02artggsuIJfLkcvlOOGEE0qUzMys9AopNHMkXSnpCEmH1r124ZiPAJ8hmQU631vA14Ef5zemc6v9AjgeOBgYI+ngXTh+k1qxYgUzZszgrLPOKnUUM7OyVEihGQIMAv4L+En6+nGjWwCSqiQ9K2mqpIWSfiepfUTMT5+muZX0jOkJYGO9VYOBv0XE8xGxAZhOMpM0ks5OZy1YIOn3ktoX8Hma1Pnnn88VV1xBmzZb/yonT57MgAEDGD9+PG+//XaxY5mZlY3tFpqI+HQDr2ML3P9BwJSIGAC8C3xtJzJ2B5bnLa9I2wBuj4hPRsRA4BlgQkM7kHSOpBpJNbVrm272nHvuuYcuXbpw2GGHbdV+7rnn8txzz5HL5ejWrRsXXnhhkx3TzKy52eZdZ5JOi4ibJX2zofUR8dOG2utZHhGPpO9vpoGhsQKoocOnP/tJ+gHJkz87APc1tIOImAJMgeT25h08/jY98sgj3H333cycOZP169fz7rvvctppp3HzzTdv6XP22WdvuUnAzKw1auyMpu7b/3s18OpQ4P7r/099Z/4nv4LkS6J1egCvpO9vBM6LiP7A94Ddd2L/O+3yyy9nxYoVLFu2jOnTp3Psscdy8803s3Llyi197rjjDvr161fMWGZmZaWx79HMAIiI79VfIemfC9z//pKOiIhHSSbm/NOOR+QJoJekjwEvA6cAp6br9gJWStoNGJuuL7lvfetb5HI5JFFVVcWvf/3rUkcyMyuZbc4MIGkJ8Nn6F+4lnQn8e0Qc2OiOpSpgJjAXOJJk9ufTSWZ//hbwUWAVMDMizpL0UaAG2BvYDPwdODgi3pV0AnAVUAFcHxE/TI9xbrqvF4FFwF4RcUZjuTwzgJnZjstqZoALgPslnRARS9MDXUJyNnFMgfvfHBFfrdf28/S1lfQBaz0a2klEzCQpWvXbfwn8ssAsZmZWAtssNBExU9L7wL2SvkhyJvJJ4OiIaLb36/bv3pEan4mYmRVNo7c3R8SDwBnAQ0BPYFihRSYilkWEr4KbmbVyjd3e/B7JXWIC2gHDgFWSBERE7F2ciGZm1pw1NnS2VzGDmJlZy1TIFDRmZmY7zYXGzMwy5UJjZmaZcqExM7NMudCYmVmmXGjMzCxTLjRmZpYpFxozM8tUY5NqtkiLXl5N1cQZBff3DM1mZrvGZzRmZpYpF5oCrF+/nsGDBzNw4ED69u3LpZdeCsBbb73F8OHD6dWrF8OHD+ftt5vtpNZmZpkpq0Ij6e/baP8XSYslPS3pL5IuSttvlHRS+v7DkuanD2ZrUu3atWP27NksWLCAXC7HrFmzeOyxx5g0aRLDhg1j6dKlDBs2jEmTJjX1oc3Mmr2yKjQNkXQ8cD5wXET0BQ4FVtfr0xG4D5gSETdkkIEOHToAsHHjRjZu3Igk7rrrLsaNGwfAuHHjuPPOO5v60GZmzV7ZFxrgEuCiiHgFICLWR8S1ees7APcC/5s+cTMTtbW1VFdX06VLF4YPH86QIUN47bXX6NatGwDdunVj1apVWR3ezKzZag6Fph/wZCPrfwr8KSJ+tq0Oks6RVCOppnbt6m11a1RFRQW5XI4VK1Ywb948Fi9evFP7MTNrbZpDodme2cAXJHXZVoeImBIRgyJiUEX7jrt0sE6dOjF06FBmzZpF165dWblyJQArV66kS5dtRjAza7WaQ6F5GjiskfXTgV8CMyVl8rC2119/nXfeeQeAdevW8cADD9C7d28+//nPM3XqVACmTp3KF77whSwOb2bWrDWHL2xeDlwhaWREvCqpHfCViPh5XYeIuEpSN+AOSSdExIamDLBy5UrGjRtHbW0tmzdv5uSTT2bkyJEcccQRnHzyyVx33XXsv//+3HbbbU15WDOzFqHcCk17SSvyln8aET+V1BV4QJKAAK6vv2FEfFvSDcBNksZExOamCjVgwADmz5//gfZ99tmHBx98sKkOY2bWIpVVoYmIBofy0luWP3DbckScUW+5yb9DY2Zmu6asCk0x9O/ekRrPX2ZmVjTN4WYAMzNrxlxozMwsUy40ZmaWKRcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqExM7NMudCYmVmmXGjMzCxTLjRmZpYpFxozM8tUqys0i15eTdXEGaWOYWbWarS6QlPfO++8w0knnUTv3r3p06cPjz76aKkjmZm1KGVRaCT9Pe/9CZKWStpf0mWSXpaUS9tul3RwXt+HJC2RtEDSE5Kqd/TY3/jGNxgxYgTPPvssCxYsoE+fPk30qczMDMqk0NSRNAy4BhgRES+lzT+LiOqI6AXcCsyW9JG8zcZGxEDgv4Erd+R47777LnPnzmXChAkAtG3blk6dOu3qxzAzszxlU2gk/RNwLfC5iHiuoT4RcSvwB+DUBlY/CnTfkWM+//zzfOQjH+HMM8/kkEMO4ayzzmLNmjU7Gt3MzBpRLoWmHXAX8MWIeHY7fZ8CejfQPgK4c0cOumnTJp566inOPfdc5s+fz5577smkSZN2ZBdmZrYd5VJoNgJ/BiYU0Ff1lqdJWgF8m2TY7YMbSOdIqpFUU7t29Zb2Hj160KNHD4YMGQLASSedxFNPPbUz+c3MbBvKpdBsBk4GPinpO9vpewjwTN7yWOBjwP8Cv2hog4iYEhGDImJQRfuOW9o/+tGPst9++7FkyRIAHnzwQQ4++OCGdmFmZjupstQB6kTEWkkjgYclvRYR19XvI+lE4DjgwnrbbpT078BzkvpExDP1t92Wa665hrFjx7JhwwZ69uzJDTfcsKsfxczM8pRNoQGIiLckjQDmSnojbb5A0mnAnsBi4NiIeL2BbddJ+glwEYUNwQFQXV1NTU1NE6Q3M7OGlEWhiYgOee+XkwyFQXKDwGWNbDe03vJPMohnZma7oFyu0ZiZWQvV6gpN/+4dWTbpc6WOYWbWarS6QmNmZsXlQmNmZplyoTEzs0y50JiZWaZcaMzMLFMuNGZmlikXGjMzy5QLjZmZZcqFxszMMuVCY2ZmmXKhMTOzTLW6QrPo5dXb72RmZk2m1RUaMzMrrlZfaGpraznkkEMYOXJkqaOYmbVImRcaSbWScnmvidvo95CkQQ20nyHp9XTbpyX9TlL7dN03Jf1F0kJJD0o6YEfzXX311fTp02fHP5iZmRWkGGc06yKiOu81qX4HSRXb2cet6bZ9gQ3A6LR9PjAoIgYAvwOu2JFgK1asYMaMGZx11lk7spmZme2Akg2dSVom6T8l/Qn4ctp8mqQ/S1osaXAD21QCewJvA0TEnIhYm65+DOixIxnOP/98rrjiCtq0afUjiGZmmSnG/2H3qDd0Njpv3fqIOCoipqfLe0bEkcDXgOvz+o2WlANeBj4M/F8Dx5kA3NtQAEnnSKqRVFO7Nrnr7J577qFLly4cdthhu/bpzMysUaUYOrs1b92t9freAhARc4G9JXWq6xcR1cBHgUXAxfkbSToNGARc2VCAiJgSEYMiYlBF+44APPLII9x9991UVVVxyimnMHv2bE477bRd+6RmZvYBpR4zWlNvORpbjoggOZs5uq5N0meA7wKfj4j3Cz3w5ZdfzooVK1i2bBnTp0/n2GOP5eabb96x9GZmtl2lLjT1jQaQdBSwOiIa+nblUcBzab9DgF+TFJlVRUtpZmYFqyzCMfZIr6/UmRURDd7iDLwt6c/A3sD4vPbRafFpA6wAzkjbrwQ6ALdJAngpIj6/owGHDh3K0KFDd3QzMzMrgJLRqNajXbde8f7KpaWOYWbWrEh6MiI+8F3HQpTb0Fnm+nfvWOoIZmatSqsrNGZmVlwuNGZmlikXGjMzy5QLjZmZZcqFxszMMtXqbm+W9B6wpNQ5tqMz8EapQxTAOZuWczad5pARmk/O3YEVETFiZzZujYWmZmfvBS+W5pARnLOpOWfTaQ4ZofXk9NCZmZllyoXGzMwy1RoLzZRSByhAc8gIztnUnLPpNIeM0EpytrprNGZmVlyt8YzGzMyKyIXGzMwy1WoKjaQRkpZI+pukbT0Pp1hZrpe0StLivLYPS7pf0tL054fy1l2S5l4i6bNFyrifpDmSnpH0tKRvlGnO3SXNk7Qgzfm9csyZd+wKSfMl3VOuOSUtk7RIUk5STRnn7CTpd5KeTf87PaKccko6KP0d1r3elXR+OWXMO+4F6d+fxZJuSf9eNV3OiGjxL6CC5KmcPYG2wALg4BLmORo4FFic13YFMDF9PxH4Ufr+4DRvO+Bj6eeoKELGbsCh6fu9gL+mWcotp4AO6fvdgMeBw8stZ17ebwL/C9xTjn/u6bGXAZ3rtZVjzqnAWen7tkCncsyZHr8CeBU4oNwyAt2BF4A90uXfkjxcsslyFuWXXOoXcARwX97yJcAlJc5UxdaFZgnQLX3fDVjSUFbgPuCIEuS9CxhezjmB9sBTwJByzAn0AB4EjuUfhaYccy7jg4WmrHKSPIX3BdIbmso1Z97xjgMeKceMJIVmOfBhkqcu35PmbbKcrWXorO4XWWdF2lZOukbESoD0Z5e0veTZJVUBh5CcLZRdznQ4KgesAu6PiLLMCVwFfAvYnNdWjjkD+IOkJyWdU6Y5ewKvAzekQ5G/kbRnGeascwpwS/q+rDJGxMvAj4GXgJXA6oj4Q1PmbC2FRg20NZf7ukuaXVIH4PfA+RHxbmNdG2grSs6IqI2IapIzhsGS+jXSvSQ5JY0EVkXEk4Vu0kBbsf7cPxURhwLHA/8q6ehG+pYqZyXJ8PMvI+IQYA3J8M62lOz3Kakt8Hngtu11baCtGP9tfgj4Askw2L7AnpJOa2yTBtoazdlaCs0KYL+85R7AKyXKsi2vSeoGkP5clbaXLLuk3UiKzLSIuL1cc9aJiHeAh4ARlF/OTwGfl7QMmA4cK+nmMsxJRLyS/lwF3AEMLsOcK0gmeXw8Xf4dSeEpt5yQFOynIuK1dLncMn4GeCEiXo+IjcDtwJFNmbO1FJongF6SPpb+6+IU4O4SZ6rvbmBc+n4cyTWRuvZTJLWT9DGgFzAv6zCSBFwHPBMRPy3jnB+R1Cl9vwfJX5pnyy1nRFwSET0ioorkv7/ZEXFaueWUtKekverek4zVLy63nBHxKrBc0kFp0zDgL+WWMzWGfwyb1WUpp4wvAYdLap/+vR8GPNOkOYt1MazUL+AEkjunngO+W+Ist5CMhW4k+dfBBGAfkgvFS9OfH87r/9009xLg+CJlPIrkdHghkEtfJ5RhzgHA/DTnYuA/0/ayylkv81D+cTNAWeUkufaxIH09Xfd3pdxypsetBmrSP/s7gQ+VW06SG1TeBDrmtZVVxvS43yP5B9pi4CaSO8qaLKenoDEzs0y1lqEzMzMrERcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqGxZktSSPpJ3vJFki5ron3fKOmkptjXdo7z5XTm4TkF9v9OEx67k6SvNdX+zLbFhcaas/eBL0nqXOog+SRV7ED3CcDXIuLTBfZvsNAosaN/nzsBO1xodvDzmbnQWLO2ieRZ5hfUX1H/jETS39OfQyX9UdJvJf1V0iRJY5U802aRpAPzdvMZSQ+n/Uam21dIulLSE5IWSvpK3n7nSPpfYFEDecak+18s6Udp23+SfDH2V5KurNe/m6S5Sp5jsljSP0maBOyRtk2TVJWeDf03yazV+9V9znQfJ0m6MX3fVdIdSp7bs0DSkcAk4MB0f1emn+GevO0nSzojfb9M0n9K+hPwZUnHSXpU0lOSblMyJx7p7/Mv6e/mx4X9MVpLV1nqAGa76BfAQklX7MA2A4E+wFvA88BvImKwkoe7/RtwftqvCjgGOBCYI+njwL+QzG77SUntgEck/SHtPxjoFxEv5B9M0r7Aj4DDgLdJZkb+YkR8X9KxwEURUVMv46kkj7b4YXoG0T4iHpZ0XiQTiNbNqn0QcGZEfC1t29Zn/jnwx4gYle6vA8kklP3y9jd0O7+39RFxVHoGeTvwmYhYI+nbwDclTQZGAb0jIuqmBjJzobFmLSLelfQ/wNeBdQVu9kSk059Leg6oKxSLgPwhrN9GxGZgqaTngd4kc38NyDtb6kgy19MGYF79IpP6JPBQRLyeHnMaycPv7mwsI3C9kolN74yI3Db6vRgRjzX2YVPHkhRJIqIWWK28JyYW6Nb05+EkD796JC1sbYFHgXeB9cBvJM0gea6JmYfOrEW4iuRax555bZtI//tOJwpsm7fu/bz3m/OWN7P1P77qz88UJFOk/1tEVKevj0Xy7A5IpqpvyDZPM7YlIuaSFKOXgZsk/cs2utY/Zn7m3XfwsFt+Z9vYvu5YInnuT93v4OCImBARm0jO6n4PfBGYtYPHtxbKhcaavYh4i+TxsxPympeRDFVB8qyN3XZi11+W1Ca9btOTZALB+4Bz0zMNJH1CySzHjXkcOEZS53TYagzwx8Y2kHQAyfNrriWZRfvQdNXGumNvw2uS+qQ3BozKa38QODfdd4WkvYH3SB7TXedF4GAls/J2JJnFtyGPAZ9KhxJRMuvvJ9LrNB0jYibJ8GN1Y5/RWg8XGmspfgLk3312Lcn/3OeRPNp5W2cbjVlCUhDuBb4aEeuB35BMR/+UpMXAr9nOEHQ6THcJMIdkVuSnIuKuxrYhmeE5J2k+cCJwddo+heSa1LRtbDeRZMhqNskM4XW+AXxa0iLgSaBvRLxJMvy1WNKVEbGcpGAvBKaRzIrd0Od5neSZ8rdIWkhSeHqTFK170rY/0sBNGtY6efZmMzPLlM9ozMwsUy40ZmaWKRcaMzPLlAuNmZllyoXGzMwy5UJjZmaZcqExM7NM/X9NBGuncZxNrQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1236,7 +1241,7 @@
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1254,7 +1259,8 @@
     "# = lower triangular matrix without the diagonal\n",
     "D_condensed = distance.squareform(D)\n",
     "# Plot pairwise distances\n",
-    "plt.boxplot(D_condensed);\n",
+    "plt.boxplot(D_condensed)\n",
+    "plt.show()\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1297,7 +1303,8 @@
    ],
    "source": [
     "print(\n",
-    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
+    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} \"\n",
+    "    f\"reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
     ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -1311,220 +1318,220 @@
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        "}\n",
-       "#T_8216f_row2_col3, #T_8216f_row8_col3 {\n",
+       "#T_d057e_row2_col3, #T_d057e_row8_col3 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row2_col5 {\n",
+       "#T_d057e_row2_col5 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row2_col6 {\n",
+       "#T_d057e_row2_col6 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row2_col8 {\n",
+       "#T_d057e_row2_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row3_col0 {\n",
+       "#T_d057e_row3_col0 {\n",
        "  background-color: #329832;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row3_col1, #T_8216f_row3_col5, #T_8216f_row3_col7, #T_8216f_row7_col0, #T_8216f_row7_col2, #T_8216f_row7_col3, #T_8216f_row7_col4, #T_8216f_row7_col6, #T_8216f_row7_col8 {\n",
+       "#T_d057e_row3_col1, #T_d057e_row3_col5, #T_d057e_row3_col7, #T_d057e_row7_col0, #T_d057e_row7_col2, #T_d057e_row7_col3, #T_d057e_row7_col4, #T_d057e_row7_col6, #T_d057e_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row3_col2 {\n",
+       "#T_d057e_row3_col2 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row3_col6, #T_8216f_row7_col5 {\n",
+       "#T_d057e_row3_col6, #T_d057e_row7_col5 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row3_col8 {\n",
+       "#T_d057e_row3_col8 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col1 {\n",
+       "#T_d057e_row4_col1 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col2, #T_8216f_row6_col4 {\n",
+       "#T_d057e_row4_col2, #T_d057e_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col3 {\n",
+       "#T_d057e_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col5 {\n",
+       "#T_d057e_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row4_col6 {\n",
+       "#T_d057e_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col7 {\n",
+       "#T_d057e_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row4_col8 {\n",
+       "#T_d057e_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row5_col0 {\n",
+       "#T_d057e_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row5_col1 {\n",
+       "#T_d057e_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row5_col2 {\n",
+       "#T_d057e_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row5_col4 {\n",
+       "#T_d057e_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row5_col6 {\n",
+       "#T_d057e_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row5_col7 {\n",
+       "#T_d057e_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row5_col8 {\n",
+       "#T_d057e_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row6_col0 {\n",
+       "#T_d057e_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row6_col1 {\n",
+       "#T_d057e_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row6_col2, #T_8216f_row6_col5 {\n",
+       "#T_d057e_row6_col2, #T_d057e_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row6_col3 {\n",
+       "#T_d057e_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row6_col7 {\n",
+       "#T_d057e_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row6_col8 {\n",
+       "#T_d057e_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row7_col1 {\n",
+       "#T_d057e_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row8_col1 {\n",
+       "#T_d057e_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row8_col4 {\n",
+       "#T_d057e_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_8216f_row8_col6 {\n",
+       "#T_d057e_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_8216f_row8_col7 {\n",
+       "#T_d057e_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_8216f_\">\n",
+       "<table id=\"T_d057e_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1541,118 +1548,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_8216f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_8216f_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_8216f_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_8216f_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_8216f_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_8216f_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_8216f_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_8216f_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_d057e_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_d057e_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_d057e_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_d057e_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_d057e_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_d057e_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_d057e_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_d057e_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_d057e_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_8216f_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_8216f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_8216f_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_8216f_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_8216f_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_8216f_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_8216f_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_8216f_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_d057e_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_d057e_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_d057e_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_d057e_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_d057e_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_d057e_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_d057e_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_d057e_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_d057e_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_8216f_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_8216f_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_8216f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_8216f_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_8216f_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_8216f_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_8216f_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_8216f_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_d057e_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_d057e_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_d057e_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_d057e_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_d057e_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_d057e_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_d057e_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_d057e_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_d057e_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_8216f_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_8216f_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_8216f_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_8216f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_8216f_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_8216f_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_8216f_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_8216f_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_d057e_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_d057e_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_d057e_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_d057e_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_d057e_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_d057e_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_d057e_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_d057e_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_d057e_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_8216f_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_8216f_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_8216f_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_8216f_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_8216f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_8216f_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_8216f_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_8216f_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_d057e_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_d057e_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_d057e_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_d057e_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_d057e_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_d057e_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_d057e_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_d057e_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_d057e_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_8216f_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_8216f_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_8216f_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_8216f_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_8216f_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_8216f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_8216f_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_8216f_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_d057e_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_d057e_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_d057e_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_d057e_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_d057e_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_d057e_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_d057e_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_d057e_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_d057e_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_8216f_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_8216f_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_8216f_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_8216f_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_8216f_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_8216f_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_8216f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_8216f_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_d057e_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_d057e_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_d057e_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_d057e_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_d057e_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_d057e_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_d057e_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_d057e_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_d057e_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_8216f_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_8216f_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_8216f_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_8216f_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_8216f_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_8216f_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_8216f_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_8216f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_8216f_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_d057e_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_d057e_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_d057e_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_d057e_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_d057e_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_d057e_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_d057e_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_d057e_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_d057e_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_d057e_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_8216f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_8216f_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_8216f_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_8216f_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_8216f_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_8216f_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_8216f_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_8216f_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_8216f_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_8216f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_d057e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d057e_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_d057e_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_d057e_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_d057e_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_d057e_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_d057e_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_d057e_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_d057e_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_d057e_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f34d3ab2bb0>"
+       "<pandas.io.formats.style.Styler at 0x7f91da3ac910>"
       ]
      },
      "execution_count": 23,
@@ -1731,7 +1738,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From fc1cb2882bc2c41f8100ba0ee080ea4ee48aa528 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 14:56:03 +0200
Subject: [PATCH 114/158] T024: Add docstring!

---
 .../talktorial.ipynb                               | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index b5bb231d..2afe8fe8 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -1739,7 +1739,19 @@
    "source": [
     "def pairwise_kinase_similarities(kinase_names, similarity_measure):\n",
     "    \"\"\"\n",
-    "    TODO\n",
+    "    Calculate pairwise similarities between a set of kinases.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_names : list of str\n",
+    "        Kinase names.\n",
+    "    similarity_measure : str\n",
+    "        Select similarity measure: \"identity\" or \"substitution\".\n",
+    "        \n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.DataFrame\n",
+    "        All-against-all similarities between input kinases.\n",
     "    \"\"\"\n",
     "    # Initialize matrix with 0\n",
     "    kinase_similarity_matrix = np.zeros((len(kinase_names),\n",

From 21fdbe8d5922da042530fce44094fc262821a36d Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 16:07:33 +0200
Subject: [PATCH 115/158] T028: Update filepaths+theory; normalize df; sort
 kinases

---
 .../talktorial.ipynb                          | 716 ++++++++++++------
 1 file changed, 475 insertions(+), 241 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 4f9826a7..ca4bf264 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -56,8 +56,7 @@
    "source": [
     "### References\n",
     "\n",
-    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Kinase similarity descriptor: XXX"
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) "
    ]
   },
   {
@@ -78,25 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use nine kinases as investigated in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629). In the study diverse kinase similarity measures were analyzed for different combinations of kinase on- and off-targets to explore the limits of multi-kinase screenings.\n",
-    "\n",
-    "> We aggregated the investigated kinases in “profiles”. Profile 1 combined **EGFR** and **ErbB2** as targets and **BRAF** as a (general) anti-target. Out of similar considerations, Profile 2 consisted of EGFR and **PI3K** as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. Profile 3, comprised of EGFR and **VEGFR2** as targets and BRAF as anti-target, was contrasted with the hit rate that we found with a standard docking against the single target VEGFR2 (Profile 4).\n",
-    "> To broaden the comparison and obtain an estimate for the promiscuity of each compound, the kinases **CDK2**, **LCK**, **MET** and **p38α** were included in the experimental assay panel and the structure-based bioinformatics comparison as commonly used anti-targets.\n",
-    "\n",
-    "*Table 1:*\n",
-    "Kinases used in this notebook, taken from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), with their synonyms, UniProt IDs, kinase groups, and full unabbreviated names.\n",
-    "\n",
-    "| Kinase                     | Synonyms               | UniProt ID | Group    | Full kinase name                                 |\n",
-    "|----------------------------|------------------------|------------|----------|--------------------------------------------------|\n",
-    "| EGFR                       | ErbB1                  | P00533     | TK       | Epidermal growth factor receptor                 |\n",
-    "| ErbB2                      | Her2                   | P04626     | TK       | Erythroblastic leukemia viral oncogene homolog 2 |\n",
-    "| PI3K                       | PI3KCA, p110a          | P42336     | Atypical | Phosphatidylinositol-3-kinase                    |\n",
-    "| VEGFR2                     | KDR                    | P35968     | TK       | Vascular endothelial growth factor receptor 2    |\n",
-    "| BRAF                       | -                      | P15056     | TKL      | Rapidly accelerated fibrosarcoma isoform B       |\n",
-    "| CDK2                       | -                      | P24941     | CMGC     | Cyclic-dependent kinase 2                        |\n",
-    "| LCK                        | -                      | P06239     | TK       | Lymphocyte-specific protein tyrosine kinase      |\n",
-    "| MET                        | -                      | P08581     | TK       | Mesenchymal-epithelial transition factor         |\n",
-    "| p38a                       | MAPK14                 | Q16539     | CMGC     | p38 mitogen activated protein kinase α           |"
+    "We use the kinase selection as defined in __Talktorial T023__."
    ]
   },
   {
@@ -105,12 +86,10 @@
    "source": [
     "### Kinase similarity descriptor (considering 4 different aspects)\n",
     "\n",
-    "Describe the dataset describing kinase similarity and how we use it.\n",
-    "\n",
-    "- XXX = KLIFS pocket sequence\n",
-    "- XXX = KiSSim fingerprint\n",
-    "- XXX = KLIFS IFP\n",
-    "- XXX = Ligand promiscuity: ChEMBL28, bioactivity"
+    "- __Talktorial T024__ = KLIFS pocket sequence\n",
+    "- __Talktorial T025__ = KiSSim fingerprint\n",
+    "- __Talktorial T026__ = KLIFS interaction fingerprint\n",
+    "- __Talktorial T027__ = Ligand profile: ChEMBL29, bioactivity"
    ]
   },
   {
@@ -152,6 +131,13 @@
     "### Load kinase matrices"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define the paths to the kinase distance matrices."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -159,15 +145,24 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrix_paths = {\n",
-    "    \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_substitution_matrix_sequence.csv\",\n",
-    "    # \"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_identity_matrix_sequence.csv\",\n",
-    "    #\"sequence\": HERE / \"../T024_kinase_similarity_sequence/data/kinase_distance_matrix_sequence.csv\",\n",
-    "    \"kissim\": HERE / \"../T025_kinase_similarity_kissim/data/kinase_distance_matrix_kissim.csv\",\n",
-    "    \"ifp\": HERE / \"../T026_kinase_similarity_ifp/data/kinase_distance_matrix_ifp.csv\",\n",
-    "    \"ligand-profile\": HERE / \"../T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_ligand_profile.csv\"\n",
+    "    \"sequence\": \"T024_kinase_similarity_sequence\",\n",
+    "    \"kissim\": \"T025_kinase_similarity_kissim\",\n",
+    "    \"ifp\": \"T026_kinase_similarity_ifp\",\n",
+    "    \"ligand-profile\": \"T027_kinase_similarity_ligand_profile\"\n",
+    "}\n",
+    "kinase_distance_matrix_paths = {\n",
+    "    perspective: HERE / f\"../{folder_name}/data/kinase_distance_matrix.csv\" \n",
+    "    for perspective, folder_name in kinase_distance_matrix_paths.items()\n",
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We load the distance matrices."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,
@@ -183,7 +178,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Check if the matrices have the same dimensions"
+    "We check if the matrices have the same dimensions"
    ]
   },
   {
@@ -203,8 +198,15 @@
     }
    ],
    "source": [
-    "for key in kinase_distance_matrix_paths.keys():\n",
-    "    print(f\"Shape of {key} matrix: {kinase_distance_matrices[key].shape}\")"
+    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
+    "    print(f\"Shape of {descriptor} matrix: {similarity_df.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check the distances."
    ]
   },
   {
@@ -215,29 +217,27 @@
     {
      "data": {
       "text/plain": [
-       "{'sequence':                   EGFR     ErbB2     p110a       KDR      BRAF      CDK2  \\\n",
-       " kinase_klifs                                                               \n",
-       " EGFR          0.000000  0.059037  0.572938  0.283953  0.344972  0.351553   \n",
-       " ErbB2         0.059037  0.000000  0.586362  0.297882  0.345400  0.369692   \n",
-       " p110a         0.572938  0.586362  0.000000  0.577295  0.563541  0.576006   \n",
-       " KDR           0.283953  0.297882  0.577295  0.000000  0.328732  0.346621   \n",
-       " BRAF          0.344972  0.345400  0.563541  0.328732  0.000000  0.353245   \n",
-       " CDK2          0.351553  0.369692  0.576006  0.346621  0.353245  0.000000   \n",
-       " LCK           0.288679  0.314925  0.548664  0.312352  0.327067  0.318747   \n",
-       " MET           0.288742  0.302033  0.606301  0.286194  0.361842  0.343975   \n",
-       " p38a          0.355356  0.364827  0.568806  0.346556  0.362088  0.276907   \n",
+       "{'sequence':            EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       " EGFR   0.000000  0.059037  0.572938  0.283953  0.344972  0.351553  0.288679   \n",
+       " ErbB2  0.059037  0.000000  0.586362  0.297882  0.345400  0.369692  0.314925   \n",
+       " p110a  0.572938  0.586362  0.000000  0.577295  0.563541  0.576006  0.548664   \n",
+       " KDR    0.283953  0.297882  0.577295  0.000000  0.328732  0.346621  0.312352   \n",
+       " BRAF   0.344972  0.345400  0.563541  0.328732  0.000000  0.353245  0.327067   \n",
+       " CDK2   0.351553  0.369692  0.576006  0.346621  0.353245  0.000000  0.318747   \n",
+       " LCK    0.288679  0.314925  0.548664  0.312352  0.327067  0.318747  0.000000   \n",
+       " MET    0.288742  0.302033  0.606301  0.286194  0.361842  0.343975  0.309121   \n",
+       " p38a   0.355356  0.364827  0.568806  0.346556  0.362088  0.276907  0.337419   \n",
        " \n",
-       "                    LCK       MET      p38a  \n",
-       " kinase_klifs                                \n",
-       " EGFR          0.288679  0.288742  0.355356  \n",
-       " ErbB2         0.314925  0.302033  0.364827  \n",
-       " p110a         0.548664  0.606301  0.568806  \n",
-       " KDR           0.312352  0.286194  0.346556  \n",
-       " BRAF          0.327067  0.361842  0.362088  \n",
-       " CDK2          0.318747  0.343975  0.276907  \n",
-       " LCK           0.000000  0.309121  0.337419  \n",
-       " MET           0.309121  0.000000  0.370645  \n",
-       " p38a          0.337419  0.370645  0.000000  ,\n",
+       "             MET      p38a  \n",
+       " EGFR   0.288742  0.355356  \n",
+       " ErbB2  0.302033  0.364827  \n",
+       " p110a  0.606301  0.568806  \n",
+       " KDR    0.286194  0.346556  \n",
+       " BRAF   0.361842  0.362088  \n",
+       " CDK2   0.343975  0.276907  \n",
+       " LCK    0.309121  0.337419  \n",
+       " MET    0.000000  0.370645  \n",
+       " p38a   0.370645  0.000000  ,\n",
        " 'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
        " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
        " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
@@ -338,14 +338,204 @@
     "print(f\"Choices of precalculated descriptors: {kinase_distance_matrix_paths.keys()}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We look at an example matrix:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "descriptor_selection = \"sequence\"\n",
-    "kinase_distance_matrix = kinase_distance_matrices[descriptor_selection]"
+    "descriptor_selection = \"sequence\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>ErbB2</th>\n",
+       "      <th>p110a</th>\n",
+       "      <th>KDR</th>\n",
+       "      <th>BRAF</th>\n",
+       "      <th>CDK2</th>\n",
+       "      <th>LCK</th>\n",
+       "      <th>MET</th>\n",
+       "      <th>p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>EGFR</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.059037</td>\n",
+       "      <td>0.572938</td>\n",
+       "      <td>0.283953</td>\n",
+       "      <td>0.344972</td>\n",
+       "      <td>0.351553</td>\n",
+       "      <td>0.288679</td>\n",
+       "      <td>0.288742</td>\n",
+       "      <td>0.355356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ErbB2</th>\n",
+       "      <td>0.059037</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.586362</td>\n",
+       "      <td>0.297882</td>\n",
+       "      <td>0.345400</td>\n",
+       "      <td>0.369692</td>\n",
+       "      <td>0.314925</td>\n",
+       "      <td>0.302033</td>\n",
+       "      <td>0.364827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p110a</th>\n",
+       "      <td>0.572938</td>\n",
+       "      <td>0.586362</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.577295</td>\n",
+       "      <td>0.563541</td>\n",
+       "      <td>0.576006</td>\n",
+       "      <td>0.548664</td>\n",
+       "      <td>0.606301</td>\n",
+       "      <td>0.568806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KDR</th>\n",
+       "      <td>0.283953</td>\n",
+       "      <td>0.297882</td>\n",
+       "      <td>0.577295</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.328732</td>\n",
+       "      <td>0.346621</td>\n",
+       "      <td>0.312352</td>\n",
+       "      <td>0.286194</td>\n",
+       "      <td>0.346556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BRAF</th>\n",
+       "      <td>0.344972</td>\n",
+       "      <td>0.345400</td>\n",
+       "      <td>0.563541</td>\n",
+       "      <td>0.328732</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.353245</td>\n",
+       "      <td>0.327067</td>\n",
+       "      <td>0.361842</td>\n",
+       "      <td>0.362088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CDK2</th>\n",
+       "      <td>0.351553</td>\n",
+       "      <td>0.369692</td>\n",
+       "      <td>0.576006</td>\n",
+       "      <td>0.346621</td>\n",
+       "      <td>0.353245</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.318747</td>\n",
+       "      <td>0.343975</td>\n",
+       "      <td>0.276907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LCK</th>\n",
+       "      <td>0.288679</td>\n",
+       "      <td>0.314925</td>\n",
+       "      <td>0.548664</td>\n",
+       "      <td>0.312352</td>\n",
+       "      <td>0.327067</td>\n",
+       "      <td>0.318747</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.309121</td>\n",
+       "      <td>0.337419</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MET</th>\n",
+       "      <td>0.288742</td>\n",
+       "      <td>0.302033</td>\n",
+       "      <td>0.606301</td>\n",
+       "      <td>0.286194</td>\n",
+       "      <td>0.361842</td>\n",
+       "      <td>0.343975</td>\n",
+       "      <td>0.309121</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.370645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>p38a</th>\n",
+       "      <td>0.355356</td>\n",
+       "      <td>0.364827</td>\n",
+       "      <td>0.568806</td>\n",
+       "      <td>0.346556</td>\n",
+       "      <td>0.362088</td>\n",
+       "      <td>0.276907</td>\n",
+       "      <td>0.337419</td>\n",
+       "      <td>0.370645</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
+       "EGFR   0.000000  0.059037  0.572938  0.283953  0.344972  0.351553  0.288679   \n",
+       "ErbB2  0.059037  0.000000  0.586362  0.297882  0.345400  0.369692  0.314925   \n",
+       "p110a  0.572938  0.586362  0.000000  0.577295  0.563541  0.576006  0.548664   \n",
+       "KDR    0.283953  0.297882  0.577295  0.000000  0.328732  0.346621  0.312352   \n",
+       "BRAF   0.344972  0.345400  0.563541  0.328732  0.000000  0.353245  0.327067   \n",
+       "CDK2   0.351553  0.369692  0.576006  0.346621  0.353245  0.000000  0.318747   \n",
+       "LCK    0.288679  0.314925  0.548664  0.312352  0.327067  0.318747  0.000000   \n",
+       "MET    0.288742  0.302033  0.606301  0.286194  0.361842  0.343975  0.309121   \n",
+       "p38a   0.355356  0.364827  0.568806  0.346556  0.362088  0.276907  0.337419   \n",
+       "\n",
+       "            MET      p38a  \n",
+       "EGFR   0.288742  0.355356  \n",
+       "ErbB2  0.302033  0.364827  \n",
+       "p110a  0.606301  0.568806  \n",
+       "KDR    0.286194  0.346556  \n",
+       "BRAF   0.361842  0.362088  \n",
+       "CDK2   0.343975  0.276907  \n",
+       "LCK    0.309121  0.337419  \n",
+       "MET    0.000000  0.370645  \n",
+       "p38a   0.370645  0.000000  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kinase_distance_matrix = kinase_distance_matrices[descriptor_selection]\n",
+    "kinase_distance_matrix"
    ]
   },
   {
@@ -361,12 +551,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -378,7 +568,6 @@
     }
    ],
    "source": [
-    "#plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
     "plt.imshow(kinase_distance_matrix)\n",
     "plt.colorbar(label=\"Kinase similarity\")\n",
     "plt.title(\"Kinase distance matrix\")\n",
@@ -405,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -419,7 +608,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -439,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -471,12 +660,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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uNDfQ+20GbAOMfdJqT+D0qvpxVf2MZtbytCSLkuwD/Dnwu8DDqmohcCcw0HRoQwwQSRotr0+yc5KHA38JnJVk/yRPSbJ5kgU0N8h/Any3PeYy4Mgk2yXZAvhDYGVVrQbmA2uBHwEPSfJWYMFUFGqASNJoORP4InBdu70DWAh8jGbm8H2aT2AdWFX3tsccT3Nz/b9oguIg4LC27QKay1nfo7kkdi+/fJmsM2+iS9Jouayq3tW375PtNq6qup3mk1fjta0DXt1uY94z2SLBGYgkqSMDRJLUiZewJGlEVNWSYdcwEc5AJEmdOAPR9Fu1Ak47eNhVrN+qq2DxHsOuQpp1DBBNr90PH3YFkqaJAaLptdfSZhtlozw7kkaY90AkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASNKISfLyJMuTrEnywySfT7J3krcnuS/JT9vte0lOSbJjz7H7Jrm55/WWSc5OclGSBUmOSnJ5kruS3JzkPUk6fSfQAJGkEZLkz4CTgL8GHkmzrO2pwCFtl7Oqaj7wcJpFoxYDl/eGSM+5tqJZH30h8PyquotmKdzjaNZEfzrwPJoFqSbMAJGkEZFkO+D/Aa+vqrOr6u6quq+qPltVb+zt2+6/GjiCZhXCN/Sdaxvgs8AWwMFVdXd73Pur6sKq+nlV3QJ8FHhml3p9lIkEo//AR20qngFsDXx60AOqal2Sc4EDenZvRbOM7Z3AS6rqZxs4xbOAqzvUaoBIPvBRI2R7YHVVrZ3gcStpLmmNmU8TRi/bUHgkWQrsBfz+RAsFA0SaHQ981NxwdDbW43ZgUZKHTDBEdgJ+3PN6NfDHwEeSrKmqC/oPSHIo8G5gv6paPYHf9QveA5Gk0fEt4F7g0EEPSLIZ8CLgwt79VXU28BpgWZLn9B1zIPAh4EVVtaJrsc5AJGlEVNWdSd4K/EOStcAXgfuA/YDnAP891jfJFsCvAW+n+STWieOc72NJtgTOTfKCqrooyXNpbpwfVlWXTqZeZyCSNEKq6kTgz4A303y66ibgWOCctssRSdYAdwCfobns9RtVtXI95/tnmk9onZfkacBbgO2A89vvmaxJ8vkutToDkaQRU1UfpZkl9LuYZsaxoWO/Buzct+9DNJesoJnJTAlnIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkSZ0YIJKkTgwQSVInBogkqRMDRJLUiQEiSerEAJEkdWKASJI6MUAkacQl2TLJsiTXJ6kk+/a1PyfJV5PcmeT6cY5f0rb/d5Jrkuw3FXUZIJI0O3wT+D1g1ThtdwP/BLxxPcd+DPh3YHvg/9Csk/6IyRY0lBUJv/PDuzjig98axq+WpJHVzh4+CLwS2JFmGdtjqupe4KS2z7r+49q1zS8db2aR5HHArwPPr6p7gE8lOQ54MfCBJI+hWa1wT6CAC4DXV9UdG6t3xmcghzx5J3bbccFM/1pJmi1eARwAPAZ4HM3a6JPxJOC6qvppz74r2/0AAd4FPAp4IrALG1k2d8yMz0Be/vRdefnTd53pXytJQ/eJ1w3U7ZSqugkgyTuBk5lciMwD7uzbdyewE0BVXQtc2+7/UZITgbcNcuKhXMKSJK3XTT0/30AzM5iMNUD/ZZ8FwE8BkuwAvA/YB5hPc2XqJ4Oc2JvokjRadun5eVdg5STPdzXwq0nm9+zbs90PzeWrAvaoqgU0N+ozyIkNEEkaLa9PsnOShwN/CZwFkGSrJFu3fbZMsnWStG2btW1bNC+zdZItAarqe8AVwNva/YcBewCfas81n2aWckeSnVj/J7kexACRpNFyJvBF4Lp2e0e7/z+Be2juXVzQ/vwrbduz2tfn08xa7mnPMealwF40l6beDRxeVT9q2/4vzae07gTOA84etFDvgUjSaLmsqt7Vv7OqlqzvgKr6Ghu47FRV1wP7rqftauA3+nb/7cbLdAYiSerIAJEkdeIlLEkaERu6TDWKnIFIkjoxQCRJnaSqJn5Q8iOab0guAlZPdVEjZC6Pby6PDeb2+Bzb7PX4qpq/8W6zQ6d7IFX1CIAky6tqr6ktaXTM5fHN5bHB3B6fY5u9kiwfdg1TyUtYkqRODBBJUieTDZD/PyVVjK65PL65PDaY2+NzbLPXnBpfp5vokiR5CUuS1IkBIknqZKAASXJgkv9Mcm2SN62nz75JrkhydZKvT22Z02djY2vHdWc7tiuSvHUYdXY1yHvX9ntqknVJDp/J+iZjgPfukCRXte/b8iR7D6POrgYY3yva8V2V5OIkew6jzi4GGNsTknwryc+SHD+MGidjgPElyfva9quS/Pow6py0qtrgBmwOfB/4VWBLmsXYd+vrsxD4DrBr+3qHjZ13FLYBx7Yv8Llh1zpd4+vp9680awkcPuy6p/C9m8cD9/n2AK4Zdt1TPL7fAh7W/vwC4NvDrnsKx7YD8FTgncDxw655GsZ3EPB5mkew/+Zsee/6t0FmIE8Drq2q66rq58DHgUP6+rwcOLuqbgSoqtsGOO8oGGRss9mg4/sjmtXJZsv7BgOMrarWVPtfK7AtzbKds8Ug47u4qsbWrr4E2HmGa+xqkLHdVlWXAfcNo8BJGuS/u0OAj1TjEmBhkh1nutDJGiRAduKXF3m/ud3X63HAw5J8LcnlSY6cqgKn2SBjA3hGkiuTfD7Jk2amtCmx0fG1S1geBnxgBuuaCgO9d0kOS3INzUprR89QbVNh0H83x7ya5v9oZ4OJjm22GWR8c+LPYJBHmYy3ylX//8k9hGZFq+cBDwW+leSSatbiHWWDjO3fgF+pqjVJDgLOAR473YVNkUHGdxLw51W1rl1eebYYZGxU1aeBTyd5FvBXwH7TXdgUGWh8AEmeQxMgs+Uez8Bjm6UGGd+c+DMYJEBuBnbpeb0zsHKcPqur6m7g7iTfAPYERj1ANjq2qrqr5+fzk5yaZFFVzYYHvg3y3u0FfLwNj0XAQUnWVtU5M1Jhd4OM7Req6htJHjPH3juS7AF8GHhBVd0+Q7VN1oTeu1lo0L8zZ/+fwQA3hB5Cs7D7o3nghtCT+vo8EfhK23cb4D+A/zXsGzxTNLbFPHAj9mnAjWOvR30bZHx9/U9n9txEH+S9+7We9+7XgVvm0nsH7ApcC/zWsOud6rH19H07s+8m+iDv3cH88k30S4ddd5dtozOQqlqb5FjgAppPF/xTVV2d5HVt+weq6rtJvgBcBdwPfLiq/mNj5x62QcYGHA4ck2QtcA/w0mr/DRh1A45vVhpwbC8GjkxyH817d8Qce+/eCmwPnNrOINfWLHiS7SBjS7IYWA4sAO5PchzNJ5nuWt95R8WA7935NJ/Euhb4b2DpsOqdDB9lIknqxG+iS5I6MUAkSZ0YIJKkTgwQSVInBogkqRMDREPVPgF47CnOVyb5sySbtW17JXnfBo5dkuTlM1etpF5+jFdDlWRNVc1rf94BOBO4qKreNsCx+9J8yeyF01qkpHE5A9HIqOYpzq8Fjm3XS9g3yecAkjy7Z02Wf08yH3g3sE+770/bGcmFSf6t3X6rPXbf9kGfy5Jck+Sjab95166DcnE7+7k0yfwkmyd5b5LL2rUa/mBYfybSKBvkWVjSjKmq69pLWDv0NR0PvL6qLkoyD7gXeBM9M5Ak2wD7V9W9SR4LfIzmWV8ATwGeRPO8oYuAZya5FDiL5hvqlyVZQPON9VcDd1bVU5NsBVyU5ItV9YPpHLs02xggGkXjPan0IuDEJB+lWXvm5nGeHrwFcEqSJwPraJYZGHNpVd0MkOQKYAlwJ/DDatadYOwxGUmeD+yRB1Zn3I7mCcwGiNTDANFISfKrNH/530bzkE4AqurdSc6jeX7QJUnGeyz7nwK30jwJejOaWcqYn/X8vI7m3/0w/iO0A/xRVV0wiaFIc573QDQykjyCZmGrU/ofepjkMVW1oqr+huYhe08AfgrM7+m2Hc2M4n7glTQPstuQa4BHJXlq+zvmJ3kIzUPwjkmyRbv/cUm2nfwIpbnFGYiG7aHtJaUtgLXAGcCJ4/Q7rl04aR3wHZpHYd8PrE1yJc2j6E8FPpXkJcBXgbs39Iur6udJjgBOTvJQmvsf+9Gsr7EE+Lf2ZvuPgEMnNUppDvJjvJKkTryEJUnqxACRJHVigEiSOjFAJEmdGCCSpE4MEElSJwaIJKmT/wHP3t2wAgH2UQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -505,26 +694,118 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Normalize different matrices [0,1]"
+    "#### Normalize different matrices \n",
+    "\n",
+    "We normalize the different matrices to values in $[0, 1]$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _min_max_normalize(distance_matrix_df):\n",
+    "    \"\"\"\n",
+    "    Apply min-max normalization to input DataFrame.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_distance_matrix_df : pd.DataFrame\n",
+    "        Kinase distance matrix.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.DataFrame\n",
+    "        Normalized kinase distance matrix.\n",
+    "    \"\"\"\n",
+    "    min_ = distance_matrix_df.min().min()\n",
+    "    max_ = distance_matrix_df.max().max()\n",
+    "    distance_matrix_normalized_df = (distance_matrix_df - min_) / (max_ - min_)\n",
+    "    return distance_matrix_normalized_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrices_normalized = {}\n",
+    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
+    "    similarity_normalized_df = _min_max_normalize(similarity_df)\n",
+    "    kinase_distance_matrices_normalized[descriptor] = similarity_normalized_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define kinase order"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "kinases_names = kinase_distance_matrices_normalized[\"sequence\"].columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _define_kinase_order(kinase_distance_matrix_df, kinase_names):\n",
+    "    \"\"\"\n",
+    "    Define the order in which kinases shall\n",
+    "    appear in the input DataFrame.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    kinase_distance_matrix_df : pd.DataFrame\n",
+    "        Kinase distance matrix.\n",
+    "    kinase_name : list of str\n",
+    "        List of kinase names to be used for sorting.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pd.DataFrame\n",
+    "        Kinase distance matrix with sorted columns/rows.\n",
+    "    \"\"\"\n",
+    "    kinase_distance_matrix_df = kinase_distance_matrix_df \\\n",
+    "        .reindex(kinases_names, axis=1) \\\n",
+    "        .reindex(kinases_names, axis=0)\n",
+    "    return kinase_distance_matrix_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrices_normalized = {\n",
+    "    descriptor: _define_kinase_order(similarity_df, kinase_names) \n",
+    "    for descriptor, similarity_df in kinase_distance_matrices_normalized.items()\n",
+    "}"
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Visualize stuff"
+    "#### Visualize kinase similarities"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -543,14 +824,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
     "def dendrogram(distance_matrix, ax=None, title=\"\"):\n",
     "    \"\"\"TODO\"\"\"\n",
     "    D = distance_matrix.values\n",
-    "    print(title)\n",
     "    D_condensed = distance.squareform(D)\n",
     "    hclust = hierarchy.linkage(D_condensed, method=\"average\")\n",
     "    tree = hierarchy.to_tree(hclust)\n",
@@ -562,7 +842,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P+4ykuyXdIekvksq5jaRZizx08FeY+YUfAkxyvZh17eFDTuSxL54FrhWzHj144nHMOOt74HoxG7R6c+ZtYURsWXidXDuDpJ7uQX1hXvYNwMvAgXn8v4HJEbE5cDFwSi/yMms7Gj6Mdb/7cYC7XS9mXdPwYaxz8rHgWjHrkYYNY8KxnwXXi9mg1e9uk5KmS/qKpBuAA/Lo90u6UdL/k7RNJ8sMBUYCTwNExF8j4oU8+WZgnf7mZdaOXC9m9XGtmNXP9WI2ePSm8bZiTbfJAwvTXoyIHSLigjw8MiK2Bz4G/G9hvgMl3QbMAlYBrujkfT4E/KGzBCQdJWmapGkvL32hs1nM2kK8vKi222RT66VYK0teeL6ET2TWGPHyotpuky3dtix53vVi7SsWLartNtnaevH2xazp+tNt8sLCtAtr5j0fICL+BoyRNLZjvojYEhgP3Al8rriQpPcDk4H/6SyBiDg7IiZHxOThy5X3dHmzsnXSbbKp9VKslSEjRpbwicwao5Nuky3dtgwZ6Xqx9tVJt8nW1ou3L2ZNV9bdJmsPvUR3wxERpCM9b+0YJ2kX4MvAuyLipZLyMmtHrhez+rhWzOrnejEbBBr1qIADASTtADwTEc90Ms8OwEN5vq2An5JWFk80KCezduV6MauPa8Wsfq4XswFoaC/mXTH3ke7wx4jo9Ba1wNOSbgTGAB8sjD8wr0SWAx4Djsjj/wcYBfxGEsCMiHhXL3Izayu117zhejHrVO01b7hWzLpUe80brhezQafuxltEdHrr2YiYWDO8UxfzTQWmdjFtl3rzMKuC9c//OgAPHXTi3RHxyrN2XC9mr/X6874BwMMHn+BaMevBBt84FYAHT/is68VskGpUt0kzMzMzMzMrkRtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBSo/5qJ7NNh8Wl1w5rpRYKy2nUuIAHLruW0qLdc6MG0qJM39pb24q2r2xyy0uLdb0xaNKi/XWFUoLxf2Lah+V03ebTpjzz+JF5a2w/mYj4+RLNykl1ntHLSglDsDua21ZWqyrZv2rlDi3vlTe+nDB0vJ+lBsOe7q0WMPKW93xzNJO72PVJ5uvN6vltQIwcqM14w0/OKKUWDdveXEpcaDcejlt+k2lxLn35TVKiQPwpX/tV1qs0SNfLC3WG8bNLS3WG8fMKC3WZyf9uS3qZdLmw+O835fzO9h8eHnrzDLr5W13LCwlzu8e26yUOGUrcz3Vroas+WBb1EtZfObNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK6HPjTdKxkh6UFJLGFcZvIukmSS9JOq5mmT0k3ZeX+2J/EjerEteLWf1cL2b1ca2YDT79OfP2d2AX4NGa8fOATwCnFkdKGgL8ENgTmAQcLGlSP97frEpcL2b1c72Y1ce1YjbI9HgPeUkTgT8CtwBbAfcDh0XEv/P018wfEU8AT0jauybUNsCDEfFwXu4CYF/gbkkfAY4ChgMPAh+IiBf6/rHMWmPWzMV85LB5bL7lcO65axHA6yWNcL2YLWvWzMV89PCn2GzLYQCTJF2Mty9my5g3ayFnH/Vv1tt8JWbd8yx422I2aNV75m1j4OyI2BxYAHysD++1NjCzMPxYHgdwSUS8KSK2AO4BPtSH+GZt4ZGHlvC+Q0Zw+dWrASzF9WLWpekPLWb/g0cC3I23L2Zd+s8jL/DmA9bmuMveDN62mA1a9TbeZkbE3/PfvwJ26MN7dfZo2I4n4v6XpOsl3QkcCryh0wDSUZKmSZo2b97SPqRg1nhrrrUcb3zT8I7Bp2hBvRRrZcG88h6sbla28WsNYas3Ld8x2JLtS7FeFj/jEw3WnsaOX57XvXFsx2BLti3w2nqZ730xs6art/EWPQzX4zFg3cLwOsDs/PdU4NiI2Az4GrBCp0lEnB0RkyNi8iqr+EaZ1qa0zLax6fVSrJUxq/TYO9qsZZYtl9bWy9CVRvTh7c0ar7ZrJG2wLzbW+2JmTVdv1U2QtF3++2Dghj68163AhpJeJ2k4cBDwuzxtNDBH0jDS0R6zypozawn//ufLHYOr4Hox69KcWUu4/Z8vdQx6+2LWhafnvMj02+Z3DHrbYjZI1dt4uwc4XNIdpBXGjyV9QtJjpKM2d0j6GYCk8Xn8Z4ATJD0maUxELAaOBf6U410UEXfl+CeSbohyDXBvWR/OrBXW32Aol1+8kH13+w+kmwK5Xsy68PoNhvK7374A6c533r6YdWGN149k2mVzOHW/m8HbFrNBq97+VEsj4piacT/Ir9eIiLmklcgyIuIq4KpOxv8Y+HGduZi1NS0HJ31nJQA2nTDnoXy3LteLWSe0HJz47ZX5za9fuDsi3ptHu17Mamg52P+kTQH47KQ/e9tiNki5s7KZmZmZmVkF9HjmLSKmA//V+FTMqm/tdYdyxZ9Xa3UaZpWw9rpDufSaNVqdhlnbW2XtFfnc77breUYzG/B85s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKqCyT+99YN549jzv06XEWjqsL8+57Nz/zTi1tFgfmbBDKXHu/8k2pcQB0ApLSovFgvJ+fkNfKO84xOLxL/c8U92+VGKsvpn11Cqc+Iv3lxLrcxu9WEocgAdm/ay0WHut/cZS4jxwxptLiQMw5KVlnz7dVyqx7JYOL29916dHBHfpuDKD9dnSZ4fy7A2rlxJrrxX3KiUOwGnTf1tarM9MLOfaqdmXTiolDsCwO0aVFmuNi54tLdbfv7p+abGe+mJ5nxH+XGKsvpu+cFWOuOPwUmI9+9yKpcQBOPKOm0qLdd3m5eS1/D6rlBIH4OkNy9t/et3jHyot1vDZw0uLtXRYaaGAz5YZrOV85s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswootfEm6bnC33tJekDSBEknSZol6bY87hJJkwrzXivpPkm3S7pV0pZl5mXWblwrZvVzvZjVz/ViNrA15MybpJ2BM4E9ImJGHv39iNgyIjYELgT+T9JqhcUOjYgtgB8B/9OIvMzajWvFrH6uF7P6uV7MBqbSG2+SdgTOAfaOiIc6myciLgSuBg7pZPJNwNpl52XWblwrZvVzvZjVz/ViNnCV/Zy35YHLgZ0i4t4e5v0XsEkn4/cALis5L7N241oxq5/rxax+rhezAazsxtsi4EbgQ8Ane5i39gm2v5Y0EhgCdPrEXUlHAUcBDB27cv8yNWut5tXKGNeKVZ7rxax+TauXYauN6V+mZtZrZXebXAq8D3iTpON7mHcr4J7C8KHA64DzgB92tkBEnB0RkyNi8pCRI8vI16xVXCtm9WtavQwd4Xqxymtevaw0oox8zawXSr/mLSJeAN4JHCrpQ53NI+m9wG7A+TXLLgJOAN4sadOyczNrJ64Vs/q5Xszq53oxG7jK7jYJQETMk7QH8DdJT+bRn5b0fmAk8P+Ad0TEfzpZdqGk7wHHkU75mw1YrhWz+rlezOrnejEbmEptvEXEqMLfM0mn3iFdOHtSN8vtVDP8vTLzMms3rhWz+rlezOrnejEb2BrynDczMzMzMzMrlxtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVQEMeFdAMMSxYNP7lVqexjPlLy/tK7//JNqXE2eiYf5QSB+DBc7cqLdbS4VFerHELS4s10I5oxHKwaHRJ3/WCYeXEAW59qbz//wfOeHMpcTb85M2lxAF47PjtS4u1cK0lpcVa7kWVFmvpiuX9H7aLpUPhpZXL+VwLF5dXL/e+vEZpsWZfOqmUOGu9++5S4gA88MNtS4t1z5dWLS3W2DHPlRbr3pPL+z/k/eWF6o8lLw/h6dkrlRJrpy3v6XmmOv3usc1Ki7X8PquUEmeFK8rbF/vlI7eUFuvAX32qtFinHTyltFjf+OoRpcV6uLRI7WGg7aeamZmZmZkNSG68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkF9LnxJmmJpNsk3S7pX5K2z+MnSlqYp90t6ZeShhWWGyrpSUnfqYl3raT78nK3Sdq/7x/LrH24Vszq53oxq5/rxWzw6c+Zt4URsWVEbAF8CSiuAB6KiC2BzYB1gPcVpu0G3Ae8T1Ltbc8OzTG3jIiL+5GbWTtxrZjVz/ViVj/Xi9kgU1a3yTHA07UjI2IJ8A9g7cLog4EzgBlAOff3NqsO14pZ/VwvZvVzvZgNAv15KNmKkm4DVgDWBN5RO4OkFYBtgU/m4RWBnYGjgbGklcdNhUV+LanjgV07R8RT/cjPrF24Vszq53oxq5/rxWyQKaPb5CbAHsAvC6fe188rk6eAGRFxRx7/TuCvEfEC8Fvg3ZKGFGIWT9Uvs7KQdJSkaZKmLXn2+X6kbtZULa2Vpc+7VqxSWrttcb1YtbS2Xp5zvZg1WyndJiPiJmAcsFoe1dHPegPgzZLelccfDOwiaTrwT2BV4O29eJ+zI2JyREweMnpkGambNVUramW5ka4Vq6aWbFtcL1ZRLamXUa4Xs2YrpfEmaRNgCOnozisiYg7wReBLksYAOwATImJiREwEPk5aiZgNCq4Vs/q5Xszq53oxGxz603hbseNWssCFwOH5othalwEjSH2t/y8iXipMuxx4l6Tl+5GHWbtzrZjVz/ViVj/Xi9kg0+cblkTEkC7GTwf+qzAcwBZdzDuPV0/v79TXXMzamWvFrH6uF7P6uV7MBp+yHhVgZmZmZmZmDeTGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVQJ9vWNJyAg2JUkINW35xKXEAxi5XXiyt0NkNo3rvwXO3KiUOwAYf+HdpsR744balxVryUqfXbPfJ8FnDS4vVDhQw9EX1PGMdlqxYThyABUtXKC3WkJfKyeux47cvJQ7AOt++sbRYM75aXl5R4iG7pYvLWQe3k+WfCSZe+VLPM9Zh610eLSUOwJf+tV9psYbdMaqUOGWuwzf8+C2lxXryqO1KizV61ujSYj27U3V3ubqy7ph5nLLzeaXEmr9kRClxAO6Zt0ZpsZ7esJz/t18+Ut5v/AuvK6/2znjwf0uLNXq5haXFOulrU0qLtfevSgvVFnzmzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrAL61HiTNF7SBZIeknS3pKskbSRpoaR/S7pH0j8kHV5Y5ghJZ+W/l5P0C0n/K2mkpCsl3SvpLkknl/XhzNrAUNeKWX28bTGrn+vFbHDq9f1PJQm4FPhFRByUx20JrAE8FBFb5XGvBy6RtFxETKlZ/ifAMOBIYAXg1Ij4q6ThwF8k7RkRf+jfRzNrrYgA2AD4iWvFrC7etpjVz/ViNgj15czb24FFEfGTjhERcRswszhTRDwMfAb4RM3yZwCrAodFxNKIeCEi/pqXeRn4F7BOH/IyaysLH34QIFwrZnUZjbctZvVyvZgNUn1pvP0X8M865/0XsElh+BBga+CgiFjmadaSxgL7AH/pQ15mbeXlx+cCvFDn7K4VG+xWxNsWs3q5XswGqUbfsEQ1w/8C1gO2WWZGaShwPvCDfKRo2WDSUZKmSZq2ZMHzpSdr1kKNq5XnXSs24DSsXl5+2fViA07D6uWZecu0/cyswfrSeLuLdMSmHlsB9xSG7wXeB1wo6Q01854NPBARp3cVLCLOjojJETF5yJiRvUjZrPmGr7EGwIg6Z29crYx0rVglLKQNti3Dh7terBLaol5WWqXXt04ws37qS+Pt/4DlJX2kY4SkN5GO4lAYNxE4FTizOD4ibgSOAa6UNCHP+01gJeBTfcjHrC2t+PoNIV0X7lox69mzeNtiVi/Xi9kg1etDJhERkt4NnC7pi8CLwHRSsa8v6d+kuxY9C5xZvLtRIcbvJa0G/FHSnsCXSUeC/pVugMRZEfGzvn0ks/aQf8sPAbu6Vszq4m2LWf1cL2aDUJ/Od0fEbNIp91ordrPMVGBqYXgK0LEyqe2PbTZQLIoI14pZHbxtMauf68VscGr0DUvMzMzMzMysBG68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFVPcBHS+LIbOXLyXUopHDSokDMH3xqNJisaCc/56lw6OUOAAP/HDb0mJt+PFbSov1wBlvLi3Wy+MXlRarHWgpDFlYUrDXv1xSINhw2NOlxdKScuIsXKukQMCMr25fWqwJX7uxtFiPfGe70mItWnngPaB3yTDx3NrDS4n1yPOrlhIHYPTIF0uLtcZFz5YS554vlff5njyqvN/luLNvKi1WmXW80gPlbYvbxfzFI7jiqS1LibX9Sg+WEgfg5i0vLi3W6x7/UClxDvzVp0qJA3DGg/9bWqzvb7BpabFGXz+utFj/nrZBabHguBJjtZ7PvJmZmZmZmVWAG29mZmZmZmYV4MabmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVVAQxpvkp7rYvxhkv6fpLsk3S3puDx+qqT989+rSPq3pCMbkZtZu3G9mNXHtWJWP9eL2cDUtDNvkvYEPgXsFhFvAN4IPFMzz0rAn4CzI2JKs3IzazeuF7P6uFbM6ud6Mau+Zj7n7UvAcRExGyAiXgTOKUwfBfwBOC8iftzEvMzakevFrD6uFbP6uV7MKq6Z17z9F/DPbqafBtwQEd/vagZJR0maJmna0uefLz1BszbSr3op1sriF1wrNqCVum1Z/JLrxQa0UuvlxfnlPTzezOrTTjcs+T9gX0mrdzVDRJwdEZMjYvJyI0c2MTWzttNtvRRrZegI14oNar3atgxd3vVig1qv6mWFsSs0MTUzg+Y23u4Ctu5m+gXAj4GrJI1uTkpmbcv1YlYf14pZ/VwvZhXXzMbbd4BTJI0HkLS8pE8UZ4iI04G/AJdKGt7E3MzajevFrD6uFbP6uV7MKq5RjbcRkh4rvD4TEVcBPwT+LOkuUp/rZW6YEhFfAGYC50pqp26dZo3iejGrj2vFrH6uF7MBqCF3m4yITgs933J2mdvORsQRNcN+rogNGq4Xs/q4Vszq53oxG5h8NMXMzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpoyA1LqkaLVFqst5b4vMqhL5TTtl46bmEpcQCWvDSktFgPnPHm0mJt+MmbS4s1/cLNS4vVDkKwdFhJsRaW9/8/rLyyY+nwKCXOci+Wl1Tntwrom0e+s11psV73pZtKi3X/j7cpLVa7WDocnl+7nP+8mc+OLSUOwBvGzS0t1t+/un4pccaOea6UOACjZ5X3SLEZX92+tFgTvnZjabFm/Gaz0mLx0/JC9cdyClYcsqiUWPuMeqiUOMnI0iINn13O0xJOO3iZe8T02ejlytuvG339uNJiPbvjk6XF+uuMy0qL9bpPlRaqLfjMm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQWU2niTFJLOLQwPlfQfSb/Pw0fk4dsKry0Kf8+T9Ej++89l5mbWblwvZvVxrZjVz/ViNrCV/aiA54H/krRiRCwEdgVm1cxzYUQcWzNuSwBJU4HfR8TFJedl1o5cL2b1ca2Y1c/1YjaANaLb5B+AvfPfBwPnN+A9zAYK14tZfVwrZvVzvZgNUI1ovF0AHCRpBWBz4Jaa6QfWnKpfsd7Ako6SNE3StKXPP19mzmat0pB6KdbKkhdcKzYgNGXb4nqxAaIp9bLw6RfLzNnM6lB2t0ki4g5JE0lHeq7qZJbOTtXXG/ts4GyA5ddZN/qcpFmbaFS9FGtlhbVcK1Z9zdq2rLim68Wqr1n1svqkVV0vZk1WeuMt+x1wKrATsGqD3sNsoHC9mNXHtWJWP9eL2QDUqMbb/wLPRMSdknZq0HuYDRSuF7P6uFbM6ud6MRuAGvKct4h4LCLO6GJybT/r7RuRg1lVuF7M6uNaMauf68VsYCr1zFtEjOpk3LXAtfnvqcDUbpY/osx8zNqZ68WsPq4Vs/q5XswGtoaceTMzMzMzM7NyufFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXQqEcFNN7wpSxd58VSQg0ZsrSUOAD3L3q+tFiLx79cSpwyW+jDZw0vLdbL4xeVFmv6hZuXFmvigXeUFuvB0iL1XQwLFq6zuJRYQ0eX93/2zNIhpcWipMfELl2xvOfNLl1cXqxFK5fz/wdw/4+3KS3WRh/9R2mxZpQWqX+WWwQj5pbzfzdv2uqlxAF443v+WVqsp764zP0s+uTek9coJQ7AszuVtzuy0gPl1d6M32xWWqwJB9xZWqz7S4vUP88tGs5Nc9crJdaNY8v7Pe038rnSYi0dVk6cb3z1iHICASd9bUppsf49bYPSYv11xmWlxfrIhB1KiwUXlxir9XzmzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrAJKb7xJ+rmk2yXdIeliSaPy+JUkXZGn3SXpyLLf26xqXC9m9XO9mNXP9WI2MDXizNunI2KLiNicdPfnY/P4jwN3R8QWwE7A9ySVd995s2pyvZjVz/ViVj/Xi9kA1OfGm6SJku6V9IvCUZ0REbEgTxewIq8+gSmA0Xn8KGAesDjPe5mkf+YjQEf16xOZtSHXi1n9XC9m9XO9mA0u/T3ztjFwdj6qswD4GICkKcBcYBPgzDzvWcCmwGzgTuCTEdHxdOwPRsTWwGTgE5JW7ezNJB0laZqkaUueLe9h2GZN0rR6eU2tPOdasUpqSb0sftH1YpXUmnp55oVGfiYz60R/G28zI+Lv+e9fATsARMSRwFrAPcCBefruwG15/JbAWZLG5GmfkHQ7cDOwLrBhZ28WEWdHxOSImDxk9Mh+pm7WdE2rl9fUyijXilVSS+pl6AquF6uk1tTLSiMa82nMrEv9bbxFV8MRsQS4EHhvHnUkcEkkDwKPAJtI2gnYBdgu97/+N7BCP/Mya0euF7P6uV7M6ud6MRsk+tt4myBpu/z3wcANkjaAV/pY7wPcm6fPAHbO09YgneJ/GFgJeDoiXpC0CfDmfuZk1q5cL2b1c72Y1c/1YjZIDO3n8vcAh0v6KfAA8GPgmnz6XcDtwEfzvN8Apkq6M0/7QkQ8KemPwDGS7gDuI52qNxuIXC9m9XO9mNXP9WI2SPS38bY0Io6pGfeWzmaMiNnAbp2MfwnYs595mFWB68Wsfq4Xs/q5XswGiUY8583MzMzMzMxK1uczbxExHfiv8lIxG7hcL2b1c72Y1c/1Yja4+MybmZmZmZlZBbjxZmZmZmZmVgGKqH00SDVI+g/waA+zjQOeLOktHav5cQZCrPUiYrWS3rNP6qwVqP533cxY7ZhT1WO1vFag8vXSjjk5VmNiDdZ6qfL/mWOVH6sy+2JlqmzjrR6SpkXEZMdqbqx2zKmdY7WLdv1+2jFWO+Y0WGK1i3b8ftoxJ8dqbax24d+4YzUi1kCslXq426SZmZmZmVkFuPFmZmZmZmZWAQO98Xa2Y7UkVjvm1M6x2kW7fj/tGKsdcxossdpFO34/7ZiTY7U2Vrvwb9yxGhFrINZKjwb0NW9mZmZmZmYDxUA/82ZmZmZmZjYguPFmg5KkzVqdg1lVuF7M6ud6MauPa6VvBlXjTZJanUOVSHqjpLa4Baukt0jaNf/dr/9HSbsDF0japJTkBijXS/3aqVbA9dJsrpXeaad6KbNWcgzXSw9cL70zUOvFtdJ3g6rxBgwvM5ik9SWtIanfD/7rTxFIWkHSsP7mUBNzD+BXwEv9jLONpA9LenNfvqfC9/IlYHWA6MeFmnllcSbw8Yi4V9Jgq4HeKK1eyqyVHK9t6qWsWsmxXC/V1LbblhzP9bLssqXWSo7peqmP66X+eAOyXlwr/TNovixJuwHnSPqmpC1KiLcHcAnwHeAiSRv1I5YiIiTtIOmQ/G9dK7dCHudIOqKvOdTE3A04GfhYRNwpaWwf4+wN/AJ4E/AF4F15fG9WjMvnf1+k/w3J3XM+DwKPSxoeEUv7EzPHVf53JUkr9DdeOyizXsqslRyvbeqlrFrJsQZ8vbhW6orneqkvVn/rpbRaye/reqmD66VXuQzIevG+WAkiYsC/gD2BfwLvBy4DTgeG9SPe9sD9wFuAlYAvA9/I09THmPsA/wJOAC4HjgdG9LDM7nmZ9wOHAv8G3trP72oU8DfgB3l4XeAOYJtextkIuBvYNg+/D7gFGNWLGJsB789/nwG8N/+9PLBc/nu5OmNtCUwH3gF8DjgLeBswpJ/fV8cdW/fLv63LgV2AFZr5Gy/zVWa9NKJW8rItr5eyaiUvO+DrxbVSVzzXS32x+lUvZdZKntf1Ut9ncr3Un8OArJdG1Erx/3sg1Uu3n7fVCTT8A8LrgQWkIxcAKwN/Bd7e2X98Tz8O0tnK9wOHFsa/B/hNP3JcL//Q1spF9f+Ac4CTgBW7WOZ1wEPAEYVxn639XH3MZwfgGuCLwE3Af/dy+Q2AEcBBwNDC+N8Bb+hFnHfn72Uf0tGsQzqZp8eCB7YDtgD+Kw+vAHyLdMr+rSWsNHYFbgXWAH4NPAAcDCzfrN95Wa+y6qVRtZJjtE299LdWcoxBUy+ulS5juV7qj9HveimrVvJ8rpf6Povrpfe5DKh6aWSt5HgDpl56/KytTqChHy4diRlLOmpyPrBVHn8J8Bvgh8CBwLg64y2f/x0KrFkYvw7wx8Jwt0cz8srhfcAbclENBTYBtiEdrdkA+GBeaZxMF0c0gB8AvwQ2zsNXAdNyUX4W2K4X39XImuG3ANcCvy6Mq2elukFeIY8hH1HrKEjgz8BO+e/NgNW6iLEDcGT+Xt4LTCEdqbkfuJR0NOp84Dzg1K6+nxxrN2AmMDkPdxwlGg58M680diiu1Or8Xa1bGP5YXintC/wdOJbUHeDDwEqtroNW1EtZtdKO9VJWrQyGenGtNHfb4nrpvl7KrBXXi+vF9eJ9sVa9Wp5Awz5YOkJwJTCS1Ar/FPBb0oWffyOd4v5BHncDPbTM8w/vN8BxwJY1014H3Jv/PjIXcKddAfJKYRbpSM7fSP2035qnvRM4ufB+PyYfoSgsP5TCCin/6C8Cfg78BXg76SjJ1fk91qjju9olf1dHd7xH/nf7XPhHA6Pr/N7XBq7L33tHnOH53/OBjXNh/amLlcWeecVwNK+u4PfOn+d7wP55ngOA/wZe300ue5OOwrwlD48HxhamDwe+RlohbV/n55tE6m5wHXB+YfzKpJXZG/Lwb3POPX7/7fAqs17KqpV2rJcya2Wg14trpbnbFtdL9/VSZq24Xlwvrhfvi7W0rlqdQEM+VFoZ3AjsXRi3Aukizbm82u+3o+XfU0HtQeqnfUxe4RxbM31l4GLS0Zlb6OZUdC6E4/PfbyQdGTiHdMRhQ2AJcArp6EZtd4KOizxvJq2U9svjPwfcA2xemHcUPfTRLsz7PuA/+T3PJK1cV8nT3kw6bf+Z7uLllcBE0tG1v9HJ0RPS6fGrSaf/N+tk+htJp7mX6SdOOir3v3mF0WMfZmBN4Dbg9Dw8HrgPOKjm/344qV/7+DpirpW/+/fl4X8A6xem/yx/xh1IRxS3bXUtNLteyqyVdqyXMmplMNSLa6X52xbXS9f1UmatuF5cL64X74u1+tXyBEr/QKk/7VLyKWrSaeNTSEdIxgGfBi4Edigs0+Up6Pwjng/sk4cPILX0DwMOKMz3KOm0+qQe8vtv4C+F4fXyCuN7eXhyXqG8rWa53YG7SEdxtgdOzCuOz+Xp3wTOzfn2eOEoqc94xwWeY3K8PUinxr+WP8/7gFWBTUmn/sd2EWsl4NvAj0in+C+kcLSr8D4/BRYBG3QR513AGTXLDClM3xf4ff4/GNrV/xv59DhpBf490srub+SjWYX5hvfyt7UhqR/1qDx8J6m7xznABGBn0pGj/0dhY9XOrzLrpexaaZd6KbNWBku9uFa6rxXXS3Prpaxacb24Xlwv3hdrh1fLE2jIh0qn3s8HViP1Ff50YdpqpOdUnEcdFzGS7oIzFfgIqU/0v0hHRE4gnZr9eC6+s8l9nTuJoZrh/wW+XBjejHTaevsu5n8LMBvYsTBuKOni3F8Cu+dxZ+fYvSqEvOw3gcvy35OAl3Mh3JVXHN3eEYrUP/yruWDuJ11ge3DOfY/8Pa5L96fWdyV1OViOwh2M8ve7a/7MewNrdRNjd9KRuR3z8Afy8AU18+0L7NTVSqeL2GuSVtDnkI7q/YLULeEC4OzCfOu0ugZaUS9l1EoV6qW/tTIY6sW10pxti+ulvnopo1ZcL64X10vrty0DuV569TtpdQKlfZDUd3nbwvCfSEd9jsrDxaMG48inoruJ13G0QaR+yD8EHufVIyuj8krkW7Xxa+KsT+p7vWrHfKRbpP4Q+FJhvu9S0wUgjx8GfJR09OStFI7i5FgnA1MK41av47t6O2mldziFPuOkIxk/IV3guXce9yZgky7irEm6Be16ubA3JK2wZpIuFv1Bjnkdqf/36zr7jgvDW+VC3CwPFz/rieRT5D18tv8mPYfkGmCPPO4DpP7qH8jDB5BO229YR7zX5e/pbaTb4m6WVzSXARML8/0D2LTVddCKeimrVtqxXsqqlcFQL66V5m5bXC9d10sjasX14npxvXhfrF1eQxkA8oMRvw7cLOmliLgtInaX9HvS6d+zI2KJpGERsSginuwhniL/CvK/f5b0NDA6Tx8VEc9JWgXYID/AcVEncV5HulvRLcAISRdFxH8k3Uwq9AMl/YF01OAI0mny4vJvB/YinQJfnnRkZzXSBZjkz3QN8BlJQyNicUQ80cNn2520grkix369pEdIt/D9P9Jp+oMi4ob8fd3aRZy9Sf3Wl+bPvoTUheBrwBOkuzZ9J3/eYTnf2u9oCLC48P/yb0m/A/4oaU9Sn+uFkj5AOmq0T3efLTufdEvimcDROfa5+dmN2+TPvwHw7oh4oIfvahNSv/p/ky7M3TAifpan7QBsKukx0kpyGPBcHfm1XJn1Ulat5Hnbql7KqpUca0DXi2uluduWPK/rpet6aUStgOulW64X10uB98UaqdWtx/6+SD/6O+niNqyk26Fe3Yt4a/DqLVWPIa2IvkU66rMD6QGFnwGOIl0w2WW/atIRneNJp4XPIB2JWC1PG0q6cPekPM87C8t1HGl6L3BK/nslUh/x75MfkJjHH0s6QlPPbfw3JxV3x92UtiFdsDoxD68J3NuRS1cxefV0+NtJdzEaTyqs+4EVSQX0bdJRnwldxBhHuii342Lc5QvTjid1t/gD6a5N99D9TWA2J18cTDri9F3SBau7kVeMedqHgeupuWNUFzHXJPXN7jhC9Mmc1xjShbX75d/WL/Pvb79W10Kz66XMWmm3eimrVgZDvbhW6o7nemlCvZRZK64X14vrxfti7fhqeQJ9TjwV8CjS6es9aqZ9H/h+YfhWch/iHuKtQTql/O5c4P8o/DAuycW9Lek2tY919yPOMVckP9QxF37HCqPTu+kUVhIdK6wPAz8rTB9TWGG8FTgUuKOnPArLj8qfo/i8kD+S+kX/N7Al6ajK2XRxFyFSl4jiSqfYBeJXOb5Iz9o4ge7vTLgPaQW1ch4urjRen7/rnehihzbPt2rOZwbp7kdvIq2If0haqR1EusB3v47vsM7vamzxd0U6WncD6SLfqaSV5ObAjsCbiv9/7fgqs14aUSvtVi9l1MpgqRfXSvO3La6X7uuljFpxvbheXC/eF2vXV8sT6PcHSP952xSGP0hq0V/Pay9cXK+HOB2F+j7SEZCppNO5HdMvAS7Kf29H4cGANXFGdRY3/70/qd/xIfl9Tupk+Y6jICvlgrqwZvrKubivAB6mvjsqvdKvnHSk4lekld4puRC+SOp3fR/pVHdXK7OtSEdxLiOtZDoe9tjxwMwN8nfXcTSrntvI7gk8VLvSIB1F6vGz5XnfkVcaXyf1xz4P+B/g/Xn6kaQNy8g64w2tGd4F+HrH74i0Qj2k1b/9VtVLWbXSjvVSVq0MlnpxrTRv2+J66V29lFErrhfXi+vF+2Lt+Gp5An1KGrYm3Z51aC6azxembVf4+1rgjXXEK16MuQXp4Y+3kPojr5THr0jq29zlnYOA/wKeAg7vJv4OpKMPC8jPuegkzrtIp38PIfVjXod09KHjItv1Sbev7fYhonnevUhHrS7i1Qt6R+Wierb4efIKqtO785DuLHQ36QLT15FWqJcVpisvfzWwdi//P2tXGh8jXajb7VHRmhg7k26pu2r+bq4jXZw7nNQ/vq6jPIV4b8krmh3IR+sK004BPtrqOujFZymtXsqqlWjDeimrVmKQ1YtrxfXSrvVSRq2E68X14nrxvlibvVqeQK8TTj+uf5LvzEM6+nAbcETNfO8hnWIf14vYbwWuyX/vTXqi/QdycR4I3N7VD490Cv0C0gMi7wYOrZnecTRpO2Axr95BqKvrynYjHcFYmH/4/84rkL+SjlyMquPz7EE6vbwv6TT8ubzabaDjqM959PwYgLflAi7eQWoUqY/xZYXPdgTpzkIr9fH/9Q5SH/Z7KNx5qRcxOvrcdzz743W9XL7jc7w55zI1f8ZfdfyO8u/tDgq3CW7nV6PqpT+1Em1YL2XVSgySenGtNG/bEq6XftVLGbUSrhfXi+ulKbVS8/8zYOql7FfLE+jlf+gepFue7paHx5GeIr9PLtAv53mOzsXd0ynsrYE3578PzIV4aGH6e0mn/K8mncru8uJKUh/sjhXAzsAjHbF47ZGedwD75r/V1coiT9+RdERoHOluQJuS+oL3WAjAKnll8+48vA0wh/TgxrPzuOGko07n9hDrM8An89/Fhz2OJN2ZaQrwftJRpc368f+7d855i37E2CuvcFYpjOvN80O2Id1Gd9s8PJH03JVvkLpZPEzhYuZ2fpVZL2XWSp6/beqlzFrJ8w6KenGtdBvP9dJG9VJGreQ4rpdwvbheGlsrhe9pQNRLI14tT6AX/5EdBbBfHt4gF/MOeXhz0pGLn+Qfb0/F3XHUqOPOOBNJrfj/rZnv3cBV5H7DXcRa5rkipCM1D/PqHXI2pnDkqacVRWG+vUhPie/yRgbdLLs36QjRFqQjMF8jPZjxFvKDEnPBd/qgRV49+nEm8M3iuMI8GwOXAy/Qi+sIusl5RAkx9iU9wHO53qws8rK7km6ze3weHpr/D84EVic/uLPdX2XWS5m1kudru3rpb6105Jj/HRT14lpxvVSpXsqolRzH9eJ6cb3UF2PQ74s16tXyBHr5n7l3/iFsngvguDy+4wnwHT/unvpC1x41Wp30HIx1SE+xP6Fm/nq6KHb0y307r16QugfpKNQpeWW0VR8/97658Jfrw7J75BXtF4ufh3REY9U6Y7yD1O1h647vu/CdfzSvbHvduGzwb6XH/7Mevu8HgYPz8FtJG5exrf5cvfwc/a6XRtRKnq/t6qWMWsnLDJp6ca10+htyvbheuvuuXS+v/Q25XgZwvfS1VgrfdeXrpSHfa6sT6MN/5msKgFfvsLMnsEv+u7tT37VHjdYn9UXeOQ+/jtRv++Q6cumsX+5UUp/o8Xna8cX368fn7k8B7Eq6e9HYPHwkcCMwus7lR5Kef3JKxwojjz8wr8S6vLC2qi9S948FpP7sU6jo6fn+1EuZtVJ8n3aul/7WSl5mUNWLa+WV5V0vrpd6Pq/rJVwvedj10vPnHRD1Uvr30uoE+vifuSvpmRRj8/ARpP69dV0UybJHjT6bx3esfCaSugH0eIEt3ffLnQj8ifwQx+5WZE34zvYkne7/GOmBhz0+HLFm+bWBr5DuHHRq/oz39jZOlV6ku0z9C/hMx/9fK/8P+/E5+lwvZdZKnr/t66W/tZJjDKp6ca28srzrxfVSz+d1vYTrxfVS9+cdEPVS6nfS6gT68Z+5J+luNh/NxV3Xg6oLy3d11OidwCTqPCVO1/1yzyZdNDshj2/5jy1/tpd7+10Vll+RdLvWk0gXIm/U6t9BE76z3UgPAX1Pq3Pp5+foc72UVSt5mUrUS39rJccYVPXiWnlleddL32K4Xir4cr306TtzvfT+8w6Ieint+2h1Av38z+xvY6Sro0Y9Pj+tJs6+LNsv91Z60Y+5id9ZKRdtD6ZX/p306jfRjq/+1EtZtZKXrUS9uFb69J0N+lopfA+uF7/q+Z24XlwvftX/O6l8vZTx6ugjXFmSRkTEC/1Yfk9S3+EfkR7CeExE3NWHOPuQ+lb/gXSnn99GxO/7mpdZI/SnXsqqlRzL9WJtrV22LTmW68XamuvFrHkq33grg6R3ApeQ7j7Up5VFjvMu0insX0XEaZIEEP6SbYAoq1ZyLNeLDWiuF7P6uV7M6uPGW9bfo0aFOLsB/wt8IiIu6X9mZu2lrFrJsVwvNqC5Xszq53ox65kbbw0gaVfgoYh4uNW5mLU714tZ/VwvZvVzvdhA5MabmZmZmZlZBSzX6gTMzMzMzMysZ268mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZDRqSpkvapZPxd0naqR9x/yDp8P7kZlZ1HXWkZIqkpyX9o9V5mdXq2BZIOl7Sz1rw/hMlhaShTXq/NST9TdKzkr5X/NzNzsX6z/9RZjboRcQb+rn8nmXlYlZVHXUkaUdgV2CdiHi+tVmZdS0ivt3qHJrkKOBJYExERKuTsf7xmTczMzMr03rAdDfczBovn+nuaX9+PeBuN9wGBjfe2pikL0ialU9z3ydpZ0nLSfqipIckPSXpIkmrFJb5gKRH87QvF7uJSZoq6ZuFeXeS9FhheC1Jv5X0H0mPSPpEYdpJ+b1+mfO5S9LkwvR1JV2Sl31K0lmFaR+UdE/uQvMnSes18nszq4ekTfLv/KCaOtlG0jRJCyQ9Lum0PH4FSb/Kv+/5km6VtEaedq2kD+e/j5D0d0nfz/M9LGn7PH6mpCfcxdIGolxHRwM/A7aT9Jykr3Vsa3JXrSfzfIe2Ol+zvG/zq8LwYYV9qBM72TbclNfrcySdJWl4YdmQdIykB/L+zg8lKU8bIunU/Pt/GNi7h7w6tiNnSnpG0r2Sdi5Mv1bStyT9HXgBeH3eztya579V0vZ53qnA4cDnc03uUvu5a957JUk/z59xlqRvShrS1+/YyufGW5uStDFwLPCmiBgN7A5MBz4B7Ae8DVgLeBr4YV5mEvBj4AN52qrAOnW+33LAFcDtwNrAzsCnJO1emO1dwAXAWOB3wFl52SHA74FHgYl5+QvytP2A44H3AKsB1wPn1/9NmJVP0huBq4H/jogLaiafAZwREWOA9YGL8vjDgZWAdUm1dQywsIu32Ba4I893Hqke3gRsALwfOEvSqNI+kFn7eIhUGzdFxKiI+GoePx4YR9o+HA6cnbdzZm0h70P9CDgUWJO0vl+7MMsS4NOk3/F2pP2kj9WEeSdpXb8F8D7SvhvAR/K0rYDJwP51pLQt8HB+v68Cl6hwsJ60r3cUMBp4FrgS+AFpu3MacKWkVSPiCODXwCm5Jv/cw/v+AlhM2l5tBewGfLiOfK1J3HhrX0uA5YFJkoZFxPSIeAg4GvhyRDwWES8BJwH7K11ouj/w+4j4W552IrC0zvd7E7BaRHw9Il6OiIeBc4CDCvPcEBFXRcQS4FzSyglgG1Jj8XMR8XxEvBgRN+RpRwPfiYh7ImIx8G1gS599sxbakXTw4fCI+H0n0xcBG0gaFxHPRcTNhfGrAhtExJKI+GdELOjiPR6JiCm5Vi4kNfi+HhEvRcTVwMukDaPZYHJiroHrSDua72t1QmYF+wNXRMQNEfEy8BXglW6GeZ1/c0QsjojpwE9JB9KLTo6I+RExA/grsGUe/z7g9IiYGRHzgO/Ukc8TeZlFEXEhcB+vPWM3NSLuyvtWuwEPRMS5Ob/zgXuBfXrzBeTeJHsCn8r7c08A3+e1+4LWYm68tamIeBD4FKlx9oSkCyStReq3fGk+bT8fuIfU0FuD1ICaWYjxPPBUnW+5HrBWR9wc+/gct8Pcwt8vACvkRuO6wKN5BdJZ3DMKMecB4rVHs8ya6Rjgxoj4axfTPwRsBNybu568M48/F/gTcIGk2ZJOkTSsixiPF/5eCBARteN85s0Gk6drroF7lLTNMmsXtftQL1DYh5K0kaTfS5oraQHpYPS4mhi1+0kd6/nXxCb9/jvi7pi7Mz4n6a7CPLNqrlGrrZlivLWKMQvz93Zfaz1gGDCnsN/2U2D1XsaxBnLjrY1FxHkRsQOpmAL4LqlY94yIsYXXChExC5hDakgBIGkE6UxBh+eBEYXh8YW/Z5LOFhTjjo6IvepIdSYwQZ3fZnYmcHRN3BUj4sY64po1wjGk3+v3O5sYEQ9ExMGkjdV3gYsljcxHP78WEZOA7UldYA5rWtZm1baypJGF4QnA7FYlY9aJORQuNZG0Iq/dh/ox6WzWhrlb/fGkg9H1xl63MDyh44+IuD53ZxxVc+fjtTuumSssU6yZYsNuNmlfsWgCMKvO/DrMBF4CxhX22cb0947MVi433tqUpI0lvUPS8sCLpCP1S4CfAN/q6HYoaTVJ++bFLgbeKWmHfBHt13nt//FtwF6SVpE0nnRmr8M/gAVKN0lZMV9c+1+S3lRHuv8grZhOljRS6cYOb8nTfgJ8SVLHLaRXknRAr78Qs/I8C+wBvFXSybUTJb1f0moRsRSYn0cvkfR2SZvlazwXkLpRLmlW0mYDwNckDVd6lMA7gd+0OiGzgouBffKNP4YDX+O1jbPRpHX/c5I2AT7ai9gXAZ+QtI6klYEv1rHM6nmZYXm/aVPgqi7mvQrYSNIhkoZKOhCYRLofQd0iYg7pevDvSRqjdJO89SXVdg+1FnLjrX0tD5xMei7HXFIRH0+6mcLvgKslPQvcTLqolYi4C/g46QYJc0g3M3msEPNc0g1JppOK88KOCfnanH1I/bMfye/7M9IFu90qLLsBMCO/54F52qWksxcX5G4G/4/Un9qsZSJiPuk5VHtK+kbN5D2AuyQ9R6q3gyLiRdKZ6otJG+97gOuATu/WZWbLmEvaJs0m3TzhmIi4t7Upmb0q70P9N+kGU3NIB/qeIJ2JAjgOOCSPP4fCPlQdziF1u78d+BdwSR3L3AJsSNof+xawf0R0eilMHv9O4LOkrp6fB94ZEU/2IscOhwHDgbtJNXsx6QYu1ibkRz4MbJKmAx+u4+5CZmZmpZO0E/CriKjr7sdm7SDfEXg+qZvkI01+7yNI+247NPN9rRp85s3MzMzMBj1J+0gaka/PPBW4k9RbyaxtuPFmZmZmZgb7krr2ziZ1WTwo3EXN2oy7TZqZmZmZmVWAz7yZmZmZmZlVgBtvZmZmZmZmFdDZQ5UrYdy4cTFx4sRWp2HWrX/+859PRsRqrczBtWJV0A61Aq4XqwbXi1n92qVeylLZxtvEiROZNm1aq9Mw65akR1udg2vFqqAdagVcL1YNrhez+rVLvZTF3SbNzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MWkjSdEkvSxpXM/42SSFpoqSpeZ7nCq/bJe1YGH4+z1+cZ0KrPpdZs0iaJGmapKfz68+SJhWmLy/pJ5IelzRP0hWS1m5lzmbNIumQXB/PSZoj6Q+SdpB0kqRFkp7Nr/slnSVpzcKyO0l6rDA8XNIlkv4uaYykw1vzqczKlffFFuY6eVrSlZLWzdOK+2DzJF0jaZNOYlybl12+Znxn+3AH9idfN97MWu8R4OCOAUmbASvWzHNKRIwqvLaIiOs7hoE35PnGFuaZ0aT8zVppNrA/sAowDvgdcEFh+ieB7YDNgbWA+cCZzU3RrPkkfQY4Hfg2sAYwAfgRsG+e5cKIGE2qnXcD44F/FhtwhVjLA5cAY4HdImIBMKLBH8GsmfbJ+1NrAo/z2u3EKXna2sAs4OfFBSVNBHYEAnhXJ7Fr9+Eu7E+iQ/uzsA1g06bAnRe3OovB4lzgMF5dURwO/BL4Zssysu65PppO0nTgp8AHSBvXy4CPRsR8UoMMSQKWABsUFn0d8KeIeDzPcwFwWiHuGcB7gJWAB7beeuvGfhBrfwOgviWtBHwdODIiLilMugK4QtJJHSMiYhFwVz4b8C/gs8BxhVgjSPUWwN4RsTAv9+PJkyf/qMEfxdrIb+7/DVc9fFWr02ioiHhR0sWkAx+10xZKugj4Tc2kw4CbgVtI+3C100vlM2/WuTsvhrl3tjqLweJmYIykTSUNAQ4EftXinKw7ro9WORTYHVgf2Ag4oWOCpPnAi6SDIN8uLPNz4C2S1so7oYcCfyhMvxXYknT24bwG5m5VMTDqeztgBeDSeheIiCXA5aQzCB2WJ9XLi8C7OhpuNjhd9fBV3Dfvvlan0VB5O3Egad+sdtpIUk+pB2smHQb8Or92l7RGI3P0mTfr2vjN4MgrW51FtX1Q9c7ZcfbtOuBe0mn5ouMkHVsYvjwifL1BK7k+ylVfrZwVETMBJH2L1FA7ASAixuYN6+HAo4Vl7gdmkGpqCXAn8EotRUTxQMn3Jk+efGo/PoUNFO1e3z3Xy6rAkxGxuJeRZ5MOZHQYTWoIHhwRL/Uylg1AG6+yMVP2mNLqNHplKlPrme0ySYuBUcATpAOFHTr2wcaQti8dXY+RtAOwHnBRRDwp6SHgEOD7nSwPsDgiXnOfg97ymTez9nAuqdiPIHWZrHVqRIwtvNxws8FoZuHvR0nXsL0iIp4HfgL8UtLqefSPSWcgVgVGkq7beeXMm6TPSrpH0jP57J3ZQPAUME5Sbw/Srw3MKww/CRwE/ELS7p0vYjYg7BcRY0lnm48FrpM0Pk87NU+bCCwENi4sdzhwdUQ8mYfPy+OKivtw/Wq4gRtvZm0hIh4l3bhkL9LOpZkta93C3xNIZwlqLUe6kULHHSW3AKZGxLx85uBMYBtJ4yTtCHwBeB+wct44mw0EN5G6Ou5X7wKSlgP2Aa4vjs/XzH0EuFjS20vM0aztRMSS/JtfAuxQM20G6SZYZ0haUdKKpO3H2yTNlTQX+DSwhaQtGpWjG29m7eNDwDvy2QMzW9bHJa0jaRXgeOBCSbtK2krSEEljSDcjeRq4Jy9zK3CYpJUkDQM+BszOR0lHA4uB/wBDJX2l6Z/IrAEi4hngK8APJe0naYSkYZL2lHRKcd48flPgfNIdJ0/rJN75pLMRl0t6S17uHQ3/IGZNpmRfYGVe3Y68IiKuIR04PIp0cGQJMIl07fSWwKakAyCHNSpHN97M2kREPBQR07qY/PmaZ4Q82cV8ZgPZecDVwMP59U3SrcvPB54BHiLdaXKPiHgxL3Mc6QzEA6RG2l6k26ID/InUhfJ+UjfMjmXMKi8iTgM+Q7ou9D+kbsfHku4cCXCgpOdId2v9Hamr5dYR0dkZbSLiF6Q7UV4paRvgxEbmb9ZkV+R6WAB8Czg8Iu7qYt7/AT5PasBNiYgZETG34wWcBRzah27LdfENS8xaKCImdjF+MdBxRfoR+dVdnOmF+c0Gqlsj4js1435DN7dljoinSHeY7GzaEtIZ7w91jJs8efJ3S8jTrC1ERMcd8GrdCJzUw7LXAuvUjDsHOCcPvn3y5MnR/yzNWqurfbE87YhOxl0IdPmstoi4CLgoDy6zfH/5zJuZmZmZmVkFuPFmZmZmZmZWAe42aWZmba+7bi1mZmaDhc+8mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkF+Jo3e61pU+DOi2HuHTB+81ZnY9YeOuqig+vDrDVqa7ERXN9WEb+5/zdc9fBVTXu/e+fdyyarbNK097PO+cybvdadF8PcO1udhVl7cV2YtQfXotkrrnr4Ku6bd1+r07Am85k3W9b4zVqdgVn7Gb8ZHHll+nvK3q3NxWwwK9ZiI7i+rUI2XmVjpuwxpSnvdeQfj2zK+1j3fObNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNrIUkTZe0S2H4IElPS3qbpJD0XH49Lun3knbtZPmFeZ65kqZKGtX8T2LWWK4Vs/rV/N47XmflaWtKOkfS7Dz+4VwPm+TpE2tq6jlJt+dpR0haksctaOVnNCtL1bYvbryZtQlJhwM/BPYGHs2jx0bEKGAL4BrgUklH1Cy6T55nS2Ar4EtNSdisRVwrZnXZJyJGFV7HSloVuBEYAewIjAbeCFwH7Fqz/NjCslsUxt+U62hsEz6DWVNVYfvixptZG5B0FPA9YPeIuLF2ekTMjYgzgJOA70papnYjYi7wJ9KKw2xAcq2Y9cungQXAByLioUjmR8SUiDizN4EiYmljUjRrjapsX4Y2KrANAHPvhCl7tzqLweCjwA7AzhFxew/zXgL8D7AxcE9xgqR1gD2B/2tEklbD9dEKrhVrjoFb37sAl5bR8JI0ZOutty4hJauS++bdx5F/PLLVaTRCZbYvbrxZ5zbbv9UZDCa7An8F7qxj3tn531UK4y6TFMAo0sriq+WmZ8twfbSKa8Uab+DU92WSFheGPweMA+Z2jJD0LuCXwBBSd8jdCvM/Kanj729GxKn57zdLmg+MbFTi1p72ev1erU6hkSqzfXHjzTo3+cj0sv75oHqeB44BTgR+JulDERHdzLt2/ndeYdx+EfFnSW8DziNtnOf3IVurl+ujfK4VaxdVqO/66mW/iPhzcYSkDwNrdgxHxO+AsXn8+2uWHxcRi1nWzRGxQ74hw7O9S9yq7ICNDuCAjQ5odRq9NpWp9cxWme2Lr3kza70ngJ1JF4//qId5353nv692QkRcB0wFTq2dZjZAuFbM+ucvwH6dXavTWxHxXAn5mLWLymxf3HgzawMRMRt4B7CHpO/XTpe0hqRjSafhv9TN9QqnA7tK2rJRuZq1kmvFrF9OA1YGzpW0vpLR+OY9ZpXZvrjbpFmbiIiZkt4B/A0Yn0fPV7ro4HlgGnBARPyxmxj/kfRL0qn/9zY6Z7NWcK2Y1eUKSUsKw9dExLslvRn4BnAD6VEBj+e/P9qCHM3aShW2L268mbVQREysGX4EWDcPHtzb5fM4b4BtwHGtmNWvs997Ydps4EPdTJ8OdHpRXURMhfouIDKriqptX9x4MzMzs/JMmwJ3XtyY2HPvgPGbNya2WQP95v7fcNXDV5Ua895597LJKpuUGtPan695MzMzs/LceXF6TpqZveKqh6/ivnnL3N/CrNd85s3MzMzKNX4zOPLK8uMOzAdn2yCx8SobM2WPKaXFG6APy7Ye+MybmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWAG29mZmZmZmYV4BuWDHa1t3T2bZjNzMzMzNqSz7wNdr6ls5mZmZlZJfjMm732ls6+DbOZmZmZWVvymTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33swqQtIRkm5odR5mZmZm1hpuvJm1kKTpkhZKeq7wOquPsa6V9GKO8Yykv0narOyczVoh18rLksbVjL9NUkiaKGlqnqdYT7dL2rEw/HyevzjPhFZ9LrNGcL2Y9Z+kSZKmSXo6v/4saVJh+vKSfiLpcUnzJF0hae1G5+XGm1nr7RMRowqvY2tnkDS0zljHRsQoYFXgWuDcEvM0a7VHgIM7BvLBiRVr5jmlpp62iIjrO4aBN+T5xhbmmdGk/M2ayfVi1j+zgf2BVYBxwO+ACwrTPwlsB2wOrAXMB85sdFL17hCaDS7TpsCdF7fs7SUdAXwE+AdwOPAj4ME0SWcChwFzgI9HxF9ql4+IxZIuAL7YtKQHsxb/XgaRc0m//Y6N4+HAL4Fvtiwja765d8KUvVudRRW4XgaB++bdx5F/PLLVaVSapOnAT4EPAGsClwEfjYj5pAYZkgQsATYoLPo64E8R8Xie5wLgtELcM4D3ACsBDwCfiojr+5uvz7yZdebOi9MOQmttCzwMrA58q2bcOOCrwCWSVqldUNJw4FDg5uakOsi1x+9lMLgZGCNpU0lDgAOBX7U4J2umzfaH8e4NXifXywC31+v3YuNVNm51GgPFocDuwPrARsAJHRMkzQdeJB0I+XZhmZ8Db5G0lqQROcYfCtNvBbYknbk7D/iNpBX6m6jPvJl1ZfxmcOSV/YvxQdUz12WSFheGPwcsAmZHRMcR08XpoA9PAKdHRAAXSvossDevdo/8gaRTgRHAQtIRnz/370NYXcr4vQxm9dUKvHo24TrgXmBWzfTjJBW7Hl8eEYf3P0FrC5OPTK/BzvViwAEbHcABGx3Q6jTa3lSm1jPbWRExE0DSt0gNtRMAImKspJGks9ePFpa5H5hBqqslwJ3AK/UUEcWDJd+TdAKwMXB7Xz8LuPFm1g72i4jXNLByt8mZncw7KzfcOjxK6mfd4RMR8TNJywFvIfXPNhtIzgX+Ruqu8stOpp8aESd0Mt5sMHK9mNWnuM9Vu29FRDwv6SfAfyRtGhFPAD8GViDdZ+B54POkM2/bAuQD7B/OsQIYQ+o51S9uvJm1r+hk3NqSVGjATaCTBlpELAWul/QgMLmBOZo1VUQ8KukRYC/gQ63Ox6ydDcZ6Oe+WGVx+W+0Jxta7b97b2Gqr61qdhnVt3cLfE0g3K6m1HKln09qknlBbAF+OiHkA+Z4EX893ed0U+AKwM3BXRCyV9DRQ92nzrviaN7NqWR34hKRhkg4grRyu6mxGSdsBkzqbZlZxHwLeERHPtzoRswoYVPVy+W2zuHvOglanYdXzcUnr5PsIHE+6NGVXSVtJGiJpDOlmJE8D9+RlbgUOk7SSpGHAx0iXvDwJjAYWA/8Bhkr6CunMW7/5zJtZ610haUlh+Brg8i7mvQXYEHgSeBzYPyKeKkw/S9Lp+e+5pP7ap2E2gETEQ91M/rykTxWGX4yIfndTMauqwVgvk9Ycw4VHb9fqNF7jyD+e3eoUrHvnAVeTujheTror696ka9/WId1H4FZgj4h4MS9zHPAD0p0khwP/D3h3nvYnUhfK+0ldKr9P55fD9Jobb2YtFBETu5k8tWbeqYVxyzwLLiJ26izI5MmT3XizyuuqViJiMa92Qzkiv7qLM50Suq2YtTPXi1mv3RoR36kZ95v86lQ+eH5oF9OWkM56F7srn9LfJMHdJs3MzMzMzCrBjTczMzMzM7MKcLdJMzMzMzMblHq4hKXt+MybmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWAr3mzZc29E6bs3eosWmvuHTB+81ZnYVUwbYp/L2ZmZtYUbrzZa222f6szMKuWOy+Glxa0OgszMzMbBNx4s9eafGR6DXaD/cyj9c7yY1qdgZmZmQ0CvubNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK8A1LBqppU9Jd8HriW5wPeufdMoPLb5vV6jTa29x9uXDC5a3OwqzXWlLfrhczs4bxmbeB6s6L0/PazHpw+W2zuHuOb3VvNhC5vs3MBhafeRvIxm8GR17Z/Ty+Jb4Bk9Ycw4VHb9fqNNrXlG+2OgOzPmt6fbtezMwaxmfezMzMzMzMKsCNN7MWkjRd0kJJzxVeZ+Vpa0o6R9LsPP5hSVMlbZKnT5QUNcvenqcdIWmJpOda+fnMypJrZZdOxo+RdLqkGbkGHszD4zpbTtJBkp6W9LZm5m/WDiRNkjQt18DTkv4saVJh+vKSfiLpcUnzJF0hae1W5mzWaFXbvrjxZtZ6+0TEqMLrWEmrAjcCI4AdgdHAG4HrgF1rlh9bWHaLwvibImJUUz6BWQtIGg78BXgDsAcwBtgeeArYppP5Dwd+COwdEdc1MVWzdjEb2B9YBRgH/A64oDD9k8B2wObAWsB84MzmpmjWeu28ffE1b2bt6dPAAuADEbE0j5sPTGlZRmbt5zBgAvD2iOg4y/wE8I3aGSUdBXwb2D0ipjUvRbPmkzQd+CnwAWBN4DLgoxExn7QtQZKAJcAGhUVfB/wpIh7P81wAnFaIewbwHmAl4IGtt966sR/ErHXadvvixptZV+be2cobuuwCXFpouFm7a+3vZbDaBfhjYcPalY8COwA7R8TtjU/LrAf1Ps6nfw4FdgeeB64ATsgvJM0HRpF6YH2lsMzPgTMkdZx1OxT4Q2H6rcDXgWdIZ+ne2MgPMNDcN+8+jvzjka1Ow+rTttsXd5s068xm+6e7dTbHZZLmF14fIXVnmdsxg6R35WnPSrq6ZvknC8seVxj/5ryBtkYbuVozfy/2qlWBOXXMtytwM+Dnp1h7aM7jfM6KiJkRMQ/4FnBwx4SIGEs6e3Ys8O/CMvcDM4BZpN4fm5Iaax3L/SoinoqIxRHxvUZ/gIFkr9fvxcarbNzqNKx+bbt98Zk3s85MPjK9+uuDqmeu/SLiz8URkj5M6uoCQET8Dhibx7+/ZvlxEbG4k7g3R8QOkydPjl5mbb01es2eH8th3auvVmo9RaFOunEMcCLwM0kfigjXhLVePY/z6Up99TKz8PejpGvYXhERz0v6CfAfSZtGxBPAj4EVSDuuzwOfJ5152xZA0meBD+dY4W6T9TtgowM4YKMDWp3GoDSVqX1ZrG23Lz7zZtae/gLsJ8k1ata1PwO7SxrZw3xPADuTbv7zo4ZnZdYe1i38PYF0s5Jay5FujNVxR8ktgKkRMS8iXiLdrGQbSeMk7Qh8AXgfsHI+e2c2ULXt9sU7hmbt6TRgZeBcSesrGQ1s2dq0zFpqmKQVOl7AuaSzC7+VtImk5SStKul4SXsVF4yI2cA7gD0kfb8FuZs128clrSNpFeB44EJJu0raStIQSWNI25qngXvyMrcCh0laSdIw4GPA7Ih4knTX48XAf4Chkr6yzDuaVVdlti9uvJm13hU1z2q7NG8o3wy8CNwAPAvcRtp4frR1qZq11FXAwsLrRNJF5fcC15Cu0fkH6ZrRW2oXjoiZpA3s/pK+06SczVrlPOBq4OH8+iYwFjifdMORh0h3mtwjIl7MyxxH2u48QGqk7QW8O0/7E6kL5f2kbpgdy5gNBJXZvviaN7MWioiJ3UybDXyom+nTgU4vfIiIqdC3Tt5m7ai7WgE+lV89LhcRj/Da7mRmA9WtEVG7E/mb/OpURDxFusNkZ9OWkLZJr2yXJk+e/N0S8jRrqaptX3zmzczMzMzMrAJ85s3MzMzM2sJ5t8zg8ttm9Xn5u+csYNKaY0rMyKy9+MybmZmZ2QASERNrH0FTFZffNou75yxodRpmbctn3szMzMysbUxacwwXHr1dn5Y98Kc3lZyNWXvxmTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwBf82Zm1lvTpsCdF6e/594B4zdvbT5m1jfFWm4mrzfMrI985s3MrLfuvBjm3tnqLMysv1zLZlYxPvNmZtYX4zeDI6+EKXu3OhMz64+OWm4mrzfMrI985s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNt4Fm2pT0/Ji5d7Q6EzMzMzOzAUPSEZJuaGUObrwNNHdeDHPvbHUWVidJ0yXt0sn4MZJOlzRD0nOSHszD4zpbTtJBkp6W9LZm5m/WbJIOkTQt18UcSX+QtIOkkyQtkvRsft0v6SxJaxaW3UnSY4Xh4ZIukfT3XHOHt+ZTmZWru22EpMj185ykxyX9XtKunSy/MM8zV9JUSaOa/0nMGq/m997xOquPsa6V9GKO8Yykv0narDD9cEn/lLRA0mOSTpE0tDfv4cbbQDR+Mxi/eauzsD6SNBz4C/AGYA9gDLA98BSwTSfzHw78ENg7Iq5rYqpmTSXpM8DpwLeBNYAJwI+AffMsF0bEaGAV4N3AeOCfxQZcIdbywCXAWGC3iFgAjGjwRzBruuI2Ang0jx4bEaOALYBrgEslHVGz6D55ni2BrYAvNSVhs9bYJyJGFV7H1s7Qi0bWsbl2VgWuBc4tTBsBfAoYB2wL7Awc15tEe9XSswFq7p2pq6W1i8NIO6Vvj4jn8rgngG/UzijpKNKO7O4RMa15KVqvTZuSzoxbn0haCfg6cGREXFKYdAVwhaSTOkZExCLgLkkHAv8CPkth4yhpBHAZEKSDHgvzcj+ePHnyjxr8UcySJmx7a7cRkiYWp0fEXOAMScOA70r6ZUQsrZ1H0p9IjThrJm83Wiof0PgI8A/gcNLBwgfTJJ1J2l+bA3w8Iv5Su3xELJZ0AfDFwrgfF2aZJenXwNt7k5fPvA12m+2fztRZO9kF+GOh4daVj5IadDu74VYB7tLcX9sBKwCX1rtARCwBLgd2LIxeHvgD8CLwro6Gm1lTNWfb25ttxCXA6sDGtRMkrQPsSdpptWbydqMdbAs8TKqPb9WMGwd8FbhE0iq1C+aeVIcCN3cT/63AXb1JyGfeBrvJR6aXNcYH1ZelVgX+Wcd8uwJ/Bbxmr4rxm8GRV7Y6i/bUc62sCjwZEYt7GXk2qRtlh9GkhuDBEfFSL2OZlaO/2976ti292UbMzv8Wa+UySQGMAv6PtJNqzebtRv/VVy+XSSpuXz4HLAJmR8SZedxiSZB6Q50eEQFcKOmzpG7JHd0jfyDpVFIXyYXAezp7Q0lHApOBD/fm4/jMm1n7eQpY5hqdThwDbAT8THltYjaAPQWM6+2F3cDawLzC8JPAQcAvJO1eVnJmbag324i187/FWtkvX0O6E7AJ6SyD2UC1X0SMLbzOyeNndjLvrNxw6/AosFZh+BMRMZbUW+SdwMWSXnMzCkn7AScDe0bEk71J1I03s/bzZ2B3SSN7mO8J0oWuO5L6YZsNZDeRujruV+8CkpYD9gGuL47P18x9hLRB7dW1BmYV0pttxLvz/PfVTsg3wpoKnFpyfmZVEJ2MW7vmgMgEXj17/eqCEUsj4npSl+PdOsZL2gM4h3STlF73nnLjzaz1hklaoeNFOu0+E/itpE0kLSdpVUnHS9qruGBEzAbeAewh6fstyN2sKSLiGeArwA8l7SdphKRhkvaUdEpx3jx+U+B80h0nT+sk3vnAscDlkt6Sl3tHwz+IWRP1tI2QtIakY0ldIr9Ue7OSgtOBXSVt2ahczSpkdeATeVtzALApcFVnM0raDphEvq4tb2d+Dbw3Iv7Rlzf3NW9mrVdb8N8i3bTka6RbOK8MPE668cIttQtHxMy8MvibpBcjwrdztgEpIk6T9DhwAmnj9yzp+tBvkY5qHpi7ooh0FPQaYOu8A9tZvF/kC8qvlLQbcGLjP4VZcxW3EaSDGQDz85mD54FpwAER8cduYvxH0i9JNfLeRuds1gJXSFpSGL6GtN/VmVuADUnd8B8H9o+IpwrTz5J0ev57LnBCRPwhD58IrARcVTh5d31E7Flvom68mbVQREzsZvKn8qvH5SLiEWDdktIya1sR8WtSw63WjcBJPSx7LbBOzbhzSN1XAN4+efLkzrrIWG9V5RE0c+8YkM9F7WEbcXBvl8/jPlpGbmbtpod9sak1804tjFvmWXARsVMP79XvrvpuvJmZmVl5Ntu/1RmYmQ1YbryZmZlZear0CJoqnB00MyvwDUvMzMzMzMwqwGfezMzM2tx5t8zg8ttm9Xq5u+csYNKaYxqQkZmZtYLPvJmZmbW5y2+bxd1zFrQ6DTMzazGfeTMzM6uASWuO4cKjt+vVMgf+9KYGZWNmZq3gxpuZWX/Vc1v0AXpLcjOzdnP3nAUD48DF3H25cEJXjxqzwcqNNzOz/vBt0c3M2sa+W67d6hTMGsqNNzOz/qj3tui+JbmZWcMdsu0EDtl2QqvTKMeUb7Y6A2tDvmGJmZmZmZlZBfjMm5mZmZmVyo+3MGsMn3kzMzMzs1L58RZmjeEzb2ZmZmZWOj/ewqx8PvNmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFm1kKSpktaKOk5SU9LulLSunnaVEkv52nzJF0jaZNOYlybl12+ZvxUSS8367OYNVIzakXSc836PGaNlOtll07Gj5F0uqQZuV4ezMPjOltO0kG5Zt7WzPzNmqlm+9LxOitPW1PSOZJm5/EP523GJnn6RElRs+ztedoRkpbkcQsk3S7pnf3N1403s9bbJyJGAWsCjwNnFqadkqetDcwCfl5cUNJEYEcggHd1EvuURiRs1iINrZW8vNmAJGk48BfgDcAewBhge+ApYJtO5j8c+CGwd0Rc18RUzVphn4gYVXgdK2lV4EZgBGn7MRp4I3AdsGvN8mMLy25RGH9T3raMBX4EXCBpbH8SdePNrE1ExIvAxcCkTqYtBC4CtqyZdBhwMzAVOLyxGZq1B9eKWZ8cBkwA3h0Rd0fE0oh4IiK+ERFXFWeUdBTwPWD3iLixFcmatYFPAwuAD0TEQ5HMj4gpEXFmTwsXRcRS4FxgJLBhf5Lyc94Gsrl3wpS9W52F1UnSCOBA0g5m7bSRwMHAgzWTDgNOA24Bbpa0RkQ83uhcB6ViPc29A8Zv3r8Y1meuFSvV4KnLXYA/RkRP3YM/CuwA7BwRtzc+LevW4Pl9tqNdgEtzw6tfJA0BjgQWAY/2J5bPvA1Um+0P4zdrdRZWn8skzScd3dkV+J/CtOPytGdJG9MPdEyQtAOwHnBRRPwTeAg4pCb2cY1LexApo55ck2VoaK3k5W0wGVx1uSowp475diUdGLmzselYjwbX77PVLpM0v/D6CDAOmNsxg6R35WnPSrq6ZvknC8sW973enLctLwKnAu+PiCf6k6jPvA1Uk49ML2utD6qeufaLiD/nozL7AtdJ6ugOdmpEnCBpAvBHYGPgjjztcODqiHgyD5+Xx32/EPtU4Mv9/BRWW099OQrqmuxeG9RKRJwwefLk6O9HsQqpal3WVy+1niJdL9qTY4ATgZ9J+lBEuCZapaq/z3bTi+1LcYSkD1OomYj4HTA2j39/zfLjImJxJ3FvjogdJI0iXYu9I6lrf5/5zJtZm4iIJRFxCbCEdOagOG0G8EngDEkrSloReB/wNklzJc0l9c3eQtIWtbHNBhLXilmf/BnYPXct7s4TwM6kncwfNTwrs/b1F2A/Sf1uL+Xuyh8DPiBpq/7EcuPNrE0o2RdYGbindnpEXAPMBo4C9iPtuE4i3ZhhS2BT4HrStT1mA5ZrxawuwySt0PEi3SxhJvBbSZtIWk7SqpKOl7RXccGImA28A9hD0vc7iW02GJxG2s6cK2n9vO0ZzbI3xKpLRDwF/Az4Sn+SGpDdJs+7ZQaX3zar1Wm0xtx9uXDC5a3OwnrnCklLSLcwfxQ4PCLukjo9zf8/pJXJ/cCUfJbhFfm5JD+Q9IUG52zWCq4Vs/pdVTP8LdINGL4GXEPaKX0cuJx0I5/XiIiZkt4B/E3SixHxpQbna9ZKHduXDtdExLslvRn4BnAD6VEBj+e/P9rH9zkdeEjS5hFxR08zd2ZANt4uv20Wd89ZwKQ1x7Q6FbNuRcTEbqYd0cm4C4ELu1nmIl7tS30EwOTJk33Nm1VeM2rFbKDorl6AT+VXj8tFxCPAuiWlZdaWeti+zAY+1M306UCnRxAjYirp8TTFcY8By/c+y1cNyMYbwKQ1x3Dh0du1Oo3mm/LNVmdgZmZmZmYN4GvezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwChrY6ATMzsyo475YZXH7brJa8991zFjBpzTEteW8zM2sfPvNmZmZWh8tvm8Xdcxa0Og0zMxvEfObNzMysTpPWHMOFR2/X9Pc98Kc3Nf09zcys/fjMm5mZmZmZWQW48WZmZmZmZlYBbryZmdmAIukISTe0Og8zM7OyufFm1oYkTZI0TdLT+fVnSZMK05eX9BNJj0uaJ+kKSWu3MmezRpI0XdJCSc8VXmf1Mda1kl7MMZ6R9DdJm5Wds1mr1NTL05KulLRunjZV0st52jxJ10japJMY1+Zll68Z/8ryzfo8Zo2U6+VlSeNqxt8mKSRNrKmbjtftknYsDD+f5y/OM6HsfN14M2tPs4H9gVWAccDvgAsK0z8JbAdsDqwFzAfObG6KZk23T0SMKryOrZ1BUr034jo2IkYBqwLXAueWmKdZO9gn/8bXBB7ntduIU/K0tYFZwM+LC0qaCOwIBPCuTmJ3LG82UDwCHNwxkA/orVgzzyk126AtIuL6jmHgDXm+sYV5ZpSdqO82aT2bNgXuvLjVWQxIkqYDPwU+QNrAXgZ8NCLmkxpkSBKwBNigsOjrgD9FxON5nguA0wpxzwDeA6y09dZbN/hTWK+5pkoj6QjgI8A/gMOBHwEPpkk6EzgMmAN8PCL+Urt8RCzO9fPFpiXdZHfPWeC7VQ5iEfGipIuB0zuZtlDSRcBvaiYdBtwM3EKqq9rpDeXfrLXAuaTffcdBjsOBXwLfbFlGXfCZN+vZnRfD3DtbncVAdiiwO7A+sBFwQscESfOBF0krk28Xlvk58BZJa0kakWP8oTD9VmBL0pk7azeuqbJtCzwMrA58q2bcOOCrwCWSlqkHScNJ9XNzc1Jtrn23XNsP9x7k8jbiQDr5jUsaSTrb8GDNpMOAX+fX7pLWaHSeHfybtRa5GRgjaVNJQ0g186sW59Qpn3mz+ozfDI68stVZVM8HVc9cZ0XETABJ3yI11E4AiIixeeN6OPBoYZn7gRmk7i5LgDuBV7qQRcQrK5zJkyf37zNYY7imXqu+WrlM0uLC8OeARcDsiOg4Wro4nazmCeD0iAjgQkmfBfbm1e6RP5B0KjACWEg6Uz3gHLLtBA7ZtvRLLqzFLjqmrtk66mUUqR52L0w7TtKxwBjStmXfjgmSdgDWAy6KiCclPQQcAny/dvlG9Ozwb9bKVme9wKtn364D7iXtYxV11E2HyyPi8H4n2Es+82bWejMLfz9KuobtFRHxPPAT4JeSVs+jfwysQLpeZyRwCYUzb5I+K+keSc80MnGzJtsvIsYWXufk8TM7mXdWbrh1qK2tT0TEWFIdvRO4WNLmDcnarDX2y7/x5UkH966TND5POzVPm0g6eLFxYbnDgasj4sk8fF4eV9SxvNlAci7pQMURpC6TtU6t2QY1veEGbryZtYN1C39PIN2spNZypDMEHXeU3AKYGhHzIuIl0tm6bSSNk7Qj8AXgfcDKjUvbrG1EJ+PWzteLdui0tiJiaURcT+o2tluD8jNrmYhYEhGXkHpp7FAzbQbpBlhnSFpR0oqkbcfbJM2VNBf4NLCFpC2anbtZM0XEo6Qbl+xFOijeltx4M2u9j0taJ1+Pczypi9eukraSNETSGNLNSJ4G7snL3AocJmklScOAj5G6jj0JjAYWA//BXaNt8Fod+ISkYZIOADYFrupsRknbAZOAu5qYn1lTKNmXdDDvntrpEXEN6cDGUcB+pEbeJNJ101uSaud6Uncys4HuQ8A7cq+ntuTGm1nrnQdcTbq5wsOkOxuNBc4HngEeIt1pco+IeDEvcxzpRiYPkBppewHvztP+ROpCeT+vvU7OrOquqHl+zqXdzHsLsCHwJOkmJvtHxFOF6Wd1xCF1lTkhIv7QSRyzqroi/74XkGrg8Ijo6gDF/wCfJzXgpkTEjIiY2/ECzgIO7cWjOMwqKSIeiohpXUz+fM026Mku5msoF6FZ690aEd+pGfcburk1c94JPbSLaUtIR44+BDB58uTOupSZVUpETOxm8tSaeacWxi3zLLiI2KmcrMzaU3f1EhFHdDLuQuDCbpa5CLgoDy6zvFmVdVUvEbEY6Oh+fwQ9/PYjYnph/obxmTczMzMzM7MKcOPNzMzMzMysAtxt0qyFeugKZmZmZmb2Cp95MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK8DVvA9HcO2HK3iXGuwPGb15ePLOBoD915poyMzOzPnDjbaDZbP9WZ2A28LnOzMzMrAXceBtoJh+ZXmUq8yye2UDQ3zpzTZmZmVkf+Jo3MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMBilJR0i6odV5mFl93HgzazFJh0iaJuk5SXMk/UHSDpJOkrRI0rP5db+ksyStWVh2J0mPFYaHS7pE0t8ljZF0eGs+lVm5JE2XtDDXScfrrDxtTUnnSJqdxz8saaqkTfL0iZKiZtnb87QjJC3J4xa08jOalaW7eulDrGslvZhjPCPpb5I2Kztns1Zr9P6YpH9KWiDpMUmnSBralzzdeDNrIUmfAU4Hvg2sAUwAfgTsm2e5MCJGA6sA7wbGA/8srjAKsZYHLgHGArtFxAJgRIM/glkz7RMRowqvYyWtCtxI+q3vCIwG3ghcB+xas/zYwrJbFMbfFBGjSLVjNlAsUy+1M/Ri5/HYXCOrAtcC55aYp1nLNWl/7FPAOGBbYGfguL7k6sabWYtIWgn4OvDxiLgkIp6PiEURcUVEfK44bx5/F3Ag8B/gszWxRgBXAMOAvSPi+bzcj5vxWcxa6NPAAuADEfFQJPMjYkpEnNmbQBGxtDEpmrWHfKb575K+L2kecNKrk3RmPrN2r6SdO1s+IhYDFwCTmpSyWcM1a38sIq6PiJcjYhbwa+Atfcm3T6frbBCaeydM2bvVWQw02wErAJfWu0BELJF0ObB7YfTywB+AZ4ADIuKlUrO0xnBNlWUX4NIyGl6Shmy99dYlpNQYd89ZwIE/vanVaVj1bUtqgK1O2sE8MI+7mHRW4D3AJZJeFxHzigtKGg4cCtzc1IxLct4tM7j8tlmtTsPaTyv2x94K3NWHXH3mzeqw2f4w3t3bG2BV4Ml8JLM3ZpNO23cYTVrx/MINt4pwTfXVZZLmF14fIe1szu2YQdK78rRnJV1ds/yThWWL3VXeLGk+8GLjP0Lf7Lvl2kxac0yr07Bq6axeAGZHxJkRsTgiFuZxTwCn57MKFwL3AcWjSz/INfIccCzwtWZ9iDJdftss7p7jS1ttGU3dH5N0JDAZOLW3iYLPvFk9Jh+ZXtZ7H1R3U58Cxkka2ssVxtpA8Wjok8AngF9Kei4i/tT7RK2pXFPL6r5WOuwXEX8ujpD0YeCVaw4i4nfA2Dz+/TXLj+ui1m6OiB0kjQKe7V3izXHIthM4ZNsJrU7D2sRFx9Q1W2f1cgQws5N5Z0VEFIYfBdYqDH8iIn4maTlSV6/fSXpbO5+p7sqkNcdw4dHbtToNa6I66qVp+2OS9gNOBnaJiCd78V6v8Jk3s9a5iXSkf796F8gbzn2A64vjI+IS4CPAxZLeXmKOZu3uL8B+uTb6JSKeKyEfs3YXnYxbW1LxCMoE0lmF1y4YsTQirgceBHZrUH5mzdaU/TFJewDnkG4mdGdfk3XjzaxFIuIZ4CvADyXtJ2mEpGGS9pR0SnHePH5T4HzSHY5O6yTe+aTuLJdLekte7h0N/yBmrXUasDJwrqT1lYwGtmxtWmaVsjrwibytOQDYFLiqsxklbUe6YUmfrtcxazdN3B/7NfDeiPhHf/J1482shSLiNOAzwAmkuxbNJBX8ZXmWAyU9B8wHfkc6tb91RCxzRDTH+wXpzkdXStoGOLGR+Zs12RV67XOrLs3dTt5MOmp6A6nb422kaw8+2rpUzVpumXrpZt5bgA1J3b6+BewfEU8Vpp/VEYf0mIATIuIPjUvdrLmatD+2EnBVoSb7VEO+5s2sxSLi16SjMbVu5NXbOHe17LXAOjXjziGdlgd4++TJkzvrImNWKRExsZtps4EPdTN9OtDpRXURMRWY2q/kzNpMd/VCze+9pgaWeRZcROxUTlZm7a3R+2P9zzDxmTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAvyoADMzMzMr3d1zFnDgT29qdRqvuHvOAiatOabVaZj1ixtvZmZmZlaqfbdcu9UpmA1IbryZmZmZWakO2XYCh2w7odVpvEY7nQU06ytf82ZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WbWhiQNl3SxpOmSQtJONdPfLumvkp6RNL2T5Sfm6S80KWWzliizViTd26S0zVrC9WJWv7LrRdIuZeTlxptZ+7oBeD8wt5NpzwP/C3yui2XPB/4NrNqY1MzaSlm18uWGZGfWXlwvZvUrs14ulrRafxNy482shfLRnC9JulvS05KmSFohIl6OiNMj4gZgSe1yEfGPiDgXeLiTmBsBbwS+GhELG/8pzBqvGbUSEb9t/CcxazzXi1n9mlgvdwLvzdPXl/R/kp6S9KSkX0saW0++Q/vxWdva3XMWcOBPb2p1Gmb1OBTYnXQE5wrghPzqqzcAD0fEs/Uu4HqxinCtmNWv5fXSjlzD1oVm1MvteTyAgO8AfwPGAL8FTgI+1VPgAXnmbd8t12bSmmNanYZZvc6KiJkRMQ/4FnBwP+ONAp6pd2bXi1WIa8Wsfi2tl3bkGrZuNKNengFGA0TEgxFxTUS8FBH/AU4D3lZP4AF55u2QbSdwyLYTWp2GGRcdU9dsMwt/Pwqs1c+3fY50FKcurhdrB64Vs/pVoV7akWt4cGqjehkDPAsgaXXgB8COpAbdcsDT9QQekGfezCpm3cLfE4DZ/Yx3F/B6SaP7Gces3bhWzOrnejGrXzPqZYs8HlKXyQA2j4gxpJuiqJ7AbryZtd7HJa0jaRXgeOBCAEnLS1ohzzNc0gqSlKctl6cNS4NaQdJwgIi4H7gN+GphebOBoKG1Iundzf5AZg3kejGrXzPqZXPStW2QzrY9B8yXtDZd37FyGW68mbXeecDVpLsVPQx8M4+/D1gIrA38Kf+9Xp721jx8FekI0cIco8NBwGTqPAVvVhGNrpWTG5u+WVO5Xszq14x62T9f3wbwNdLdKJ8BrgQuqTfRAXnNm1nF3BoR36kdGRETu1ogIq6lm9PrETEd2Alg8uTJ0d8EzdpEQ2sFXC82oLhezOrX8HqpmXYXsHXN6O/1nKbPvJmZmZmZmVWCG29mZmZmZmYV4G6TZi3U3el4M3uVa8Wsfq4Xs/pVrV585s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwCFFHN5ytK+g/waIvTGAc82eIcwHnUapc8ADaOiNGtTKANaqWd/j/aJZd2yQPaJ5eW1wp0Wy/t8j11xzmWowo5tnu99Ec7fv/OqX7tmFdb1EtZKvuogIhYrdU5SJoWEZOdh/PoiqRprc6h1bXSbv8f7ZBLu+QB7ZNLO9QKdF0v7fI9dcc5lqMqObY6B2jM9qUdv3/nVL92zKtd6qUs7jZpZmZmZmZWAW68mZmZmZmZVYAbb/1zdqsTyJzHa7VLHtBeubRKO30H7ZJLu+QB7ZNLu+TRlXbPD5xjWZxja7XjZ3NO9WvHvNoxpz6r7A1LzMzMzMzMBhOfeTMzMzMzM6sAN97qIGkPSfdJelDSFzuZvpKkKyTdLukuSUe2Io88z06Sbst5XNeqPPJ8b5K0RNL+rchD0qGS7sivGyVt0aI8JOkHefodkt7YiDxazXXSt1zyfIOiVurMpaX10i6/4/7kmOdpym+8Pznm+Rr62+9OO9VFP3Ks5PalXeusHWurHWupXWtnoNbLMiLCr25ewBDgIeD1wHDgdmBSzTzHA9/Nf68GzAOGtyCPscDdwIQ8vHorvo/CfP8HXAXs36L/l+2BlfPfewK3tCiPvYA/AALe3Ig8Wv1ynfQtl8J8A75WepFLy+qlXX7HJeTYlN94f3IszNew334J32NT6qKfOVZu+9KuddaOtdWOtdSutTNQ66Wzl8+89Wwb4MGIeDgiXgYuAPatmSeA0ZIEjCKtZBa3II9DgEsiYgZARDxRcg715gHw38BvgUbkUFceEXFjRDydB28G1mlFHnn4l5HcDIyVtGYDcmkl10nfcoHBUyt15UJr66Vdfsf9zbFZv/H+5AiN/+13p53qos85Us3tS7vWWTvWVjvWUrvWzkCtl2W48daztYGZheHH8riis4BNgdnAncAnI2JpC/LYCFhZ0rWS/inpsJJzqCsPSWsD7wZ+0oD3rzuPGh8iHW1pRR69zbWKXCd9yGWQ1Uq9ubSyXtrld9yddvqNd6Vdfvvdaae66Eq710tftWudtWNttWMttWvtDNR6WcbQVidQAepkXO0tOncHbgPeAawPXCPp+ohY0OQ8hgJbAzsDKwI3Sbo5Iu5vch6nA1+IiCXpoFlD1JNHmlF6O2nlsUOL8qg71wpznfQtl9MZPLVSby6trJd2+R13p51+411pl99+d9qpLrrS7vXSV+1aZ+1YW+1YS+1aOwO1XpbhxlvPHgPWLQyvQzoSVHQkcHJEBPCgpEeATYB/NDmPx4AnI+J54HlJfwO2AMpcqdSTx2TggrwSGQfsJWnx/2/vTkLkKMMwjv8fk7gmenADEUkUxaCoB3OJiiOI4HLwoAiCVxdQUBG8iApeFMGDBvUQJCB6UhFccxAFiUoSYha3g+hFETdEjSuTvB66xHGcjJX0THdV5/+DD4papt5qvqeb76up7qp6ccR1kOQcYD1weVV9v4Dn3586WtXac+bkwGo5mLLStpZx5qUr/Xg+Xerj+9KVvj+fLuViX7qelwPV1Zx1MVtdzFJXszOpefmv/X1I7mBrDAa4nwGr+OcByLNm7fMEcH+zfCLwJXDcGOpYDbzR7Hsk8AFw9qjrmLX/BhbnSxjavB6nAJ8Ca8fcP67k3w/Ibh5HX17MZk4OrJZZ+090VvajlrHlpSv9eAFqHEkfH6bGWfsvSt9fgNdxJLkYssbefb50NWddzFYXs9TV7ExqXuZq3nn7H1U1neRWYCODb7J5qqo+THJzs/1J4AFgQ5JdDDrE3VX13ajrqKqPk7wO7AT2Auur6oNR17GQ5xuyjnuBY4HHmxmp6ao6fwx1vMrgG44+BX5lMKM4UczJgdWy0Occoo5Fz8p+1DK2vHSlHw9b46j6+DA1jqqWfelSLoassXefL13NWRez1cUsdTU7k5qXuaQZiUqSJEmSOsxvm5QkSZKkHnDwJkmSJEk94OBNkiRJknrAwZskSZIk9YCDN0mSJEnqAQdvPZVkT5LtST5MsiPJnUkOabadn+TReY5dmeT60VUrjY9ZkdozL1J75kXj4E8F9FSS3VW1vFk+AXgW2FRV97U4dgq4q6quWtQipQ4wK1J75kVqz7xoHLzzNgGq6hvgRuDWDEwleRkgycXNrND2JO8nWQE8CFzUrLujmf15O8m2pq1tjp1K8laS55J8kuSZNL+2mGRNkneamabNSVYkWZLk4SRbkuxMctO4XhNpLmZFas+8SO2ZF41MVdl62IDdc6z7ATgRmAJebta9BFzQLC8Hls7c3qw/Eji8WT4d2NosTwE/AiczGOi/C1wIHAp8Bqxp9ju6+bs3Avc06w4DtgKrxv1a2Q7uZlZstvbNvNhs7Zt5sY2jLUWTJHOs2wQ8kuQZ4IWq+qKZsJlpGbAuyXnAHuCMGds2V9UXAEm2AysZvIl8VVVbAKrqp2b7ZcA5Sa5pjj2GwRvQ50NfmbSwzIrUnnmR2jMvWlQO3iZEklMZhP0bYPXf66vqwSSvAFcA7yW5dI7D7wC+Bs5lMKvz+4xtf8xY3sOgzwSY62HJALdV1cYhLkVaVGZFas+8SO2ZF42Cz7xNgCTHA08C66qqZm07rap2VdVDDG6dnwn8DKyYsdsxDGZv9gI3AEv+55SfACclWdOcY0WSpcBG4JYky5r1ZyQ5avgrlBaGWZHaMy9Se+ZFo+Kdt/46orl1vgyYBp4GHpljv9uTXMJgpuYj4DVgLzCdZAewAXgceD7JtcCbwC/znbiq/kxyHfBYkiOA34BLgfUMbuVvax6m/Ra4eqirlIZnVqT2zIvUnnnRyPlTAZIkSZLUA/7bpCRJkiT1gIM3SZIkSeoBB2+SJEmS1AMO3iRJkiSpBxy8SZIkSVIPOHiTJEmSpB5w8CZJkiRJPeDgTZIkSZJ64C/jPKa1DyDrhQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1008x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "n_perspectives = len(kinase_distance_matrices_normalized)\n",
+    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*3.5, 10))\n",
+    "for i, (perspective, matrix) in enumerate(kinase_distance_matrices_normalized.items()):\n",
+    "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
+    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We load here again the input kinase information that we defined in __Talktorial T023__."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -586,202 +899,122 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>BRAF</th>\n",
-       "      <th>CDK2</th>\n",
-       "      <th>EGFR</th>\n",
-       "      <th>ErbB2</th>\n",
-       "      <th>KDR</th>\n",
-       "      <th>LCK</th>\n",
-       "      <th>MET</th>\n",
-       "      <th>p110a</th>\n",
-       "      <th>p38a</th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>BRAF</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>17.156339</td>\n",
-       "      <td>19.515299</td>\n",
-       "      <td>21.382788</td>\n",
-       "      <td>21.089165</td>\n",
-       "      <td>21.582682</td>\n",
-       "      <td>20.297483</td>\n",
-       "      <td>37.610863</td>\n",
-       "      <td>21.731421</td>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>CDK2</th>\n",
-       "      <td>17.156339</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>18.146634</td>\n",
-       "      <td>21.106349</td>\n",
-       "      <td>19.880467</td>\n",
-       "      <td>17.972925</td>\n",
-       "      <td>18.304288</td>\n",
-       "      <td>36.780071</td>\n",
-       "      <td>19.480656</td>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>EGFR</th>\n",
-       "      <td>19.515299</td>\n",
-       "      <td>18.146634</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>16.391994</td>\n",
-       "      <td>17.281650</td>\n",
-       "      <td>16.467225</td>\n",
-       "      <td>17.498395</td>\n",
-       "      <td>36.046207</td>\n",
-       "      <td>22.127617</td>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>ErbB2</th>\n",
-       "      <td>21.382788</td>\n",
-       "      <td>21.106349</td>\n",
-       "      <td>16.391994</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>23.850592</td>\n",
-       "      <td>23.880970</td>\n",
-       "      <td>22.563236</td>\n",
-       "      <td>41.277474</td>\n",
-       "      <td>24.681708</td>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>KDR</th>\n",
-       "      <td>21.089165</td>\n",
-       "      <td>19.880467</td>\n",
-       "      <td>17.281650</td>\n",
-       "      <td>23.850592</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>19.254898</td>\n",
-       "      <td>20.430532</td>\n",
-       "      <td>41.104455</td>\n",
-       "      <td>20.262541</td>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>LCK</th>\n",
-       "      <td>21.582682</td>\n",
-       "      <td>17.972925</td>\n",
-       "      <td>16.467225</td>\n",
-       "      <td>23.880970</td>\n",
-       "      <td>19.254898</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>19.221161</td>\n",
-       "      <td>39.022018</td>\n",
-       "      <td>22.456693</td>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>MET</th>\n",
-       "      <td>20.297483</td>\n",
-       "      <td>18.304288</td>\n",
-       "      <td>17.498395</td>\n",
-       "      <td>22.563236</td>\n",
-       "      <td>20.430532</td>\n",
-       "      <td>19.221161</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>39.414278</td>\n",
-       "      <td>21.982655</td>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p110a</th>\n",
-       "      <td>37.610863</td>\n",
-       "      <td>36.780071</td>\n",
-       "      <td>36.046207</td>\n",
-       "      <td>41.277474</td>\n",
-       "      <td>41.104455</td>\n",
-       "      <td>39.022018</td>\n",
-       "      <td>39.414278</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>38.529748</td>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>p38a</th>\n",
-       "      <td>21.731421</td>\n",
-       "      <td>19.480656</td>\n",
-       "      <td>22.127617</td>\n",
-       "      <td>24.681708</td>\n",
-       "      <td>20.262541</td>\n",
-       "      <td>22.456693</td>\n",
-       "      <td>21.982655</td>\n",
-       "      <td>38.529748</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "            BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
-       "BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
-       "CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
-       "EGFR   19.515299  18.146634   0.000000  16.391994  17.281650  16.467225   \n",
-       "ErbB2  21.382788  21.106349  16.391994   0.000000  23.850592  23.880970   \n",
-       "KDR    21.089165  19.880467  17.281650  23.850592   0.000000  19.254898   \n",
-       "LCK    21.582682  17.972925  16.467225  23.880970  19.254898   0.000000   \n",
-       "MET    20.297483  18.304288  17.498395  22.563236  20.430532  19.221161   \n",
-       "p110a  37.610863  36.780071  36.046207  41.277474  41.104455  39.022018   \n",
-       "p38a   21.731421  19.480656  22.127617  24.681708  20.262541  22.456693   \n",
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
        "\n",
-       "             MET      p110a       p38a  \n",
-       "BRAF   20.297483  37.610863  21.731421  \n",
-       "CDK2   18.304288  36.780071  19.480656  \n",
-       "EGFR   17.498395  36.046207  22.127617  \n",
-       "ErbB2  22.563236  41.277474  24.681708  \n",
-       "KDR    20.430532  41.104455  20.262541  \n",
-       "LCK    19.221161  39.022018  22.456693  \n",
-       "MET     0.000000  39.414278  21.982655  \n",
-       "p110a  39.414278   0.000000  38.529748  \n",
-       "p38a   21.982655  38.529748   0.000000  "
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_distance_matrices[\"kissim\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "27.09695\n",
-      "sequence\n",
-      "1751.566954\n",
-      "kissim\n",
-      "22.258792\n",
-      "ifp\n",
-      "52.39304\n",
-      "ligand-profile\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x720 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "n_perspectives = len(kinase_distance_matrices)\n",
-    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*3.5, 10))\n",
-    "for i, (perspective, matrix) in enumerate(kinase_distance_matrices.items()):\n",
-    "    print(matrix.sum().sum())\n",
-    "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
-    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
+    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
+    "kinase_selection_df"
    ]
   },
   {
@@ -790,7 +1023,8 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges."
+    "- Both \"sequence\" and \"kissim\" group members of the same family close together, while showing dissimilarity to Atypical kinase (PI3K)\n",
+    "- "
    ]
   },
   {
@@ -823,7 +1057,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From 40d02eb392896e79358a0c7e9748efab94505309 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Tue, 28 Sep 2021 18:40:01 +0200
Subject: [PATCH 116/158] T028: Add discussion; finalize code+text

---
 .../talktorial.ipynb                          | 309 +++++++++---------
 1 file changed, 146 insertions(+), 163 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index ca4bf264..103d61ae 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -43,11 +43,16 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Retrieve and preprocess data\n",
-    "* Show kinase coverage\n",
-    "* Compare kinases\n",
-    "* Visualize similarity as kinase matrix\n",
-    "* Visualize similarity as phylogenetic tree"
+    "* Load kinase matrices\n",
+    "* Visualize similarity for example perspective\n",
+    "  * Visualize kinase matrix\n",
+    "  * Visualize similarity as dendrograms\n",
+    "* Visualize similarities from different perspectives\n",
+    "  * Preprocess distance matrices\n",
+    "    * Normalize different matrices\n",
+    "    * Define kinase order\n",
+    "  * Visualize kinase similarities\n",
+    "  * Analyze of results"
    ]
   },
   {
@@ -56,7 +61,8 @@
    "source": [
     "### References\n",
     "\n",
-    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) "
+    "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
+    "* Clustering and dendrograms with `scipy`: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html"
    ]
   },
   {
@@ -107,9 +113,10 @@
    "source": [
     "from pathlib import Path\n",
     "\n",
+    "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
+    "import seaborn as sns\n",
     "from scipy.spatial import distance_matrix, distance\n",
     "from scipy.cluster import hierarchy"
    ]
@@ -209,109 +216,6 @@
     "We check the distances."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'sequence':            EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
-       " EGFR   0.000000  0.059037  0.572938  0.283953  0.344972  0.351553  0.288679   \n",
-       " ErbB2  0.059037  0.000000  0.586362  0.297882  0.345400  0.369692  0.314925   \n",
-       " p110a  0.572938  0.586362  0.000000  0.577295  0.563541  0.576006  0.548664   \n",
-       " KDR    0.283953  0.297882  0.577295  0.000000  0.328732  0.346621  0.312352   \n",
-       " BRAF   0.344972  0.345400  0.563541  0.328732  0.000000  0.353245  0.327067   \n",
-       " CDK2   0.351553  0.369692  0.576006  0.346621  0.353245  0.000000  0.318747   \n",
-       " LCK    0.288679  0.314925  0.548664  0.312352  0.327067  0.318747  0.000000   \n",
-       " MET    0.288742  0.302033  0.606301  0.286194  0.361842  0.343975  0.309121   \n",
-       " p38a   0.355356  0.364827  0.568806  0.346556  0.362088  0.276907  0.337419   \n",
-       " \n",
-       "             MET      p38a  \n",
-       " EGFR   0.288742  0.355356  \n",
-       " ErbB2  0.302033  0.364827  \n",
-       " p110a  0.606301  0.568806  \n",
-       " KDR    0.286194  0.346556  \n",
-       " BRAF   0.361842  0.362088  \n",
-       " CDK2   0.343975  0.276907  \n",
-       " LCK    0.309121  0.337419  \n",
-       " MET    0.000000  0.370645  \n",
-       " p38a   0.370645  0.000000  ,\n",
-       " 'kissim':             BRAF       CDK2       EGFR      ErbB2        KDR        LCK  \\\n",
-       " BRAF    0.000000  17.156339  19.515299  21.382788  21.089165  21.582682   \n",
-       " CDK2   17.156339   0.000000  18.146634  21.106349  19.880467  17.972925   \n",
-       " EGFR   19.515299  18.146634   0.000000  16.391994  17.281650  16.467225   \n",
-       " ErbB2  21.382788  21.106349  16.391994   0.000000  23.850592  23.880970   \n",
-       " KDR    21.089165  19.880467  17.281650  23.850592   0.000000  19.254898   \n",
-       " LCK    21.582682  17.972925  16.467225  23.880970  19.254898   0.000000   \n",
-       " MET    20.297483  18.304288  17.498395  22.563236  20.430532  19.221161   \n",
-       " p110a  37.610863  36.780071  36.046207  41.277474  41.104455  39.022018   \n",
-       " p38a   21.731421  19.480656  22.127617  24.681708  20.262541  22.456693   \n",
-       " \n",
-       "              MET      p110a       p38a  \n",
-       " BRAF   20.297483  37.610863  21.731421  \n",
-       " CDK2   18.304288  36.780071  19.480656  \n",
-       " EGFR   17.498395  36.046207  22.127617  \n",
-       " ErbB2  22.563236  41.277474  24.681708  \n",
-       " KDR    20.430532  41.104455  20.262541  \n",
-       " LCK    19.221161  39.022018  22.456693  \n",
-       " MET     0.000000  39.414278  21.982655  \n",
-       " p110a  39.414278   0.000000  38.529748  \n",
-       " p38a   21.982655  38.529748   0.000000  ,\n",
-       " 'ifp':            BRAF      CDK2      EGFR     ErbB2       KDR       LCK       MET  \\\n",
-       " BRAF   0.000000  0.300000  0.277778  0.393939  0.363636  0.320000  0.368421   \n",
-       " CDK2   0.300000  0.000000  0.111111  0.451613  0.238095  0.190476  0.133333   \n",
-       " EGFR   0.277778  0.111111  0.000000  0.258065  0.238095  0.166667  0.227273   \n",
-       " ErbB2  0.393939  0.451613  0.258065  0.000000  0.406250  0.419355  0.382353   \n",
-       " KDR    0.363636  0.238095  0.238095  0.406250  0.000000  0.058824  0.333333   \n",
-       " LCK    0.320000  0.190476  0.166667  0.419355  0.058824  0.000000  0.250000   \n",
-       " MET    0.368421  0.133333  0.227273  0.382353  0.333333  0.250000  0.000000   \n",
-       " p110a  0.500000  0.347826  0.380952  0.571429  0.533333  0.375000  0.428571   \n",
-       " p38a   0.333333  0.277778  0.173913  0.258065  0.043478  0.190476  0.352941   \n",
-       " \n",
-       "           p110a      p38a  \n",
-       " BRAF   0.500000  0.333333  \n",
-       " CDK2   0.347826  0.277778  \n",
-       " EGFR   0.380952  0.173913  \n",
-       " ErbB2  0.571429  0.258065  \n",
-       " KDR    0.533333  0.043478  \n",
-       " LCK    0.375000  0.190476  \n",
-       " MET    0.428571  0.352941  \n",
-       " p110a  0.000000  0.473684  \n",
-       " p38a   0.473684  0.000000  ,\n",
-       " 'ligand-profile':            EGFR     ErbB2     p110a       KDR      BRAF      CDK2       LCK  \\\n",
-       " EGFR   0.000000  0.434188  0.927374  0.649496  0.542373  0.875000  0.753968   \n",
-       " ErbB2  0.434188  0.000000  1.000000  0.600000  0.750000  0.851852  0.848485   \n",
-       " p110a  0.927374  1.000000  0.000000  0.816092  0.666667  0.666667  1.000000   \n",
-       " KDR    0.649496  0.600000  0.816092  0.000000  0.240458  0.382609  0.566586   \n",
-       " BRAF   0.542373  0.750000  0.666667  0.240458  0.000000  0.923077  0.450000   \n",
-       " CDK2   0.875000  0.851852  0.666667  0.382609  0.923077  0.000000  0.888889   \n",
-       " LCK    0.753968  0.848485  1.000000  0.566586  0.450000  0.888889  0.000000   \n",
-       " MET    0.771739  0.964286  1.000000  0.458824  0.884615  0.909091  0.730159   \n",
-       " p38a   0.653846  0.875000  1.000000  0.483607  0.292683  0.888889  0.500000   \n",
-       " \n",
-       "             MET      p38a  \n",
-       " EGFR   0.771739  0.653846  \n",
-       " ErbB2  0.964286  0.875000  \n",
-       " p110a  1.000000  1.000000  \n",
-       " KDR    0.458824  0.483607  \n",
-       " BRAF   0.884615  0.292683  \n",
-       " CDK2   0.909091  0.888889  \n",
-       " LCK    0.730159  0.500000  \n",
-       " MET    0.000000  0.950000  \n",
-       " p38a   0.950000  0.000000  }"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kinase_distance_matrices"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -323,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -347,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -528,7 +432,7 @@
        "p38a   0.370645  0.000000  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,12 +455,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -568,12 +472,13 @@
     }
    ],
    "source": [
-    "plt.imshow(kinase_distance_matrix)\n",
-    "plt.colorbar(label=\"Kinase similarity\")\n",
-    "plt.title(\"Kinase distance matrix\")\n",
-    "kinase_names = kinase_distance_matrix.columns.to_list()\n",
-    "plt.xticks(ticks=range(0, len(kinase_names)), labels=kinase_names, rotation=45)\n",
-    "plt.yticks(ticks=range(0, len(kinase_names)), labels=kinase_names);"
+    "sns.heatmap(\n",
+    "    kinase_distance_matrix, \n",
+    "    linewidths=0, \n",
+    "    annot=True, \n",
+    "    square=True, cbar_kws={'label': 'Kinase distance'}, \n",
+    "    cmap='viridis', \n",
+    ");"
    ]
   },
   {
@@ -594,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -608,7 +513,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -644,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -694,14 +599,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Normalize different matrices \n",
+    "#### Preprocess distance matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Normalize different matrices \n",
     "\n",
     "We normalize the different matrices to values in $[0, 1]$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -727,7 +639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,21 +653,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Define kinase order"
+    "##### Define kinase order\n",
+    "\n",
+    "Define in which order to display the kinases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinases_names = kinase_distance_matrices_normalized[\"sequence\"].columns"
+    "kinase_names = kinase_distance_matrices_normalized[\"sequence\"].columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -777,14 +691,14 @@
     "        Kinase distance matrix with sorted columns/rows.\n",
     "    \"\"\"\n",
     "    kinase_distance_matrix_df = kinase_distance_matrix_df \\\n",
-    "        .reindex(kinases_names, axis=1) \\\n",
-    "        .reindex(kinases_names, axis=0)\n",
+    "        .reindex(kinase_names, axis=1) \\\n",
+    "        .reindex(kinase_names, axis=0)\n",
     "    return kinase_distance_matrix_df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
@@ -805,31 +719,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
     "def heatmap(distance_matrix, ax=None, title=\"\"):\n",
-    "    \"\"\"TODO\"\"\"\n",
-    "    #plt.imshow(kinase_distance_matrix, vmin=0.0, vmax=1.0)\n",
-    "    ax.imshow(distance_matrix)\n",
-    "    #ax.colorbar(label=\"Kinase similarity\")\n",
-    "    ax.set_title(title)\n",
-    "    kinase_names = distance_matrix.columns.to_list()\n",
-    "    ax.set_xticks(ticks=range(0, len(kinase_names)))\n",
-    "    ax.set_xticklabels(labels=kinase_names, rotation=45)\n",
-    "    ax.set_yticks(ticks=range(0, len(kinase_names)))\n",
-    "    ax.set_yticklabels(labels=kinase_names)"
+    "    \"\"\"\n",
+    "    Generate heatmap from distance matrix.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    distance_matrix : pd.DataFrame\n",
+    "        Distance matrix.\n",
+    "    ax : matplotlib.axes\n",
+    "        Plot axis to use!\n",
+    "    title : str\n",
+    "        Plot title.\n",
+    "    \"\"\"\n",
+    "    sns.heatmap(\n",
+    "        distance_matrix, \n",
+    "        linewidths=0, \n",
+    "        annot=True, \n",
+    "        square=True, \n",
+    "        cmap='viridis', \n",
+    "        ax=ax\n",
+    "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "def dendrogram(distance_matrix, ax=None, title=\"\"):\n",
-    "    \"\"\"TODO\"\"\"\n",
+    "    \"\"\"\n",
+    "    Generate dendrogram from distance matrix.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    distance_matrix : pd.DataFrame\n",
+    "        Distance matrix.\n",
+    "    ax : matplotlib.axes\n",
+    "        Plot axis to use!\n",
+    "    title : str\n",
+    "        Plot title.\n",
+    "    \"\"\"\n",
     "    D = distance_matrix.values\n",
     "    D_condensed = distance.squareform(D)\n",
     "    hclust = hierarchy.linkage(D_condensed, method=\"average\")\n",
@@ -842,14 +777,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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P+4ykuyXdIekvksq5jaRZizx08FeY+YUfAkxyvZh17eFDTuSxL54FrhWzHj144nHMOOt74HoxG7R6c+ZtYURsWXidXDuDpJ7uQX1hXvYNwMvAgXn8v4HJEbE5cDFwSi/yMms7Gj6Mdb/7cYC7XS9mXdPwYaxz8rHgWjHrkYYNY8KxnwXXi9mg1e9uk5KmS/qKpBuAA/Lo90u6UdL/k7RNJ8sMBUYCTwNExF8j4oU8+WZgnf7mZdaOXC9m9XGtmNXP9WI2ePSm8bZiTbfJAwvTXoyIHSLigjw8MiK2Bz4G/G9hvgMl3QbMAlYBrujkfT4E/KGzBCQdJWmapGkvL32hs1nM2kK8vKi222RT66VYK0teeL6ET2TWGPHyotpuky3dtix53vVi7SsWLartNtnaevH2xazp+tNt8sLCtAtr5j0fICL+BoyRNLZjvojYEhgP3Al8rriQpPcDk4H/6SyBiDg7IiZHxOThy5X3dHmzsnXSbbKp9VKslSEjRpbwicwao5Nuky3dtgwZ6Xqx9tVJt8nW1ou3L2ZNV9bdJmsPvUR3wxERpCM9b+0YJ2kX4MvAuyLipZLyMmtHrhez+rhWzOrnejEbBBr1qIADASTtADwTEc90Ms8OwEN5vq2An5JWFk80KCezduV6MauPa8Wsfq4XswFoaC/mXTH3ke7wx4jo9Ba1wNOSbgTGAB8sjD8wr0SWAx4Djsjj/wcYBfxGEsCMiHhXL3Izayu117zhejHrVO01b7hWzLpUe80brhezQafuxltEdHrr2YiYWDO8UxfzTQWmdjFtl3rzMKuC9c//OgAPHXTi3RHxyrN2XC9mr/X6874BwMMHn+BaMevBBt84FYAHT/is68VskGpUt0kzMzMzMzMrkRtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBSo/5qJ7NNh8Wl1w5rpRYKy2nUuIAHLruW0qLdc6MG0qJM39pb24q2r2xyy0uLdb0xaNKi/XWFUoLxf2Lah+V03ebTpjzz+JF5a2w/mYj4+RLNykl1ntHLSglDsDua21ZWqyrZv2rlDi3vlTe+nDB0vJ+lBsOe7q0WMPKW93xzNJO72PVJ5uvN6vltQIwcqM14w0/OKKUWDdveXEpcaDcejlt+k2lxLn35TVKiQPwpX/tV1qs0SNfLC3WG8bNLS3WG8fMKC3WZyf9uS3qZdLmw+O835fzO9h8eHnrzDLr5W13LCwlzu8e26yUOGUrcz3Vroas+WBb1EtZfObNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK6HPjTdKxkh6UFJLGFcZvIukmSS9JOq5mmT0k3ZeX+2J/EjerEteLWf1cL2b1ca2YDT79OfP2d2AX4NGa8fOATwCnFkdKGgL8ENgTmAQcLGlSP97frEpcL2b1c72Y1ce1YjbI9HgPeUkTgT8CtwBbAfcDh0XEv/P018wfEU8AT0jauybUNsCDEfFwXu4CYF/gbkkfAY4ChgMPAh+IiBf6/rHMWmPWzMV85LB5bL7lcO65axHA6yWNcL2YLWvWzMV89PCn2GzLYQCTJF2Mty9my5g3ayFnH/Vv1tt8JWbd8yx422I2aNV75m1j4OyI2BxYAHysD++1NjCzMPxYHgdwSUS8KSK2AO4BPtSH+GZt4ZGHlvC+Q0Zw+dWrASzF9WLWpekPLWb/g0cC3I23L2Zd+s8jL/DmA9bmuMveDN62mA1a9TbeZkbE3/PfvwJ26MN7dfZo2I4n4v6XpOsl3QkcCryh0wDSUZKmSZo2b97SPqRg1nhrrrUcb3zT8I7Bp2hBvRRrZcG88h6sbla28WsNYas3Ld8x2JLtS7FeFj/jEw3WnsaOX57XvXFsx2BLti3w2nqZ730xs6art/EWPQzX4zFg3cLwOsDs/PdU4NiI2Az4GrBCp0lEnB0RkyNi8iqr+EaZ1qa0zLax6fVSrJUxq/TYO9qsZZYtl9bWy9CVRvTh7c0ar7ZrJG2wLzbW+2JmTVdv1U2QtF3++2Dghj68163AhpJeJ2k4cBDwuzxtNDBH0jDS0R6zypozawn//ufLHYOr4Hox69KcWUu4/Z8vdQx6+2LWhafnvMj02+Z3DHrbYjZI1dt4uwc4XNIdpBXGjyV9QtJjpKM2d0j6GYCk8Xn8Z4ATJD0maUxELAaOBf6U410UEXfl+CeSbohyDXBvWR/OrBXW32Aol1+8kH13+w+kmwK5Xsy68PoNhvK7374A6c533r6YdWGN149k2mVzOHW/m8HbFrNBq97+VEsj4piacT/Ir9eIiLmklcgyIuIq4KpOxv8Y+HGduZi1NS0HJ31nJQA2nTDnoXy3LteLWSe0HJz47ZX5za9fuDsi3ptHu17Mamg52P+kTQH47KQ/e9tiNki5s7KZmZmZmVkF9HjmLSKmA//V+FTMqm/tdYdyxZ9Xa3UaZpWw9rpDufSaNVqdhlnbW2XtFfnc77breUYzG/B85s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKqCyT+99YN549jzv06XEWjqsL8+57Nz/zTi1tFgfmbBDKXHu/8k2pcQB0ApLSovFgvJ+fkNfKO84xOLxL/c8U92+VGKsvpn11Cqc+Iv3lxLrcxu9WEocgAdm/ay0WHut/cZS4jxwxptLiQMw5KVlnz7dVyqx7JYOL29916dHBHfpuDKD9dnSZ4fy7A2rlxJrrxX3KiUOwGnTf1tarM9MLOfaqdmXTiolDsCwO0aVFmuNi54tLdbfv7p+abGe+mJ5nxH+XGKsvpu+cFWOuOPwUmI9+9yKpcQBOPKOm0qLdd3m5eS1/D6rlBIH4OkNy9t/et3jHyot1vDZw0uLtXRYaaGAz5YZrOV85s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswootfEm6bnC33tJekDSBEknSZol6bY87hJJkwrzXivpPkm3S7pV0pZl5mXWblwrZvVzvZjVz/ViNrA15MybpJ2BM4E9ImJGHv39iNgyIjYELgT+T9JqhcUOjYgtgB8B/9OIvMzajWvFrH6uF7P6uV7MBqbSG2+SdgTOAfaOiIc6myciLgSuBg7pZPJNwNpl52XWblwrZvVzvZjVz/ViNnCV/Zy35YHLgZ0i4t4e5v0XsEkn4/cALis5L7N241oxq5/rxax+rhezAazsxtsi4EbgQ8Ane5i39gm2v5Y0EhgCdPrEXUlHAUcBDB27cv8yNWut5tXKGNeKVZ7rxax+TauXYauN6V+mZtZrZXebXAq8D3iTpON7mHcr4J7C8KHA64DzgB92tkBEnB0RkyNi8pCRI8vI16xVXCtm9WtavQwd4Xqxymtevaw0oox8zawXSr/mLSJeAN4JHCrpQ53NI+m9wG7A+TXLLgJOAN4sadOyczNrJ64Vs/q5Xszq53oxG7jK7jYJQETMk7QH8DdJT+bRn5b0fmAk8P+Ad0TEfzpZdqGk7wHHkU75mw1YrhWz+rlezOrnejEbmEptvEXEqMLfM0mn3iFdOHtSN8vtVDP8vTLzMms3rhWz+rlezOrnejEb2BrynDczMzMzMzMrlxtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVQEMeFdAMMSxYNP7lVqexjPlLy/tK7//JNqXE2eiYf5QSB+DBc7cqLdbS4VFerHELS4s10I5oxHKwaHRJ3/WCYeXEAW59qbz//wfOeHMpcTb85M2lxAF47PjtS4u1cK0lpcVa7kWVFmvpiuX9H7aLpUPhpZXL+VwLF5dXL/e+vEZpsWZfOqmUOGu9++5S4gA88MNtS4t1z5dWLS3W2DHPlRbr3pPL+z/k/eWF6o8lLw/h6dkrlRJrpy3v6XmmOv3usc1Ki7X8PquUEmeFK8rbF/vlI7eUFuvAX32qtFinHTyltFjf+OoRpcV6uLRI7WGg7aeamZmZmZkNSG68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkF9LnxJmmJpNsk3S7pX5K2z+MnSlqYp90t6ZeShhWWGyrpSUnfqYl3raT78nK3Sdq/7x/LrH24Vszq53oxq5/rxWzw6c+Zt4URsWVEbAF8CSiuAB6KiC2BzYB1gPcVpu0G3Ae8T1Ltbc8OzTG3jIiL+5GbWTtxrZjVz/ViVj/Xi9kgU1a3yTHA07UjI2IJ8A9g7cLog4EzgBlAOff3NqsO14pZ/VwvZvVzvZgNAv15KNmKkm4DVgDWBN5RO4OkFYBtgU/m4RWBnYGjgbGklcdNhUV+LanjgV07R8RT/cjPrF24Vszq53oxq5/rxWyQKaPb5CbAHsAvC6fe188rk6eAGRFxRx7/TuCvEfEC8Fvg3ZKGFGIWT9Uvs7KQdJSkaZKmLXn2+X6kbtZULa2Vpc+7VqxSWrttcb1YtbS2Xp5zvZg1WyndJiPiJmAcsFoe1dHPegPgzZLelccfDOwiaTrwT2BV4O29eJ+zI2JyREweMnpkGambNVUramW5ka4Vq6aWbFtcL1ZRLamXUa4Xs2YrpfEmaRNgCOnozisiYg7wReBLksYAOwATImJiREwEPk5aiZgNCq4Vs/q5Xszq53oxGxz603hbseNWssCFwOH5othalwEjSH2t/y8iXipMuxx4l6Tl+5GHWbtzrZjVz/ViVj/Xi9kg0+cblkTEkC7GTwf+qzAcwBZdzDuPV0/v79TXXMzamWvFrH6uF7P6uV7MBp+yHhVgZmZmZmZmDeTGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVQJ9vWNJyAg2JUkINW35xKXEAxi5XXiyt0NkNo3rvwXO3KiUOwAYf+HdpsR744balxVryUqfXbPfJ8FnDS4vVDhQw9EX1PGMdlqxYThyABUtXKC3WkJfKyeux47cvJQ7AOt++sbRYM75aXl5R4iG7pYvLWQe3k+WfCSZe+VLPM9Zh610eLSUOwJf+tV9psYbdMaqUOGWuwzf8+C2lxXryqO1KizV61ujSYj27U3V3ubqy7ph5nLLzeaXEmr9kRClxAO6Zt0ZpsZ7esJz/t18+Ut5v/AuvK6/2znjwf0uLNXq5haXFOulrU0qLtfevSgvVFnzmzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrAL61HiTNF7SBZIeknS3pKskbSRpoaR/S7pH0j8kHV5Y5ghJZ+W/l5P0C0n/K2mkpCsl3SvpLkknl/XhzNrAUNeKWX28bTGrn+vFbHDq9f1PJQm4FPhFRByUx20JrAE8FBFb5XGvBy6RtFxETKlZ/ifAMOBIYAXg1Ij4q6ThwF8k7RkRf+jfRzNrrYgA2AD4iWvFrC7etpjVz/ViNgj15czb24FFEfGTjhERcRswszhTRDwMfAb4RM3yZwCrAodFxNKIeCEi/pqXeRn4F7BOH/IyaysLH34QIFwrZnUZjbctZvVyvZgNUn1pvP0X8M865/0XsElh+BBga+CgiFjmadaSxgL7AH/pQ15mbeXlx+cCvFDn7K4VG+xWxNsWs3q5XswGqUbfsEQ1w/8C1gO2WWZGaShwPvCDfKRo2WDSUZKmSZq2ZMHzpSdr1kKNq5XnXSs24DSsXl5+2fViA07D6uWZecu0/cyswfrSeLuLdMSmHlsB9xSG7wXeB1wo6Q01854NPBARp3cVLCLOjojJETF5yJiRvUjZrPmGr7EGwIg6Z29crYx0rVglLKQNti3Dh7terBLaol5WWqXXt04ws37qS+Pt/4DlJX2kY4SkN5GO4lAYNxE4FTizOD4ibgSOAa6UNCHP+01gJeBTfcjHrC2t+PoNIV0X7lox69mzeNtiVi/Xi9kg1etDJhERkt4NnC7pi8CLwHRSsa8v6d+kuxY9C5xZvLtRIcbvJa0G/FHSnsCXSUeC/pVugMRZEfGzvn0ks/aQf8sPAbu6Vszq4m2LWf1cL2aDUJ/Od0fEbNIp91ordrPMVGBqYXgK0LEyqe2PbTZQLIoI14pZHbxtMauf68VscGr0DUvMzMzMzMysBG68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFVPcBHS+LIbOXLyXUopHDSokDMH3xqNJisaCc/56lw6OUOAAP/HDb0mJt+PFbSov1wBlvLi3Wy+MXlRarHWgpDFlYUrDXv1xSINhw2NOlxdKScuIsXKukQMCMr25fWqwJX7uxtFiPfGe70mItWnngPaB3yTDx3NrDS4n1yPOrlhIHYPTIF0uLtcZFz5YS554vlff5njyqvN/luLNvKi1WmXW80gPlbYvbxfzFI7jiqS1LibX9Sg+WEgfg5i0vLi3W6x7/UClxDvzVp0qJA3DGg/9bWqzvb7BpabFGXz+utFj/nrZBabHguBJjtZ7PvJmZmZmZmVWAG29mZmZmZmYV4MabmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVVAQxpvkp7rYvxhkv6fpLsk3S3puDx+qqT989+rSPq3pCMbkZtZu3G9mNXHtWJWP9eL2cDUtDNvkvYEPgXsFhFvAN4IPFMzz0rAn4CzI2JKs3IzazeuF7P6uFbM6ud6Mau+Zj7n7UvAcRExGyAiXgTOKUwfBfwBOC8iftzEvMzakevFrD6uFbP6uV7MKq6Z17z9F/DPbqafBtwQEd/vagZJR0maJmna0uefLz1BszbSr3op1sriF1wrNqCVum1Z/JLrxQa0UuvlxfnlPTzezOrTTjcs+T9gX0mrdzVDRJwdEZMjYvJyI0c2MTWzttNtvRRrZegI14oNar3atgxd3vVig1qv6mWFsSs0MTUzg+Y23u4Ctu5m+gXAj4GrJI1uTkpmbcv1YlYf14pZ/VwvZhXXzMbbd4BTJI0HkLS8pE8UZ4iI04G/AJdKGt7E3MzajevFrD6uFbP6uV7MKq5RjbcRkh4rvD4TEVcBPwT+LOkuUp/rZW6YEhFfAGYC50pqp26dZo3iejGrj2vFrH6uF7MBqCF3m4yITgs933J2mdvORsQRNcN+rogNGq4Xs/q4Vszq53oxG5h8NMXMzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpoyA1LqkaLVFqst5b4vMqhL5TTtl46bmEpcQCWvDSktFgPnPHm0mJt+MmbS4s1/cLNS4vVDkKwdFhJsRaW9/8/rLyyY+nwKCXOci+Wl1Tntwrom0e+s11psV73pZtKi3X/j7cpLVa7WDocnl+7nP+8mc+OLSUOwBvGzS0t1t+/un4pccaOea6UOACjZ5X3SLEZX92+tFgTvnZjabFm/Gaz0mLx0/JC9cdyClYcsqiUWPuMeqiUOMnI0iINn13O0xJOO3iZe8T02ejlytuvG339uNJiPbvjk6XF+uuMy0qL9bpPlRaqLfjMm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQWU2niTFJLOLQwPlfQfSb/Pw0fk4dsKry0Kf8+T9Ej++89l5mbWblwvZvVxrZjVz/ViNrCV/aiA54H/krRiRCwEdgVm1cxzYUQcWzNuSwBJU4HfR8TFJedl1o5cL2b1ca2Y1c/1YjaANaLb5B+AvfPfBwPnN+A9zAYK14tZfVwrZvVzvZgNUI1ovF0AHCRpBWBz4Jaa6QfWnKpfsd7Ako6SNE3StKXPP19mzmat0pB6KdbKkhdcKzYgNGXb4nqxAaIp9bLw6RfLzNnM6lB2t0ki4g5JE0lHeq7qZJbOTtXXG/ts4GyA5ddZN/qcpFmbaFS9FGtlhbVcK1Z9zdq2rLim68Wqr1n1svqkVV0vZk1WeuMt+x1wKrATsGqD3sNsoHC9mNXHtWJWP9eL2QDUqMbb/wLPRMSdknZq0HuYDRSuF7P6uFbM6ud6MRuAGvKct4h4LCLO6GJybT/r7RuRg1lVuF7M6uNaMauf68VsYCr1zFtEjOpk3LXAtfnvqcDUbpY/osx8zNqZ68WsPq4Vs/q5XswGtoaceTMzMzMzM7NyufFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXQqEcFNN7wpSxd58VSQg0ZsrSUOAD3L3q+tFiLx79cSpwyW+jDZw0vLdbL4xeVFmv6hZuXFmvigXeUFuvB0iL1XQwLFq6zuJRYQ0eX93/2zNIhpcWipMfELl2xvOfNLl1cXqxFK5fz/wdw/4+3KS3WRh/9R2mxZpQWqX+WWwQj5pbzfzdv2uqlxAF443v+WVqsp764zP0s+uTek9coJQ7AszuVtzuy0gPl1d6M32xWWqwJB9xZWqz7S4vUP88tGs5Nc9crJdaNY8v7Pe038rnSYi0dVk6cb3z1iHICASd9bUppsf49bYPSYv11xmWlxfrIhB1KiwUXlxir9XzmzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrAJKb7xJ+rmk2yXdIeliSaPy+JUkXZGn3SXpyLLf26xqXC9m9XO9mNXP9WI2MDXizNunI2KLiNicdPfnY/P4jwN3R8QWwE7A9ySVd995s2pyvZjVz/ViVj/Xi9kA1OfGm6SJku6V9IvCUZ0REbEgTxewIq8+gSmA0Xn8KGAesDjPe5mkf+YjQEf16xOZtSHXi1n9XC9m9XO9mA0u/T3ztjFwdj6qswD4GICkKcBcYBPgzDzvWcCmwGzgTuCTEdHxdOwPRsTWwGTgE5JW7ezNJB0laZqkaUueLe9h2GZN0rR6eU2tPOdasUpqSb0sftH1YpXUmnp55oVGfiYz60R/G28zI+Lv+e9fATsARMSRwFrAPcCBefruwG15/JbAWZLG5GmfkHQ7cDOwLrBhZ28WEWdHxOSImDxk9Mh+pm7WdE2rl9fUyijXilVSS+pl6AquF6uk1tTLSiMa82nMrEv9bbxFV8MRsQS4EHhvHnUkcEkkDwKPAJtI2gnYBdgu97/+N7BCP/Mya0euF7P6uV7M6ud6MRsk+tt4myBpu/z3wcANkjaAV/pY7wPcm6fPAHbO09YgneJ/GFgJeDoiXpC0CfDmfuZk1q5cL2b1c72Y1c/1YjZIDO3n8vcAh0v6KfAA8GPgmnz6XcDtwEfzvN8Apkq6M0/7QkQ8KemPwDGS7gDuI52qNxuIXC9m9XO9mNXP9WI2SPS38bY0Io6pGfeWzmaMiNnAbp2MfwnYs595mFWB68Wsfq4Xs/q5XswGiUY8583MzMzMzMxK1uczbxExHfiv8lIxG7hcL2b1c72Y1c/1Yja4+MybmZmZmZlZBbjxZmZmZmZmVgGKqH00SDVI+g/waA+zjQOeLOktHav5cQZCrPUiYrWS3rNP6qwVqP533cxY7ZhT1WO1vFag8vXSjjk5VmNiDdZ6qfL/mWOVH6sy+2JlqmzjrR6SpkXEZMdqbqx2zKmdY7WLdv1+2jFWO+Y0WGK1i3b8ftoxJ8dqbax24d+4YzUi1kCslXq426SZmZmZmVkFuPFmZmZmZmZWAQO98Xa2Y7UkVjvm1M6x2kW7fj/tGKsdcxossdpFO34/7ZiTY7U2Vrvwb9yxGhFrINZKjwb0NW9mZmZmZmYDxUA/82ZmZmZmZjYguPFmg5KkzVqdg1lVuF7M6ud6MauPa6VvBlXjTZJanUOVSHqjpLa4Baukt0jaNf/dr/9HSbsDF0japJTkBijXS/3aqVbA9dJsrpXeaad6KbNWcgzXSw9cL70zUOvFtdJ3g6rxBgwvM5ik9SWtIanfD/7rTxFIWkHSsP7mUBNzD+BXwEv9jLONpA9LenNfvqfC9/IlYHWA6MeFmnllcSbw8Yi4V9Jgq4HeKK1eyqyVHK9t6qWsWsmxXC/V1LbblhzP9bLssqXWSo7peqmP66X+eAOyXlwr/TNovixJuwHnSPqmpC1KiLcHcAnwHeAiSRv1I5YiIiTtIOmQ/G9dK7dCHudIOqKvOdTE3A04GfhYRNwpaWwf4+wN/AJ4E/AF4F15fG9WjMvnf1+k/w3J3XM+DwKPSxoeEUv7EzPHVf53JUkr9DdeOyizXsqslRyvbeqlrFrJsQZ8vbhW6orneqkvVn/rpbRaye/reqmD66VXuQzIevG+WAkiYsC/gD2BfwLvBy4DTgeG9SPe9sD9wFuAlYAvA9/I09THmPsA/wJOAC4HjgdG9LDM7nmZ9wOHAv8G3trP72oU8DfgB3l4XeAOYJtextkIuBvYNg+/D7gFGNWLGJsB789/nwG8N/+9PLBc/nu5OmNtCUwH3gF8DjgLeBswpJ/fV8cdW/fLv63LgV2AFZr5Gy/zVWa9NKJW8rItr5eyaiUvO+DrxbVSVzzXS32x+lUvZdZKntf1Ut9ncr3Un8OArJdG1Erx/3sg1Uu3n7fVCTT8A8LrgQWkIxcAKwN/Bd7e2X98Tz8O0tnK9wOHFsa/B/hNP3JcL//Q1spF9f+Ac4CTgBW7WOZ1wEPAEYVxn639XH3MZwfgGuCLwE3Af/dy+Q2AEcBBwNDC+N8Bb+hFnHfn72Uf0tGsQzqZp8eCB7YDtgD+Kw+vAHyLdMr+rSWsNHYFbgXWAH4NPAAcDCzfrN95Wa+y6qVRtZJjtE299LdWcoxBUy+ulS5juV7qj9HveimrVvJ8rpf6Povrpfe5DKh6aWSt5HgDpl56/KytTqChHy4diRlLOmpyPrBVHn8J8Bvgh8CBwLg64y2f/x0KrFkYvw7wx8Jwt0cz8srhfcAbclENBTYBtiEdrdkA+GBeaZxMF0c0gB8AvwQ2zsNXAdNyUX4W2K4X39XImuG3ANcCvy6Mq2elukFeIY8hH1HrKEjgz8BO+e/NgNW6iLEDcGT+Xt4LTCEdqbkfuJR0NOp84Dzg1K6+nxxrN2AmMDkPdxwlGg58M680diiu1Or8Xa1bGP5YXintC/wdOJbUHeDDwEqtroNW1EtZtdKO9VJWrQyGenGtNHfb4nrpvl7KrBXXi+vF9eJ9sVa9Wp5Awz5YOkJwJTCS1Ar/FPBb0oWffyOd4v5BHncDPbTM8w/vN8BxwJY1014H3Jv/PjIXcKddAfJKYRbpSM7fSP2035qnvRM4ufB+PyYfoSgsP5TCCin/6C8Cfg78BXg76SjJ1fk91qjju9olf1dHd7xH/nf7XPhHA6Pr/N7XBq7L33tHnOH53/OBjXNh/amLlcWeecVwNK+u4PfOn+d7wP55ngOA/wZe300ue5OOwrwlD48HxhamDwe+RlohbV/n55tE6m5wHXB+YfzKpJXZG/Lwb3POPX7/7fAqs17KqpV2rJcya2Wg14trpbnbFtdL9/VSZq24Xlwvrhfvi7W0rlqdQEM+VFoZ3AjsXRi3Aukizbm82u+3o+XfU0HtQeqnfUxe4RxbM31l4GLS0Zlb6OZUdC6E4/PfbyQdGTiHdMRhQ2AJcArp6EZtd4KOizxvJq2U9svjPwfcA2xemHcUPfTRLsz7PuA/+T3PJK1cV8nT3kw6bf+Z7uLllcBE0tG1v9HJ0RPS6fGrSaf/N+tk+htJp7mX6SdOOir3v3mF0WMfZmBN4Dbg9Dw8HrgPOKjm/344qV/7+DpirpW/+/fl4X8A6xem/yx/xh1IRxS3bXUtNLteyqyVdqyXMmplMNSLa6X52xbXS9f1UmatuF5cL64X74u1+tXyBEr/QKk/7VLyKWrSaeNTSEdIxgGfBi4Edigs0+Up6Pwjng/sk4cPILX0DwMOKMz3KOm0+qQe8vtv4C+F4fXyCuN7eXhyXqG8rWa53YG7SEdxtgdOzCuOz+Xp3wTOzfn2eOEoqc94xwWeY3K8PUinxr+WP8/7gFWBTUmn/sd2EWsl4NvAj0in+C+kcLSr8D4/BRYBG3QR513AGTXLDClM3xf4ff4/GNrV/xv59DhpBf490srub+SjWYX5hvfyt7UhqR/1qDx8J6m7xznABGBn0pGj/0dhY9XOrzLrpexaaZd6KbNWBku9uFa6rxXXS3Prpaxacb24Xlwv3hdrh1fLE2jIh0qn3s8HViP1Ff50YdpqpOdUnEcdFzGS7oIzFfgIqU/0v0hHRE4gnZr9eC6+s8l9nTuJoZrh/wW+XBjejHTaevsu5n8LMBvYsTBuKOni3F8Cu+dxZ+fYvSqEvOw3gcvy35OAl3Mh3JVXHN3eEYrUP/yruWDuJ11ge3DOfY/8Pa5L96fWdyV1OViOwh2M8ve7a/7MewNrdRNjd9KRuR3z8Afy8AU18+0L7NTVSqeL2GuSVtDnkI7q/YLULeEC4OzCfOu0ugZaUS9l1EoV6qW/tTIY6sW10pxti+ulvnopo1ZcL64X10vrty0DuV569TtpdQKlfZDUd3nbwvCfSEd9jsrDxaMG48inoruJ13G0QaR+yD8EHufVIyuj8krkW7Xxa+KsT+p7vWrHfKRbpP4Q+FJhvu9S0wUgjx8GfJR09OStFI7i5FgnA1MK41av47t6O2mldziFPuOkIxk/IV3guXce9yZgky7irEm6Be16ubA3JK2wZpIuFv1Bjnkdqf/36zr7jgvDW+VC3CwPFz/rieRT5D18tv8mPYfkGmCPPO4DpP7qH8jDB5BO229YR7zX5e/pbaTb4m6WVzSXARML8/0D2LTVddCKeimrVtqxXsqqlcFQL66V5m5bXC9d10sjasX14npxvXhfrF1eQxkA8oMRvw7cLOmliLgtInaX9HvS6d+zI2KJpGERsSginuwhniL/CvK/f5b0NDA6Tx8VEc9JWgXYID/AcVEncV5HulvRLcAISRdFxH8k3Uwq9AMl/YF01OAI0mny4vJvB/YinQJfnnRkZzXSBZjkz3QN8BlJQyNicUQ80cNn2520grkix369pEdIt/D9P9Jp+oMi4ob8fd3aRZy9Sf3Wl+bPvoTUheBrwBOkuzZ9J3/eYTnf2u9oCLC48P/yb0m/A/4oaU9Sn+uFkj5AOmq0T3efLTufdEvimcDROfa5+dmN2+TPvwHw7oh4oIfvahNSv/p/ky7M3TAifpan7QBsKukx0kpyGPBcHfm1XJn1Ulat5Hnbql7KqpUca0DXi2uluduWPK/rpet6aUStgOulW64X10uB98UaqdWtx/6+SD/6O+niNqyk26Fe3Yt4a/DqLVWPIa2IvkU66rMD6QGFnwGOIl0w2WW/atIRneNJp4XPIB2JWC1PG0q6cPekPM87C8t1HGl6L3BK/nslUh/x75MfkJjHH0s6QlPPbfw3JxV3x92UtiFdsDoxD68J3NuRS1cxefV0+NtJdzEaTyqs+4EVSQX0bdJRnwldxBhHuii342Lc5QvTjid1t/gD6a5N99D9TWA2J18cTDri9F3SBau7kVeMedqHgeupuWNUFzHXJPXN7jhC9Mmc1xjShbX75d/WL/Pvb79W10Kz66XMWmm3eimrVgZDvbhW6o7nemlCvZRZK64X14vrxfti7fhqeQJ9TjwV8CjS6es9aqZ9H/h+YfhWch/iHuKtQTql/O5c4P8o/DAuycW9Lek2tY919yPOMVckP9QxF37HCqPTu+kUVhIdK6wPAz8rTB9TWGG8FTgUuKOnPArLj8qfo/i8kD+S+kX/N7Al6ajK2XRxFyFSl4jiSqfYBeJXOb5Iz9o4ge7vTLgPaQW1ch4urjRen7/rnehihzbPt2rOZwbp7kdvIq2If0haqR1EusB3v47vsM7vamzxd0U6WncD6SLfqaSV5ObAjsCbiv9/7fgqs14aUSvtVi9l1MpgqRfXSvO3La6X7uuljFpxvbheXC/eF2vXV8sT6PcHSP952xSGP0hq0V/Pay9cXK+HOB2F+j7SEZCppNO5HdMvAS7Kf29H4cGANXFGdRY3/70/qd/xIfl9Tupk+Y6jICvlgrqwZvrKubivAB6mvjsqvdKvnHSk4lekld4puRC+SOp3fR/pVHdXK7OtSEdxLiOtZDoe9tjxwMwN8nfXcTSrntvI7gk8VLvSIB1F6vGz5XnfkVcaXyf1xz4P+B/g/Xn6kaQNy8g64w2tGd4F+HrH74i0Qj2k1b/9VtVLWbXSjvVSVq0MlnpxrTRv2+J66V29lFErrhfXi+vF+2Lt+Gp5An1KGrYm3Z51aC6azxembVf4+1rgjXXEK16MuQXp4Y+3kPojr5THr0jq29zlnYOA/wKeAg7vJv4OpKMPC8jPuegkzrtIp38PIfVjXod09KHjItv1Sbev7fYhonnevUhHrS7i1Qt6R+Wierb4efIKqtO785DuLHQ36QLT15FWqJcVpisvfzWwdi//P2tXGh8jXajb7VHRmhg7k26pu2r+bq4jXZw7nNQ/vq6jPIV4b8krmh3IR+sK004BPtrqOujFZymtXsqqlWjDeimrVmKQ1YtrxfXSrvVSRq2E68X14nrxvlibvVqeQK8TTj+uf5LvzEM6+nAbcETNfO8hnWIf14vYbwWuyX/vTXqi/QdycR4I3N7VD490Cv0C0gMi7wYOrZnecTRpO2Axr95BqKvrynYjHcFYmH/4/84rkL+SjlyMquPz7EE6vbwv6TT8ubzabaDjqM959PwYgLflAi7eQWoUqY/xZYXPdgTpzkIr9fH/9Q5SH/Z7KNx5qRcxOvrcdzz743W9XL7jc7w55zI1f8ZfdfyO8u/tDgq3CW7nV6PqpT+1Em1YL2XVSgySenGtNG/bEq6XftVLGbUSrhfXi+ulKbVS8/8zYOql7FfLE+jlf+gepFue7paHx5GeIr9PLtAv53mOzsXd0ynsrYE3578PzIV4aGH6e0mn/K8mncru8uJKUh/sjhXAzsAjHbF47ZGedwD75r/V1coiT9+RdERoHOluQJuS+oL3WAjAKnll8+48vA0wh/TgxrPzuOGko07n9hDrM8An89/Fhz2OJN2ZaQrwftJRpc368f+7d855i37E2CuvcFYpjOvN80O2Id1Gd9s8PJH03JVvkLpZPEzhYuZ2fpVZL2XWSp6/beqlzFrJ8w6KenGtdBvP9dJG9VJGreQ4rpdwvbheGlsrhe9pQNRLI14tT6AX/5EdBbBfHt4gF/MOeXhz0pGLn+Qfb0/F3XHUqOPOOBNJrfj/rZnv3cBV5H7DXcRa5rkipCM1D/PqHXI2pnDkqacVRWG+vUhPie/yRgbdLLs36QjRFqQjMF8jPZjxFvKDEnPBd/qgRV49+nEm8M3iuMI8GwOXAy/Qi+sIusl5RAkx9iU9wHO53qws8rK7km6ze3weHpr/D84EVic/uLPdX2XWS5m1kudru3rpb6105Jj/HRT14lpxvVSpXsqolRzH9eJ6cb3UF2PQ74s16tXyBHr5n7l3/iFsngvguDy+4wnwHT/unvpC1x41Wp30HIx1SE+xP6Fm/nq6KHb0y307r16QugfpKNQpeWW0VR8/97658Jfrw7J75BXtF4ufh3REY9U6Y7yD1O1h647vu/CdfzSvbHvduGzwb6XH/7Mevu8HgYPz8FtJG5exrf5cvfwc/a6XRtRKnq/t6qWMWsnLDJp6ca10+htyvbheuvuuXS+v/Q25XgZwvfS1VgrfdeXrpSHfa6sT6MN/5msKgFfvsLMnsEv+u7tT37VHjdYn9UXeOQ+/jtRv++Q6cumsX+5UUp/o8Xna8cX368fn7k8B7Eq6e9HYPHwkcCMwus7lR5Kef3JKxwojjz8wr8S6vLC2qi9S948FpP7sU6jo6fn+1EuZtVJ8n3aul/7WSl5mUNWLa+WV5V0vrpd6Pq/rJVwvedj10vPnHRD1Uvr30uoE+vifuSvpmRRj8/ARpP69dV0UybJHjT6bx3esfCaSugH0eIEt3ffLnQj8ifwQx+5WZE34zvYkne7/GOmBhz0+HLFm+bWBr5DuHHRq/oz39jZOlV6ku0z9C/hMx/9fK/8P+/E5+lwvZdZKnr/t66W/tZJjDKp6ca28srzrxfVSz+d1vYTrxfVS9+cdEPVS6nfS6gT68Z+5J+luNh/NxV3Xg6oLy3d11OidwCTqPCVO1/1yzyZdNDshj2/5jy1/tpd7+10Vll+RdLvWk0gXIm/U6t9BE76z3UgPAX1Pq3Pp5+foc72UVSt5mUrUS39rJccYVPXiWnlleddL32K4Xir4cr306TtzvfT+8w6Ieint+2h1Av38z+xvY6Sro0Y9Pj+tJs6+LNsv91Z60Y+5id9ZKRdtD6ZX/p306jfRjq/+1EtZtZKXrUS9uFb69J0N+lopfA+uF7/q+Z24XlwvftX/O6l8vZTx6ugjXFmSRkTEC/1Yfk9S3+EfkR7CeExE3NWHOPuQ+lb/gXSnn99GxO/7mpdZI/SnXsqqlRzL9WJtrV22LTmW68XamuvFrHkq33grg6R3ApeQ7j7Up5VFjvMu0insX0XEaZIEEP6SbYAoq1ZyLNeLDWiuF7P6uV7M6uPGW9bfo0aFOLsB/wt8IiIu6X9mZu2lrFrJsVwvNqC5Xszq53ox65kbbw0gaVfgoYh4uNW5mLU714tZ/VwvZvVzvdhA5MabmZmZmZlZBSzX6gTMzMzMzMysZ268mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZDRqSpkvapZPxd0naqR9x/yDp8P7kZlZ1HXWkZIqkpyX9o9V5mdXq2BZIOl7Sz1rw/hMlhaShTXq/NST9TdKzkr5X/NzNzsX6z/9RZjboRcQb+rn8nmXlYlZVHXUkaUdgV2CdiHi+tVmZdS0ivt3qHJrkKOBJYExERKuTsf7xmTczMzMr03rAdDfczBovn+nuaX9+PeBuN9wGBjfe2pikL0ialU9z3ydpZ0nLSfqipIckPSXpIkmrFJb5gKRH87QvF7uJSZoq6ZuFeXeS9FhheC1Jv5X0H0mPSPpEYdpJ+b1+mfO5S9LkwvR1JV2Sl31K0lmFaR+UdE/uQvMnSes18nszq4ekTfLv/KCaOtlG0jRJCyQ9Lum0PH4FSb/Kv+/5km6VtEaedq2kD+e/j5D0d0nfz/M9LGn7PH6mpCfcxdIGolxHRwM/A7aT9Jykr3Vsa3JXrSfzfIe2Ol+zvG/zq8LwYYV9qBM72TbclNfrcySdJWl4YdmQdIykB/L+zg8lKU8bIunU/Pt/GNi7h7w6tiNnSnpG0r2Sdi5Mv1bStyT9HXgBeH3eztya579V0vZ53qnA4cDnc03uUvu5a957JUk/z59xlqRvShrS1+/YyufGW5uStDFwLPCmiBgN7A5MBz4B7Ae8DVgLeBr4YV5mEvBj4AN52qrAOnW+33LAFcDtwNrAzsCnJO1emO1dwAXAWOB3wFl52SHA74FHgYl5+QvytP2A44H3AKsB1wPn1/9NmJVP0huBq4H/jogLaiafAZwREWOA9YGL8vjDgZWAdUm1dQywsIu32Ba4I893Hqke3gRsALwfOEvSqNI+kFn7eIhUGzdFxKiI+GoePx4YR9o+HA6cnbdzZm0h70P9CDgUWJO0vl+7MMsS4NOk3/F2pP2kj9WEeSdpXb8F8D7SvhvAR/K0rYDJwP51pLQt8HB+v68Cl6hwsJ60r3cUMBp4FrgS+AFpu3MacKWkVSPiCODXwCm5Jv/cw/v+AlhM2l5tBewGfLiOfK1J3HhrX0uA5YFJkoZFxPSIeAg4GvhyRDwWES8BJwH7K11ouj/w+4j4W552IrC0zvd7E7BaRHw9Il6OiIeBc4CDCvPcEBFXRcQS4FzSyglgG1Jj8XMR8XxEvBgRN+RpRwPfiYh7ImIx8G1gS599sxbakXTw4fCI+H0n0xcBG0gaFxHPRcTNhfGrAhtExJKI+GdELOjiPR6JiCm5Vi4kNfi+HhEvRcTVwMukDaPZYHJiroHrSDua72t1QmYF+wNXRMQNEfEy8BXglW6GeZ1/c0QsjojpwE9JB9KLTo6I+RExA/grsGUe/z7g9IiYGRHzgO/Ukc8TeZlFEXEhcB+vPWM3NSLuyvtWuwEPRMS5Ob/zgXuBfXrzBeTeJHsCn8r7c08A3+e1+4LWYm68tamIeBD4FKlx9oSkCyStReq3fGk+bT8fuIfU0FuD1ICaWYjxPPBUnW+5HrBWR9wc+/gct8Pcwt8vACvkRuO6wKN5BdJZ3DMKMecB4rVHs8ya6Rjgxoj4axfTPwRsBNybu568M48/F/gTcIGk2ZJOkTSsixiPF/5eCBARteN85s0Gk6drroF7lLTNMmsXtftQL1DYh5K0kaTfS5oraQHpYPS4mhi1+0kd6/nXxCb9/jvi7pi7Mz4n6a7CPLNqrlGrrZlivLWKMQvz93Zfaz1gGDCnsN/2U2D1XsaxBnLjrY1FxHkRsQOpmAL4LqlY94yIsYXXChExC5hDakgBIGkE6UxBh+eBEYXh8YW/Z5LOFhTjjo6IvepIdSYwQZ3fZnYmcHRN3BUj4sY64po1wjGk3+v3O5sYEQ9ExMGkjdV3gYsljcxHP78WEZOA7UldYA5rWtZm1baypJGF4QnA7FYlY9aJORQuNZG0Iq/dh/ox6WzWhrlb/fGkg9H1xl63MDyh44+IuD53ZxxVc+fjtTuumSssU6yZYsNuNmlfsWgCMKvO/DrMBF4CxhX22cb0947MVi433tqUpI0lvUPS8sCLpCP1S4CfAN/q6HYoaTVJ++bFLgbeKWmHfBHt13nt//FtwF6SVpE0nnRmr8M/gAVKN0lZMV9c+1+S3lRHuv8grZhOljRS6cYOb8nTfgJ8SVLHLaRXknRAr78Qs/I8C+wBvFXSybUTJb1f0moRsRSYn0cvkfR2SZvlazwXkLpRLmlW0mYDwNckDVd6lMA7gd+0OiGzgouBffKNP4YDX+O1jbPRpHX/c5I2AT7ai9gXAZ+QtI6klYEv1rHM6nmZYXm/aVPgqi7mvQrYSNIhkoZKOhCYRLofQd0iYg7pevDvSRqjdJO89SXVdg+1FnLjrX0tD5xMei7HXFIRH0+6mcLvgKslPQvcTLqolYi4C/g46QYJc0g3M3msEPNc0g1JppOK88KOCfnanH1I/bMfye/7M9IFu90qLLsBMCO/54F52qWksxcX5G4G/4/Un9qsZSJiPuk5VHtK+kbN5D2AuyQ9R6q3gyLiRdKZ6otJG+97gOuATu/WZWbLmEvaJs0m3TzhmIi4t7Upmb0q70P9N+kGU3NIB/qeIJ2JAjgOOCSPP4fCPlQdziF1u78d+BdwSR3L3AJsSNof+xawf0R0eilMHv9O4LOkrp6fB94ZEU/2IscOhwHDgbtJNXsx6QYu1ibkRz4MbJKmAx+u4+5CZmZmpZO0E/CriKjr7sdm7SDfEXg+qZvkI01+7yNI+247NPN9rRp85s3MzMzMBj1J+0gaka/PPBW4k9RbyaxtuPFmZmZmZgb7krr2ziZ1WTwo3EXN2oy7TZqZmZmZmVWAz7yZmZmZmZlVgBtvZmZmZmZmFdDZQ5UrYdy4cTFx4sRWp2HWrX/+859PRsRqrczBtWJV0A61Aq4XqwbXi1n92qVeylLZxtvEiROZNm1aq9Mw65akR1udg2vFqqAdagVcL1YNrhez+rVLvZTF3SbNzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MWkjSdEkvSxpXM/42SSFpoqSpeZ7nCq/bJe1YGH4+z1+cZ0KrPpdZs0iaJGmapKfz68+SJhWmLy/pJ5IelzRP0hWS1m5lzmbNIumQXB/PSZoj6Q+SdpB0kqRFkp7Nr/slnSVpzcKyO0l6rDA8XNIlkv4uaYykw1vzqczKlffFFuY6eVrSlZLWzdOK+2DzJF0jaZNOYlybl12+Znxn+3AH9idfN97MWu8R4OCOAUmbASvWzHNKRIwqvLaIiOs7hoE35PnGFuaZ0aT8zVppNrA/sAowDvgdcEFh+ieB7YDNgbWA+cCZzU3RrPkkfQY4Hfg2sAYwAfgRsG+e5cKIGE2qnXcD44F/FhtwhVjLA5cAY4HdImIBMKLBH8GsmfbJ+1NrAo/z2u3EKXna2sAs4OfFBSVNBHYEAnhXJ7Fr9+Eu7E+iQ/uzsA1g06bAnRe3OovB4lzgMF5dURwO/BL4Zssysu65PppO0nTgp8AHSBvXy4CPRsR8UoMMSQKWABsUFn0d8KeIeDzPcwFwWiHuGcB7gJWAB7beeuvGfhBrfwOgviWtBHwdODIiLilMugK4QtJJHSMiYhFwVz4b8C/gs8BxhVgjSPUWwN4RsTAv9+PJkyf/qMEfxdrIb+7/DVc9fFWr02ioiHhR0sWkAx+10xZKugj4Tc2kw4CbgVtI+3C100vlM2/WuTsvhrl3tjqLweJmYIykTSUNAQ4EftXinKw7ro9WORTYHVgf2Ag4oWOCpPnAi6SDIN8uLPNz4C2S1so7oYcCfyhMvxXYknT24bwG5m5VMTDqeztgBeDSeheIiCXA5aQzCB2WJ9XLi8C7OhpuNjhd9fBV3Dfvvlan0VB5O3Egad+sdtpIUk+pB2smHQb8Or92l7RGI3P0mTfr2vjN4MgrW51FtX1Q9c7ZcfbtOuBe0mn5ouMkHVsYvjwifL1BK7k+ylVfrZwVETMBJH2L1FA7ASAixuYN6+HAo4Vl7gdmkGpqCXAn8EotRUTxQMn3Jk+efGo/PoUNFO1e3z3Xy6rAkxGxuJeRZ5MOZHQYTWoIHhwRL/Uylg1AG6+yMVP2mNLqNHplKlPrme0ySYuBUcATpAOFHTr2wcaQti8dXY+RtAOwHnBRRDwp6SHgEOD7nSwPsDgiXnOfg97ymTez9nAuqdiPIHWZrHVqRIwtvNxws8FoZuHvR0nXsL0iIp4HfgL8UtLqefSPSWcgVgVGkq7beeXMm6TPSrpH0jP57J3ZQPAUME5Sbw/Srw3MKww/CRwE/ELS7p0vYjYg7BcRY0lnm48FrpM0Pk87NU+bCCwENi4sdzhwdUQ8mYfPy+OKivtw/Wq4gRtvZm0hIh4l3bhkL9LOpZkta93C3xNIZwlqLUe6kULHHSW3AKZGxLx85uBMYBtJ4yTtCHwBeB+wct44mw0EN5G6Ou5X7wKSlgP2Aa4vjs/XzH0EuFjS20vM0aztRMSS/JtfAuxQM20G6SZYZ0haUdKKpO3H2yTNlTQX+DSwhaQtGpWjG29m7eNDwDvy2QMzW9bHJa0jaRXgeOBCSbtK2krSEEljSDcjeRq4Jy9zK3CYpJUkDQM+BszOR0lHA4uB/wBDJX2l6Z/IrAEi4hngK8APJe0naYSkYZL2lHRKcd48flPgfNIdJ0/rJN75pLMRl0t6S17uHQ3/IGZNpmRfYGVe3Y68IiKuIR04PIp0cGQJMIl07fSWwKakAyCHNSpHN97M2kREPBQR07qY/PmaZ4Q82cV8ZgPZecDVwMP59U3SrcvPB54BHiLdaXKPiHgxL3Mc6QzEA6RG2l6k26ID/InUhfJ+UjfMjmXMKi8iTgM+Q7ou9D+kbsfHku4cCXCgpOdId2v9Hamr5dYR0dkZbSLiF6Q7UV4paRvgxEbmb9ZkV+R6WAB8Czg8Iu7qYt7/AT5PasBNiYgZETG34wWcBRzah27LdfENS8xaKCImdjF+MdBxRfoR+dVdnOmF+c0Gqlsj4js1435DN7dljoinSHeY7GzaEtIZ7w91jJs8efJ3S8jTrC1ERMcd8GrdCJzUw7LXAuvUjDsHOCcPvn3y5MnR/yzNWqurfbE87YhOxl0IdPmstoi4CLgoDy6zfH/5zJuZmZmZmVkFuPFmZmZmZmZWAe42aWZmba+7bi1mZmaDhc+8mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkF+Jo3e61pU+DOi2HuHTB+81ZnY9YeOuqig+vDrDVqa7ERXN9WEb+5/zdc9fBVTXu/e+fdyyarbNK097PO+cybvdadF8PcO1udhVl7cV2YtQfXotkrrnr4Ku6bd1+r07Am85k3W9b4zVqdgVn7Gb8ZHHll+nvK3q3NxWwwK9ZiI7i+rUI2XmVjpuwxpSnvdeQfj2zK+1j3fObNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNrIUkTZe0S2H4IElPS3qbpJD0XH49Lun3knbtZPmFeZ65kqZKGtX8T2LWWK4Vs/rV/N47XmflaWtKOkfS7Dz+4VwPm+TpE2tq6jlJt+dpR0haksctaOVnNCtL1bYvbryZtQlJhwM/BPYGHs2jx0bEKGAL4BrgUklH1Cy6T55nS2Ar4EtNSdisRVwrZnXZJyJGFV7HSloVuBEYAewIjAbeCFwH7Fqz/NjCslsUxt+U62hsEz6DWVNVYfvixptZG5B0FPA9YPeIuLF2ekTMjYgzgJOA70papnYjYi7wJ9KKw2xAcq2Y9cungQXAByLioUjmR8SUiDizN4EiYmljUjRrjapsX4Y2KrANAHPvhCl7tzqLweCjwA7AzhFxew/zXgL8D7AxcE9xgqR1gD2B/2tEklbD9dEKrhVrjoFb37sAl5bR8JI0ZOutty4hJauS++bdx5F/PLLVaTRCZbYvbrxZ5zbbv9UZDCa7An8F7qxj3tn531UK4y6TFMAo0sriq+WmZ8twfbSKa8Uab+DU92WSFheGPweMA+Z2jJD0LuCXwBBSd8jdCvM/Kanj729GxKn57zdLmg+MbFTi1p72ev1erU6hkSqzfXHjzTo3+cj0sv75oHqeB44BTgR+JulDERHdzLt2/ndeYdx+EfFnSW8DziNtnOf3IVurl+ujfK4VaxdVqO/66mW/iPhzcYSkDwNrdgxHxO+AsXn8+2uWHxcRi1nWzRGxQ74hw7O9S9yq7ICNDuCAjQ5odRq9NpWp9cxWme2Lr3kza70ngJ1JF4//qId5353nv692QkRcB0wFTq2dZjZAuFbM+ucvwH6dXavTWxHxXAn5mLWLymxf3HgzawMRMRt4B7CHpO/XTpe0hqRjSafhv9TN9QqnA7tK2rJRuZq1kmvFrF9OA1YGzpW0vpLR+OY9ZpXZvrjbpFmbiIiZkt4B/A0Yn0fPV7ro4HlgGnBARPyxmxj/kfRL0qn/9zY6Z7NWcK2Y1eUKSUsKw9dExLslvRn4BnAD6VEBj+e/P9qCHM3aShW2L268mbVQREysGX4EWDcPHtzb5fM4b4BtwHGtmNWvs997Ydps4EPdTJ8OdHpRXURMhfouIDKriqptX9x4MzMzs/JMmwJ3XtyY2HPvgPGbNya2WQP95v7fcNXDV5Ua895597LJKpuUGtPan695MzMzs/LceXF6TpqZveKqh6/ivnnL3N/CrNd85s3MzMzKNX4zOPLK8uMOzAdn2yCx8SobM2WPKaXFG6APy7Ye+MybmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWAG29mZmZmZmYV4BuWDHa1t3T2bZjNzMzMzNqSz7wNdr6ls5mZmZlZJfjMm732ls6+DbOZmZmZWVvymTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33swqQtIRkm5odR5mZmZm1hpuvJm1kKTpkhZKeq7wOquPsa6V9GKO8Yykv0narOyczVoh18rLksbVjL9NUkiaKGlqnqdYT7dL2rEw/HyevzjPhFZ9LrNGcL2Y9Z+kSZKmSXo6v/4saVJh+vKSfiLpcUnzJF0hae1G5+XGm1nr7RMRowqvY2tnkDS0zljHRsQoYFXgWuDcEvM0a7VHgIM7BvLBiRVr5jmlpp62iIjrO4aBN+T5xhbmmdGk/M2ayfVi1j+zgf2BVYBxwO+ACwrTPwlsB2wOrAXMB85sdFL17hCaDS7TpsCdF7fs7SUdAXwE+AdwOPAj4ME0SWcChwFzgI9HxF9ql4+IxZIuAL7YtKQHsxb/XgaRc0m//Y6N4+HAL4Fvtiwja765d8KUvVudRRW4XgaB++bdx5F/PLLVaVSapOnAT4EPAGsClwEfjYj5pAYZkgQsATYoLPo64E8R8Xie5wLgtELcM4D3ACsBDwCfiojr+5uvz7yZdebOi9MOQmttCzwMrA58q2bcOOCrwCWSVqldUNJw4FDg5uakOsi1x+9lMLgZGCNpU0lDgAOBX7U4J2umzfaH8e4NXifXywC31+v3YuNVNm51GgPFocDuwPrARsAJHRMkzQdeJB0I+XZhmZ8Db5G0lqQROcYfCtNvBbYknbk7D/iNpBX6m6jPvJl1ZfxmcOSV/YvxQdUz12WSFheGPwcsAmZHRMcR08XpoA9PAKdHRAAXSvossDevdo/8gaRTgRHAQtIRnz/370NYXcr4vQxm9dUKvHo24TrgXmBWzfTjJBW7Hl8eEYf3P0FrC5OPTK/BzvViwAEbHcABGx3Q6jTa3lSm1jPbWRExE0DSt0gNtRMAImKspJGks9ePFpa5H5hBqqslwJ3AK/UUEcWDJd+TdAKwMXB7Xz8LuPFm1g72i4jXNLByt8mZncw7KzfcOjxK6mfd4RMR8TNJywFvIfXPNhtIzgX+Ruqu8stOpp8aESd0Mt5sMHK9mNWnuM9Vu29FRDwv6SfAfyRtGhFPAD8GViDdZ+B54POkM2/bAuQD7B/OsQIYQ+o51S9uvJm1r+hk3NqSVGjATaCTBlpELAWul/QgMLmBOZo1VUQ8KukRYC/gQ63Ox6ydDcZ6Oe+WGVx+W+0Jxta7b97b2Gqr61qdhnVt3cLfE0g3K6m1HKln09qknlBbAF+OiHkA+Z4EX893ed0U+AKwM3BXRCyV9DRQ92nzrviaN7NqWR34hKRhkg4grRyu6mxGSdsBkzqbZlZxHwLeERHPtzoRswoYVPVy+W2zuHvOglanYdXzcUnr5PsIHE+6NGVXSVtJGiJpDOlmJE8D9+RlbgUOk7SSpGHAx0iXvDwJjAYWA/8Bhkr6CunMW7/5zJtZ610haUlh+Brg8i7mvQXYEHgSeBzYPyKeKkw/S9Lp+e+5pP7ap2E2gETEQ91M/rykTxWGX4yIfndTMauqwVgvk9Ycw4VHb9fqNF7jyD+e3eoUrHvnAVeTujheTror696ka9/WId1H4FZgj4h4MS9zHPAD0p0khwP/D3h3nvYnUhfK+0ldKr9P55fD9Jobb2YtFBETu5k8tWbeqYVxyzwLLiJ26izI5MmT3XizyuuqViJiMa92Qzkiv7qLM50Suq2YtTPXi1mv3RoR36kZ95v86lQ+eH5oF9OWkM56F7srn9LfJMHdJs3MzMzMzCrBjTczMzMzM7MKcLdJMzMzMzMblHq4hKXt+MybmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWAr3mzZc29E6bs3eosWmvuHTB+81ZnYVUwbYp/L2ZmZtYUbrzZa222f6szMKuWOy+Glxa0OgszMzMbBNx4s9eafGR6DXaD/cyj9c7yY1qdgZmZmQ0CvubNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK8A1LBqppU9Jd8HriW5wPeufdMoPLb5vV6jTa29x9uXDC5a3OwqzXWlLfrhczs4bxmbeB6s6L0/PazHpw+W2zuHuOb3VvNhC5vs3MBhafeRvIxm8GR17Z/Ty+Jb4Bk9Ycw4VHb9fqNNrXlG+2OgOzPmt6fbtezMwaxmfezMzMzMzMKsCNN7MWkjRd0kJJzxVeZ+Vpa0o6R9LsPP5hSVMlbZKnT5QUNcvenqcdIWmJpOda+fnMypJrZZdOxo+RdLqkGbkGHszD4zpbTtJBkp6W9LZm5m/WDiRNkjQt18DTkv4saVJh+vKSfiLpcUnzJF0hae1W5mzWaFXbvrjxZtZ6+0TEqMLrWEmrAjcCI4AdgdHAG4HrgF1rlh9bWHaLwvibImJUUz6BWQtIGg78BXgDsAcwBtgeeArYppP5Dwd+COwdEdc1MVWzdjEb2B9YBRgH/A64oDD9k8B2wObAWsB84MzmpmjWeu28ffE1b2bt6dPAAuADEbE0j5sPTGlZRmbt5zBgAvD2iOg4y/wE8I3aGSUdBXwb2D0ipjUvRbPmkzQd+CnwAWBN4DLgoxExn7QtQZKAJcAGhUVfB/wpIh7P81wAnFaIewbwHmAl4IGtt966sR/ErHXadvvixptZV+be2cobuuwCXFpouFm7a+3vZbDaBfhjYcPalY8COwA7R8TtjU/LrAf1Ps6nfw4FdgeeB64ATsgvJM0HRpF6YH2lsMzPgTMkdZx1OxT4Q2H6rcDXgWdIZ+ne2MgPMNDcN+8+jvzjka1Ow+rTttsXd5s068xm+6e7dTbHZZLmF14fIXVnmdsxg6R35WnPSrq6ZvknC8seVxj/5ryBtkYbuVozfy/2qlWBOXXMtytwM+Dnp1h7aM7jfM6KiJkRMQ/4FnBwx4SIGEs6e3Ys8O/CMvcDM4BZpN4fm5Iaax3L/SoinoqIxRHxvUZ/gIFkr9fvxcarbNzqNKx+bbt98Zk3s85MPjK9+uuDqmeu/SLiz8URkj5M6uoCQET8Dhibx7+/ZvlxEbG4k7g3R8QOkydPjl5mbb01es2eH8th3auvVmo9RaFOunEMcCLwM0kfigjXhLVePY/z6Up99TKz8PejpGvYXhERz0v6CfAfSZtGxBPAj4EVSDuuzwOfJ5152xZA0meBD+dY4W6T9TtgowM4YKMDWp3GoDSVqX1ZrG23Lz7zZtae/gLsJ8k1ata1PwO7SxrZw3xPADuTbv7zo4ZnZdYe1i38PYF0s5Jay5FujNVxR8ktgKkRMS8iXiLdrGQbSeMk7Qh8AXgfsHI+e2c2ULXt9sU7hmbt6TRgZeBcSesrGQ1s2dq0zFpqmKQVOl7AuaSzC7+VtImk5SStKul4SXsVF4yI2cA7gD0kfb8FuZs128clrSNpFeB44EJJu0raStIQSWNI25qngXvyMrcCh0laSdIw4GPA7Ih4knTX48XAf4Chkr6yzDuaVVdlti9uvJm13hU1z2q7NG8o3wy8CNwAPAvcRtp4frR1qZq11FXAwsLrRNJF5fcC15Cu0fkH6ZrRW2oXjoiZpA3s/pK+06SczVrlPOBq4OH8+iYwFjifdMORh0h3mtwjIl7MyxxH2u48QGqk7QW8O0/7E6kL5f2kbpgdy5gNBJXZvviaN7MWioiJ3UybDXyom+nTgU4vfIiIqdC3Tt5m7ai7WgE+lV89LhcRj/Da7mRmA9WtEVG7E/mb/OpURDxFusNkZ9OWkLZJr2yXJk+e/N0S8jRrqaptX3zmzczMzMzMrAJ85s3MzMzM2sJ5t8zg8ttm9Xn5u+csYNKaY0rMyKy9+MybmZmZ2QASERNrH0FTFZffNou75yxodRpmbctn3szMzMysbUxacwwXHr1dn5Y98Kc3lZyNWXvxmTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwBf82Zm1lvTpsCdF6e/594B4zdvbT5m1jfFWm4mrzfMrI985s3MrLfuvBjm3tnqLMysv1zLZlYxPvNmZtYX4zeDI6+EKXu3OhMz64+OWm4mrzfMrI985s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNt4Fm2pT0/Ji5d7Q6EzMzMzOzAUPSEZJuaGUObrwNNHdeDHPvbHUWVidJ0yXt0sn4MZJOlzRD0nOSHszD4zpbTtJBkp6W9LZm5m/WbJIOkTQt18UcSX+QtIOkkyQtkvRsft0v6SxJaxaW3UnSY4Xh4ZIukfT3XHOHt+ZTmZWru22EpMj185ykxyX9XtKunSy/MM8zV9JUSaOa/0nMGq/m997xOquPsa6V9GKO8Yykv0narDD9cEn/lLRA0mOSTpE0tDfv4cbbQDR+Mxi/eauzsD6SNBz4C/AGYA9gDLA98BSwTSfzHw78ENg7Iq5rYqpmTSXpM8DpwLeBNYAJwI+AffMsF0bEaGAV4N3AeOCfxQZcIdbywCXAWGC3iFgAjGjwRzBruuI2Ang0jx4bEaOALYBrgEslHVGz6D55ni2BrYAvNSVhs9bYJyJGFV7H1s7Qi0bWsbl2VgWuBc4tTBsBfAoYB2wL7Awc15tEe9XSswFq7p2pq6W1i8NIO6Vvj4jn8rgngG/UzijpKNKO7O4RMa15KVqvTZuSzoxbn0haCfg6cGREXFKYdAVwhaSTOkZExCLgLkkHAv8CPkth4yhpBHAZEKSDHgvzcj+ePHnyjxr8UcySJmx7a7cRkiYWp0fEXOAMScOA70r6ZUQsrZ1H0p9IjThrJm83Wiof0PgI8A/gcNLBwgfTJJ1J2l+bA3w8Iv5Su3xELJZ0AfDFwrgfF2aZJenXwNt7k5fPvA12m+2fztRZO9kF+GOh4daVj5IadDu74VYB7tLcX9sBKwCX1rtARCwBLgd2LIxeHvgD8CLwro6Gm1lTNWfb25ttxCXA6sDGtRMkrQPsSdpptWbydqMdbAs8TKqPb9WMGwd8FbhE0iq1C+aeVIcCN3cT/63AXb1JyGfeBrvJR6aXNcYH1ZelVgX+Wcd8uwJ/Bbxmr4rxm8GRV7Y6i/bUc62sCjwZEYt7GXk2qRtlh9GkhuDBEfFSL2OZlaO/2976ti292UbMzv8Wa+UySQGMAv6PtJNqzebtRv/VVy+XSSpuXz4HLAJmR8SZedxiSZB6Q50eEQFcKOmzpG7JHd0jfyDpVFIXyYXAezp7Q0lHApOBD/fm4/jMm1n7eQpY5hqdThwDbAT8THltYjaAPQWM6+2F3cDawLzC8JPAQcAvJO1eVnJmbag324i187/FWtkvX0O6E7AJ6SyD2UC1X0SMLbzOyeNndjLvrNxw6/AosFZh+BMRMZbUW+SdwMWSXnMzCkn7AScDe0bEk71J1I03s/bzZ2B3SSN7mO8J0oWuO5L6YZsNZDeRujruV+8CkpYD9gGuL47P18x9hLRB7dW1BmYV0pttxLvz/PfVTsg3wpoKnFpyfmZVEJ2MW7vmgMgEXj17/eqCEUsj4npSl+PdOsZL2gM4h3STlF73nnLjzaz1hklaoeNFOu0+E/itpE0kLSdpVUnHS9qruGBEzAbeAewh6fstyN2sKSLiGeArwA8l7SdphKRhkvaUdEpx3jx+U+B80h0nT+sk3vnAscDlkt6Sl3tHwz+IWRP1tI2QtIakY0ldIr9Ue7OSgtOBXSVt2ahczSpkdeATeVtzALApcFVnM0raDphEvq4tb2d+Dbw3Iv7Rlzf3NW9mrVdb8N8i3bTka6RbOK8MPE668cIttQtHxMy8MvibpBcjwrdztgEpIk6T9DhwAmnj9yzp+tBvkY5qHpi7ooh0FPQaYOu8A9tZvF/kC8qvlLQbcGLjP4VZcxW3EaSDGQDz85mD54FpwAER8cduYvxH0i9JNfLeRuds1gJXSFpSGL6GtN/VmVuADUnd8B8H9o+IpwrTz5J0ev57LnBCRPwhD58IrARcVTh5d31E7Flvom68mbVQREzsZvKn8qvH5SLiEWDdktIya1sR8WtSw63WjcBJPSx7LbBOzbhzSN1XAN4+efLkzrrIWG9V5RE0c+8YkM9F7WEbcXBvl8/jPlpGbmbtpod9sak1804tjFvmWXARsVMP79XvrvpuvJmZmVl5Ntu/1RmYmQ1YbryZmZlZear0CJoqnB00MyvwDUvMzMzMzMwqwGfezMzM2tx5t8zg8ttm9Xq5u+csYNKaYxqQkZmZtYLPvJmZmbW5y2+bxd1zFrQ6DTMzazGfeTMzM6uASWuO4cKjt+vVMgf+9KYGZWNmZq3gxpuZWX/Vc1v0AXpLcjOzdnP3nAUD48DF3H25cEJXjxqzwcqNNzOz/vBt0c3M2sa+W67d6hTMGsqNNzOz/qj3tui+JbmZWcMdsu0EDtl2QqvTKMeUb7Y6A2tDvmGJmZmZmZlZBfjMm5mZmZmVyo+3MGsMn3kzMzMzs1L58RZmjeEzb2ZmZmZWOj/ewqx8PvNmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFm1kKSpktaKOk5SU9LulLSunnaVEkv52nzJF0jaZNOYlybl12+ZvxUSS8367OYNVIzakXSc836PGaNlOtll07Gj5F0uqQZuV4ezMPjOltO0kG5Zt7WzPzNmqlm+9LxOitPW1PSOZJm5/EP523GJnn6RElRs+ztedoRkpbkcQsk3S7pnf3N1403s9bbJyJGAWsCjwNnFqadkqetDcwCfl5cUNJEYEcggHd1EvuURiRs1iINrZW8vNmAJGk48BfgDcAewBhge+ApYJtO5j8c+CGwd0Rc18RUzVphn4gYVXgdK2lV4EZgBGn7MRp4I3AdsGvN8mMLy25RGH9T3raMBX4EXCBpbH8SdePNrE1ExIvAxcCkTqYtBC4CtqyZdBhwMzAVOLyxGZq1B9eKWZ8cBkwA3h0Rd0fE0oh4IiK+ERFXFWeUdBTwPWD3iLixFcmatYFPAwuAD0TEQ5HMj4gpEXFmTwsXRcRS4FxgJLBhf5Lyc94Gsrl3wpS9W52F1UnSCOBA0g5m7bSRwMHAgzWTDgNOA24Bbpa0RkQ83uhcB6ViPc29A8Zv3r8Y1meuFSvV4KnLXYA/RkRP3YM/CuwA7BwRtzc+LevW4Pl9tqNdgEtzw6tfJA0BjgQWAY/2J5bPvA1Um+0P4zdrdRZWn8skzScd3dkV+J/CtOPytGdJG9MPdEyQtAOwHnBRRPwTeAg4pCb2cY1LexApo55ck2VoaK3k5W0wGVx1uSowp475diUdGLmzselYjwbX77PVLpM0v/D6CDAOmNsxg6R35WnPSrq6ZvknC8sW973enLctLwKnAu+PiCf6k6jPvA1Uk49ML2utD6qeufaLiD/nozL7AtdJ6ugOdmpEnCBpAvBHYGPgjjztcODqiHgyD5+Xx32/EPtU4Mv9/BRWW099OQrqmuxeG9RKRJwwefLk6O9HsQqpal3WVy+1niJdL9qTY4ATgZ9J+lBEuCZapaq/z3bTi+1LcYSkD1OomYj4HTA2j39/zfLjImJxJ3FvjogdJI0iXYu9I6lrf5/5zJtZm4iIJRFxCbCEdOagOG0G8EngDEkrSloReB/wNklzJc0l9c3eQtIWtbHNBhLXilmf/BnYPXct7s4TwM6kncwfNTwrs/b1F2A/Sf1uL+Xuyh8DPiBpq/7EcuPNrE0o2RdYGbindnpEXAPMBo4C9iPtuE4i3ZhhS2BT4HrStT1mA5ZrxawuwySt0PEi3SxhJvBbSZtIWk7SqpKOl7RXccGImA28A9hD0vc7iW02GJxG2s6cK2n9vO0ZzbI3xKpLRDwF/Az4Sn+SGpDdJs+7ZQaX3zar1Wm0xtx9uXDC5a3OwnrnCklLSLcwfxQ4PCLukjo9zf8/pJXJ/cCUfJbhFfm5JD+Q9IUG52zWCq4Vs/pdVTP8LdINGL4GXEPaKX0cuJx0I5/XiIiZkt4B/E3SixHxpQbna9ZKHduXDtdExLslvRn4BnAD6VEBj+e/P9rH9zkdeEjS5hFxR08zd2ZANt4uv20Wd89ZwKQ1x7Q6FbNuRcTEbqYd0cm4C4ELu1nmIl7tS30EwOTJk33Nm1VeM2rFbKDorl6AT+VXj8tFxCPAuiWlZdaWeti+zAY+1M306UCnRxAjYirp8TTFcY8By/c+y1cNyMYbwKQ1x3Dh0du1Oo3mm/LNVmdgZmZmZmYN4GvezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwChrY6ATMzsyo475YZXH7brJa8991zFjBpzTEteW8zM2sfPvNmZmZWh8tvm8Xdcxa0Og0zMxvEfObNzMysTpPWHMOFR2/X9Pc98Kc3Nf09zcys/fjMm5mZmZmZWQW48WZmZmZmZlYBbryZmdmAIukISTe0Og8zM7OyufFm1oYkTZI0TdLT+fVnSZMK05eX9BNJj0uaJ+kKSWu3MmezRpI0XdJCSc8VXmf1Mda1kl7MMZ6R9DdJm5Wds1mr1NTL05KulLRunjZV0st52jxJ10japJMY1+Zll68Z/8ryzfo8Zo2U6+VlSeNqxt8mKSRNrKmbjtftknYsDD+f5y/OM6HsfN14M2tPs4H9gVWAccDvgAsK0z8JbAdsDqwFzAfObG6KZk23T0SMKryOrZ1BUr034jo2IkYBqwLXAueWmKdZO9gn/8bXBB7ntduIU/K0tYFZwM+LC0qaCOwIBPCuTmJ3LG82UDwCHNwxkA/orVgzzyk126AtIuL6jmHgDXm+sYV5ZpSdqO82aT2bNgXuvLjVWQxIkqYDPwU+QNrAXgZ8NCLmkxpkSBKwBNigsOjrgD9FxON5nguA0wpxzwDeA6y09dZbN/hTWK+5pkoj6QjgI8A/gMOBHwEPpkk6EzgMmAN8PCL+Urt8RCzO9fPFpiXdZHfPWeC7VQ5iEfGipIuB0zuZtlDSRcBvaiYdBtwM3EKqq9rpDeXfrLXAuaTffcdBjsOBXwLfbFlGXfCZN+vZnRfD3DtbncVAdiiwO7A+sBFwQscESfOBF0krk28Xlvk58BZJa0kakWP8oTD9VmBL0pk7azeuqbJtCzwMrA58q2bcOOCrwCWSlqkHScNJ9XNzc1Jtrn23XNsP9x7k8jbiQDr5jUsaSTrb8GDNpMOAX+fX7pLWaHSeHfybtRa5GRgjaVNJQ0g186sW59Qpn3mz+ozfDI68stVZVM8HVc9cZ0XETABJ3yI11E4AiIixeeN6OPBoYZn7gRmk7i5LgDuBV7qQRcQrK5zJkyf37zNYY7imXqu+WrlM0uLC8OeARcDsiOg4Wro4nazmCeD0iAjgQkmfBfbm1e6RP5B0KjACWEg6Uz3gHLLtBA7ZtvRLLqzFLjqmrtk66mUUqR52L0w7TtKxwBjStmXfjgmSdgDWAy6KiCclPQQcAny/dvlG9Ozwb9bKVme9wKtn364D7iXtYxV11E2HyyPi8H4n2Es+82bWejMLfz9KuobtFRHxPPAT4JeSVs+jfwysQLpeZyRwCYUzb5I+K+keSc80MnGzJtsvIsYWXufk8TM7mXdWbrh1qK2tT0TEWFIdvRO4WNLmDcnarDX2y7/x5UkH966TND5POzVPm0g6eLFxYbnDgasj4sk8fF4eV9SxvNlAci7pQMURpC6TtU6t2QY1veEGbryZtYN1C39PIN2spNZypDMEHXeU3AKYGhHzIuIl0tm6bSSNk7Qj8AXgfcDKjUvbrG1EJ+PWzteLdui0tiJiaURcT+o2tluD8jNrmYhYEhGXkHpp7FAzbQbpBlhnSFpR0oqkbcfbJM2VNBf4NLCFpC2anbtZM0XEo6Qbl+xFOijeltx4M2u9j0taJ1+Pczypi9eukraSNETSGNLNSJ4G7snL3AocJmklScOAj5G6jj0JjAYWA//BXaNt8Fod+ISkYZIOADYFrupsRknbAZOAu5qYn1lTKNmXdDDvntrpEXEN6cDGUcB+pEbeJNJ101uSaud6Uncys4HuQ8A7cq+ntuTGm1nrnQdcTbq5wsOkOxuNBc4HngEeIt1pco+IeDEvcxzpRiYPkBppewHvztP+ROpCeT+vvU7OrOquqHl+zqXdzHsLsCHwJOkmJvtHxFOF6Wd1xCF1lTkhIv7QSRyzqroi/74XkGrg8Ijo6gDF/wCfJzXgpkTEjIiY2/ECzgIO7cWjOMwqKSIeiohpXUz+fM026Mku5msoF6FZ690aEd+pGfcburk1c94JPbSLaUtIR44+BDB58uTOupSZVUpETOxm8tSaeacWxi3zLLiI2KmcrMzaU3f1EhFHdDLuQuDCbpa5CLgoDy6zvFmVdVUvEbEY6Oh+fwQ9/PYjYnph/obxmTczMzMzM7MKcOPNzMzMzMysAtxt0qyFeugKZmZmZmb2Cp95MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK8DVvA9HcO2HK3iXGuwPGb15ePLOBoD915poyMzOzPnDjbaDZbP9WZ2A28LnOzMzMrAXceBtoJh+ZXmUq8yye2UDQ3zpzTZmZmVkf+Jo3MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzczMzMzMrALceDMzMzMzM6sAN97MzMzMBilJR0i6odV5mFl93HgzazFJh0iaJuk5SXMk/UHSDpJOkrRI0rP5db+ksyStWVh2J0mPFYaHS7pE0t8ljZF0eGs+lVm5JE2XtDDXScfrrDxtTUnnSJqdxz8saaqkTfL0iZKiZtnb87QjJC3J4xa08jOalaW7eulDrGslvZhjPCPpb5I2Kztns1Zr9P6YpH9KWiDpMUmnSBralzzdeDNrIUmfAU4Hvg2sAUwAfgTsm2e5MCJGA6sA7wbGA/8srjAKsZYHLgHGArtFxAJgRIM/glkz7RMRowqvYyWtCtxI+q3vCIwG3ghcB+xas/zYwrJbFMbfFBGjSLVjNlAsUy+1M/Ri5/HYXCOrAtcC55aYp1nLNWl/7FPAOGBbYGfguL7k6sabWYtIWgn4OvDxiLgkIp6PiEURcUVEfK44bx5/F3Ag8B/gszWxRgBXAMOAvSPi+bzcj5vxWcxa6NPAAuADEfFQJPMjYkpEnNmbQBGxtDEpmrWHfKb575K+L2kecNKrk3RmPrN2r6SdO1s+IhYDFwCTmpSyWcM1a38sIq6PiJcjYhbwa+Atfcm3T6frbBCaeydM2bvVWQw02wErAJfWu0BELJF0ObB7YfTywB+AZ4ADIuKlUrO0xnBNlWUX4NIyGl6Shmy99dYlpNQYd89ZwIE/vanVaVj1bUtqgK1O2sE8MI+7mHRW4D3AJZJeFxHzigtKGg4cCtzc1IxLct4tM7j8tlmtTsPaTyv2x94K3NWHXH3mzeqw2f4w3t3bG2BV4Ml8JLM3ZpNO23cYTVrx/MINt4pwTfXVZZLmF14fIe1szu2YQdK78rRnJV1ds/yThWWL3VXeLGk+8GLjP0Lf7Lvl2kxac0yr07Bq6axeAGZHxJkRsTgiFuZxTwCn57MKFwL3AcWjSz/INfIccCzwtWZ9iDJdftss7p7jS1ttGU3dH5N0JDAZOLW3iYLPvFk9Jh+ZXtZ7H1R3U58Cxkka2ssVxtpA8Wjok8AngF9Kei4i/tT7RK2pXFPL6r5WOuwXEX8ujpD0YeCVaw4i4nfA2Dz+/TXLj+ui1m6OiB0kjQKe7V3izXHIthM4ZNsJrU7D2sRFx9Q1W2f1cgQws5N5Z0VEFIYfBdYqDH8iIn4maTlSV6/fSXpbO5+p7sqkNcdw4dHbtToNa6I66qVp+2OS9gNOBnaJiCd78V6v8Jk3s9a5iXSkf796F8gbzn2A64vjI+IS4CPAxZLeXmKOZu3uL8B+uTb6JSKeKyEfs3YXnYxbW1LxCMoE0lmF1y4YsTQirgceBHZrUH5mzdaU/TFJewDnkG4mdGdfk3XjzaxFIuIZ4CvADyXtJ2mEpGGS9pR0SnHePH5T4HzSHY5O6yTe+aTuLJdLekte7h0N/yBmrXUasDJwrqT1lYwGtmxtWmaVsjrwibytOQDYFLiqsxklbUe6YUmfrtcxazdN3B/7NfDeiPhHf/J1482shSLiNOAzwAmkuxbNJBX8ZXmWAyU9B8wHfkc6tb91RCxzRDTH+wXpzkdXStoGOLGR+Zs12RV67XOrLs3dTt5MOmp6A6nb422kaw8+2rpUzVpumXrpZt5bgA1J3b6+BewfEU8Vpp/VEYf0mIATIuIPjUvdrLmatD+2EnBVoSb7VEO+5s2sxSLi16SjMbVu5NXbOHe17LXAOjXjziGdlgd4++TJkzvrImNWKRExsZtps4EPdTN9OtDpRXURMRWY2q/kzNpMd/VCze+9pgaWeRZcROxUTlZm7a3R+2P9zzDxmTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAvyoADMzMzMr3d1zFnDgT29qdRqvuHvOAiatOabVaZj1ixtvZmZmZlaqfbdcu9UpmA1IbryZmZmZWakO2XYCh2w7odVpvEY7nQU06ytf82ZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WbWhiQNl3SxpOmSQtJONdPfLumvkp6RNL2T5Sfm6S80KWWzliizViTd26S0zVrC9WJWv7LrRdIuZeTlxptZ+7oBeD8wt5NpzwP/C3yui2XPB/4NrNqY1MzaSlm18uWGZGfWXlwvZvUrs14ulrRafxNy482shfLRnC9JulvS05KmSFohIl6OiNMj4gZgSe1yEfGPiDgXeLiTmBsBbwS+GhELG/8pzBqvGbUSEb9t/CcxazzXi1n9mlgvdwLvzdPXl/R/kp6S9KSkX0saW0++Q/vxWdva3XMWcOBPb2p1Gmb1OBTYnXQE5wrghPzqqzcAD0fEs/Uu4HqxinCtmNWv5fXSjlzD1oVm1MvteTyAgO8AfwPGAL8FTgI+1VPgAXnmbd8t12bSmmNanYZZvc6KiJkRMQ/4FnBwP+ONAp6pd2bXi1WIa8Wsfi2tl3bkGrZuNKNengFGA0TEgxFxTUS8FBH/AU4D3lZP4AF55u2QbSdwyLYTWp2GGRcdU9dsMwt/Pwqs1c+3fY50FKcurhdrB64Vs/pVoV7akWt4cGqjehkDPAsgaXXgB8COpAbdcsDT9QQekGfezCpm3cLfE4DZ/Yx3F/B6SaP7Gces3bhWzOrnejGrXzPqZYs8HlKXyQA2j4gxpJuiqJ7AbryZtd7HJa0jaRXgeOBCAEnLS1ohzzNc0gqSlKctl6cNS4NaQdJwgIi4H7gN+GphebOBoKG1Iundzf5AZg3kejGrXzPqZXPStW2QzrY9B8yXtDZd37FyGW68mbXeecDVpLsVPQx8M4+/D1gIrA38Kf+9Xp721jx8FekI0cIco8NBwGTqPAVvVhGNrpWTG5u+WVO5Xszq14x62T9f3wbwNdLdKJ8BrgQuqTfRAXnNm1nF3BoR36kdGRETu1ogIq6lm9PrETEd2Alg8uTJ0d8EzdpEQ2sFXC82oLhezOrX8HqpmXYXsHXN6O/1nKbPvJmZmZmZmVWCG29mZmZmZmYV4G6TZi3U3el4M3uVa8Wsfq4Xs/pVrV585s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwCFFHN5ytK+g/waIvTGAc82eIcwHnUapc8ADaOiNGtTKANaqWd/j/aJZd2yQPaJ5eW1wp0Wy/t8j11xzmWowo5tnu99Ec7fv/OqX7tmFdb1EtZKvuogIhYrdU5SJoWEZOdh/PoiqRprc6h1bXSbv8f7ZBLu+QB7ZNLO9QKdF0v7fI9dcc5lqMqObY6B2jM9qUdv3/nVL92zKtd6qUs7jZpZmZmZmZWAW68mZmZmZmZVYAbb/1zdqsTyJzHa7VLHtBeubRKO30H7ZJLu+QB7ZNLu+TRlXbPD5xjWZxja7XjZ3NO9WvHvNoxpz6r7A1LzMzMzMzMBhOfeTMzMzMzM6sAN97qIGkPSfdJelDSFzuZvpKkKyTdLukuSUe2Io88z06Sbst5XNeqPPJ8b5K0RNL+rchD0qGS7sivGyVt0aI8JOkHefodkt7YiDxazXXSt1zyfIOiVurMpaX10i6/4/7kmOdpym+8Pznm+Rr62+9OO9VFP3Ks5PalXeusHWurHWupXWtnoNbLMiLCr25ewBDgIeD1wHDgdmBSzTzHA9/Nf68GzAOGtyCPscDdwIQ8vHorvo/CfP8HXAXs36L/l+2BlfPfewK3tCiPvYA/AALe3Ig8Wv1ynfQtl8J8A75WepFLy+qlXX7HJeTYlN94f3IszNew334J32NT6qKfOVZu+9KuddaOtdWOtdSutTNQ66Wzl8+89Wwb4MGIeDgiXgYuAPatmSeA0ZIEjCKtZBa3II9DgEsiYgZARDxRcg715gHw38BvgUbkUFceEXFjRDydB28G1mlFHnn4l5HcDIyVtGYDcmkl10nfcoHBUyt15UJr66Vdfsf9zbFZv/H+5AiN/+13p53qos85Us3tS7vWWTvWVjvWUrvWzkCtl2W48daztYGZheHH8riis4BNgdnAncAnI2JpC/LYCFhZ0rWS/inpsJJzqCsPSWsD7wZ+0oD3rzuPGh8iHW1pRR69zbWKXCd9yGWQ1Uq9ubSyXtrld9yddvqNd6Vdfvvdaae66Eq710tftWudtWNttWMttWvtDNR6WcbQVidQAepkXO0tOncHbgPeAawPXCPp+ohY0OQ8hgJbAzsDKwI3Sbo5Iu5vch6nA1+IiCXpoFlD1JNHmlF6O2nlsUOL8qg71wpznfQtl9MZPLVSby6trJd2+R13p51+411pl99+d9qpLrrS7vXSV+1aZ+1YW+1YS+1aOwO1XpbhxlvPHgPWLQyvQzoSVHQkcHJEBPCgpEeATYB/NDmPx4AnI+J54HlJfwO2AMpcqdSTx2TggrwSGQfsJWnx/2/vTkLkKMMwjv8fk7gmenADEUkUxaCoB3OJiiOI4HLwoAiCVxdQUBG8iApeFMGDBvUQJCB6UhFccxAFiUoSYha3g+hFETdEjSuTvB66xHGcjJX0THdV5/+DD4papt5qvqeb76up7qp6ccR1kOQcYD1weVV9v4Dn3586WtXac+bkwGo5mLLStpZx5qUr/Xg+Xerj+9KVvj+fLuViX7qelwPV1Zx1MVtdzFJXszOpefmv/X1I7mBrDAa4nwGr+OcByLNm7fMEcH+zfCLwJXDcGOpYDbzR7Hsk8AFw9qjrmLX/BhbnSxjavB6nAJ8Ca8fcP67k3w/Ibh5HX17MZk4OrJZZ+090VvajlrHlpSv9eAFqHEkfH6bGWfsvSt9fgNdxJLkYssbefb50NWddzFYXs9TV7ExqXuZq3nn7H1U1neRWYCODb7J5qqo+THJzs/1J4AFgQ5JdDDrE3VX13ajrqKqPk7wO7AT2Auur6oNR17GQ5xuyjnuBY4HHmxmp6ao6fwx1vMrgG44+BX5lMKM4UczJgdWy0Occoo5Fz8p+1DK2vHSlHw9b46j6+DA1jqqWfelSLoassXefL13NWRez1cUsdTU7k5qXuaQZiUqSJEmSOsxvm5QkSZKkHnDwJkmSJEk94OBNkiRJknrAwZskSZIk9YCDN0mSJEnqAQdvPZVkT5LtST5MsiPJnUkOabadn+TReY5dmeT60VUrjY9ZkdozL1J75kXj4E8F9FSS3VW1vFk+AXgW2FRV97U4dgq4q6quWtQipQ4wK1J75kVqz7xoHLzzNgGq6hvgRuDWDEwleRkgycXNrND2JO8nWQE8CFzUrLujmf15O8m2pq1tjp1K8laS55J8kuSZNL+2mGRNkneamabNSVYkWZLk4SRbkuxMctO4XhNpLmZFas+8SO2ZF41MVdl62IDdc6z7ATgRmAJebta9BFzQLC8Hls7c3qw/Eji8WT4d2NosTwE/AiczGOi/C1wIHAp8Bqxp9ju6+bs3Avc06w4DtgKrxv1a2Q7uZlZstvbNvNhs7Zt5sY2jLUWTJHOs2wQ8kuQZ4IWq+qKZsJlpGbAuyXnAHuCMGds2V9UXAEm2AysZvIl8VVVbAKrqp2b7ZcA5Sa5pjj2GwRvQ50NfmbSwzIrUnnmR2jMvWlQO3iZEklMZhP0bYPXf66vqwSSvAFcA7yW5dI7D7wC+Bs5lMKvz+4xtf8xY3sOgzwSY62HJALdV1cYhLkVaVGZFas+8SO2ZF42Cz7xNgCTHA08C66qqZm07rap2VdVDDG6dnwn8DKyYsdsxDGZv9gI3AEv+55SfACclWdOcY0WSpcBG4JYky5r1ZyQ5avgrlBaGWZHaMy9Se+ZFo+Kdt/46orl1vgyYBp4GHpljv9uTXMJgpuYj4DVgLzCdZAewAXgceD7JtcCbwC/znbiq/kxyHfBYkiOA34BLgfUMbuVvax6m/Ra4eqirlIZnVqT2zIvUnnnRyPlTAZIkSZLUA/7bpCRJkiT1gIM3SZIkSeoBB2+SJEmS1AMO3iRJkiSpBxy8SZIkSVIPOHiTJEmSpB5w8CZJkiRJPeDgTZIkSZJ64C/jPKa1DyDrhQAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 1008x720 with 8 Axes>"
+       "<Figure size 1440x720 with 12 Axes>"
       ]
      },
      "metadata": {
@@ -860,7 +795,7 @@
    ],
    "source": [
     "n_perspectives = len(kinase_distance_matrices_normalized)\n",
-    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*3.5, 10))\n",
+    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*5, 10))\n",
     "for i, (perspective, matrix) in enumerate(kinase_distance_matrices_normalized.items()):\n",
     "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
     "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
@@ -875,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1007,7 +942,7 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1020,11 +955,61 @@
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "#### Analyze of results\n",
+    "\n",
+    "- The _sequence_ approach reproduces nicely the Manning _et al._ tree\n",
+    "  - The TK kinases EGFR, ErbB2, MET, KDR are grouped together with highest similarities for EGFR and ErbB2\n",
+    "  - The TKL kinase LCK groups next to the TK kinases\n",
+    "  - The CMGC kinases p38a and CDK2 are grouped together\n",
+    "  - The atypical kinase p110a is a singleton\n",
+    "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made, the typical kinases are grouped together, while the atypical kinase p110a is a singleton. Within the typical kinase grouping, EGFR and ErbB2 are the most similar.\n",
+    "- The _ifp_ view:\n",
+    "  - BRAF and the atypical kinase p110a form a singletons each\n",
+    "  - Surprisingly, ErbB2 forms a singleton (=not grouped with EGFR), but we have to keep in mind that we have the least data points ($4$) for ErbB2.\n",
+    "- The _ligand-profile_ view:\n",
+    "  - CDK2 and p110a create their own cluster early on (i.e. low similarity to the rest)\n",
+    "  - BRAF, p38a, KDR, LCK (TKL, CMGC and TK group) as well as EGFR and ErbB2 (TK) form one cluster\n",
+    "  - Surprisingly, MET (TK) forms a singleton \n",
+    "- Overall observations:\n",
+    "  - Members of the same family (here: EGFR and ErbB2) are grouped together closely in the _sequence_ _kissim_, and _ligand-profile_ approaches.\n",
+    "  - The atypical kinase p110a show high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches\n",
+    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (minus _sequence_)\n",
+    "\n",
+    "<!---\n",
+    "- Comparison to multi-target profiles in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
+    "  - Profile 1: \n",
+    "    - EGFR/ErbB2 as on-targets and BRAF as anti-target\n",
+    "    - Should be doable from \"sequence\", \"ligand-profile\" view (less clear from \"kisim\" and \"ifp\" view)\n",
+    "  - Profile 2:\n",
+    "    - EGFR/p110a as on-targets and BRAF as anti-target\n",
+    "    - Should be hard to design from all views\n",
+    "  - Profile 3: \n",
+    "    - EGFR/KDR as on-targets and BRAF as anti-target\n",
+    "    - Should be hard to design except from a \"sequence\" view\n",
+    "--->"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## Discussion\n",
     "\n",
-    "- Both \"sequence\" and \"kissim\" group members of the same family close together, while showing dissimilarity to Atypical kinase (PI3K)\n",
-    "- "
+    "We have assessed kinase similarity from many different angles: the pocket sequence, the pocket structure, ligand binding modes (interaction fingerprints), and ligand bioactivities. We see from our results, that different conclusions can be drawn for each perspective, while some observations agree.\n",
+    "Thus, it is beneficial to consider different metrics to study kinase similarity.\n",
+    "\n",
+    "The different approaches have strengths and drawbacks:\n",
+    "\n",
+    "- The _sequence_ view: Sequence data is available for all kinases, however no information about structure or ligand binding is included.\n",
+    "- The _kissim_, _ifp_, and _ligand-profile_ views: Information about structure, ligand binding, or ligand bioactivity is included, however some kinases may not have any data, some kinases may have only few data, and most kinases may not be fully explored (even if a lot of data is available).\n",
+    "- All views rely on simplifications! \n",
+    "  - Dealing with multiple measurements\n",
+    "    - _ligand-profile_ view: Only one measurement per kinase-ligand is used here.\n",
+    "    - _kissim_ and _ifp_ view: Only one structure/IFP pair represents one kinase pair here.\n",
+    "  - Bioactivity cutoffs to define active/inactive compounds in case of the _ligand-profile_ view"
    ]
   },
   {
@@ -1033,11 +1018,9 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n",
-    "\n",
-    "1. Question\n",
-    "2. Question\n",
-    "3. Question"
+    "1. Can you think of another perspective to assess kinase similarity?\n",
+    "2. Are there another method to assess and visualize the similarities?\n",
+    "3. Can you name at least one advantage and one disadvantage per perspective discussed here?"
    ]
   }
  ],

From 34b130d972e81a261d09f5eee915ea6515e1244b Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 29 Sep 2021 09:09:46 +0200
Subject: [PATCH 117/158] Add matrix checks.

---
 .../talktorial.ipynb                          | 238 ++++++++++++------
 1 file changed, 168 insertions(+), 70 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 103d61ae..f116469a 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -34,7 +34,7 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor (considering 4 different aspects)"
+    "* Kinase similarity descriptor (considering 4 different methods)"
    ]
   },
   {
@@ -43,16 +43,16 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Load kinase matrices\n",
+    "* Load kinase distance matrices\n",
     "* Visualize similarity for example perspective\n",
-    "  * Visualize kinase matrix\n",
-    "  * Visualize similarity as dendrograms\n",
+    "  * Visualize kinase distance matrix as heatmap\n",
+    "  * Visualize similarity as dendrogram\n",
     "* Visualize similarities from different perspectives\n",
     "  * Preprocess distance matrices\n",
-    "    * Normalize different matrices\n",
+    "    * Normalize matrices\n",
     "    * Define kinase order\n",
     "  * Visualize kinase similarities\n",
-    "  * Analyze of results"
+    "  * Analysis of results"
    ]
   },
   {
@@ -90,12 +90,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor (considering 4 different aspects)\n",
+    "### Kinase similarity descriptor (considering 4 different methods)\n",
     "\n",
     "- __Talktorial T024__ = KLIFS pocket sequence\n",
     "- __Talktorial T025__ = KiSSim fingerprint\n",
     "- __Talktorial T026__ = KLIFS interaction fingerprint\n",
-    "- __Talktorial T027__ = Ligand profile: ChEMBL29, bioactivity"
+    "- __Talktorial T027__ = Ligand profile: ChEMBL29, bioactivity\n",
+    "\n",
+    "Please refer to the talktorials for more details on each method."
    ]
   },
   {
@@ -135,7 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load kinase matrices"
+    "### Load kinase distance matrices"
    ]
   },
   {
@@ -147,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,12 +169,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We load the distance matrices."
+    "We load the distance matrices which were generated in the previous notebooks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,12 +187,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check if the matrices have the same dimensions"
+    "We now make a few checks to verify matrix distance conditions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check that the matrices have the same dimensions."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -213,7 +222,94 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check the distances."
+    "We check that the matrices are symmetric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetry of sequence matrix: True\n",
+      "Symmetry of kissim matrix: True\n",
+      "Symmetry of ifp matrix: True\n",
+      "Symmetry of ligand-profile matrix: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
+    "    print(f\"Symmetry of {descriptor} matrix: \"\n",
+    "          f\"{(similarity_df.values == similarity_df.values.transpose()).all()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We check that all values are positive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Symmetry of sequence matrix: True\n",
+      "Symmetry of kissim matrix: True\n",
+      "Symmetry of ifp matrix: True\n",
+      "Symmetry of ligand-profile matrix: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
+    "    print(f\"Symmetry of {descriptor} matrix: \"\n",
+    "          f\"{(similarity_df.values >= 0).all()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we check that the diagonal values are zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null diagonal of sequence matrix: True\n",
+      "Null diagonal of kissim matrix: True\n",
+      "Null diagonal of ifp matrix: True\n",
+      "Null diagonal of ligand-profile matrix: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
+    "    print(f\"Null diagonal of {descriptor} matrix: \"\n",
+    "          f\"{(np.diagonal(similarity_df) == 0).all()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If one of the checks (dimension, symmetry, positivity, diagonal) fails, the rest of the notebook will not run and the matrix generation from the previous notebooks should be investigated."
    ]
   },
   {
@@ -227,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -251,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -432,7 +528,7 @@
        "p38a   0.370645  0.000000  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,7 +544,7 @@
     "tags": []
    },
    "source": [
-    "#### Visualize kinase matrix\n",
+    "#### Visualize kinase distance matrix as heatmap\n",
     "\n",
     "We visualize the kinase matrix in the form of a heatmap."
    ]
@@ -494,12 +590,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use the `scipy` function `squareform` to create a condensed vector of the distance matrix of shape $n*(n-1)/2$, where $n$ is the shape of the quadratic distance matrix. The values in this vector correspond to the values of the lower triangular matrix."
+    "Since distance matrices are symmetric, we use the `scipy` function `squareform` to create a condensed vector of the distance matrix of shape $n*(n-1)/2$, where $n$ is the shape of the distance matrix. The values in this vector correspond to the values of the lower triangular matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -513,7 +609,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -533,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -544,12 +640,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now generate a phylogenetic tree based on our clustering."
+    "We now generate a phylogenetic tree based on the clustering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -560,17 +656,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We visualize this tree as dendrogram."
+    "We visualize the tree as a dendrogram."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -585,7 +681,8 @@
     "fig, ax = plt.subplots()\n",
     "labels = kinase_distance_matrix.columns.to_list()\n",
     "hierarchy.dendrogram(hclust, labels=labels, orientation=\"left\", ax=ax)\n",
-    "ax.set_xlabel(\"Distance\");"
+    "ax.set_xlabel(\"Distance\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -606,14 +703,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### Normalize different matrices \n",
+    "##### Normalize matrices \n",
     "\n",
     "We normalize the different matrices to values in $[0, 1]$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -639,7 +736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -698,7 +795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 37,
    "metadata": {
     "tags": []
    },
@@ -719,13 +816,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
     "def heatmap(distance_matrix, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
-    "    Generate heatmap from distance matrix.\n",
+    "    Generate a heatmap from a distance matrix.\n",
     "    \n",
     "    Parameters\n",
     "    ----------\n",
@@ -748,13 +845,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
     "def dendrogram(distance_matrix, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
-    "    Generate dendrogram from distance matrix.\n",
+    "    Generate a dendrogram from a distance matrix.\n",
     "    \n",
     "    Parameters\n",
     "    ----------\n",
@@ -777,12 +874,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 12 Axes>"
       ]
@@ -805,12 +902,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We load here again the input kinase information that we defined in __Talktorial T023__."
+    "We load here again the input kinase information that we defined in __Talktorial T023__, to determine, among other, to which group each kinase belongs to."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -942,7 +1039,7 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -956,25 +1053,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Analyze of results\n",
+    "#### Analysis of results\n",
     "\n",
-    "- The _sequence_ approach reproduces nicely the Manning _et al._ tree\n",
-    "  - The TK kinases EGFR, ErbB2, MET, KDR are grouped together with highest similarities for EGFR and ErbB2\n",
-    "  - The TKL kinase LCK groups next to the TK kinases\n",
-    "  - The CMGC kinases p38a and CDK2 are grouped together\n",
-    "  - The atypical kinase p110a is a singleton\n",
-    "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made, the typical kinases are grouped together, while the atypical kinase p110a is a singleton. Within the typical kinase grouping, EGFR and ErbB2 are the most similar.\n",
-    "- The _ifp_ view:\n",
-    "  - BRAF and the atypical kinase p110a form a singletons each\n",
+    "- The _sequence_ approach reproduces nicely the Manning tree.\n",
+    "  - The TK kinases EGFR, ErbB2, MET, KDR are grouped together with highest similarities for EGFR and ErbB2.\n",
+    "  - The TKL kinase LCK groups next to the TK kinases.\n",
+    "  - The CMGC kinases p38a and CDK2 are grouped together.\n",
+    "  - The atypical kinase p110a is a singleton, i.e., forms its own cluster.\n",
+    "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made: the typical kinases are grouped together, while the atypical kinase p110a is a singleton. Within the typical kinase grouping, EGFR and ErbB2 are the most similar.\n",
+    "- The _ifp_ method:\n",
+    "  - BRAF and the atypical kinase p110a form a singletons each.\n",
     "  - Surprisingly, ErbB2 forms a singleton (=not grouped with EGFR), but we have to keep in mind that we have the least data points ($4$) for ErbB2.\n",
-    "- The _ligand-profile_ view:\n",
-    "  - CDK2 and p110a create their own cluster early on (i.e. low similarity to the rest)\n",
-    "  - BRAF, p38a, KDR, LCK (TKL, CMGC and TK group) as well as EGFR and ErbB2 (TK) form one cluster\n",
-    "  - Surprisingly, MET (TK) forms a singleton \n",
+    "- The _ligand-profile_ method:\n",
+    "  - CDK2 and p110a create their own cluster early on (i.e. low similarity to the rest).\n",
+    "  - BRAF, p38a, KDR, LCK (TKL, CMGC and TK group) as well as EGFR and ErbB2 (TK) form one cluster.\n",
+    "  - Surprisingly, MET (TK) forms a singleton.\n",
     "- Overall observations:\n",
     "  - Members of the same family (here: EGFR and ErbB2) are grouped together closely in the _sequence_ _kissim_, and _ligand-profile_ approaches.\n",
-    "  - The atypical kinase p110a show high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches\n",
-    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (minus _sequence_)\n",
+    "  - The atypical kinase p110a show high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches.\n",
+    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (excpect for _sequence_).\n",
     "\n",
     "<!---\n",
     "- Comparison to multi-target profiles in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
@@ -998,18 +1095,18 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "We have assessed kinase similarity from many different angles: the pocket sequence, the pocket structure, ligand binding modes (interaction fingerprints), and ligand bioactivities. We see from our results, that different conclusions can be drawn for each perspective, while some observations agree.\n",
-    "Thus, it is beneficial to consider different metrics to study kinase similarity.\n",
+    "We have assessed kinase similarity from many different angles: the pocket sequence, the pocket structure, ligand binding modes (interaction fingerprints), and ligand bioactivities. We see from our results that different conclusions can be drawn for each methods, while some observations agree.\n",
+    "Thus, it is beneficial to consider different strategies to study kinase similarity.\n",
     "\n",
     "The different approaches have strengths and drawbacks:\n",
     "\n",
-    "- The _sequence_ view: Sequence data is available for all kinases, however no information about structure or ligand binding is included.\n",
-    "- The _kissim_, _ifp_, and _ligand-profile_ views: Information about structure, ligand binding, or ligand bioactivity is included, however some kinases may not have any data, some kinases may have only few data, and most kinases may not be fully explored (even if a lot of data is available).\n",
-    "- All views rely on simplifications! \n",
+    "- The _sequence_ method: Sequence data is available for all kinases, however no information about structure or ligand binding is included.\n",
+    "- The _kissim_, _ifp_, and _ligand-profile_ methods: Information about structure, ligand binding, or ligand bioactivity is included, however some kinases may not have any data, some kinases may have only few data, and most kinases may not be fully explored (even if a lot of data is available).\n",
+    "- All methods rely on simplifications! \n",
     "  - Dealing with multiple measurements\n",
-    "    - _ligand-profile_ view: Only one measurement per kinase-ligand is used here.\n",
-    "    - _kissim_ and _ifp_ view: Only one structure/IFP pair represents one kinase pair here.\n",
-    "  - Bioactivity cutoffs to define active/inactive compounds in case of the _ligand-profile_ view"
+    "    - _ligand-profile_ method: Only one measurement per kinase-ligand is used here.\n",
+    "    - _kissim_ and _ifp_ method: Only one structure/IFP pair represents one kinase pair here.\n",
+    "  - Bioactivity cutoffs to define active/inactive compounds in case of the _ligand-profile_ method."
    ]
   },
   {
@@ -1020,7 +1117,8 @@
     "\n",
     "1. Can you think of another perspective to assess kinase similarity?\n",
     "2. Are there another method to assess and visualize the similarities?\n",
-    "3. Can you name at least one advantage and one disadvantage per perspective discussed here?"
+    "3. Can you name at least one advantage and one disadvantage per perspective discussed here?\n",
+    "4. Could the different methods be applied to proteins in general?"
    ]
   }
  ],
@@ -1040,7 +1138,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 38f72c51afeb9d0b19807e64ee5d3314ecf8e189 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 29 Sep 2021 09:13:08 +0200
Subject: [PATCH 118/158] flake8 checks.

---
 .../talktorial.ipynb                          | 141 ++++++++++++------
 1 file changed, 93 insertions(+), 48 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index f116469a..390b3cb9 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,7 +160,7 @@
     "    \"ligand-profile\": \"T027_kinase_similarity_ligand_profile\"\n",
     "}\n",
     "kinase_distance_matrix_paths = {\n",
-    "    perspective: HERE / f\"../{folder_name}/data/kinase_distance_matrix.csv\" \n",
+    "    perspective: HERE / f\"../{folder_name}/data/kinase_distance_matrix.csv\"\n",
     "    for perspective, folder_name in kinase_distance_matrix_paths.items()\n",
     "}"
    ]
@@ -174,9 +174,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (90 > 79 characters)\n"
+     ]
+    }
+   ],
    "source": [
     "kinase_distance_matrices = {}\n",
     "for kinsim_perspective, path in kinase_distance_matrix_paths.items():\n",
@@ -199,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -227,9 +235,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (80 > 79 characters)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -256,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -323,9 +338,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (85 > 79 characters)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -347,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -356,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -528,7 +550,7 @@
        "p38a   0.370645  0.000000  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,12 +573,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -569,12 +591,13 @@
    ],
    "source": [
     "sns.heatmap(\n",
-    "    kinase_distance_matrix, \n",
-    "    linewidths=0, \n",
-    "    annot=True, \n",
-    "    square=True, cbar_kws={'label': 'Kinase distance'}, \n",
-    "    cmap='viridis', \n",
-    ");"
+    "    kinase_distance_matrix,\n",
+    "    linewidths=0,\n",
+    "    annot=True,\n",
+    "    square=True, cbar_kws={'label': 'Kinase distance'},\n",
+    "    cmap='viridis',\n",
+    ")\n",
+    "plt.show()"
    ]
   },
   {
@@ -595,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +632,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -629,7 +652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -645,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -661,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -710,14 +733,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
     "def _min_max_normalize(distance_matrix_df):\n",
     "    \"\"\"\n",
     "    Apply min-max normalization to input DataFrame.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    kinase_distance_matrix_df : pd.DataFrame\n",
@@ -736,7 +759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -757,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -766,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -774,7 +797,7 @@
     "    \"\"\"\n",
     "    Define the order in which kinases shall\n",
     "    appear in the input DataFrame.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    kinase_distance_matrix_df : pd.DataFrame\n",
@@ -795,14 +818,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 28,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (80 > 79 characters)\n"
+     ]
+    }
+   ],
    "source": [
     "kinase_distance_matrices_normalized = {\n",
-    "    descriptor: _define_kinase_order(similarity_df, kinase_names) \n",
+    "    descriptor: _define_kinase_order(similarity_df, kinase_names)\n",
     "    for descriptor, similarity_df in kinase_distance_matrices_normalized.items()\n",
     "}"
    ]
@@ -816,14 +847,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
     "def heatmap(distance_matrix, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
     "    Generate a heatmap from a distance matrix.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    distance_matrix : pd.DataFrame\n",
@@ -834,25 +865,25 @@
     "        Plot title.\n",
     "    \"\"\"\n",
     "    sns.heatmap(\n",
-    "        distance_matrix, \n",
-    "        linewidths=0, \n",
-    "        annot=True, \n",
-    "        square=True, \n",
-    "        cmap='viridis', \n",
+    "        distance_matrix,\n",
+    "        linewidths=0,\n",
+    "        annot=True,\n",
+    "        square=True,\n",
+    "        cmap='viridis',\n",
     "        ax=ax\n",
     "    )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
     "def dendrogram(distance_matrix, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
     "    Generate a dendrogram from a distance matrix.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    distance_matrix : pd.DataFrame\n",
@@ -874,7 +905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -888,6 +919,13 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "3:80: E501 line too long (87 > 79 characters)\n"
+     ]
     }
    ],
    "source": [
@@ -907,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1039,9 +1077,16 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 42,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "1:80: E501 line too long (94 > 79 characters)\n"
+     ]
     }
    ],
    "source": [

From 787eb827a340aafb1e6ca2180772d7675c8eee73 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 29 Sep 2021 09:15:56 +0200
Subject: [PATCH 119/158] :pencil2: fix typo.

---
 .../talktorial.ipynb                                            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 390b3cb9..2436e411 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -1116,7 +1116,7 @@
     "- Overall observations:\n",
     "  - Members of the same family (here: EGFR and ErbB2) are grouped together closely in the _sequence_ _kissim_, and _ligand-profile_ approaches.\n",
     "  - The atypical kinase p110a show high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches.\n",
-    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (excpect for _sequence_).\n",
+    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (except for _sequence_).\n",
     "\n",
     "<!---\n",
     "- Comparison to multi-target profiles in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",

From b26041e42e6aeb37c339ef5e0415f452c8c298eb Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 29 Sep 2021 09:18:08 +0200
Subject: [PATCH 120/158] running notebook from top to bottom.

---
 .../talktorial.ipynb                          | 104 +++++-------------
 1 file changed, 30 insertions(+), 74 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 2436e411..e650edfc 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,17 +174,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (90 > 79 characters)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "kinase_distance_matrices = {}\n",
     "for kinsim_perspective, path in kinase_distance_matrix_paths.items():\n",
@@ -207,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -235,16 +227,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (80 > 79 characters)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -271,7 +256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -300,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -338,16 +323,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (85 > 79 characters)\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -369,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -378,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -550,7 +528,7 @@
        "p38a   0.370645  0.000000  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -573,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -632,7 +610,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -652,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -668,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,7 +662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -733,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -759,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -818,19 +796,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 21,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (80 > 79 characters)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "kinase_distance_matrices_normalized = {\n",
     "    descriptor: _define_kinase_order(similarity_df, kinase_names)\n",
@@ -847,7 +817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -905,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -919,13 +889,6 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "3:80: E501 line too long (87 > 79 characters)\n"
-     ]
     }
    ],
    "source": [
@@ -945,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1077,16 +1040,9 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 34,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1:80: E501 line too long (94 > 79 characters)\n"
-     ]
     }
    ],
    "source": [

From e94b6b1f36098aacfe07069e9e11d72e61be3d28 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Fri, 1 Oct 2021 14:18:34 +0200
Subject: [PATCH 121/158] very minor textual adaptions in T023

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 77 ++++++++-----------
 1 file changed, 34 insertions(+), 43 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 47d5bf34..4d3f42ac 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -19,7 +19,8 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available?"
+    "In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available?\n",
+    "Finally, we select a set of kinases which will be analyzed in the forthcoming talktorials **T024**-**T028** w.r.t. their similarity, with the goal of generating a broad insight into potential off-target effects."
    ]
   },
   {
@@ -88,7 +89,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating and thereby activating themselves or other proteins and are the most frequently mutated proteins in tumors.\n",
+    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating, and thereby activating, themselves or other proteins and are among the most frequently mutated proteins in tumors.\n",
     "\n",
     "As of September 2021, $5782$ X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and $67$ FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
     "\n",
@@ -107,7 +108,7 @@
     "\n",
     "The human kinome consists of over $500$ protein kinases (this number may vary depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
     "\n",
-    "In [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning _et al._ clustered the human protein kinases based on their sequence into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
+    "As reported in [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning _et al._ clustered the human protein kinases based on their sequence similarity into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases, as well as atypical kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
     "![Manning tree with number of structures per kinase (KinMap)](images/kinmap_n_structures_per_kinase.png)\n",
     "\n",
@@ -136,7 +137,7 @@
     "![Kinase structure with key motifs](images/T023_kinase_structure.png)\n",
     "\n",
     "*Figure 2:* \n",
-    "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop. The example structure shown here represents CDK2, PDB ID: 1FIN. Check the appendix on how to generate this visualization with `opencadd`."
+    "Kinase structure with important key motifs: Hinge region, DFG motif, αC-helix, and G-rich loop. The example structure shown here represents CDK2, PDB ID: [1FIN](https://www.rcsb.org/structure/1fin). Check the appendix on how to generate this visualization with `opencadd`."
    ]
   },
   {
@@ -145,7 +146,7 @@
    "source": [
     "### Kinase resources\n",
     "\n",
-    "Nevertheless the focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development ([<i>Annual Reports in Medicinal Chemistry</i> 50 (2017): 197-236](https://doi.org/10.1016/bs.armc.2017.08.001))."
+    "The focus on this protein family has led to a plethora of freely available data on compounds, bioactivity, and structures that are being used for computational drug development ([<i>Annual Reports in Medicinal Chemistry</i> 50 (2017): 197-236](https://doi.org/10.1016/bs.armc.2017.08.001))."
    ]
   },
   {
@@ -219,7 +220,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, ChEMBL is not the only available database. Below is an non-exhaustive list of available kinase data sets.\n",
+    "However, ChEMBL is not the only available bioactivity database. Below is an non-exhaustive list of available kinase profiling data sets.\n",
     "\n",
     "\n",
     "- Karaman _et al._ dataset\n",
@@ -242,9 +243,9 @@
    "source": [
     "### Kinase similarity: Off-targets and promiscuous binding\n",
     "\n",
-    "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also other targets (off-targets). This promiscuous binding can cause mild to severe side effects.\n",
+    "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also with other targets (off-targets). This promiscuous binding can cause mild to severe side effects.\n",
     "\n",
-    "Predicting such side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib produces side effects in the highly sequentially-similar TK kinase group. However, it also affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
+    "Predicting such side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
     "\n",
     "![Erlotinib profiling data from Karaman dataset (KinMap)](images/kinmap_erlotinib_karaman.png)\n",
     "\n",
@@ -276,7 +277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the course of the kinase similarity talktorials (__Talktorials T024-T028__), we will use nine kinases from [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
+    "In the course of the kinase similarity talktorials (__Talktorials T024-T028__), we will use nine kinases from a study published in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629), which were selected for the following reasons:\n",
     "\n",
     "> - Profile 1 combined __EGFR__ and __ErbB2__ as targets and __BRAF__ as a (general) anti-target. \n",
     "> - Out of similar considerations, Profile 2 consisted of EGFR and __PI3K__ as targets and BRAF as anti-target. This profile is expected to be more challenging as PI3K is an atypical kinase and thus less similar to EGFR than for example ErbB2 used in Profile 1. \n",
@@ -336,7 +337,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "_Note_: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. Please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
+    "_Note_: You can run the kinase similarity __Talktorials T024-T028__ with your own set of kinases. To do so, please update the CSV file with your kinases; the only mandatory columns are `kinase_klifs` and `uniprot_id`."
    ]
   },
   {
@@ -560,7 +561,17 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n",
+      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
+      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
+     ]
+    }
+   ],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -602,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,36 +669,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51787e3622e445b2a4e6a934f21be93c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f3ee7ef0e80c4b55ae90db00bd2f1b04",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "NGLWidget()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from opencadd.structure.pocket import PocketKlifs, PocketViewer\n",
     "\n",
@@ -704,11 +688,18 @@
     "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
     "viewer.viewer"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -722,7 +713,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From 3d66b32de4da1b3f951e568aeab3f88e17748263 Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Fri, 1 Oct 2021 14:47:59 +0200
Subject: [PATCH 122/158] T023: Update KLIFS image URL, add cell output

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 51 ++++++++++++-------
 1 file changed, 34 insertions(+), 17 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 4d3f42ac..c50ac57a 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -162,7 +162,7 @@
    "source": [
     "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily and quickly looked up in any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
     "\n",
-    "![KLIFS binding site](https://klifs.net/images/faq/xcolors.png.pagespeed.ic.dprMuoZGzn.webp)\n",
+    "![KLIFS binding site](https://klifs.net/images/faq/colors.png)\n",
     "\n",
     "*Figure 1:* \n",
     "Kinase binding site residues as defined by KLIFS.\n",
@@ -561,17 +561,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:opencadd.databases.klifs.api:If you want to see an non-truncated version of the DataFrames in this module, use `pd.set_option('display.max_columns', 50)` in your notebook.\n",
-      "INFO:opencadd.databases.klifs.api:Set up remote session...\n",
-      "INFO:opencadd.databases.klifs.api:Remote session is ready!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from opencadd.databases.klifs import setup_remote\n",
     "\n",
@@ -613,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,9 +659,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c842ece1bf864f258dc9c457898ca680",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "25bb28b90afc440cb2bdc16e9cb55a88",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "NGLWidget()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from opencadd.structure.pocket import PocketKlifs, PocketViewer\n",
     "\n",
@@ -699,7 +716,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -713,7 +730,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.7"
   },
   "toc-autonumbering": true,
   "widgets": {

From bba443b6f02921eb15f08de62b184affc4d8b4ec Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Fri, 1 Oct 2021 14:50:24 +0200
Subject: [PATCH 123/158] T023: Add comment on KLIFS ID 4367

---
 .../talktorials/T023_what_is_a_kinase/talktorial.ipynb        | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index c50ac57a..a3d7e29c 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -654,7 +654,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase structure visualization with `opencadd`"
+    "### Kinase structure visualization with `opencadd`\n",
+    "\n",
+    "We are using as an example the ATP-bound CDK2 structure with the KLIFS ID [4367](https://klifs.net/details.php?structure_id=4367)."
    ]
   },
   {

From aa41f288c3596db1250b702c272e9010d43e9af5 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Fri, 1 Oct 2021 14:53:11 +0200
Subject: [PATCH 124/158] very minor adaptions in T024

---
 .../talktorial.ipynb                          | 2949 ++++++++---------
 1 file changed, 1369 insertions(+), 1580 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 2afe8fe8..3dbdf162 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the $85$ residues pocket sequence for each kinase. \n",
+    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the $85$ residues of the pocket sequence for each kinase. \n",
     "\n",
     "Two similarity measures are implemented:\n",
     "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.\n",
@@ -100,7 +100,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Kinase similarity descriptor: sequence\n",
+    "### Kinase similarity descriptor: Sequence\n",
     "\n",
     "As mentioned in the previous talktorial, sequence is often used to assess kinase similarity (see the phylogenetic tree developed by Manning _et al._ [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762))."
    ]
@@ -109,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, the KLIFS' pocket sequence is used for two main reasons:\n",
+    "In this talktorial, the KLIFS pocket sequence is used for two main reasons:\n",
     "\n",
     "1. The sequence is of fixed length (it contains $85$ residues), which makes computation for pairwise similarity between two sequences easy.\n",
     "\n",
@@ -144,7 +144,7 @@
     "A simple way of assessing the similarity between two sequences is to use the so-called identity score.\n",
     "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If they are, the entry is set to $1$, and $0$ otherwise.\n",
     "\n",
-    "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which, in the case of KLIFS pocket sequence is $85$."
+    "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which in the case of KLIFS pocket sequence is $85$."
    ]
   },
   {
@@ -777,692 +777,660 @@
     {
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-       "      <th class=\"col_heading level0 col13\" >Q</th>\n",
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-       "      <th class=\"col_heading level0 col15\" >S</th>\n",
-       "      <th class=\"col_heading level0 col16\" >T</th>\n",
-       "      <th class=\"col_heading level0 col17\" >V</th>\n",
-       "      <th class=\"col_heading level0 col18\" >W</th>\n",
-       "      <th class=\"col_heading level0 col19\" >Y</th>\n",
-       "      <th class=\"col_heading level0 col20\" >B</th>\n",
-       "      <th class=\"col_heading level0 col21\" >Z</th>\n",
-       "      <th class=\"col_heading level0 col22\" >X</th>\n",
-       "      <th class=\"col_heading level0 col23\" >*</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
-       "      <td id=\"T_2f87e_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_2f87e_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
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-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_2f87e_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_2f87e_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_2f87e_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_2f87e_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_2f87e_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_2f87e_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_2f87e_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_2f87e_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_2f87e_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_2f87e_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_2f87e_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_2f87e_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_2f87e_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_2f87e_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_2f87e_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_2f87e_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_2f87e_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_2f87e_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_2f87e_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_2f87e_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_2f87e_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_2f87e_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_2f87e_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_2f87e_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_2f87e_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_2f87e_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_2f87e_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_2f87e_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_2f87e_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_2f87e_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_2f87e_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_2f87e_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_2f87e_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_2f87e_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_2f87e_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_2f87e_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_2f87e_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_2f87e_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_2f87e_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_2f87e_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_2f87e_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_2f87e_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_2f87e_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_2f87e_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_2f87e_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_2f87e_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_2f87e_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_2f87e_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_2f87e_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_2f87e_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_2f87e_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_2f87e_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_2f87e_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_2f87e_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_2f87e_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_2f87e_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_2f87e_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_2f87e_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_2f87e_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_2f87e_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_2f87e_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_2f87e_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_2f87e_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_2f87e_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_2f87e_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_2f87e_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_2f87e_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_2f87e_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_2f87e_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_2f87e_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_2f87e_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_2f87e_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_2f87e_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_2f87e_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_2f87e_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_2f87e_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_2f87e_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_2f87e_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_2f87e_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_2f87e_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_2f87e_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_2f87e_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_2f87e_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_2f87e_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_2f87e_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_2f87e_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_2f87e_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_2f87e_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_2f87e_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_2f87e_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_2f87e_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_2f87e_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_2f87e_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_2f87e_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_2f87e_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_2f87e_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_2f87e_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_2f87e_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_2f87e_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_2f87e_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_2f87e_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_2f87e_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_2f87e_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_2f87e_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_2f87e_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_2f87e_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_2f87e_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_2f87e_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_2f87e_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2f87e_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_2f87e_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_2f87e_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
+       "</style><table id=\"T_b14ab_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >A</th>        <th class=\"col_heading level0 col1\" >C</th>        <th class=\"col_heading level0 col2\" >D</th>        <th class=\"col_heading level0 col3\" >E</th>        <th class=\"col_heading level0 col4\" >F</th>        <th class=\"col_heading level0 col5\" >G</th>        <th class=\"col_heading level0 col6\" >H</th>        <th class=\"col_heading level0 col7\" >I</th>        <th class=\"col_heading level0 col8\" >K</th>        <th class=\"col_heading level0 col9\" >L</th>        <th class=\"col_heading level0 col10\" >M</th>        <th class=\"col_heading level0 col11\" >N</th>        <th class=\"col_heading level0 col12\" >P</th>        <th class=\"col_heading level0 col13\" >Q</th>        <th class=\"col_heading level0 col14\" >R</th>        <th class=\"col_heading level0 col15\" >S</th>        <th class=\"col_heading level0 col16\" >T</th>        <th class=\"col_heading level0 col17\" >V</th>        <th class=\"col_heading level0 col18\" >W</th>        <th class=\"col_heading level0 col19\" >Y</th>        <th class=\"col_heading level0 col20\" >B</th>        <th class=\"col_heading level0 col21\" >Z</th>        <th class=\"col_heading level0 col22\" >X</th>        <th class=\"col_heading level0 col23\" >*</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "                        <td id=\"T_b14ab_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "                        <td id=\"T_b14ab_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "                        <td id=\"T_b14ab_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "                        <td id=\"T_b14ab_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "                        <td id=\"T_b14ab_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "                        <td id=\"T_b14ab_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "                        <td id=\"T_b14ab_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "                        <td id=\"T_b14ab_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "                        <td id=\"T_b14ab_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "                        <td id=\"T_b14ab_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "                        <td id=\"T_b14ab_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "                        <td id=\"T_b14ab_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "                        <td id=\"T_b14ab_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "                        <td id=\"T_b14ab_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "                        <td id=\"T_b14ab_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "                        <td id=\"T_b14ab_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "                        <td id=\"T_b14ab_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "                        <td id=\"T_b14ab_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "                        <td id=\"T_b14ab_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "                        <td id=\"T_b14ab_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "                        <td id=\"T_b14ab_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "                        <td id=\"T_b14ab_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "                        <td id=\"T_b14ab_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "                        <td id=\"T_b14ab_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "                        <td id=\"T_b14ab_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "                        <td id=\"T_b14ab_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "                        <td id=\"T_b14ab_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "                        <td id=\"T_b14ab_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "                        <td id=\"T_b14ab_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "                        <td id=\"T_b14ab_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "                        <td id=\"T_b14ab_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "                        <td id=\"T_b14ab_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "                        <td id=\"T_b14ab_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "                        <td id=\"T_b14ab_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "                        <td id=\"T_b14ab_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "                        <td id=\"T_b14ab_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "                        <td id=\"T_b14ab_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "                        <td id=\"T_b14ab_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "                        <td id=\"T_b14ab_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "                        <td id=\"T_b14ab_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "                        <td id=\"T_b14ab_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "                        <td id=\"T_b14ab_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "                        <td id=\"T_b14ab_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "                        <td id=\"T_b14ab_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "                        <td id=\"T_b14ab_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "                        <td id=\"T_b14ab_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "                        <td id=\"T_b14ab_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "                        <td id=\"T_b14ab_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "                        <td id=\"T_b14ab_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "                        <td id=\"T_b14ab_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "                        <td id=\"T_b14ab_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "                        <td id=\"T_b14ab_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "                        <td id=\"T_b14ab_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "                        <td id=\"T_b14ab_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "                        <td id=\"T_b14ab_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "                        <td id=\"T_b14ab_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "                        <td id=\"T_b14ab_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "                        <td id=\"T_b14ab_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "                        <td id=\"T_b14ab_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "                        <td id=\"T_b14ab_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "                        <td id=\"T_b14ab_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "                        <td id=\"T_b14ab_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "                        <td id=\"T_b14ab_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "                        <td id=\"T_b14ab_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "                        <td id=\"T_b14ab_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "                        <td id=\"T_b14ab_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "                        <td id=\"T_b14ab_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "                        <td id=\"T_b14ab_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "                        <td id=\"T_b14ab_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "                        <td id=\"T_b14ab_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "                        <td id=\"T_b14ab_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "                        <td id=\"T_b14ab_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "                        <td id=\"T_b14ab_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "                        <td id=\"T_b14ab_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "                        <td id=\"T_b14ab_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "                        <td id=\"T_b14ab_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "                        <td id=\"T_b14ab_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "                        <td id=\"T_b14ab_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "                        <td id=\"T_b14ab_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "                        <td id=\"T_b14ab_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "                        <td id=\"T_b14ab_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "                        <td id=\"T_b14ab_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "                        <td id=\"T_b14ab_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "                        <td id=\"T_b14ab_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "                        <td id=\"T_b14ab_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "                        <td id=\"T_b14ab_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "                        <td id=\"T_b14ab_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "                        <td id=\"T_b14ab_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "                        <td id=\"T_b14ab_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "                        <td id=\"T_b14ab_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "                        <td id=\"T_b14ab_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "                        <td id=\"T_b14ab_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "                        <td id=\"T_b14ab_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "                        <td id=\"T_b14ab_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "                        <td id=\"T_b14ab_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "                        <td id=\"T_b14ab_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "                        <td id=\"T_b14ab_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "                        <td id=\"T_b14ab_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "                        <td id=\"T_b14ab_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "                        <td id=\"T_b14ab_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "                        <td id=\"T_b14ab_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "                        <td id=\"T_b14ab_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "                        <td id=\"T_b14ab_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "                        <td id=\"T_b14ab_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "                        <td id=\"T_b14ab_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "                        <td id=\"T_b14ab_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "                        <td id=\"T_b14ab_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "                        <td id=\"T_b14ab_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "                        <td id=\"T_b14ab_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "                        <td id=\"T_b14ab_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "                        <td id=\"T_b14ab_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b14ab_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "                        <td id=\"T_b14ab_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "                        <td id=\"T_b14ab_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc951ee7f70>"
+       "<pandas.io.formats.style.Styler at 0x7fe2b8b0e640>"
       ]
      },
      "execution_count": 10,
@@ -1737,7 +1705,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def pairwise_kinase_similarities(kinase_names, similarity_measure):\n",
+    "def pairwise_kinase_similarities(kinase_names, kinase_sequences, similarity_measure):\n",
     "    \"\"\"\n",
     "    Calculate pairwise similarities between a set of kinases.\n",
     "    \n",
@@ -1745,6 +1713,8 @@
     "    ----------\n",
     "    kinase_names : list of str\n",
     "        Kinase names.\n",
+    "    kinase_sequences : list of str\n",
+    "        Kinase pocket sequences.\n",
     "    similarity_measure : str\n",
     "        Select similarity measure: \"identity\" or \"substitution\".\n",
     "        \n",
@@ -1757,16 +1727,16 @@
     "    kinase_similarity_matrix = np.zeros((len(kinase_names),\n",
     "                                         len(kinase_names)))\n",
     "    # Iterate over matrix and fill with similarity values\n",
-    "    for i, kinase_name1 in enumerate(kinase_selection_df.kinase_klifs):\n",
-    "        for j, kinase_name2 in enumerate(kinase_selection_df.kinase_klifs):\n",
+    "    for i, kinase_name1 in enumerate(kinase_names):\n",
+    "        for j, kinase_name2 in enumerate(kinase_names):\n",
     "            kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
     "                                                                 kinase_sequences[kinase_name2],\n",
     "                                                                 similarity_measure)\n",
     "\n",
     "    # Cast matrix to DataFrame\n",
     "    kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                               index=kinase_selection_df.kinase_klifs,\n",
-    "                                               columns=kinase_selection_df.kinase_klifs)\n",
+    "                                               index=kinase_names,\n",
+    "                                               columns=kinase_names)\n",
     "    kinase_similarity_matrix_df.index.name = None\n",
     "    kinase_similarity_matrix_df.columns.name = None\n",
     "\n",
@@ -1983,7 +1953,7 @@
    ],
    "source": [
     "kinase_similarity_matrix_identity_df = pairwise_kinase_similarities(\n",
-    "    kinase_names, similarity_measure=\"identity\"\n",
+    "    kinase_names, kinase_sequences, similarity_measure=\"identity\"\n",
     ")\n",
     "kinase_similarity_matrix_identity_df\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -1997,273 +1967,222 @@
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-       "<table id=\"T_13e6e_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_13e6e_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_13e6e_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_13e6e_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_13e6e_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_13e6e_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_13e6e_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_13e6e_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_13e6e_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_13e6e_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_13e6e_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_13e6e_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_13e6e_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_13e6e_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_13e6e_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_13e6e_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_13e6e_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_13e6e_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_13e6e_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_13e6e_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_13e6e_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_13e6e_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_13e6e_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_13e6e_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_13e6e_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_13e6e_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_13e6e_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_13e6e_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_13e6e_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_13e6e_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_13e6e_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_13e6e_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_13e6e_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_13e6e_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_13e6e_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_13e6e_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_13e6e_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_13e6e_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_13e6e_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_13e6e_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_13e6e_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_13e6e_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_13e6e_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_13e6e_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_13e6e_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_13e6e_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_13e6e_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_13e6e_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13e6e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_13e6e_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_13e6e_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_13e6e_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_13e6e_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_13e6e_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_13e6e_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_13e6e_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_13e6e_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_13e6e_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
+       "#T_c1dca_row0_col0,#T_c1dca_row1_col1,#T_c1dca_row2_col2,#T_c1dca_row3_col3,#T_c1dca_row4_col4,#T_c1dca_row5_col5,#T_c1dca_row6_col6,#T_c1dca_row7_col7,#T_c1dca_row8_col8{\n",
+       "            background-color:  #008000;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_c1dca_row0_col1,#T_c1dca_row1_col0{\n",
+       "            background-color:  #1c8e1c;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col2,#T_c1dca_row1_col2{\n",
+       "            background-color:  #e8f2e8;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col3,#T_c1dca_row7_col3{\n",
+       "            background-color:  #92c892;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col4,#T_c1dca_row3_col5,#T_c1dca_row7_col4,#T_c1dca_row8_col4{\n",
+       "            background-color:  #add5ad;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col5{\n",
+       "            background-color:  #b3d8b3;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col6{\n",
+       "            background-color:  #97ca97;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col7{\n",
+       "            background-color:  #8ec58e;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row0_col8,#T_c1dca_row3_col8,#T_c1dca_row4_col6,#T_c1dca_row6_col8,#T_c1dca_row8_col6{\n",
+       "            background-color:  #a7d2a7;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col3,#T_c1dca_row6_col3{\n",
+       "            background-color:  #9ccd9c;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col4,#T_c1dca_row3_col4,#T_c1dca_row4_col3,#T_c1dca_row5_col7,#T_c1dca_row7_col5,#T_c1dca_row8_col7{\n",
+       "            background-color:  #a6d2a6;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col5,#T_c1dca_row4_col5{\n",
+       "            background-color:  #b1d7b1;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col6,#T_c1dca_row6_col7{\n",
+       "            background-color:  #9dcd9d;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col7,#T_c1dca_row3_col7,#T_c1dca_row8_col5{\n",
+       "            background-color:  #8bc48b;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row1_col8,#T_c1dca_row4_col7,#T_c1dca_row6_col5,#T_c1dca_row7_col6{\n",
+       "            background-color:  #a4d0a4;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row2_col0,#T_c1dca_row2_col1,#T_c1dca_row2_col3,#T_c1dca_row2_col4,#T_c1dca_row2_col5,#T_c1dca_row2_col6,#T_c1dca_row2_col7,#T_c1dca_row2_col8,#T_c1dca_row5_col2,#T_c1dca_row7_col2{\n",
+       "            background-color:  #ebf3eb;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row3_col0,#T_c1dca_row7_col1{\n",
+       "            background-color:  #8dc58d;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row3_col1{\n",
+       "            background-color:  #96c996;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row3_col2,#T_c1dca_row4_col2{\n",
+       "            background-color:  #dfeddf;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row3_col6{\n",
+       "            background-color:  #9bcc9b;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row4_col0{\n",
+       "            background-color:  #a6d1a6;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row4_col1,#T_c1dca_row8_col1{\n",
+       "            background-color:  #a0cea0;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row4_col8,#T_c1dca_row5_col6{\n",
+       "            background-color:  #aad3aa;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row5_col0{\n",
+       "            background-color:  #b5d9b5;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row5_col1{\n",
+       "            background-color:  #b2d7b2;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row5_col3{\n",
+       "            background-color:  #b7dab7;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row5_col4{\n",
+       "            background-color:  #badbba;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row5_col8,#T_c1dca_row7_col0{\n",
+       "            background-color:  #90c790;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row6_col0{\n",
+       "            background-color:  #93c893;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row6_col1{\n",
+       "            background-color:  #9acb9a;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row6_col2,#T_c1dca_row8_col2{\n",
+       "            background-color:  #e1eee1;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row6_col4,#T_c1dca_row8_col3{\n",
+       "            background-color:  #a9d3a9;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row7_col8{\n",
+       "            background-color:  #aed5ae;\n",
+       "            color:  #000000;\n",
+       "        }#T_c1dca_row8_col0{\n",
+       "            background-color:  #a3d0a3;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_c1dca_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_c1dca_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "                        <td id=\"T_c1dca_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "                        <td id=\"T_c1dca_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "                        <td id=\"T_c1dca_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "                        <td id=\"T_c1dca_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "                        <td id=\"T_c1dca_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_c1dca_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "                        <td id=\"T_c1dca_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "                        <td id=\"T_c1dca_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "                        <td id=\"T_c1dca_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "                        <td id=\"T_c1dca_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "                        <td id=\"T_c1dca_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "                        <td id=\"T_c1dca_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "                        <td id=\"T_c1dca_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "                        <td id=\"T_c1dca_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "                        <td id=\"T_c1dca_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "                        <td id=\"T_c1dca_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "                        <td id=\"T_c1dca_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "                        <td id=\"T_c1dca_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "                        <td id=\"T_c1dca_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "                        <td id=\"T_c1dca_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "                        <td id=\"T_c1dca_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "                        <td id=\"T_c1dca_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "                        <td id=\"T_c1dca_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "                        <td id=\"T_c1dca_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "                        <td id=\"T_c1dca_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "                        <td id=\"T_c1dca_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "                        <td id=\"T_c1dca_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "                        <td id=\"T_c1dca_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "                        <td id=\"T_c1dca_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "                        <td id=\"T_c1dca_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "                        <td id=\"T_c1dca_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "                        <td id=\"T_c1dca_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "                        <td id=\"T_c1dca_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "                        <td id=\"T_c1dca_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "                        <td id=\"T_c1dca_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "                        <td id=\"T_c1dca_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "                        <td id=\"T_c1dca_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "                        <td id=\"T_c1dca_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "                        <td id=\"T_c1dca_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "                        <td id=\"T_c1dca_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "                        <td id=\"T_c1dca_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "                        <td id=\"T_c1dca_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "                        <td id=\"T_c1dca_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "                        <td id=\"T_c1dca_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "                        <td id=\"T_c1dca_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_c1dca_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_c1dca_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "                        <td id=\"T_c1dca_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "                        <td id=\"T_c1dca_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "                        <td id=\"T_c1dca_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "                        <td id=\"T_c1dca_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "                        <td id=\"T_c1dca_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "                        <td id=\"T_c1dca_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "                        <td id=\"T_c1dca_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "                        <td id=\"T_c1dca_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "                        <td id=\"T_c1dca_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc9514ab730>"
+       "<pandas.io.formats.style.Styler at 0x7fe25bb05ee0>"
       ]
      },
      "execution_count": 21,
@@ -2467,7 +2386,7 @@
    ],
    "source": [
     "kinase_similarity_matrix_substitution_df = pairwise_kinase_similarities(\n",
-    "    kinase_names, similarity_measure=\"substitution\"\n",
+    "    kinase_names, kinase_sequences, similarity_measure=\"substitution\"\n",
     ")\n",
     "kinase_similarity_matrix_substitution_df\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -2481,317 +2400,252 @@
     {
      "data": {
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-       "  background-color: #008000;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row0_col1 {\n",
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-       "#T_e02c4_row0_col2 {\n",
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-       "  background-color: #73b873;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_e02c4_row0_col4, #T_e02c4_row0_col5, #T_e02c4_row1_col4, #T_e02c4_row3_col8, #T_e02c4_row4_col5, #T_e02c4_row8_col7 {\n",
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-       "#T_e02c4_row0_col6 {\n",
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-       "  background-color: #6fb76f;\n",
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-       "#T_e02c4_row0_col8, #T_e02c4_row8_col1 {\n",
-       "  background-color: #92c892;\n",
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-       "#T_e02c4_row1_col0 {\n",
-       "  background-color: #188c18;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row1_col2 {\n",
-       "  background-color: #e3efe3;\n",
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-       "#T_e02c4_row1_col3 {\n",
-       "  background-color: #79bc79;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row1_col5, #T_e02c4_row1_col8, #T_e02c4_row7_col4, #T_e02c4_row8_col4 {\n",
-       "  background-color: #97ca97;\n",
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-       "#T_e02c4_row1_col6 {\n",
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-       "#T_e02c4_row1_col7 {\n",
-       "  background-color: #75b975;\n",
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-       "}\n",
-       "#T_e02c4_row2_col0, #T_e02c4_row2_col1, #T_e02c4_row2_col3, #T_e02c4_row2_col4, #T_e02c4_row2_col5, #T_e02c4_row2_col6, #T_e02c4_row2_col7, #T_e02c4_row2_col8, #T_e02c4_row7_col2 {\n",
-       "  background-color: #ebf3eb;\n",
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-       "}\n",
-       "#T_e02c4_row3_col0, #T_e02c4_row7_col3 {\n",
-       "  background-color: #74b974;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row3_col1, #T_e02c4_row6_col7 {\n",
-       "  background-color: #78bb78;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row3_col2, #T_e02c4_row5_col2 {\n",
-       "  background-color: #e0eedf;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row3_col4 {\n",
-       "  background-color: #89c389;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row3_col5, #T_e02c4_row4_col0 {\n",
-       "  background-color: #8ec58e;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row3_col6, #T_e02c4_row4_col3, #T_e02c4_row5_col7 {\n",
-       "  background-color: #85c185;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row3_col7 {\n",
-       "  background-color: #6eb66e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row4_col1 {\n",
-       "  background-color: #8ac48a;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row4_col2 {\n",
-       "  background-color: #daebda;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row4_col6, #T_e02c4_row4_col7, #T_e02c4_row7_col5 {\n",
-       "  background-color: #8cc58c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row4_col8 {\n",
-       "  background-color: #95c995;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row5_col0, #T_e02c4_row8_col6 {\n",
-       "  background-color: #90c790;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row5_col1 {\n",
-       "  background-color: #94c994;\n",
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-       "#T_e02c4_row5_col3, #T_e02c4_row8_col3 {\n",
-       "  background-color: #8dc58d;\n",
-       "  color: #000000;\n",
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-       "#T_e02c4_row5_col4 {\n",
-       "  background-color: #93c893;\n",
-       "  color: #000000;\n",
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-       "  background-color: #88c388;\n",
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-       "  background-color: #72b872;\n",
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-       "  background-color: #76ba76;\n",
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-       "#T_e02c4_row6_col1 {\n",
-       "  background-color: #7ebe7e;\n",
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-       "#T_e02c4_row6_col2 {\n",
-       "  background-color: #d4e8d4;\n",
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-       "#T_e02c4_row6_col3 {\n",
-       "  background-color: #7fbe7f;\n",
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-       "#T_e02c4_row6_col5 {\n",
-       "  background-color: #82c082;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row6_col8 {\n",
-       "  background-color: #8bc48b;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row7_col0 {\n",
-       "  background-color: #77ba77;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_e02c4_row7_col1 {\n",
-       "  background-color: #78bb78;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row7_col6 {\n",
-       "  background-color: #84c184;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row7_col8 {\n",
-       "  background-color: #99cb99;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row8_col0 {\n",
-       "  background-color: #91c791;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row8_col2 {\n",
-       "  background-color: #ddecdd;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_e02c4_row8_col5 {\n",
-       "  background-color: #71b771;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_e02c4_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_e02c4_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_e02c4_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_e02c4_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_e02c4_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_e02c4_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_e02c4_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_e02c4_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_e02c4_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_e02c4_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_e02c4_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_e02c4_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_e02c4_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_e02c4_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_e02c4_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_e02c4_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_e02c4_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_e02c4_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_e02c4_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_e02c4_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_e02c4_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_e02c4_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_e02c4_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_e02c4_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_e02c4_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_e02c4_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_e02c4_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_e02c4_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_e02c4_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_e02c4_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_e02c4_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_e02c4_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_e02c4_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_e02c4_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_e02c4_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_e02c4_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_e02c4_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_e02c4_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_e02c4_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_e02c4_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_e02c4_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_e02c4_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_e02c4_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_e02c4_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_e02c4_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_e02c4_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_e02c4_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_e02c4_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_e02c4_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_e02c4_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_e02c4_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_e02c4_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_e02c4_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_e02c4_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_e02c4_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_e02c4_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_e02c4_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_e02c4_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_e02c4_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_e02c4_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_e02c4_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_e02c4_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_e02c4_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_e02c4_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_e02c4_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_e02c4_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_e02c4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_e02c4_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_e02c4_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_e02c4_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_e02c4_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_e02c4_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_e02c4_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_e02c4_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_e02c4_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_e02c4_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
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+       "            background-color:  #008000;\n",
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+       "            background-color:  #8fc68f;\n",
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+       "        }#T_0f456_row6_col0{\n",
+       "            background-color:  #76ba76;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row6_col1{\n",
+       "            background-color:  #7ebe7e;\n",
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+       "        }#T_0f456_row6_col2{\n",
+       "            background-color:  #d4e8d4;\n",
+       "            color:  #000000;\n",
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+       "            color:  #000000;\n",
+       "        }#T_0f456_row6_col5{\n",
+       "            background-color:  #82c082;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row6_col8{\n",
+       "            background-color:  #8bc48b;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row7_col0{\n",
+       "            background-color:  #77ba77;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row7_col6{\n",
+       "            background-color:  #84c184;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row7_col8{\n",
+       "            background-color:  #99cb99;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row8_col0{\n",
+       "            background-color:  #91c791;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row8_col2{\n",
+       "            background-color:  #ddecdd;\n",
+       "            color:  #000000;\n",
+       "        }#T_0f456_row8_col5{\n",
+       "            background-color:  #71b771;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_0f456_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_0f456_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_0f456_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "                        <td id=\"T_0f456_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "                        <td id=\"T_0f456_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "                        <td id=\"T_0f456_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "                        <td id=\"T_0f456_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "                        <td id=\"T_0f456_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "                        <td id=\"T_0f456_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "                        <td id=\"T_0f456_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_0f456_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "                        <td id=\"T_0f456_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "                        <td id=\"T_0f456_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "                        <td id=\"T_0f456_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "                        <td id=\"T_0f456_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "                        <td id=\"T_0f456_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "                        <td id=\"T_0f456_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "                        <td id=\"T_0f456_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "                        <td id=\"T_0f456_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "                        <td id=\"T_0f456_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "                        <td id=\"T_0f456_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "                        <td id=\"T_0f456_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "                        <td id=\"T_0f456_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "                        <td id=\"T_0f456_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "                        <td id=\"T_0f456_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "                        <td id=\"T_0f456_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "                        <td id=\"T_0f456_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "                        <td id=\"T_0f456_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "                        <td id=\"T_0f456_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "                        <td id=\"T_0f456_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "                        <td id=\"T_0f456_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "                        <td id=\"T_0f456_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "                        <td id=\"T_0f456_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "                        <td id=\"T_0f456_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "                        <td id=\"T_0f456_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "                        <td id=\"T_0f456_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "                        <td id=\"T_0f456_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "                        <td id=\"T_0f456_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "                        <td id=\"T_0f456_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "                        <td id=\"T_0f456_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "                        <td id=\"T_0f456_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "                        <td id=\"T_0f456_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "                        <td id=\"T_0f456_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "                        <td id=\"T_0f456_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "                        <td id=\"T_0f456_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "                        <td id=\"T_0f456_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "                        <td id=\"T_0f456_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "                        <td id=\"T_0f456_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "                        <td id=\"T_0f456_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "                        <td id=\"T_0f456_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "                        <td id=\"T_0f456_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "                        <td id=\"T_0f456_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "                        <td id=\"T_0f456_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "                        <td id=\"T_0f456_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "                        <td id=\"T_0f456_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "                        <td id=\"T_0f456_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "                        <td id=\"T_0f456_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "                        <td id=\"T_0f456_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "                        <td id=\"T_0f456_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "                        <td id=\"T_0f456_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "                        <td id=\"T_0f456_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "                        <td id=\"T_0f456_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "                        <td id=\"T_0f456_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "                        <td id=\"T_0f456_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "                        <td id=\"T_0f456_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "                        <td id=\"T_0f456_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "                        <td id=\"T_0f456_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_0f456_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "                        <td id=\"T_0f456_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "                        <td id=\"T_0f456_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "                        <td id=\"T_0f456_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "                        <td id=\"T_0f456_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "                        <td id=\"T_0f456_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "                        <td id=\"T_0f456_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "                        <td id=\"T_0f456_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "                        <td id=\"T_0f456_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "                        <td id=\"T_0f456_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc9514b5e20>"
+       "<pandas.io.formats.style.Styler at 0x7fe25baf8d00>"
       ]
      },
      "execution_count": 23,
@@ -2893,317 +2747,252 @@
     {
      "data": {
       "text/html": [
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-       "#T_71344_row0_col3 {\n",
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-       "  background-color: #5bad5b;\n",
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-       "#T_71344_row0_col7 {\n",
-       "  background-color: #7bbc7b;\n",
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-       "#T_71344_row1_col3 {\n",
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-       "  background-color: #54a954;\n",
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-       "#T_71344_row1_col7 {\n",
-       "  background-color: #76ba76;\n",
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-       "  background-color: #008000;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #77ba77;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row3_col1, #T_71344_row6_col7 {\n",
-       "  background-color: #73b873;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row3_col2, #T_71344_row5_col2 {\n",
-       "  background-color: #0b850b;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row3_col4 {\n",
-       "  background-color: #61b061;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row3_col5, #T_71344_row4_col0 {\n",
-       "  background-color: #5dae5d;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row3_col6, #T_71344_row4_col3, #T_71344_row5_col7 {\n",
-       "  background-color: #65b265;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row3_col7 {\n",
-       "  background-color: #7cbd7c;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "#T_71344_row3_col8 {\n",
-       "  background-color: #5cad5c;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row4_col1 {\n",
-       "  background-color: #61af61;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row4_col2 {\n",
-       "  background-color: #118811;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row4_col6, #T_71344_row4_col7, #T_71344_row7_col5 {\n",
-       "  background-color: #5fae5f;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row4_col8, #T_71344_row5_col1 {\n",
-       "  background-color: #56aa56;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row5_col0, #T_71344_row8_col6 {\n",
-       "  background-color: #5aac5a;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row5_col3, #T_71344_row8_col3 {\n",
-       "  background-color: #5eae5e;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row5_col4 {\n",
-       "  background-color: #57ab57;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row5_col6, #T_71344_row6_col4 {\n",
-       "  background-color: #62b062;\n",
-       "  color: #f1f1f1;\n",
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-       "  background-color: #78bb78;\n",
-       "  color: #000000;\n",
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-       "#T_71344_row6_col0 {\n",
-       "  background-color: #75b975;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row6_col1 {\n",
-       "  background-color: #6db56d;\n",
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-       "#T_71344_row6_col2 {\n",
-       "  background-color: #168b16;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row6_col3 {\n",
-       "  background-color: #6cb56c;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row6_col5 {\n",
-       "  background-color: #69b369;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row6_col8 {\n",
-       "  background-color: #60af60;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row7_col0 {\n",
-       "  background-color: #74b974;\n",
-       "  color: #f1f1f1;\n",
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-       "#T_71344_row7_col1 {\n",
-       "  background-color: #72b872;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row7_col6 {\n",
-       "  background-color: #66b266;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row7_col8 {\n",
-       "  background-color: #52a852;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row8_col0 {\n",
-       "  background-color: #59ac59;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row8_col2 {\n",
-       "  background-color: #0e870e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_71344_row8_col5 {\n",
-       "  background-color: #79bc79;\n",
-       "  color: #000000;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_71344_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_71344_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_71344_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_71344_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_71344_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_71344_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_71344_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_71344_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_71344_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_71344_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_71344_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_71344_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_71344_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_71344_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_71344_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_71344_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_71344_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_71344_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_71344_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_71344_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_71344_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_71344_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_71344_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_71344_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_71344_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_71344_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_71344_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_71344_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_71344_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_71344_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_71344_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_71344_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_71344_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_71344_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_71344_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_71344_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_71344_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_71344_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_71344_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_71344_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_71344_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_71344_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_71344_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_71344_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_71344_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_71344_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_71344_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_71344_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_71344_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_71344_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_71344_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_71344_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_71344_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_71344_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_71344_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_71344_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_71344_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_71344_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_71344_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_71344_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_71344_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_71344_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_71344_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_71344_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_71344_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_71344_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_71344_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_71344_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_71344_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_71344_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_71344_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_71344_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_71344_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_71344_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_71344_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_71344_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_71344_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_71344_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_71344_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_71344_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_71344_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_71344_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_71344_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
+       "<style  type=\"text/css\" >\n",
+       "#T_b889a_row0_col0,#T_b889a_row1_col1,#T_b889a_row2_col2,#T_b889a_row3_col3,#T_b889a_row4_col4,#T_b889a_row5_col5,#T_b889a_row6_col6,#T_b889a_row7_col7,#T_b889a_row8_col8{\n",
+       "            background-color:  #ebf3eb;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col1{\n",
+       "            background-color:  #d4e8d4;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col2{\n",
+       "            background-color:  #0d860d;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row0_col3,#T_b889a_row5_col8{\n",
+       "            background-color:  #78bb78;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col4,#T_b889a_row0_col5,#T_b889a_row1_col4,#T_b889a_row4_col5,#T_b889a_row8_col7{\n",
+       "            background-color:  #5bad5b;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col6{\n",
+       "            background-color:  #6fb76f;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col7{\n",
+       "            background-color:  #7bbc7b;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row0_col8,#T_b889a_row8_col1{\n",
+       "            background-color:  #58ab58;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row1_col0{\n",
+       "            background-color:  #d3e7d3;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row1_col2{\n",
+       "            background-color:  #078407;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row1_col3{\n",
+       "            background-color:  #71b771;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row1_col5,#T_b889a_row1_col8,#T_b889a_row7_col4,#T_b889a_row8_col4{\n",
+       "            background-color:  #54a954;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row1_col6{\n",
+       "            background-color:  #64b164;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row1_col7{\n",
+       "            background-color:  #76ba76;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row2_col0,#T_b889a_row2_col1,#T_b889a_row2_col3,#T_b889a_row2_col4,#T_b889a_row2_col5,#T_b889a_row2_col6,#T_b889a_row2_col7,#T_b889a_row2_col8,#T_b889a_row7_col2{\n",
+       "            background-color:  #008000;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row3_col0,#T_b889a_row7_col3{\n",
+       "            background-color:  #77ba77;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col1,#T_b889a_row6_col7{\n",
+       "            background-color:  #73b873;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col2,#T_b889a_row5_col2{\n",
+       "            background-color:  #0b850b;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row3_col4{\n",
+       "            background-color:  #61b061;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col5,#T_b889a_row4_col0{\n",
+       "            background-color:  #5dae5d;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col6,#T_b889a_row4_col3,#T_b889a_row5_col7{\n",
+       "            background-color:  #65b265;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col7{\n",
+       "            background-color:  #7cbd7c;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row3_col8{\n",
+       "            background-color:  #5cad5c;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row4_col1{\n",
+       "            background-color:  #61af61;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row4_col2{\n",
+       "            background-color:  #118811;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row4_col6,#T_b889a_row4_col7,#T_b889a_row7_col5{\n",
+       "            background-color:  #5fae5f;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row4_col8,#T_b889a_row5_col1{\n",
+       "            background-color:  #56aa56;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row5_col0,#T_b889a_row8_col6{\n",
+       "            background-color:  #5aac5a;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row5_col3,#T_b889a_row8_col3{\n",
+       "            background-color:  #5eae5e;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row5_col4{\n",
+       "            background-color:  #57ab57;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row5_col6,#T_b889a_row6_col4{\n",
+       "            background-color:  #62b062;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col0{\n",
+       "            background-color:  #75b975;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col1{\n",
+       "            background-color:  #6db56d;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col2{\n",
+       "            background-color:  #168b16;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col3{\n",
+       "            background-color:  #6cb56c;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col5{\n",
+       "            background-color:  #69b369;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row6_col8{\n",
+       "            background-color:  #60af60;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row7_col0{\n",
+       "            background-color:  #74b974;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row7_col1{\n",
+       "            background-color:  #72b872;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row7_col6{\n",
+       "            background-color:  #66b266;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row7_col8{\n",
+       "            background-color:  #52a852;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row8_col0{\n",
+       "            background-color:  #59ac59;\n",
+       "            color:  #000000;\n",
+       "        }#T_b889a_row8_col2{\n",
+       "            background-color:  #0e870e;\n",
+       "            color:  #f1f1f1;\n",
+       "        }#T_b889a_row8_col5{\n",
+       "            background-color:  #79bc79;\n",
+       "            color:  #000000;\n",
+       "        }</style><table id=\"T_b889a_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
+       "                <tr>\n",
+       "                        <th id=\"T_b889a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "                        <td id=\"T_b889a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "                        <td id=\"T_b889a_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "                        <td id=\"T_b889a_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "                        <td id=\"T_b889a_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "                        <td id=\"T_b889a_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "                        <td id=\"T_b889a_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "                        <td id=\"T_b889a_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "                        <td id=\"T_b889a_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "                        <td id=\"T_b889a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "                        <td id=\"T_b889a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "                        <td id=\"T_b889a_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "                        <td id=\"T_b889a_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "                        <td id=\"T_b889a_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "                        <td id=\"T_b889a_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "                        <td id=\"T_b889a_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "                        <td id=\"T_b889a_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "                        <td id=\"T_b889a_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "                        <td id=\"T_b889a_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "                        <td id=\"T_b889a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "                        <td id=\"T_b889a_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "                        <td id=\"T_b889a_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "                        <td id=\"T_b889a_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "                        <td id=\"T_b889a_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "                        <td id=\"T_b889a_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "                        <td id=\"T_b889a_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "                        <td id=\"T_b889a_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "                        <td id=\"T_b889a_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "                        <td id=\"T_b889a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "                        <td id=\"T_b889a_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "                        <td id=\"T_b889a_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "                        <td id=\"T_b889a_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "                        <td id=\"T_b889a_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "                        <td id=\"T_b889a_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "                        <td id=\"T_b889a_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "                        <td id=\"T_b889a_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "                        <td id=\"T_b889a_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "                        <td id=\"T_b889a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "                        <td id=\"T_b889a_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "                        <td id=\"T_b889a_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "                        <td id=\"T_b889a_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "                        <td id=\"T_b889a_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "                        <td id=\"T_b889a_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "                        <td id=\"T_b889a_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "                        <td id=\"T_b889a_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "                        <td id=\"T_b889a_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "                        <td id=\"T_b889a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "                        <td id=\"T_b889a_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "                        <td id=\"T_b889a_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "                        <td id=\"T_b889a_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "                        <td id=\"T_b889a_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "                        <td id=\"T_b889a_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "                        <td id=\"T_b889a_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "                        <td id=\"T_b889a_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "                        <td id=\"T_b889a_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "                        <td id=\"T_b889a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "                        <td id=\"T_b889a_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "                        <td id=\"T_b889a_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "                        <td id=\"T_b889a_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "                        <td id=\"T_b889a_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "                        <td id=\"T_b889a_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "                        <td id=\"T_b889a_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "                        <td id=\"T_b889a_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "                        <td id=\"T_b889a_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "                        <td id=\"T_b889a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "                        <td id=\"T_b889a_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                        <th id=\"T_b889a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "                        <td id=\"T_b889a_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "                        <td id=\"T_b889a_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "                        <td id=\"T_b889a_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "                        <td id=\"T_b889a_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "                        <td id=\"T_b889a_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "                        <td id=\"T_b889a_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "                        <td id=\"T_b889a_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "                        <td id=\"T_b889a_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "                        <td id=\"T_b889a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "            </tr>\n",
+       "    </tbody></table>"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc951f1dc10>"
+       "<pandas.io.formats.style.Styler at 0x7fe25ba2fe20>"
       ]
      },
      "execution_count": 28,
@@ -3259,7 +3048,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -3273,7 +3062,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.5"
   },
   "toc-autonumbering": true,
   "widgets": {

From bb19ccaac8ea623bd46998fdd25a33996badd9f6 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Fri, 1 Oct 2021 17:38:08 +0200
Subject: [PATCH 125/158] very minor adaptions in T025

---
 .../talktorial.ipynb                          | 301 +++++++++---------
 1 file changed, 155 insertions(+), 146 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index f76f2757..8813b252 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -691,7 +691,7 @@
     "- Resolution: $\\le 3$ Angström\n",
     "- Quality score*: $\\ge 6$\n",
     "\n",
-    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms (the higher the better the structure quality)."
+    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms. A higher score indicates a better structure quality."
    ]
   },
   {
@@ -828,12 +828,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -926,7 +926,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprinting if any structures cannot be encoded."
+    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprint generation, if any structures cannot be encoded."
    ]
   },
   {
@@ -1293,7 +1293,9 @@
    "source": [
     "### Map structure to kinase distance matrix\n",
     "\n",
-    "First, as an intermediate step, we will use the structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we add the corresponding kinase name."
+    "Note: So far we compared individual structures, but we want to compare kinases (which can be represented by several structures, as plotted above).\n",
+    "\n",
+    "First, as an intermediate step, we will use the structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we use the corresponding kinase name."
    ]
   },
   {
@@ -1445,20 +1447,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Structure matrix of shape (1611, 1611)reduced to kinase matrix of shape (9, 9).\n"
+      "Structure matrix of shape (1611, 1611) reduced to kinase matrix of shape (9, 9).\n"
      ]
     }
    ],
    "source": [
     "print(\n",
-    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape}\"\n",
+    "    f\"Structure matrix of shape {structure_distance_matrix_df.shape} \"\n",
     "    f\"reduced to kinase matrix of shape {kinase_distance_matrix_df.shape}.\"\n",
     ")\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -1473,188 +1475,188 @@
      "data": {
       "text/html": [
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-       "#T_71223_row0_col1, #T_71223_row5_col4 {\n",
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-       "#T_71223_row0_col3, #T_71223_row2_col0, #T_71223_row4_col6 {\n",
+       "#T_83d37_row0_col3, #T_83d37_row2_col0, #T_83d37_row4_col6 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row0_col4, #T_71223_row0_col6 {\n",
+       "#T_83d37_row0_col4, #T_83d37_row0_col6 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row0_col5 {\n",
+       "#T_83d37_row0_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row0_col7 {\n",
+       "#T_83d37_row0_col7 {\n",
        "  background-color: #148a14;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row0_col8 {\n",
+       "#T_83d37_row0_col8 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row1_col0, #T_71223_row3_col2, #T_71223_row5_col2 {\n",
+       "#T_83d37_row1_col0, #T_83d37_row3_col2, #T_83d37_row5_col2 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row1_col2 {\n",
+       "#T_83d37_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row1_col3 {\n",
+       "#T_83d37_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row1_col4 {\n",
+       "#T_83d37_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row1_col5 {\n",
+       "#T_83d37_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row1_col6 {\n",
+       "#T_83d37_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row1_col7 {\n",
+       "#T_83d37_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row1_col8 {\n",
+       "#T_83d37_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row2_col1, #T_71223_row4_col5, #T_71223_row6_col5, #T_71223_row8_col4 {\n",
+       "#T_83d37_row2_col1, #T_83d37_row4_col5, #T_83d37_row6_col5, #T_83d37_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row2_col3 {\n",
+       "#T_83d37_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row2_col4 {\n",
+       "#T_83d37_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row2_col5 {\n",
+       "#T_83d37_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row2_col6 {\n",
+       "#T_83d37_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row2_col7 {\n",
+       "#T_83d37_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row2_col8, #T_71223_row4_col3, #T_71223_row8_col0, #T_71223_row8_col5 {\n",
+       "#T_83d37_row2_col8, #T_83d37_row4_col3, #T_83d37_row8_col0, #T_83d37_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col0 {\n",
+       "#T_83d37_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col1, #T_71223_row3_col6, #T_71223_row5_col0, #T_71223_row6_col8 {\n",
+       "#T_83d37_row3_col1, #T_83d37_row3_col6, #T_83d37_row5_col0, #T_83d37_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col4, #T_71223_row5_col3 {\n",
+       "#T_83d37_row3_col4, #T_83d37_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col5 {\n",
+       "#T_83d37_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col7, #T_71223_row7_col0, #T_71223_row7_col1, #T_71223_row7_col2, #T_71223_row7_col3, #T_71223_row7_col4, #T_71223_row7_col5, #T_71223_row7_col6, #T_71223_row7_col8 {\n",
+       "#T_83d37_row3_col7, #T_83d37_row7_col0, #T_83d37_row7_col1, #T_83d37_row7_col2, #T_83d37_row7_col3, #T_83d37_row7_col4, #T_83d37_row7_col5, #T_83d37_row7_col6, #T_83d37_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row3_col8 {\n",
+       "#T_83d37_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row4_col0 {\n",
+       "#T_83d37_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row4_col2 {\n",
+       "#T_83d37_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row4_col7 {\n",
+       "#T_83d37_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row4_col8 {\n",
+       "#T_83d37_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row5_col1 {\n",
+       "#T_83d37_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row5_col6, #T_71223_row6_col2 {\n",
+       "#T_83d37_row5_col6, #T_83d37_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_71223_row5_col7 {\n",
+       "#T_83d37_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row5_col8 {\n",
+       "#T_83d37_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row6_col1, #T_71223_row6_col4 {\n",
+       "#T_83d37_row6_col1, #T_83d37_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row6_col3 {\n",
+       "#T_83d37_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row6_col7 {\n",
+       "#T_83d37_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row8_col1 {\n",
+       "#T_83d37_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row8_col2 {\n",
+       "#T_83d37_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row8_col3 {\n",
+       "#T_83d37_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row8_col6 {\n",
+       "#T_83d37_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_71223_row8_col7 {\n",
+       "#T_83d37_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_71223_\">\n",
+       "<table id=\"T_83d37_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1671,118 +1673,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_71223_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_71223_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_71223_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_71223_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_71223_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_71223_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_71223_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_71223_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_71223_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_83d37_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_83d37_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_83d37_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_83d37_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_83d37_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_83d37_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_83d37_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_83d37_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_83d37_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_71223_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_71223_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_71223_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_71223_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_71223_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_71223_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_71223_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_71223_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_71223_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_83d37_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_83d37_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_83d37_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_83d37_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_83d37_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_83d37_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_83d37_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_83d37_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_83d37_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_71223_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_71223_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_71223_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_71223_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_71223_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_71223_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_71223_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_71223_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_71223_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_83d37_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_83d37_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_83d37_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_83d37_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_83d37_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_83d37_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_83d37_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_83d37_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_83d37_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_71223_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_71223_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_71223_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_71223_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_71223_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_71223_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_71223_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_71223_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_71223_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_83d37_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_83d37_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_83d37_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_83d37_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_83d37_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_83d37_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_83d37_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_83d37_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_83d37_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_71223_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_71223_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_71223_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_71223_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_71223_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_71223_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_71223_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_71223_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_71223_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_83d37_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_83d37_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_83d37_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_83d37_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_83d37_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_83d37_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_83d37_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_83d37_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_83d37_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_71223_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_71223_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_71223_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_71223_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_71223_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_71223_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_71223_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_71223_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_71223_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_83d37_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_83d37_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_83d37_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_83d37_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_83d37_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_83d37_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_83d37_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_83d37_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_83d37_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_71223_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_71223_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_71223_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_71223_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_71223_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_71223_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_71223_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_71223_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_71223_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_83d37_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_83d37_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_83d37_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_83d37_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_83d37_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_83d37_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_83d37_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_83d37_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_83d37_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_71223_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_71223_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_71223_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_71223_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_71223_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_71223_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_71223_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_71223_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_71223_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_83d37_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_83d37_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_83d37_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_83d37_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_83d37_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_83d37_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_83d37_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_83d37_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_83d37_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_83d37_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_71223_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_71223_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_71223_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_71223_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_71223_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_71223_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_71223_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_71223_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_71223_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_71223_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_83d37_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_83d37_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_83d37_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_83d37_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_83d37_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_83d37_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_83d37_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_83d37_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_83d37_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_83d37_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fcdc9dc93a0>"
+       "<pandas.io.formats.style.Styler at 0x7f3ad8e3a520>"
       ]
      },
      "execution_count": 21,
@@ -1797,6 +1799,13 @@
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1864,7 +1873,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From 202f921dcfbc80a7fcdf4fea49702896e9d30d0c Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Fri, 1 Oct 2021 17:56:25 +0200
Subject: [PATCH 126/158] very minor adaptions in T026

---
 .../talktorial.ipynb                          | 313 +++++++++---------
 1 file changed, 161 insertions(+), 152 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index dff58ad1..2aa37bb6 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -110,8 +110,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between the co-crystallized binding modes of the ligands. \n",
-    "We will make use of the KLIFS IFP: For each kinase structure that is co-crystallized with a ligand, all interactions between the $85$ KLIFS pocket residues (see more details in __Talktorial T023__) and the co-crystallized ligand are described using the IFP by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e))."
+    "In this talktorial, we are interested in assessing the similarity between a set of kinases based on the similarity between the binding modes of the co-crystallized ligands. \n",
+    "We will make use of the KLIFS IFP: For each kinase structure that is co-crystallized with a ligand, all interactions between the $85$ KLIFS pocket residues (see more details in __Talktorial T023__) and the ligand are described using the IFP by Marcou and Rognan ([<i>JCIM</i> (2007), <b>71(1)</b>, 195-207](https://pubs.acs.org/doi/10.1021/ci600342e))."
    ]
   },
   {
@@ -127,7 +127,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This results in a $85 \\times 7 = 595$ bit vector. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will now do in the practical part of this tutorial."
+    "This results in a $85 \\times 7 = 595$ bit vector. Since the binding site is aligned across all kinases, each bit position in this IFP can be directly compared across all IFPs in KLIFS. This is what we will do in the practical part of this tutorial."
    ]
   },
   {
@@ -137,7 +137,7 @@
     "![KLIFS IFP](images/T026_KLIFS_IFP.png)\n",
     "\n",
     "*Figure 1:* \n",
-    "KLIFS interaction fingerprint (IFP) definition: Seven interaction types are detected between each of a kinase structure's $85$ pocket residues and a co-crystallized ligand. Interaction types include: hydrophobic contact (HYD), face to face aromatic interactions (F−F), face to edge aromatic interactions (F−E), protein H-bond donor (DON), protein H- bond acceptor (ACC), protein cationic interactions (ION+), and protein anionic interactions (ION−).\n",
+    "KLIFS interaction fingerprint (IFP) definition: Seven interaction types are detected between each of a kinase structure's $85$ pocket residues and a co-crystallized ligand. Interaction types include: hydrophobic contact (HYD), face to face aromatic interactions (F−F), face to edge aromatic interactions (F−E), protein H-bond donor (DON), protein H-bond acceptor (ACC), protein cationic interactions (ION+), and protein anionic interactions (ION−).\n",
     "Figure by Dominique Sydow, adapted from: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)."
    ]
   },
@@ -440,7 +440,7 @@
     "- Resolution: $\\le 3$ Angström\n",
     "- Quality score*: $\\ge 6$\n",
     "\n",
-    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms (the higher the better the structure quality)."
+    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms. A higher score indicates a better structure quality."
    ]
   },
   {
@@ -863,7 +863,7 @@
    "source": [
     "#### Prepare IFPs as `numpy` array\n",
     "\n",
-    "KLIFS deposits the IFP as a string of 0 and 1. We have to prepare our IFPs as an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
+    "KLIFS deposits the IFP as a string of 0's and 1's. We have to prepare our IFPs as an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
    ]
   },
   {
@@ -1104,7 +1104,9 @@
    "source": [
     "### Map structure to kinase distance matrix\n",
     "\n",
-    "First, as an intermediate step, we will create a structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we add the corresponding kinase name."
+    "Note: So far we compared individual structures, but we want to compare kinases (which can be represented by several structures, as plotted above).\n",
+    "\n",
+    "First, as an intermediate step, we will create a structure distance matrix but &mdash; instead of labeling the data with structure KLIFS IDs &mdash; we use the corresponding kinase name."
    ]
   },
   {
@@ -1318,220 +1320,220 @@
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+       "#T_47595_row4_col2, #T_47595_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row4_col3 {\n",
+       "#T_47595_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row4_col5 {\n",
+       "#T_47595_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row4_col6 {\n",
+       "#T_47595_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row4_col7 {\n",
+       "#T_47595_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row4_col8 {\n",
+       "#T_47595_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row5_col0 {\n",
+       "#T_47595_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row5_col1 {\n",
+       "#T_47595_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row5_col2 {\n",
+       "#T_47595_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row5_col4 {\n",
+       "#T_47595_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row5_col6 {\n",
+       "#T_47595_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row5_col7 {\n",
+       "#T_47595_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row5_col8 {\n",
+       "#T_47595_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row6_col0 {\n",
+       "#T_47595_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row6_col1 {\n",
+       "#T_47595_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row6_col2, #T_d057e_row6_col5 {\n",
+       "#T_47595_row6_col2, #T_47595_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row6_col3 {\n",
+       "#T_47595_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row6_col7 {\n",
+       "#T_47595_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row6_col8 {\n",
+       "#T_47595_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row7_col1 {\n",
+       "#T_47595_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row8_col1 {\n",
+       "#T_47595_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row8_col4 {\n",
+       "#T_47595_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d057e_row8_col6 {\n",
+       "#T_47595_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d057e_row8_col7 {\n",
+       "#T_47595_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d057e_\">\n",
+       "<table id=\"T_47595_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1548,118 +1550,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_d057e_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_d057e_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_d057e_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_d057e_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_d057e_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_d057e_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_d057e_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_d057e_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_47595_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_47595_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_47595_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_47595_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_47595_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_47595_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_47595_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_47595_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_47595_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_47595_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_d057e_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_d057e_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_d057e_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_d057e_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_d057e_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_d057e_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_d057e_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_d057e_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_47595_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_47595_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_47595_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_47595_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_47595_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_47595_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_47595_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_47595_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_47595_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_47595_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_d057e_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_d057e_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_d057e_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_d057e_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_d057e_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_d057e_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_d057e_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_d057e_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_47595_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_47595_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_47595_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_47595_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_47595_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_47595_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_47595_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_47595_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_47595_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_47595_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_d057e_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_d057e_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_d057e_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_d057e_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_d057e_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_d057e_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_d057e_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_d057e_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_47595_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_47595_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_47595_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_47595_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_47595_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_47595_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_47595_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_47595_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_47595_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_47595_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_d057e_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_d057e_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_d057e_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_d057e_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_d057e_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_d057e_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_d057e_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_d057e_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_47595_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_47595_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_47595_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_47595_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_47595_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_47595_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_47595_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_47595_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_47595_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_47595_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_d057e_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_d057e_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_d057e_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_d057e_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_d057e_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_d057e_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_d057e_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_d057e_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_47595_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_47595_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_47595_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_47595_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_47595_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_47595_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_47595_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_47595_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_47595_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_47595_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_d057e_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_d057e_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_d057e_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_d057e_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_d057e_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_d057e_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_d057e_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_d057e_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_47595_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_47595_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_47595_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_47595_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_47595_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_47595_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_47595_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_47595_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_47595_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_47595_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_d057e_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_d057e_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_d057e_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_d057e_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_d057e_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_d057e_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_d057e_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_d057e_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_d057e_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_47595_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_47595_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_47595_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_47595_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_47595_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_47595_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_47595_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_47595_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_47595_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_47595_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d057e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d057e_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_d057e_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_d057e_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_d057e_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_d057e_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_d057e_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_d057e_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_d057e_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_d057e_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_47595_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_47595_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_47595_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_47595_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_47595_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_47595_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_47595_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_47595_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_47595_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_47595_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f91da3ac910>"
+       "<pandas.io.formats.style.Styler at 0x7fc0219a15b0>"
       ]
      },
      "execution_count": 23,
@@ -1673,6 +1675,13 @@
     "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

From 8437deb9cccfa6023e037613695a9be2c130f3af Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Sat, 2 Oct 2021 14:36:52 +0200
Subject: [PATCH 127/158] very minor adaptions in T027

---
 .../talktorial.ipynb                          | 1228 +++++++++--------
 1 file changed, 646 insertions(+), 582 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index eff6ec3f..c6ec81fe 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -115,7 +115,7 @@
     "\n",
     "\n",
     "*Figure 3:* \n",
-    "Number of ChEMBL29 bioactivities per kinase mapped onto the Manning kinome tree using KinMap. The figure is generated in __Talktorial T023__."
+    "Number of ChEMBL29 bioactivities per kinase - as collected in [kinodata](https://github.com/openkinome/kinodata) - mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). The figure has been generated in __Talktorial T023__."
    ]
   },
   {
@@ -141,7 +141,7 @@
    "source": [
     "#### Kinase similarity\n",
     "\n",
-    "We use the following metric as similarity between kinases $K_i$ and $K_j$:\n",
+    "We use the following metric as similarity between two kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
     "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on both } K_i \\text{ and } K_j}\n",
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -186,7 +186,11 @@
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import seaborn as sns"
+    "import seaborn as sns\n",
+    "\n",
+    "from rdkit import Chem\n",
+    "from rdkit.Chem import Draw\n",
+    "from rdkit.Chem.Draw import IPythonConsole"
    ]
   },
   {
@@ -215,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -347,7 +351,7 @@
        "8        p38 mitogen activated protein kinase alpha  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -585,7 +589,7 @@
        "4  CHEMBL3639077     2014.0          NaN    O75116  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,14 +620,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
+      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -645,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -671,7 +675,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -686,12 +690,12 @@
    "metadata": {},
    "source": [
     "The DataFrame contains many columns that won't be necessary for the rest of the notebook which are therefore removed. \n",
-    "Only relevant information is kept, namely the canonical SMILES of the compound, the measured activity and the UniProt ID of the kinase. These columns are renamed for readability."
+    "Only relevant information is kept, namely the `canonical SMILES` of the compound, the `measured activity` and the * UniProt ID` of the kinase. These columns are renamed for readability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -706,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -786,7 +790,7 @@
        "4  CCCC(=O)Nc1cccc(-c2nc(Nc3ccc4[nH]ncc4c3)c3cc(O...  13.920819    O75116"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -806,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -832,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -912,7 +916,7 @@
        "141  Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1  11.096910    P00533"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -933,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -949,7 +953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -962,7 +966,7 @@
        " ('CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1', 8)]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -977,12 +981,50 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a simple workaround, we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value."
+    "We can also have a look at those compounds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mols = []\n",
+    "for entry in measured_compounds.most_common()[0:5]:    \n",
+    "    mols.append(Chem.MolFromSmiles(entry[0]))\n",
+    "Draw.MolsToGridImage(mols, molsPerRow=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In our example, the firt molecules is actually gefitinib, a known FDA approved drug against EGFR."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a simple workaround - since we prefer to have one activity value per compound-kinase pair - we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1055,7 +1097,7 @@
        "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  8.420216"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1077,12 +1119,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cut-off. We use a pIC50 cut-off of $6.3$, similarly to the cutoff used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
+    "Finally, we binarize the pIC50 values to obtain hit or non-hit using a cutoff. We use a pIC50 cutoff of $6.3$, similarly to the cutoff used in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1091,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1120,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1129,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1222,7 +1264,7 @@
        "4                1  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1250,12 +1292,12 @@
     "\n",
     "1. the total number of measured compounds against the given kinase,\n",
     "2. the number of active compounds against the kinase, and\n",
-    "3. the fraction of active compounds, i.e., the ratio of active compounds over the total number of measured compounds."
+    "3. the fraction of active compounds, i.e., the ratio of active compounds over the total number of measured compounds per kinase."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1298,13 +1340,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "EGFR (P00533):\n",
       "Total number of measured compounds:     5965 \n",
       "Number of active compounds:             3635 \n",
       "Fraction of active compounds:           0.61 \n",
@@ -1314,6 +1357,7 @@
    ],
    "source": [
     "uniprot_id = kinase_selection_df[\"uniprot_id\"][0]\n",
+    "print(f\"{kinase_selection_df['kinase'][0]} ({uniprot_id}):\")\n",
     "egfr_metrics = kinase_to_activity_numbers(uniprot_id, data)\n",
     "print(\n",
     "    f\"{'Total number of measured compounds:' : <40}\"\n",
@@ -1335,7 +1379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1369,7 +1413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1470,7 +1514,7 @@
        "p38a   3637    2778  0.763816"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1490,53 +1534,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_7ce86_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_7ce86_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_7ce86_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_884d7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_884d7_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_884d7_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_884d7_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_7ce86_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_7ce86_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_7ce86_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_884d7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_884d7_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_884d7_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_884d7_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_7ce86_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_7ce86_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_7ce86_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_884d7_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_884d7_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_884d7_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_884d7_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_7ce86_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_7ce86_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_7ce86_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_884d7_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_884d7_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_884d7_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_884d7_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_7ce86_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_7ce86_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_7ce86_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_884d7_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_884d7_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_884d7_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_884d7_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_7ce86_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_7ce86_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_7ce86_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_884d7_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_884d7_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_884d7_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_884d7_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_7ce86_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_7ce86_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_7ce86_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_884d7_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_884d7_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_884d7_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_884d7_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_7ce86_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_7ce86_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_7ce86_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_884d7_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_884d7_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_884d7_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_884d7_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7ce86_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_7ce86_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_7ce86_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_7ce86_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_884d7_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_884d7_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_884d7_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_884d7_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff2aed8b5b0>"
+       "<pandas.io.formats.style.Styler at 0x7f3b9d5c8160>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1624,7 +1668,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From the table, we notice that CDK2 and BRAF are the least (in yellow) and most (in red) promiscuous kinases, respectively."
+    "From the table, we notice that CDK2 is the least (in yellow) and BRAF the most (in red) promiscuous kinase."
    ]
   },
   {
@@ -1643,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1705,12 +1749,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at the values and similarity between EGFR and MET (which corresponds to the first and 7th row in the DataFrame)."
+    "Let's look at the values and similarity between EGFR and MET (which corresponds to the first and seventh row in the DataFrame)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1760,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1940,7 +1984,7 @@
        "p38a     69/138       1/20  2778/3637  "
       ]
      },
-     "execution_count": 31,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1965,6 +2009,16 @@
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that the quantities of pairwise tested as well as pairwise active compounds on two kinases vary largely. \n",
+    "* For the pair p110a and ErbB2 there are none.\n",
+    "* Also, for p110a and [BRAF, CDK2, LCK, MET and p38a], there are less than 13 commonly tested compounds.\n",
+    "* In contrast, EGFR and ErbB2 have 1170 commonly tested compounds, of which 662 were active on both."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1974,7 +2028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1997,7 +2051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2169,7 +2223,7 @@
        "p38a   0.050000  0.763816  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2188,215 +2242,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
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        "}\n",
-       "#T_84296_row7_col3 {\n",
+       "#T_679c5_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_84296_row7_col4 {\n",
+       "#T_679c5_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_84296_row7_col5 {\n",
+       "#T_679c5_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_84296_row7_col6 {\n",
+       "#T_679c5_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_84296_row8_col0 {\n",
+       "#T_679c5_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_84296_row8_col1 {\n",
+       "#T_679c5_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_84296_row8_col3 {\n",
+       "#T_679c5_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_84296_row8_col4 {\n",
+       "#T_679c5_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_84296_row8_col6 {\n",
+       "#T_679c5_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_84296_\">\n",
+       "<table id=\"T_679c5_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2413,121 +2467,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_84296_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_84296_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_84296_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_84296_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_84296_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_84296_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_84296_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_84296_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_84296_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_84296_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_84296_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_84296_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_84296_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_84296_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_84296_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_84296_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_84296_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_84296_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_84296_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_84296_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_84296_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_84296_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_84296_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_84296_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_84296_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_84296_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_84296_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_84296_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_84296_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_84296_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_84296_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_84296_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_84296_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_84296_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_84296_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_84296_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_84296_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_84296_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_84296_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_84296_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_84296_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_84296_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_84296_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_84296_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_84296_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_84296_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_84296_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_84296_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_84296_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_84296_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_84296_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_84296_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_84296_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_84296_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_84296_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_84296_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_84296_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_84296_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_84296_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_84296_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_84296_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_84296_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_84296_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_84296_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_84296_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_84296_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_84296_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_84296_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_84296_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_84296_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_84296_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_84296_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_84296_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_84296_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_84296_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_84296_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_84296_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_84296_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_84296_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_84296_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_84296_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_84296_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_84296_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_679c5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_679c5_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_679c5_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_679c5_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_679c5_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_679c5_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_679c5_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_679c5_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_679c5_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_679c5_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_679c5_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_679c5_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_679c5_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_679c5_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_679c5_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_679c5_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_679c5_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_679c5_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_679c5_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_679c5_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_679c5_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_679c5_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_679c5_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_679c5_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_679c5_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_679c5_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_679c5_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_679c5_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_679c5_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_679c5_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_679c5_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_679c5_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_679c5_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_679c5_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_679c5_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_679c5_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_679c5_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_679c5_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_679c5_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_679c5_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_679c5_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_679c5_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_679c5_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_679c5_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_679c5_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_679c5_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_679c5_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_679c5_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_679c5_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_679c5_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_679c5_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_679c5_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_679c5_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_679c5_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_679c5_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_679c5_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_679c5_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_679c5_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_679c5_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_679c5_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_679c5_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_679c5_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_679c5_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_679c5_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_679c5_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_679c5_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_679c5_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_679c5_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_679c5_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_679c5_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_679c5_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_679c5_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_679c5_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679c5_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_679c5_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_679c5_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_679c5_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_679c5_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_679c5_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_679c5_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_679c5_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_679c5_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_679c5_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff296608d60>"
+       "<pandas.io.formats.style.Styler at 0x7f3b9d44fc10>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2551,219 +2605,219 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_ec019_row0_col0, #T_ec019_row1_col1, #T_ec019_row2_col2, #T_ec019_row3_col5, #T_ec019_row4_col3, #T_ec019_row4_col4, #T_ec019_row6_col6, #T_ec019_row7_col7, #T_ec019_row8_col8 {\n",
+       "#T_be23a_row0_col0, #T_be23a_row1_col1, #T_be23a_row2_col2, #T_be23a_row3_col5, #T_be23a_row4_col3, #T_be23a_row4_col4, #T_be23a_row6_col6, #T_be23a_row7_col7, #T_be23a_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row0_col1 {\n",
+       "#T_be23a_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row0_col2 {\n",
+       "#T_be23a_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row0_col3, #T_ec019_row7_col0 {\n",
+       "#T_be23a_row0_col3, #T_be23a_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row0_col4, #T_ec019_row3_col0, #T_ec019_row4_col2, #T_ec019_row5_col2 {\n",
+       "#T_be23a_row0_col4, #T_be23a_row3_col0, #T_be23a_row4_col2, #T_be23a_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row0_col5 {\n",
+       "#T_be23a_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row0_col6, #T_ec019_row4_col1 {\n",
+       "#T_be23a_row0_col6, #T_be23a_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row0_col7, #T_ec019_row3_col2 {\n",
+       "#T_be23a_row0_col7, #T_be23a_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row0_col8 {\n",
+       "#T_be23a_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col0 {\n",
+       "#T_be23a_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row1_col2, #T_ec019_row2_col0, #T_ec019_row2_col1, #T_ec019_row2_col3, #T_ec019_row2_col6, #T_ec019_row2_col7, #T_ec019_row2_col8, #T_ec019_row4_col5, #T_ec019_row5_col4, #T_ec019_row6_col2, #T_ec019_row7_col2, #T_ec019_row8_col2 {\n",
+       "#T_be23a_row1_col2, #T_be23a_row2_col0, #T_be23a_row2_col1, #T_be23a_row2_col3, #T_be23a_row2_col6, #T_be23a_row2_col7, #T_be23a_row2_col8, #T_be23a_row4_col5, #T_be23a_row5_col4, #T_be23a_row6_col2, #T_be23a_row7_col2, #T_be23a_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col3 {\n",
+       "#T_be23a_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col4 {\n",
+       "#T_be23a_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col5 {\n",
+       "#T_be23a_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col6 {\n",
+       "#T_be23a_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col7 {\n",
+       "#T_be23a_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row1_col8 {\n",
+       "#T_be23a_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row2_col4 {\n",
+       "#T_be23a_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row2_col5 {\n",
+       "#T_be23a_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row3_col1 {\n",
+       "#T_be23a_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row3_col3 {\n",
+       "#T_be23a_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row3_col4, #T_ec019_row4_col8 {\n",
+       "#T_be23a_row3_col4, #T_be23a_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row3_col6 {\n",
+       "#T_be23a_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row3_col7 {\n",
+       "#T_be23a_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row3_col8 {\n",
+       "#T_be23a_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row4_col0 {\n",
+       "#T_be23a_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row4_col6 {\n",
+       "#T_be23a_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row4_col7, #T_ec019_row5_col8 {\n",
+       "#T_be23a_row4_col7, #T_be23a_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row5_col0 {\n",
+       "#T_be23a_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row5_col1 {\n",
+       "#T_be23a_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row5_col3 {\n",
+       "#T_be23a_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row5_col5 {\n",
+       "#T_be23a_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row5_col6 {\n",
+       "#T_be23a_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row5_col7 {\n",
+       "#T_be23a_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col0 {\n",
+       "#T_be23a_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col1 {\n",
+       "#T_be23a_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col3 {\n",
+       "#T_be23a_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col4 {\n",
+       "#T_be23a_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row6_col5, #T_ec019_row7_col8, #T_ec019_row8_col5, #T_ec019_row8_col7 {\n",
+       "#T_be23a_row6_col5, #T_be23a_row7_col8, #T_be23a_row8_col5, #T_be23a_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col7 {\n",
+       "#T_be23a_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row6_col8 {\n",
+       "#T_be23a_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row7_col1 {\n",
+       "#T_be23a_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row7_col3 {\n",
+       "#T_be23a_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row7_col4 {\n",
+       "#T_be23a_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row7_col5 {\n",
+       "#T_be23a_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row7_col6 {\n",
+       "#T_be23a_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row8_col0 {\n",
+       "#T_be23a_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row8_col1 {\n",
+       "#T_be23a_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ec019_row8_col3 {\n",
+       "#T_be23a_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row8_col4 {\n",
+       "#T_be23a_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ec019_row8_col6 {\n",
+       "#T_be23a_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_ec019_\">\n",
+       "<table id=\"T_be23a_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2780,121 +2834,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_ec019_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_ec019_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_ec019_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_ec019_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_ec019_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_ec019_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_ec019_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_ec019_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_ec019_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_ec019_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_ec019_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_ec019_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_ec019_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_ec019_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_ec019_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_ec019_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_ec019_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_ec019_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_ec019_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_ec019_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_ec019_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_ec019_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_ec019_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_ec019_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_ec019_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_ec019_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_ec019_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_ec019_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_ec019_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_ec019_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_ec019_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_ec019_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_ec019_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_ec019_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_ec019_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_ec019_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_ec019_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_ec019_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_ec019_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_ec019_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_ec019_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_ec019_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_ec019_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_ec019_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_ec019_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_ec019_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_ec019_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_ec019_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_ec019_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_ec019_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_ec019_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_ec019_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_ec019_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_ec019_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_ec019_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_ec019_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_ec019_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_ec019_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_ec019_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_ec019_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_ec019_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_ec019_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_ec019_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_ec019_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_ec019_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_ec019_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ec019_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_ec019_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_ec019_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_ec019_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_ec019_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_ec019_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_ec019_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_ec019_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_ec019_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_ec019_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_be23a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_be23a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_be23a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_be23a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_be23a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_be23a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_be23a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_be23a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_be23a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_be23a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_be23a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_be23a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_be23a_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_be23a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_be23a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_be23a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_be23a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_be23a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_be23a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_be23a_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_be23a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_be23a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_be23a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_be23a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_be23a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_be23a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_be23a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_be23a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_be23a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_be23a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_be23a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_be23a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_be23a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_be23a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_be23a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_be23a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_be23a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_be23a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_be23a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_be23a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_be23a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_be23a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_be23a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_be23a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_be23a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_be23a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_be23a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_be23a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_be23a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_be23a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_be23a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_be23a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_be23a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_be23a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_be23a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_be23a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_be23a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_be23a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_be23a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_be23a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_be23a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_be23a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_be23a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_be23a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_be23a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_be23a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_be23a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be23a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_be23a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_be23a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_be23a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_be23a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_be23a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_be23a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_be23a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_be23a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_be23a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff296608dc0>"
+       "<pandas.io.formats.style.Styler at 0x7f3b9d3c5f70>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2914,7 +2968,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2937,7 +2991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -2959,7 +3013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2975,7 +3029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2984,211 +3038,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_a7172_row0_col0, #T_a7172_row1_col1, #T_a7172_row2_col2, #T_a7172_row3_col3, #T_a7172_row4_col4, #T_a7172_row5_col5, #T_a7172_row6_col6, #T_a7172_row7_col7, #T_a7172_row8_col8 {\n",
+       "#T_b7ffd_row0_col0, #T_b7ffd_row1_col1, #T_b7ffd_row2_col2, #T_b7ffd_row3_col3, #T_b7ffd_row4_col4, #T_b7ffd_row5_col5, #T_b7ffd_row6_col6, #T_b7ffd_row7_col7, #T_b7ffd_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row0_col1 {\n",
+       "#T_b7ffd_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row0_col2 {\n",
+       "#T_b7ffd_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col3 {\n",
+       "#T_b7ffd_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col4 {\n",
+       "#T_b7ffd_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col5 {\n",
+       "#T_b7ffd_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col6 {\n",
+       "#T_b7ffd_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col7 {\n",
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        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row0_col8 {\n",
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        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col0 {\n",
+       "#T_b7ffd_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row1_col2, #T_a7172_row2_col0, #T_a7172_row2_col1, #T_a7172_row2_col3, #T_a7172_row2_col6, #T_a7172_row2_col7, #T_a7172_row2_col8, #T_a7172_row4_col5, #T_a7172_row5_col4, #T_a7172_row6_col2, #T_a7172_row7_col2, #T_a7172_row8_col2 {\n",
+       "#T_b7ffd_row1_col2, #T_b7ffd_row2_col0, #T_b7ffd_row2_col1, #T_b7ffd_row2_col3, #T_b7ffd_row2_col6, #T_b7ffd_row2_col7, #T_b7ffd_row2_col8, #T_b7ffd_row4_col5, #T_b7ffd_row5_col4, #T_b7ffd_row6_col2, #T_b7ffd_row7_col2, #T_b7ffd_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col3 {\n",
+       "#T_b7ffd_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col4, #T_a7172_row3_col2, #T_a7172_row6_col0 {\n",
+       "#T_b7ffd_row1_col4, #T_b7ffd_row3_col2, #T_b7ffd_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col5 {\n",
+       "#T_b7ffd_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col6, #T_a7172_row6_col1 {\n",
+       "#T_b7ffd_row1_col6, #T_b7ffd_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col7, #T_a7172_row6_col5, #T_a7172_row7_col1, #T_a7172_row8_col5 {\n",
+       "#T_b7ffd_row1_col7, #T_b7ffd_row6_col5, #T_b7ffd_row7_col1, #T_b7ffd_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row1_col8, #T_a7172_row8_col1 {\n",
+       "#T_b7ffd_row1_col8, #T_b7ffd_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row2_col4, #T_a7172_row2_col5 {\n",
+       "#T_b7ffd_row2_col4, #T_b7ffd_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row3_col0 {\n",
+       "#T_b7ffd_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row3_col1 {\n",
+       "#T_b7ffd_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row3_col4 {\n",
+       "#T_b7ffd_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row3_col5 {\n",
+       "#T_b7ffd_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row3_col6 {\n",
+       "#T_b7ffd_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row3_col7 {\n",
+       "#T_b7ffd_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row3_col8 {\n",
+       "#T_b7ffd_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row4_col0 {\n",
+       "#T_b7ffd_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row4_col1 {\n",
+       "#T_b7ffd_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row4_col2, #T_a7172_row5_col2 {\n",
+       "#T_b7ffd_row4_col2, #T_b7ffd_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row4_col3 {\n",
+       "#T_b7ffd_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row4_col6 {\n",
+       "#T_b7ffd_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row4_col7 {\n",
+       "#T_b7ffd_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row4_col8 {\n",
+       "#T_b7ffd_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row5_col0 {\n",
+       "#T_b7ffd_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row5_col1 {\n",
+       "#T_b7ffd_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row5_col3 {\n",
+       "#T_b7ffd_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row5_col6, #T_a7172_row5_col8 {\n",
+       "#T_b7ffd_row5_col6, #T_b7ffd_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row5_col7 {\n",
+       "#T_b7ffd_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row6_col3 {\n",
+       "#T_b7ffd_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row6_col4 {\n",
+       "#T_b7ffd_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7172_row6_col7, #T_a7172_row7_col6 {\n",
+       "#T_b7ffd_row6_col7, #T_b7ffd_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row6_col8, #T_a7172_row8_col6 {\n",
+       "#T_b7ffd_row6_col8, #T_b7ffd_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row7_col0 {\n",
+       "#T_b7ffd_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row7_col3 {\n",
+       "#T_b7ffd_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row7_col4 {\n",
+       "#T_b7ffd_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row7_col5 {\n",
+       "#T_b7ffd_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row7_col8, #T_a7172_row8_col7 {\n",
+       "#T_b7ffd_row7_col8, #T_b7ffd_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row8_col0 {\n",
+       "#T_b7ffd_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row8_col3 {\n",
+       "#T_b7ffd_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7172_row8_col4 {\n",
+       "#T_b7ffd_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_a7172_\">\n",
+       "<table id=\"T_b7ffd_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3205,121 +3259,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_a7172_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_a7172_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_a7172_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_a7172_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_a7172_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_a7172_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_a7172_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_a7172_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_a7172_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_a7172_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_a7172_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_a7172_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_a7172_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_a7172_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_a7172_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_a7172_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_a7172_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_a7172_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_a7172_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_a7172_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_a7172_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_a7172_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_a7172_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_a7172_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_a7172_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_a7172_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_a7172_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_a7172_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_a7172_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_a7172_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_a7172_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_a7172_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_a7172_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_a7172_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_a7172_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_a7172_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_a7172_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_a7172_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_a7172_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_a7172_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_a7172_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_a7172_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_a7172_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_a7172_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_a7172_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_a7172_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_a7172_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_a7172_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_a7172_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_a7172_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_a7172_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_a7172_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_a7172_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_a7172_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_a7172_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_a7172_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_a7172_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_a7172_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_a7172_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7172_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_a7172_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_a7172_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_a7172_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_a7172_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_a7172_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_a7172_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_a7172_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_a7172_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_a7172_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_b7ffd_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_b7ffd_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_b7ffd_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_b7ffd_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_b7ffd_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_b7ffd_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_b7ffd_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_b7ffd_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_b7ffd_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_b7ffd_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_b7ffd_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_b7ffd_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_b7ffd_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_b7ffd_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_b7ffd_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_b7ffd_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_b7ffd_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_b7ffd_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_b7ffd_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_b7ffd_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_b7ffd_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_b7ffd_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_b7ffd_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_b7ffd_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_b7ffd_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_b7ffd_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_b7ffd_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_b7ffd_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_b7ffd_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_b7ffd_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_b7ffd_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_b7ffd_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_b7ffd_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_b7ffd_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_b7ffd_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_b7ffd_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_b7ffd_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_b7ffd_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_b7ffd_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_b7ffd_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_b7ffd_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_b7ffd_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_b7ffd_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_b7ffd_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_b7ffd_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_b7ffd_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_b7ffd_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_b7ffd_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_b7ffd_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_b7ffd_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_b7ffd_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_b7ffd_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_b7ffd_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_b7ffd_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_b7ffd_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_b7ffd_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_b7ffd_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_b7ffd_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_b7ffd_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_b7ffd_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b7ffd_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_b7ffd_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_b7ffd_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_b7ffd_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_b7ffd_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_b7ffd_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_b7ffd_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_b7ffd_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_b7ffd_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_b7ffd_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7ff296674970>"
+       "<pandas.io.formats.style.Styler at 0x7f3b9d3dd100>"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3337,7 +3391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3352,6 +3406,9 @@
     "\n",
     "In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
+    "When working with these data, we have to keep in mind that some kinases have much higher coverage w.r.t. how many compounds were tested against them, leading to an imbalance in information content.\n",
+    "This cannot be inferred from the calculated fraction, i.e., the pairs EGFR-KDR and EGFR-p38a both have a profile similarity of 0.35, whereas the first was calculated based on 893 tested compounds, the latter on 52 only.\n",
+    "\n",
     "The kinase distance matrix above will be reloaded in __Talktorial T028__, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
   },
@@ -3361,10 +3418,17 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "1. Is there be an optimal way to deal with multiple kinase-ligand measurements?\n",
+    "1. Is there an optimal way to deal with multiple kinase-ligand measurements?\n",
     "2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few? \n",
     "3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of $0$, as is the case for p110a and LCK?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 96829d8489c8865badfafe13795d0e8f01fc8749 Mon Sep 17 00:00:00 2001
From: Andrea Volkamer <andreavolkamer@users.noreply.github.com>
Date: Sat, 2 Oct 2021 15:22:43 +0200
Subject: [PATCH 128/158] very minor adaptions in T028

---
 .../talktorial.ipynb                          | 90 ++++++++++++-------
 1 file changed, 57 insertions(+), 33 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index e650edfc..3f11a5eb 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -24,7 +24,7 @@
     "* **Talktorial T024**: Kinase pocket sequences (KLIFS pocket sequences)\n",
     "* **Talktorial T025**: Kinase pocket structures (KiSSim fingerprint based on KLIFS pocket residues)\n",
     "* **Talktorial T026**: Kinase-ligand interaction profiles (KLIFS IFPs based on KLIFS pocket residues)\n",
-    "* **Talktorial T027**: Ligand profiling data"
+    "* **Talktorial T027**: Ligand profiling data (using ChEMBL29 bioactivity data)"
    ]
   },
   {
@@ -43,7 +43,7 @@
    "source": [
     "### Contents in *Practical*\n",
     "\n",
-    "* Load kinase distance matrices\n",
+    "* Load kinase similarity and distance matrices\n",
     "* Visualize similarity for example perspective\n",
     "  * Visualize kinase distance matrix as heatmap\n",
     "  * Visualize similarity as dendrogram\n",
@@ -137,7 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Load kinase distance matrices"
+    "### Load kinase similarity and distance matrices"
    ]
   },
   {
@@ -153,15 +153,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "kinase_distance_matrix_paths = {\n",
+    "kinase_matrix_paths = {\n",
     "    \"sequence\": \"T024_kinase_similarity_sequence\",\n",
     "    \"kissim\": \"T025_kinase_similarity_kissim\",\n",
     "    \"ifp\": \"T026_kinase_similarity_ifp\",\n",
     "    \"ligand-profile\": \"T027_kinase_similarity_ligand_profile\"\n",
     "}\n",
+    "\n",
+    "# Distance matrices (for dendrogram)\n",
     "kinase_distance_matrix_paths = {\n",
     "    perspective: HERE / f\"../{folder_name}/data/kinase_distance_matrix.csv\"\n",
-    "    for perspective, folder_name in kinase_distance_matrix_paths.items()\n",
+    "    for perspective, folder_name in kinase_matrix_paths.items()\n",
     "}"
    ]
   },
@@ -309,7 +311,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If one of the checks (dimension, symmetry, positivity, diagonal) fails, the rest of the notebook will not run and the matrix generation from the previous notebooks should be investigated."
+    "If one of the checks (dimension, symmetry, positivity, or diagonal) fails, the rest of the notebook will not run properly and the matrix generation from the previous notebooks should be investigated."
    ]
   },
   {
@@ -556,7 +558,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -742,9 +744,9 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrices_normalized = {}\n",
-    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
-    "    similarity_normalized_df = _min_max_normalize(similarity_df)\n",
-    "    kinase_distance_matrices_normalized[descriptor] = similarity_normalized_df"
+    "for descriptor, score_df in kinase_distance_matrices.items():\n",
+    "    score_normalized_df = _min_max_normalize(score_df)\n",
+    "    kinase_distance_matrices_normalized[descriptor] = score_normalized_df"
    ]
   },
   {
@@ -803,8 +805,8 @@
    "outputs": [],
    "source": [
     "kinase_distance_matrices_normalized = {\n",
-    "    descriptor: _define_kinase_order(similarity_df, kinase_names)\n",
-    "    for descriptor, similarity_df in kinase_distance_matrices_normalized.items()\n",
+    "    descriptor: _define_kinase_order(score_df, kinase_names)\n",
+    "    for descriptor, score_df in kinase_distance_matrices_normalized.items()\n",
     "}"
    ]
   },
@@ -821,21 +823,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def heatmap(distance_matrix, ax=None, title=\"\"):\n",
+    "def heatmap(score_df, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
     "    Generate a heatmap from a distance matrix.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
-    "    distance_matrix : pd.DataFrame\n",
-    "        Distance matrix.\n",
+    "    score_df : pd.DataFrame\n",
+    "        Distance or similarity score matrix.\n",
     "    ax : matplotlib.axes\n",
     "        Plot axis to use!\n",
     "    title : str\n",
     "        Plot title.\n",
     "    \"\"\"\n",
     "    sns.heatmap(\n",
-    "        distance_matrix,\n",
+    "        score_df,\n",
     "        linewidths=0,\n",
     "        annot=True,\n",
     "        square=True,\n",
@@ -873,6 +875,15 @@
     "    ax.set_xlabel(\"Distance\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note for ease of interpretability, we show below:\n",
+    "* Heatmaps based on the **similarity** matrix (higher value ~ more similar)\n",
+    "* Dendrograms calculated on the **distance** matrix, but again the neighborhood describes the similarity between the kinases"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -880,7 +891,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x720 with 12 Axes>"
       ]
@@ -894,9 +905,12 @@
    "source": [
     "n_perspectives = len(kinase_distance_matrices_normalized)\n",
     "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*5, 10))\n",
-    "for i, (perspective, matrix) in enumerate(kinase_distance_matrices_normalized.items()):\n",
-    "    heatmap(matrix, ax=axes[0][i], title=perspective)\n",
-    "    dendrogram(matrix, ax=axes[1][i], title=perspective)"
+    "for i, (perspective, distance_matrix) in enumerate(kinase_distance_matrices_normalized.items()):\n",
+    "    # heatmap based on similarity matrix\n",
+    "    similarity_matrix = 1 - distance_matrix\n",
+    "    heatmap(similarity_matrix, ax=axes[0][i], title=perspective)\n",
+    "    # dendrogram based on distance matrix\n",
+    "    dendrogram(distance_matrix, ax=axes[1][i], title=perspective)"
    ]
   },
   {
@@ -1056,23 +1070,26 @@
    "source": [
     "#### Analysis of results\n",
     "\n",
-    "- The _sequence_ approach reproduces nicely the Manning tree.\n",
-    "  - The TK kinases EGFR, ErbB2, MET, KDR are grouped together with highest similarities for EGFR and ErbB2.\n",
+    "- The _sequence_ approach reproduces nicely the Manning tree relationships.\n",
+    "  - The TK kinases EGFR, ErbB2, MET, and KDR are grouped together with highest similarities for EGFR and ErbB2.\n",
     "  - The TKL kinase LCK groups next to the TK kinases.\n",
     "  - The CMGC kinases p38a and CDK2 are grouped together.\n",
     "  - The atypical kinase p110a is a singleton, i.e., forms its own cluster.\n",
-    "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made: the typical kinases are grouped together, while the atypical kinase p110a is a singleton. Within the typical kinase grouping, EGFR and ErbB2 are the most similar.\n",
+    "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made: \n",
+    "  - The typical kinases are grouped together, while the atypical kinase p110a is a singleton. \n",
+    "  - Within the typical kinase grouping, EGFR and ErbB2 are the most similar (but not as prominent as in the _sequence_ approach).\n",
+    "  - But kinases from different groups, e.g. CDK2 (CMGC) and BRAF (TKL), cluster together.\n",
     "- The _ifp_ method:\n",
-    "  - BRAF and the atypical kinase p110a form a singletons each.\n",
+    "  - BRAF and the atypical kinase p110a form a singleton each.\n",
     "  - Surprisingly, ErbB2 forms a singleton (=not grouped with EGFR), but we have to keep in mind that we have the least data points ($4$) for ErbB2.\n",
     "- The _ligand-profile_ method:\n",
-    "  - CDK2 and p110a create their own cluster early on (i.e. low similarity to the rest).\n",
-    "  - BRAF, p38a, KDR, LCK (TKL, CMGC and TK group) as well as EGFR and ErbB2 (TK) form one cluster.\n",
-    "  - Surprisingly, MET (TK) forms a singleton.\n",
+    "  - Interestingly, CDK2 and p110a form their own cluster early on (i.e. low similarity to the rest).\n",
+    "  - BRAF (TKL), p38a (CMGC), KDR and LCK (both TK) as well as EGFR and ErbB2 (TK) form one cluster each.\n",
+    "  - Surprisingly, MET (TK) forms a singleton (not clustering with any of the other TK kinases).\n",
     "- Overall observations:\n",
-    "  - Members of the same family (here: EGFR and ErbB2) are grouped together closely in the _sequence_ _kissim_, and _ligand-profile_ approaches.\n",
-    "  - The atypical kinase p110a show high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches.\n",
-    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (except for _sequence_).\n",
+    "  - Members of the same family (here: EGFR and ErbB2) are grouped together closely in the _sequence_, _kissim_, and _ligand-profile_ approaches.\n",
+    "  - The atypical kinase p110a shows high dissimilarity in the _sequence_, _kissim_, and _ifp_ approaches.\n",
+    "  - Unexpectedly, KDR (TK) and p38a (CMGC) cluster together in all views (except for _sequence_). Thus, it would be interesting to explore this relationship further.\n",
     "\n",
     "<!---\n",
     "- Comparison to multi-target profiles in [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)\n",
@@ -1096,7 +1113,7 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "We have assessed kinase similarity from many different angles: the pocket sequence, the pocket structure, ligand binding modes (interaction fingerprints), and ligand bioactivities. We see from our results that different conclusions can be drawn for each methods, while some observations agree.\n",
+    "We have assessed kinase similarity from many different angles: the pocket sequence, the pocket structure, ligand binding modes (interaction fingerprints), and ligand bioactivities. We see from our results that different conclusions can be drawn for each method, while some observations agree.\n",
     "Thus, it is beneficial to consider different strategies to study kinase similarity.\n",
     "\n",
     "The different approaches have strengths and drawbacks:\n",
@@ -1117,10 +1134,17 @@
     "## Quiz\n",
     "\n",
     "1. Can you think of another perspective to assess kinase similarity?\n",
-    "2. Are there another method to assess and visualize the similarities?\n",
+    "2. Are there other methods to assess and visualize the similarities?\n",
     "3. Can you name at least one advantage and one disadvantage per perspective discussed here?\n",
-    "4. Could the different methods be applied to proteins in general?"
+    "4. Could the different methods be applied to other proteins in general?"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From c361f31e028f9312f8835d72b104db30d0884fce Mon Sep 17 00:00:00 2001
From: Unknown <dominique.sydow@posteo.de>
Date: Mon, 4 Oct 2021 09:03:26 +0200
Subject: [PATCH 129/158] T026+T028: Move heatmap/dendrogram quiz T026>T028;
 add new quiz to T026

---
 .../talktorials/T026_kinase_similarity_ifp/talktorial.ipynb | 6 +++---
 .../talktorial.ipynb                                        | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 2aa37bb6..f4b34c76 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -1725,9 +1725,9 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "1. Why is the structural coverage between kinases so different?\n",
-    "2. At one point, we are mapping structure pairs to kinase pairs. We use here the minimum structure distance. What other mapping strategies can you think of? What advantages and disadvantages could they have?\n",
-    "3. What is the difference between the distances that we show in the heatmap and the dendrogram? "
+    "1. What are the advantages and disadvantages of describing kinase similarity with interaction fingerprints\n",
+    "2. Why is the structural coverage between kinases so different?\n",
+    "3. At one point, we are mapping structure pairs to kinase pairs. We use here the minimum structure distance. What other mapping strategies can you think of? What advantages and disadvantages could they have?"
    ]
   }
  ],
diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 3f11a5eb..6727de2a 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -1136,7 +1136,8 @@
     "1. Can you think of another perspective to assess kinase similarity?\n",
     "2. Are there other methods to assess and visualize the similarities?\n",
     "3. Can you name at least one advantage and one disadvantage per perspective discussed here?\n",
-    "4. Could the different methods be applied to other proteins in general?"
+    "4. Could the different methods be applied to other proteins in general?\n",
+    "5. What is the difference between the distances that we show in the heatmap and the dendrogram? "
    ]
   },
   {

From 9fe8f5dd2371fbda20712a22e92ed648eeaaf905 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 5 Oct 2021 14:32:03 +0200
Subject: [PATCH 130/158] change kinase_name, kinase_seq to kinase_dict

---
 .../talktorial.ipynb                          | 3027 +++++++++--------
 1 file changed, 1615 insertions(+), 1412 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 3dbdf162..c3d08bf1 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -519,7 +519,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's look at these pocket sequences."
+    "We create a dictionary made of the kinase name and its associated pocket sequence. This dictionary is used throughout this notebook."
    ]
   },
   {
@@ -547,10 +547,10 @@
     }
    ],
    "source": [
-    "kinase_sequences = {}\n",
+    "kinase_sequences_dict = {}\n",
     "for kinase in kinase_selection_df[\"kinase_klifs\"]:\n",
-    "    kinase_sequences[kinase] = klifs_pocket_sequence(kinase)\n",
-    "kinase_sequences\n",
+    "    kinase_sequences_dict[kinase] = klifs_pocket_sequence(kinase)\n",
+    "kinase_sequences_dict\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -777,660 +777,692 @@
     {
      "data": {
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-       "                        <td id=\"T_b14ab_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
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-       "                        <td id=\"T_b14ab_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
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-       "            <tr>\n",
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-       "                        <td id=\"T_b14ab_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
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-       "                        <td id=\"T_b14ab_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
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-       "                        <td id=\"T_b14ab_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "                        <td id=\"T_b14ab_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
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-       "                        <td id=\"T_b14ab_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
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-       "                        <td id=\"T_b14ab_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
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-       "                        <td id=\"T_b14ab_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
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-       "                        <td id=\"T_b14ab_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
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-       "                        <th id=\"T_b14ab_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "                        <td id=\"T_b14ab_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "                        <td id=\"T_b14ab_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "                        <td id=\"T_b14ab_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "                        <td id=\"T_b14ab_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "                        <td id=\"T_b14ab_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "                        <td id=\"T_b14ab_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "                        <td id=\"T_b14ab_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "                        <td id=\"T_b14ab_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "                        <td id=\"T_b14ab_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "                        <td id=\"T_b14ab_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "                        <td id=\"T_b14ab_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "                        <td id=\"T_b14ab_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "                        <td id=\"T_b14ab_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "                        <td id=\"T_b14ab_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "                        <td id=\"T_b14ab_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "                        <td id=\"T_b14ab_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "                        <td id=\"T_b14ab_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "                        <td id=\"T_b14ab_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "                        <td id=\"T_b14ab_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "                        <td id=\"T_b14ab_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "                        <td id=\"T_b14ab_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "                        <td id=\"T_b14ab_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "                        <td id=\"T_b14ab_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "                        <td id=\"T_b14ab_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "                        <td id=\"T_b14ab_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "                        <td id=\"T_b14ab_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "                        <td id=\"T_b14ab_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "                        <td id=\"T_b14ab_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "                        <td id=\"T_b14ab_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "                        <td id=\"T_b14ab_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "                        <td id=\"T_b14ab_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "                        <td id=\"T_b14ab_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "                        <td id=\"T_b14ab_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "                        <td id=\"T_b14ab_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "                        <td id=\"T_b14ab_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "                        <td id=\"T_b14ab_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "                        <td id=\"T_b14ab_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "                        <td id=\"T_b14ab_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "                        <td id=\"T_b14ab_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "                        <td id=\"T_b14ab_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "                        <td id=\"T_b14ab_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "                        <td id=\"T_b14ab_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "                        <td id=\"T_b14ab_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "                        <td id=\"T_b14ab_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "                        <td id=\"T_b14ab_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "                        <td id=\"T_b14ab_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "                        <td id=\"T_b14ab_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "                        <td id=\"T_b14ab_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "                        <td id=\"T_b14ab_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "                        <td id=\"T_b14ab_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "                        <td id=\"T_b14ab_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "                        <td id=\"T_b14ab_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "                        <td id=\"T_b14ab_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "                        <td id=\"T_b14ab_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "                        <td id=\"T_b14ab_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "                        <td id=\"T_b14ab_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "                        <td id=\"T_b14ab_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "                        <td id=\"T_b14ab_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "                        <td id=\"T_b14ab_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "                        <td id=\"T_b14ab_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "                        <td id=\"T_b14ab_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "                        <td id=\"T_b14ab_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "                        <td id=\"T_b14ab_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "                        <td id=\"T_b14ab_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "                        <td id=\"T_b14ab_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "                        <td id=\"T_b14ab_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "                        <td id=\"T_b14ab_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "                        <td id=\"T_b14ab_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "                        <td id=\"T_b14ab_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "                        <td id=\"T_b14ab_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "                        <td id=\"T_b14ab_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "                        <td id=\"T_b14ab_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "                        <td id=\"T_b14ab_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "                        <td id=\"T_b14ab_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "                        <td id=\"T_b14ab_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "                        <td id=\"T_b14ab_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "                        <td id=\"T_b14ab_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "                        <td id=\"T_b14ab_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "                        <td id=\"T_b14ab_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "                        <td id=\"T_b14ab_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "                        <td id=\"T_b14ab_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "                        <td id=\"T_b14ab_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "                        <td id=\"T_b14ab_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "                        <td id=\"T_b14ab_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "                        <td id=\"T_b14ab_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "                        <td id=\"T_b14ab_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "                        <td id=\"T_b14ab_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "                        <td id=\"T_b14ab_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "                        <td id=\"T_b14ab_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "                        <td id=\"T_b14ab_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "                        <td id=\"T_b14ab_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "                        <td id=\"T_b14ab_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "                        <td id=\"T_b14ab_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "                        <td id=\"T_b14ab_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "                        <td id=\"T_b14ab_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "                        <td id=\"T_b14ab_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "                        <td id=\"T_b14ab_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "                        <td id=\"T_b14ab_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "                        <td id=\"T_b14ab_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "                        <td id=\"T_b14ab_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "                        <td id=\"T_b14ab_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "                        <td id=\"T_b14ab_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "                        <td id=\"T_b14ab_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "                        <td id=\"T_b14ab_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "                        <td id=\"T_b14ab_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "                        <td id=\"T_b14ab_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "                        <td id=\"T_b14ab_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "                        <td id=\"T_b14ab_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "                        <td id=\"T_b14ab_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "                        <td id=\"T_b14ab_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b14ab_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "                        <td id=\"T_b14ab_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "                        <td id=\"T_b14ab_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "</style>\n",
+       "<table id=\"T_aa646_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >A</th>\n",
+       "      <th class=\"col_heading level0 col1\" >C</th>\n",
+       "      <th class=\"col_heading level0 col2\" >D</th>\n",
+       "      <th class=\"col_heading level0 col3\" >E</th>\n",
+       "      <th class=\"col_heading level0 col4\" >F</th>\n",
+       "      <th class=\"col_heading level0 col5\" >G</th>\n",
+       "      <th class=\"col_heading level0 col6\" >H</th>\n",
+       "      <th class=\"col_heading level0 col7\" >I</th>\n",
+       "      <th class=\"col_heading level0 col8\" >K</th>\n",
+       "      <th class=\"col_heading level0 col9\" >L</th>\n",
+       "      <th class=\"col_heading level0 col10\" >M</th>\n",
+       "      <th class=\"col_heading level0 col11\" >N</th>\n",
+       "      <th class=\"col_heading level0 col12\" >P</th>\n",
+       "      <th class=\"col_heading level0 col13\" >Q</th>\n",
+       "      <th class=\"col_heading level0 col14\" >R</th>\n",
+       "      <th class=\"col_heading level0 col15\" >S</th>\n",
+       "      <th class=\"col_heading level0 col16\" >T</th>\n",
+       "      <th class=\"col_heading level0 col17\" >V</th>\n",
+       "      <th class=\"col_heading level0 col18\" >W</th>\n",
+       "      <th class=\"col_heading level0 col19\" >Y</th>\n",
+       "      <th class=\"col_heading level0 col20\" >B</th>\n",
+       "      <th class=\"col_heading level0 col21\" >Z</th>\n",
+       "      <th class=\"col_heading level0 col22\" >X</th>\n",
+       "      <th class=\"col_heading level0 col23\" >*</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_aa646_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_aa646_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_aa646_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_aa646_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_aa646_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_aa646_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_aa646_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_aa646_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_aa646_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_aa646_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_aa646_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_aa646_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_aa646_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_aa646_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_aa646_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_aa646_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_aa646_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_aa646_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_aa646_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_aa646_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_aa646_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_aa646_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_aa646_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_aa646_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_aa646_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_aa646_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_aa646_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_aa646_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_aa646_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_aa646_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_aa646_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_aa646_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_aa646_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_aa646_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_aa646_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_aa646_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_aa646_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_aa646_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_aa646_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_aa646_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_aa646_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_aa646_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_aa646_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_aa646_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_aa646_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_aa646_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_aa646_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_aa646_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_aa646_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_aa646_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_aa646_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_aa646_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_aa646_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_aa646_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_aa646_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_aa646_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_aa646_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_aa646_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_aa646_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_aa646_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_aa646_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_aa646_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_aa646_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_aa646_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_aa646_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_aa646_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_aa646_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_aa646_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_aa646_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_aa646_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_aa646_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_aa646_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_aa646_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_aa646_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_aa646_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_aa646_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_aa646_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_aa646_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_aa646_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_aa646_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_aa646_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_aa646_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_aa646_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_aa646_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_aa646_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_aa646_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_aa646_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_aa646_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_aa646_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_aa646_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_aa646_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_aa646_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_aa646_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_aa646_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_aa646_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_aa646_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_aa646_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_aa646_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_aa646_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_aa646_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_aa646_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_aa646_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_aa646_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_aa646_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_aa646_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_aa646_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_aa646_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_aa646_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_aa646_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_aa646_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_aa646_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa646_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_aa646_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_aa646_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe2b8b0e640>"
+       "<pandas.io.formats.style.Styler at 0x7fdd39651fa0>"
       ]
      },
      "execution_count": 10,
@@ -1585,7 +1617,7 @@
    "source": [
     "print(\"The sequences are:\\n\")\n",
     "for key in ('EGFR', 'MET'):\n",
-    "    print(f\"{key:5s}: {kinase_sequences[key]}\")\n",
+    "    print(f\"{key:5s}: {kinase_sequences_dict[key]}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1603,8 +1635,8 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                              kinase_sequences[\"MET\"],\n",
+    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
+    "                                              kinase_sequences_dict[\"MET\"],\n",
     "                                              \"identity\")\n",
     "\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
@@ -1626,8 +1658,8 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                              kinase_sequences[\"MET\"],\n",
+    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
+    "                                              kinase_sequences_dict[\"MET\"],\n",
     "                                              \"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
     "      f\"{egfr_met_seq_similarity:.2f} using substitution.\")\n",
@@ -1655,8 +1687,8 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                          kinase_sequences[\"EGFR\"],\n",
+    "egfr_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
+    "                                          kinase_sequences_dict[\"EGFR\"],\n",
     "                                          type_=\"identity\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{egfr_seq_similarity:.2f} using identity.\")\n",
@@ -1677,8 +1709,8 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(kinase_sequences[\"EGFR\"],\n",
-    "                                          kinase_sequences[\"EGFR\"],\n",
+    "egfr_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
+    "                                          kinase_sequences_dict[\"EGFR\"],\n",
     "                                          type_=\"substitution\")\n",
     "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
     "      f\"{egfr_seq_similarity:.2f} using substitution.\")\n",
@@ -1705,16 +1737,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def pairwise_kinase_similarities(kinase_names, kinase_sequences, similarity_measure):\n",
+    "def pairwise_kinase_similarities(kinase_sequence_dictionary, similarity_measure):\n",
     "    \"\"\"\n",
     "    Calculate pairwise similarities between a set of kinases.\n",
     "    \n",
     "    Parameters\n",
     "    ----------\n",
-    "    kinase_names : list of str\n",
-    "        Kinase names.\n",
-    "    kinase_sequences : list of str\n",
-    "        Kinase pocket sequences.\n",
+    "    kinase_sequence_dictionary : dict\n",
+    "        A dictionary containing the kinase name as key\n",
+    "        and the kinase sequence as value.\n",
     "    similarity_measure : str\n",
     "        Select similarity measure: \"identity\" or \"substitution\".\n",
     "        \n",
@@ -1724,19 +1755,19 @@
     "        All-against-all similarities between input kinases.\n",
     "    \"\"\"\n",
     "    # Initialize matrix with 0\n",
-    "    kinase_similarity_matrix = np.zeros((len(kinase_names),\n",
-    "                                         len(kinase_names)))\n",
+    "    kinase_similarity_matrix = np.zeros((len(kinase_sequence_dictionary),\n",
+    "                                         len(kinase_sequence_dictionary)))\n",
     "    # Iterate over matrix and fill with similarity values\n",
-    "    for i, kinase_name1 in enumerate(kinase_names):\n",
-    "        for j, kinase_name2 in enumerate(kinase_names):\n",
-    "            kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequences[kinase_name1],\n",
-    "                                                                 kinase_sequences[kinase_name2],\n",
+    "    for i, kinase_sequence1 in enumerate(kinase_sequence_dictionary.values()):\n",
+    "        for j, kinase_sequence2 in enumerate(kinase_sequence_dictionary.values()):\n",
+    "            kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequence1,\n",
+    "                                                                 kinase_sequence2,\n",
     "                                                                 similarity_measure)\n",
     "\n",
     "    # Cast matrix to DataFrame\n",
     "    kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                               index=kinase_names,\n",
-    "                                               columns=kinase_names)\n",
+    "                                               index=kinase_sequences_dict.keys(),\n",
+    "                                               columns=kinase_sequences_dict.keys())\n",
     "    kinase_similarity_matrix_df.index.name = None\n",
     "    kinase_similarity_matrix_df.columns.name = None\n",
     "\n",
@@ -1756,15 +1787,6 @@
     "We visualize the similarity matrices using identity and substitution:"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kinase_names = kinase_selection_df[\"kinase_klifs\"]"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1774,7 +1796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1946,14 +1968,14 @@
        "p38a   0.364706  1.000000  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "kinase_similarity_matrix_identity_df = pairwise_kinase_similarities(\n",
-    "    kinase_names, kinase_sequences, similarity_measure=\"identity\"\n",
+    "    kinase_sequences_dict, similarity_measure=\"identity\"\n",
     ")\n",
     "kinase_similarity_matrix_identity_df\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -1961,231 +1983,282 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "            color:  #000000;\n",
-       "        }#T_c1dca_row6_col4,#T_c1dca_row8_col3{\n",
-       "            background-color:  #a9d3a9;\n",
-       "            color:  #000000;\n",
-       "        }#T_c1dca_row7_col8{\n",
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-       "            color:  #000000;\n",
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-       "            background-color:  #a3d0a3;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_c1dca_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_c1dca_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "                        <td id=\"T_c1dca_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "                        <td id=\"T_c1dca_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "                        <td id=\"T_c1dca_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "                        <td id=\"T_c1dca_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "                        <td id=\"T_c1dca_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_c1dca_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "                        <td id=\"T_c1dca_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "                        <td id=\"T_c1dca_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "                        <td id=\"T_c1dca_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "                        <td id=\"T_c1dca_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "                        <td id=\"T_c1dca_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "                        <td id=\"T_c1dca_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "                        <td id=\"T_c1dca_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "                        <td id=\"T_c1dca_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "                        <td id=\"T_c1dca_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "                        <td id=\"T_c1dca_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "                        <td id=\"T_c1dca_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "                        <td id=\"T_c1dca_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "                        <td id=\"T_c1dca_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "                        <td id=\"T_c1dca_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "                        <td id=\"T_c1dca_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "                        <td id=\"T_c1dca_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "                        <td id=\"T_c1dca_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "                        <td id=\"T_c1dca_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "                        <td id=\"T_c1dca_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "                        <td id=\"T_c1dca_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "                        <td id=\"T_c1dca_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "                        <td id=\"T_c1dca_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "                        <td id=\"T_c1dca_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "                        <td id=\"T_c1dca_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "                        <td id=\"T_c1dca_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "                        <td id=\"T_c1dca_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "                        <td id=\"T_c1dca_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "                        <td id=\"T_c1dca_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "                        <td id=\"T_c1dca_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "                        <td id=\"T_c1dca_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "                        <td id=\"T_c1dca_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "                        <td id=\"T_c1dca_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "                        <td id=\"T_c1dca_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "                        <td id=\"T_c1dca_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "                        <td id=\"T_c1dca_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "                        <td id=\"T_c1dca_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "                        <td id=\"T_c1dca_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "                        <td id=\"T_c1dca_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "                        <td id=\"T_c1dca_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "                        <td id=\"T_c1dca_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_c1dca_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "                        <td id=\"T_c1dca_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "                        <td id=\"T_c1dca_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "                        <td id=\"T_c1dca_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "                        <td id=\"T_c1dca_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "                        <td id=\"T_c1dca_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "                        <td id=\"T_c1dca_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "                        <td id=\"T_c1dca_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "                        <td id=\"T_c1dca_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "                        <td id=\"T_c1dca_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_cb06b_row0_col0, #T_cb06b_row1_col1, #T_cb06b_row2_col2, #T_cb06b_row3_col3, #T_cb06b_row4_col4, #T_cb06b_row5_col5, #T_cb06b_row6_col6, #T_cb06b_row7_col7, #T_cb06b_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_cb06b_row0_col1, #T_cb06b_row1_col0 {\n",
+       "  background-color: #1c8e1c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_cb06b_row0_col2, #T_cb06b_row1_col2 {\n",
+       "  background-color: #e8f2e8;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col3, #T_cb06b_row7_col3 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col4, #T_cb06b_row3_col5, #T_cb06b_row7_col4, #T_cb06b_row8_col4 {\n",
+       "  background-color: #add5ad;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col5 {\n",
+       "  background-color: #b3d8b3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col6 {\n",
+       "  background-color: #97ca97;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col7 {\n",
+       "  background-color: #8ec58e;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row0_col8, #T_cb06b_row3_col8, #T_cb06b_row4_col6, #T_cb06b_row6_col8, #T_cb06b_row8_col6 {\n",
+       "  background-color: #a7d2a7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col3, #T_cb06b_row6_col3 {\n",
+       "  background-color: #9ccd9c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col4, #T_cb06b_row3_col4, #T_cb06b_row4_col3, #T_cb06b_row5_col7, #T_cb06b_row7_col5, #T_cb06b_row8_col7 {\n",
+       "  background-color: #a6d2a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col5, #T_cb06b_row4_col5 {\n",
+       "  background-color: #b1d7b1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col6, #T_cb06b_row6_col7 {\n",
+       "  background-color: #9dcd9d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col7, #T_cb06b_row3_col7, #T_cb06b_row8_col5 {\n",
+       "  background-color: #8bc48b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row1_col8, #T_cb06b_row4_col7, #T_cb06b_row6_col5, #T_cb06b_row7_col6 {\n",
+       "  background-color: #a4d0a4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row2_col0, #T_cb06b_row2_col1, #T_cb06b_row2_col3, #T_cb06b_row2_col4, #T_cb06b_row2_col5, #T_cb06b_row2_col6, #T_cb06b_row2_col7, #T_cb06b_row2_col8, #T_cb06b_row5_col2, #T_cb06b_row7_col2 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row3_col0, #T_cb06b_row7_col1 {\n",
+       "  background-color: #8dc58d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row3_col1 {\n",
+       "  background-color: #96c996;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row3_col2, #T_cb06b_row4_col2 {\n",
+       "  background-color: #dfeddf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row3_col6 {\n",
+       "  background-color: #9bcc9b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row4_col0 {\n",
+       "  background-color: #a6d1a6;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row4_col1, #T_cb06b_row8_col1 {\n",
+       "  background-color: #a0cea0;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row4_col8, #T_cb06b_row5_col6 {\n",
+       "  background-color: #aad3aa;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row5_col0 {\n",
+       "  background-color: #b5d9b5;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row5_col1 {\n",
+       "  background-color: #b2d7b2;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row5_col3 {\n",
+       "  background-color: #b7dab7;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row5_col4 {\n",
+       "  background-color: #badbba;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row5_col8, #T_cb06b_row7_col0 {\n",
+       "  background-color: #90c790;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row6_col0 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row6_col1 {\n",
+       "  background-color: #9acb9a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row6_col2, #T_cb06b_row8_col2 {\n",
+       "  background-color: #e1eee1;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row6_col4, #T_cb06b_row8_col3 {\n",
+       "  background-color: #a9d3a9;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row7_col8 {\n",
+       "  background-color: #aed5ae;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_cb06b_row8_col0 {\n",
+       "  background-color: #a3d0a3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_cb06b_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_cb06b_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_cb06b_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_cb06b_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_cb06b_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_cb06b_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_cb06b_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_cb06b_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_cb06b_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_cb06b_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_cb06b_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_cb06b_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_cb06b_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_cb06b_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_cb06b_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_cb06b_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_cb06b_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_cb06b_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_cb06b_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_cb06b_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_cb06b_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_cb06b_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_cb06b_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_cb06b_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_cb06b_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_cb06b_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_cb06b_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_cb06b_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_cb06b_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_cb06b_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_cb06b_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_cb06b_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_cb06b_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_cb06b_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_cb06b_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_cb06b_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_cb06b_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_cb06b_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_cb06b_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_cb06b_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_cb06b_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_cb06b_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_cb06b_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_cb06b_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_cb06b_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_cb06b_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_cb06b_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_cb06b_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cb06b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_cb06b_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_cb06b_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_cb06b_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_cb06b_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_cb06b_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_cb06b_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_cb06b_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_cb06b_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_cb06b_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe25bb05ee0>"
+       "<pandas.io.formats.style.Styler at 0x7fdd39a41220>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2207,7 +2280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2379,14 +2452,14 @@
        "p38a   0.629355  1.000000  "
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "kinase_similarity_matrix_substitution_df = pairwise_kinase_similarities(\n",
-    "    kinase_names, kinase_sequences, similarity_measure=\"substitution\"\n",
+    "    kinase_sequences_dict, similarity_measure=\"substitution\"\n",
     ")\n",
     "kinase_similarity_matrix_substitution_df\n",
     "# NBVAL_CHECK_OUTPUT"
@@ -2394,261 +2467,326 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "        }</style><table id=\"T_0f456_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_0f456_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_0f456_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "                        <td id=\"T_0f456_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "                        <td id=\"T_0f456_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "                        <td id=\"T_0f456_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "                        <td id=\"T_0f456_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "                        <td id=\"T_0f456_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "                        <td id=\"T_0f456_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "                        <td id=\"T_0f456_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_0f456_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "                        <td id=\"T_0f456_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "                        <td id=\"T_0f456_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "                        <td id=\"T_0f456_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "                        <td id=\"T_0f456_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "                        <td id=\"T_0f456_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "                        <td id=\"T_0f456_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "                        <td id=\"T_0f456_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "                        <td id=\"T_0f456_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "                        <td id=\"T_0f456_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "                        <td id=\"T_0f456_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "                        <td id=\"T_0f456_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "                        <td id=\"T_0f456_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "                        <td id=\"T_0f456_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "                        <td id=\"T_0f456_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "                        <td id=\"T_0f456_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "                        <td id=\"T_0f456_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "                        <td id=\"T_0f456_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "                        <td id=\"T_0f456_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "                        <td id=\"T_0f456_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "                        <td id=\"T_0f456_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "                        <td id=\"T_0f456_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "                        <td id=\"T_0f456_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "                        <td id=\"T_0f456_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "                        <td id=\"T_0f456_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "                        <td id=\"T_0f456_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "                        <td id=\"T_0f456_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "                        <td id=\"T_0f456_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "                        <td id=\"T_0f456_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "                        <td id=\"T_0f456_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "                        <td id=\"T_0f456_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "                        <td id=\"T_0f456_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "                        <td id=\"T_0f456_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "                        <td id=\"T_0f456_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "                        <td id=\"T_0f456_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "                        <td id=\"T_0f456_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "                        <td id=\"T_0f456_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "                        <td id=\"T_0f456_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "                        <td id=\"T_0f456_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "                        <td id=\"T_0f456_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "                        <td id=\"T_0f456_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "                        <td id=\"T_0f456_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "                        <td id=\"T_0f456_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "                        <td id=\"T_0f456_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "                        <td id=\"T_0f456_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "                        <td id=\"T_0f456_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "                        <td id=\"T_0f456_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "                        <td id=\"T_0f456_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "                        <td id=\"T_0f456_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "                        <td id=\"T_0f456_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "                        <td id=\"T_0f456_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "                        <td id=\"T_0f456_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "                        <td id=\"T_0f456_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "                        <td id=\"T_0f456_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "                        <td id=\"T_0f456_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "                        <td id=\"T_0f456_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "                        <td id=\"T_0f456_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0f456_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "                        <td id=\"T_0f456_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "                        <td id=\"T_0f456_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "                        <td id=\"T_0f456_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "                        <td id=\"T_0f456_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "                        <td id=\"T_0f456_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "                        <td id=\"T_0f456_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "                        <td id=\"T_0f456_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "                        <td id=\"T_0f456_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "                        <td id=\"T_0f456_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_ef15b_row0_col0, #T_ef15b_row1_col1, #T_ef15b_row2_col2, #T_ef15b_row3_col3, #T_ef15b_row4_col4, #T_ef15b_row5_col5, #T_ef15b_row6_col6, #T_ef15b_row7_col7, #T_ef15b_row8_col8 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row0_col1 {\n",
+       "  background-color: #178b17;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row0_col2 {\n",
+       "  background-color: #deedde;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row0_col3 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row0_col4, #T_ef15b_row0_col5, #T_ef15b_row1_col4, #T_ef15b_row3_col8, #T_ef15b_row4_col5, #T_ef15b_row8_col7 {\n",
+       "  background-color: #8fc68f;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row0_col6 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row0_col7 {\n",
+       "  background-color: #6fb76f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row0_col8, #T_ef15b_row8_col1 {\n",
+       "  background-color: #92c892;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row1_col0 {\n",
+       "  background-color: #188c18;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row1_col2 {\n",
+       "  background-color: #e3efe3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row1_col3 {\n",
+       "  background-color: #79bc79;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row1_col5, #T_ef15b_row1_col8, #T_ef15b_row7_col4, #T_ef15b_row8_col4 {\n",
+       "  background-color: #97ca97;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row1_col6 {\n",
+       "  background-color: #86c286;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row1_col7 {\n",
+       "  background-color: #75b975;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row2_col0, #T_ef15b_row2_col1, #T_ef15b_row2_col3, #T_ef15b_row2_col4, #T_ef15b_row2_col5, #T_ef15b_row2_col6, #T_ef15b_row2_col7, #T_ef15b_row2_col8, #T_ef15b_row7_col2 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row3_col0, #T_ef15b_row7_col3 {\n",
+       "  background-color: #74b974;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row3_col1, #T_ef15b_row6_col7 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row3_col2, #T_ef15b_row5_col2 {\n",
+       "  background-color: #e0eedf;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row3_col4 {\n",
+       "  background-color: #89c389;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row3_col5, #T_ef15b_row4_col0 {\n",
+       "  background-color: #8ec58e;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row3_col6, #T_ef15b_row4_col3, #T_ef15b_row5_col7 {\n",
+       "  background-color: #85c185;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row3_col7 {\n",
+       "  background-color: #6eb66e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row4_col1 {\n",
+       "  background-color: #8ac48a;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row4_col2 {\n",
+       "  background-color: #daebda;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row4_col6, #T_ef15b_row4_col7, #T_ef15b_row7_col5 {\n",
+       "  background-color: #8cc58c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row4_col8 {\n",
+       "  background-color: #95c995;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col0, #T_ef15b_row8_col6 {\n",
+       "  background-color: #90c790;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col1 {\n",
+       "  background-color: #94c994;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col3, #T_ef15b_row8_col3 {\n",
+       "  background-color: #8dc58d;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col4 {\n",
+       "  background-color: #93c893;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col6, #T_ef15b_row6_col4 {\n",
+       "  background-color: #88c388;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row5_col8 {\n",
+       "  background-color: #72b872;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row6_col0 {\n",
+       "  background-color: #76ba76;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row6_col1 {\n",
+       "  background-color: #7ebe7e;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row6_col2 {\n",
+       "  background-color: #d4e8d4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row6_col3 {\n",
+       "  background-color: #7fbe7f;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row6_col5 {\n",
+       "  background-color: #82c082;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row6_col8 {\n",
+       "  background-color: #8bc48b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row7_col0 {\n",
+       "  background-color: #77ba77;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_ef15b_row7_col1 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row7_col6 {\n",
+       "  background-color: #84c184;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row7_col8 {\n",
+       "  background-color: #99cb99;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row8_col0 {\n",
+       "  background-color: #91c791;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row8_col2 {\n",
+       "  background-color: #ddecdd;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_ef15b_row8_col5 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_ef15b_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_ef15b_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_ef15b_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_ef15b_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_ef15b_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_ef15b_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_ef15b_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_ef15b_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_ef15b_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_ef15b_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_ef15b_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_ef15b_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_ef15b_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_ef15b_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_ef15b_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_ef15b_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_ef15b_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_ef15b_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_ef15b_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_ef15b_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_ef15b_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_ef15b_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_ef15b_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_ef15b_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_ef15b_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_ef15b_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_ef15b_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_ef15b_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_ef15b_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_ef15b_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_ef15b_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_ef15b_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_ef15b_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_ef15b_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_ef15b_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_ef15b_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_ef15b_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_ef15b_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_ef15b_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_ef15b_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_ef15b_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_ef15b_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_ef15b_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_ef15b_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_ef15b_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_ef15b_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_ef15b_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_ef15b_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_ef15b_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_ef15b_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_ef15b_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_ef15b_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_ef15b_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_ef15b_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_ef15b_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_ef15b_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_ef15b_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_ef15b_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_ef15b_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_ef15b_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_ef15b_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_ef15b_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_ef15b_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_ef15b_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_ef15b_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_ef15b_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_ef15b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_ef15b_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_ef15b_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_ef15b_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_ef15b_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_ef15b_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_ef15b_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_ef15b_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_ef15b_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_ef15b_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe25baf8d00>"
+       "<pandas.io.formats.style.Styler at 0x7fdd39b0c3a0>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2672,7 +2810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2688,12 +2826,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_similarity_matrix.csv\"\n",
-    "kinase_similarity_matrix_df.to_csv(DATA / f_name)"
+    "file_name = f\"kinase_similarity_matrix.csv\"\n",
+    "kinase_similarity_matrix_df.to_csv(DATA / file_name)"
    ]
   },
   {
@@ -2712,7 +2850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2732,7 +2870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2741,261 +2879,326 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style  type=\"text/css\" >\n",
-       "#T_b889a_row0_col0,#T_b889a_row1_col1,#T_b889a_row2_col2,#T_b889a_row3_col3,#T_b889a_row4_col4,#T_b889a_row5_col5,#T_b889a_row6_col6,#T_b889a_row7_col7,#T_b889a_row8_col8{\n",
-       "            background-color:  #ebf3eb;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row0_col1{\n",
-       "            background-color:  #d4e8d4;\n",
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-       "            background-color:  #0d860d;\n",
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-       "            background-color:  #78bb78;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row0_col4,#T_b889a_row0_col5,#T_b889a_row1_col4,#T_b889a_row4_col5,#T_b889a_row8_col7{\n",
-       "            background-color:  #5bad5b;\n",
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-       "            background-color:  #7bbc7b;\n",
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-       "            background-color:  #58ab58;\n",
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-       "        }#T_b889a_row1_col0{\n",
-       "            background-color:  #d3e7d3;\n",
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-       "        }#T_b889a_row1_col2{\n",
-       "            background-color:  #078407;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_b889a_row1_col3{\n",
-       "            background-color:  #71b771;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row1_col5,#T_b889a_row1_col8,#T_b889a_row7_col4,#T_b889a_row8_col4{\n",
-       "            background-color:  #54a954;\n",
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-       "        }#T_b889a_row1_col6{\n",
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-       "        }#T_b889a_row1_col7{\n",
-       "            background-color:  #76ba76;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row2_col0,#T_b889a_row2_col1,#T_b889a_row2_col3,#T_b889a_row2_col4,#T_b889a_row2_col5,#T_b889a_row2_col6,#T_b889a_row2_col7,#T_b889a_row2_col8,#T_b889a_row7_col2{\n",
-       "            background-color:  #008000;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_b889a_row3_col0,#T_b889a_row7_col3{\n",
-       "            background-color:  #77ba77;\n",
-       "            color:  #000000;\n",
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-       "            background-color:  #73b873;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row3_col2,#T_b889a_row5_col2{\n",
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-       "            color:  #f1f1f1;\n",
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-       "            background-color:  #5dae5d;\n",
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-       "        }#T_b889a_row3_col6,#T_b889a_row4_col3,#T_b889a_row5_col7{\n",
-       "            background-color:  #65b265;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row3_col7{\n",
-       "            background-color:  #7cbd7c;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row3_col8{\n",
-       "            background-color:  #5cad5c;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row4_col1{\n",
-       "            background-color:  #61af61;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row4_col2{\n",
-       "            background-color:  #118811;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_b889a_row4_col6,#T_b889a_row4_col7,#T_b889a_row7_col5{\n",
-       "            background-color:  #5fae5f;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row4_col8,#T_b889a_row5_col1{\n",
-       "            background-color:  #56aa56;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row5_col0,#T_b889a_row8_col6{\n",
-       "            background-color:  #5aac5a;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row5_col3,#T_b889a_row8_col3{\n",
-       "            background-color:  #5eae5e;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row5_col4{\n",
-       "            background-color:  #57ab57;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row5_col6,#T_b889a_row6_col4{\n",
-       "            background-color:  #62b062;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col0{\n",
-       "            background-color:  #75b975;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col1{\n",
-       "            background-color:  #6db56d;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col2{\n",
-       "            background-color:  #168b16;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col3{\n",
-       "            background-color:  #6cb56c;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col5{\n",
-       "            background-color:  #69b369;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row6_col8{\n",
-       "            background-color:  #60af60;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row7_col0{\n",
-       "            background-color:  #74b974;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row7_col1{\n",
-       "            background-color:  #72b872;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row7_col6{\n",
-       "            background-color:  #66b266;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row7_col8{\n",
-       "            background-color:  #52a852;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row8_col0{\n",
-       "            background-color:  #59ac59;\n",
-       "            color:  #000000;\n",
-       "        }#T_b889a_row8_col2{\n",
-       "            background-color:  #0e870e;\n",
-       "            color:  #f1f1f1;\n",
-       "        }#T_b889a_row8_col5{\n",
-       "            background-color:  #79bc79;\n",
-       "            color:  #000000;\n",
-       "        }</style><table id=\"T_b889a_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >EGFR</th>        <th class=\"col_heading level0 col1\" >ErbB2</th>        <th class=\"col_heading level0 col2\" >p110a</th>        <th class=\"col_heading level0 col3\" >KDR</th>        <th class=\"col_heading level0 col4\" >BRAF</th>        <th class=\"col_heading level0 col5\" >CDK2</th>        <th class=\"col_heading level0 col6\" >LCK</th>        <th class=\"col_heading level0 col7\" >MET</th>        <th class=\"col_heading level0 col8\" >p38a</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_b889a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "                        <td id=\"T_b889a_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "                        <td id=\"T_b889a_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "                        <td id=\"T_b889a_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "                        <td id=\"T_b889a_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "                        <td id=\"T_b889a_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "                        <td id=\"T_b889a_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "                        <td id=\"T_b889a_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "                        <td id=\"T_b889a_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "                        <td id=\"T_b889a_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "                        <td id=\"T_b889a_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "                        <td id=\"T_b889a_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "                        <td id=\"T_b889a_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "                        <td id=\"T_b889a_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "                        <td id=\"T_b889a_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "                        <td id=\"T_b889a_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "                        <td id=\"T_b889a_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "                        <td id=\"T_b889a_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "                        <td id=\"T_b889a_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "                        <td id=\"T_b889a_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "                        <td id=\"T_b889a_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "                        <td id=\"T_b889a_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "                        <td id=\"T_b889a_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "                        <td id=\"T_b889a_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "                        <td id=\"T_b889a_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "                        <td id=\"T_b889a_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "                        <td id=\"T_b889a_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "                        <td id=\"T_b889a_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "                        <td id=\"T_b889a_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "                        <td id=\"T_b889a_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "                        <td id=\"T_b889a_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "                        <td id=\"T_b889a_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "                        <td id=\"T_b889a_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "                        <td id=\"T_b889a_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "                        <td id=\"T_b889a_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "                        <td id=\"T_b889a_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "                        <td id=\"T_b889a_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "                        <td id=\"T_b889a_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "                        <td id=\"T_b889a_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "                        <td id=\"T_b889a_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "                        <td id=\"T_b889a_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "                        <td id=\"T_b889a_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "                        <td id=\"T_b889a_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "                        <td id=\"T_b889a_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "                        <td id=\"T_b889a_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "                        <td id=\"T_b889a_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "                        <td id=\"T_b889a_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "                        <td id=\"T_b889a_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "                        <td id=\"T_b889a_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "                        <td id=\"T_b889a_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "                        <td id=\"T_b889a_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "                        <td id=\"T_b889a_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "                        <td id=\"T_b889a_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "                        <td id=\"T_b889a_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "                        <td id=\"T_b889a_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "                        <td id=\"T_b889a_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "                        <td id=\"T_b889a_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "                        <td id=\"T_b889a_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "                        <td id=\"T_b889a_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "                        <td id=\"T_b889a_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "                        <td id=\"T_b889a_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "                        <td id=\"T_b889a_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "                        <td id=\"T_b889a_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "                        <td id=\"T_b889a_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "                        <td id=\"T_b889a_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "                        <td id=\"T_b889a_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b889a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "                        <td id=\"T_b889a_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "                        <td id=\"T_b889a_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "                        <td id=\"T_b889a_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "                        <td id=\"T_b889a_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "                        <td id=\"T_b889a_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "                        <td id=\"T_b889a_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "                        <td id=\"T_b889a_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "                        <td id=\"T_b889a_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "                        <td id=\"T_b889a_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_93a1c_row0_col0, #T_93a1c_row1_col1, #T_93a1c_row2_col2, #T_93a1c_row3_col3, #T_93a1c_row4_col4, #T_93a1c_row5_col5, #T_93a1c_row6_col6, #T_93a1c_row7_col7, #T_93a1c_row8_col8 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row0_col1 {\n",
+       "  background-color: #d4e8d4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row0_col2 {\n",
+       "  background-color: #0d860d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row0_col3 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row0_col4, #T_93a1c_row0_col5, #T_93a1c_row1_col4, #T_93a1c_row4_col5, #T_93a1c_row8_col7 {\n",
+       "  background-color: #5bad5b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row0_col6 {\n",
+       "  background-color: #6fb76f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row0_col7 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row0_col8, #T_93a1c_row8_col1 {\n",
+       "  background-color: #58ab58;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row1_col0 {\n",
+       "  background-color: #d3e7d3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row1_col2 {\n",
+       "  background-color: #078407;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row1_col3 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row1_col5, #T_93a1c_row1_col8, #T_93a1c_row7_col4, #T_93a1c_row8_col4 {\n",
+       "  background-color: #54a954;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row1_col6 {\n",
+       "  background-color: #64b164;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row1_col7 {\n",
+       "  background-color: #76ba76;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row2_col0, #T_93a1c_row2_col1, #T_93a1c_row2_col3, #T_93a1c_row2_col4, #T_93a1c_row2_col5, #T_93a1c_row2_col6, #T_93a1c_row2_col7, #T_93a1c_row2_col8, #T_93a1c_row7_col2 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col0, #T_93a1c_row7_col3 {\n",
+       "  background-color: #77ba77;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col1, #T_93a1c_row6_col7 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col2, #T_93a1c_row5_col2 {\n",
+       "  background-color: #0b850b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col4 {\n",
+       "  background-color: #61b061;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col5, #T_93a1c_row4_col0 {\n",
+       "  background-color: #5dae5d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col6, #T_93a1c_row4_col3, #T_93a1c_row5_col7 {\n",
+       "  background-color: #65b265;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row3_col7 {\n",
+       "  background-color: #7cbd7c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row3_col8 {\n",
+       "  background-color: #5cad5c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row4_col1 {\n",
+       "  background-color: #61af61;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row4_col2 {\n",
+       "  background-color: #118811;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row4_col6, #T_93a1c_row4_col7, #T_93a1c_row7_col5 {\n",
+       "  background-color: #5fae5f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row4_col8, #T_93a1c_row5_col1 {\n",
+       "  background-color: #56aa56;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row5_col0, #T_93a1c_row8_col6 {\n",
+       "  background-color: #5aac5a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row5_col3, #T_93a1c_row8_col3 {\n",
+       "  background-color: #5eae5e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row5_col4 {\n",
+       "  background-color: #57ab57;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row5_col6, #T_93a1c_row6_col4 {\n",
+       "  background-color: #62b062;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row5_col8 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_93a1c_row6_col0 {\n",
+       "  background-color: #75b975;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row6_col1 {\n",
+       "  background-color: #6db56d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row6_col2 {\n",
+       "  background-color: #168b16;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row6_col3 {\n",
+       "  background-color: #6cb56c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row6_col5 {\n",
+       "  background-color: #69b369;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row6_col8 {\n",
+       "  background-color: #60af60;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row7_col0 {\n",
+       "  background-color: #74b974;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row7_col1 {\n",
+       "  background-color: #72b872;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row7_col6 {\n",
+       "  background-color: #66b266;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row7_col8 {\n",
+       "  background-color: #52a852;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row8_col0 {\n",
+       "  background-color: #59ac59;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row8_col2 {\n",
+       "  background-color: #0e870e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_93a1c_row8_col5 {\n",
+       "  background-color: #79bc79;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_93a1c_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_93a1c_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_93a1c_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_93a1c_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_93a1c_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_93a1c_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_93a1c_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_93a1c_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_93a1c_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_93a1c_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_93a1c_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_93a1c_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_93a1c_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_93a1c_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_93a1c_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_93a1c_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_93a1c_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_93a1c_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_93a1c_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_93a1c_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_93a1c_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_93a1c_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_93a1c_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_93a1c_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_93a1c_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_93a1c_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_93a1c_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_93a1c_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_93a1c_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_93a1c_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_93a1c_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_93a1c_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_93a1c_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_93a1c_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_93a1c_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_93a1c_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_93a1c_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_93a1c_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_93a1c_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_93a1c_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_93a1c_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_93a1c_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_93a1c_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_93a1c_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_93a1c_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_93a1c_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_93a1c_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_93a1c_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_93a1c_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_93a1c_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_93a1c_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_93a1c_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_93a1c_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_93a1c_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_93a1c_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_93a1c_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_93a1c_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_93a1c_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_93a1c_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_93a1c_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_93a1c_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_93a1c_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_93a1c_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_93a1c_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_93a1c_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_93a1c_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93a1c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_93a1c_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_93a1c_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_93a1c_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_93a1c_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_93a1c_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_93a1c_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_93a1c_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_93a1c_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_93a1c_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fe25ba2fe20>"
+       "<pandas.io.formats.style.Styler at 0x7fdd39a8f1c0>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3015,12 +3218,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
-    "f_name = f\"kinase_distance_matrix.csv\"\n",
-    "kinase_distance_matrix_df.to_csv(DATA / f_name)"
+    "file_name = f\"kinase_distance_matrix.csv\"\n",
+    "kinase_distance_matrix_df.to_csv(DATA / file_name)"
    ]
   },
   {
@@ -3048,7 +3251,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3062,7 +3265,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From c95d6938a708542631369951ebf88ce3465cb8ec Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 6 Oct 2021 08:44:22 +0200
Subject: [PATCH 131/158] :pencil2: fix typos and nb in math mode.

---
 .../talktorial.ipynb                          | 1146 ++++++++---------
 1 file changed, 570 insertions(+), 576 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index c6ec81fe..2718dad2 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,7 +627,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pIC50', 'pKd', 'pKi'}\n",
+      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -690,7 +690,7 @@
    "metadata": {},
    "source": [
     "The DataFrame contains many columns that won't be necessary for the rest of the notebook which are therefore removed. \n",
-    "Only relevant information is kept, namely the `canonical SMILES` of the compound, the `measured activity` and the * UniProt ID` of the kinase. These columns are renamed for readability."
+    "Only relevant information is kept, namely the canonical SMILES of the compound (`compound_structures.canonical_smiles`), the measured activity (`activities.standard_value`) and the UniProt ID of the kinase (`UniprotID`). These columns are renamed for readability."
    ]
   },
   {
@@ -981,22 +981,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can also have a look at those compounds."
+    "We have a look at those compounds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1012,19 +1012,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In our example, the firt molecules is actually gefitinib, a known FDA approved drug against EGFR."
+    "In this example, the first molecule is gefitinib, a known FDA approved drug against EGFR."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As a simple workaround - since we prefer to have one activity value per compound-kinase pair - we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value."
+    "As a simple workaround — since we prefer to have one activity value per compound-kinase pair — we keep the value for which the compound has the best activity value, i.e., the highest pIC50 value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1097,7 +1097,7 @@
        "4    P00533  Br.C[C@@H](Nc1ncnc2[nH]c(-c3ccc(O)cc3)cc12)c1c...  8.420216"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1133,7 +1133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1162,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1171,7 +1171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1264,7 +1264,7 @@
        "4                1  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1297,7 +1297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1340,7 +1340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1379,7 +1379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1413,7 +1413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1514,7 +1514,7 @@
        "p38a   3637    2778  0.763816"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1534,53 +1534,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "  color: #000000;\n",
        "}\n",
-       "#T_884d7_row1_col2 {\n",
+       "#T_d0ee5_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_884d7_row2_col2 {\n",
+       "#T_d0ee5_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
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        "}\n",
-       "#T_884d7_row3_col2 {\n",
+       "#T_d0ee5_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_884d7_row4_col2 {\n",
+       "#T_d0ee5_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_884d7_row5_col2 {\n",
+       "#T_d0ee5_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_884d7_row6_col2 {\n",
+       "#T_d0ee5_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_884d7_row7_col2 {\n",
+       "#T_d0ee5_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_884d7_row8_col2 {\n",
+       "#T_d0ee5_row8_col2 {\n",
        "  background-color: #5d3897;\n",
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        "}\n",
        "</style>\n",
-       "<table id=\"T_884d7_\">\n",
+       "<table id=\"T_d0ee5_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1591,67 +1591,67 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_884d7_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_884d7_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_884d7_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_d0ee5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d0ee5_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_d0ee5_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_d0ee5_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
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-       "      <td id=\"T_884d7_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_884d7_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_d0ee5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d0ee5_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_d0ee5_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_d0ee5_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_884d7_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_884d7_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_884d7_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_d0ee5_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_d0ee5_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_d0ee5_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_d0ee5_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_884d7_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_884d7_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_884d7_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_d0ee5_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_d0ee5_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_d0ee5_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_d0ee5_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_884d7_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_884d7_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_884d7_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_d0ee5_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_d0ee5_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_d0ee5_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_d0ee5_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_884d7_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_884d7_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_884d7_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_d0ee5_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_d0ee5_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_d0ee5_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_d0ee5_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_884d7_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_884d7_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_884d7_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_d0ee5_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_d0ee5_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_d0ee5_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_d0ee5_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_884d7_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_884d7_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_884d7_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_d0ee5_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_d0ee5_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_d0ee5_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_d0ee5_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_884d7_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_884d7_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_884d7_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_884d7_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_d0ee5_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d0ee5_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_d0ee5_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_d0ee5_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f3b9d5c8160>"
+       "<pandas.io.formats.style.Styler at 0x7fd70fcb2e80>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1687,7 +1687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1754,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1804,7 +1804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1984,7 +1984,7 @@
        "p38a     69/138       1/20  2778/3637  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2013,10 +2013,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that the quantities of pairwise tested as well as pairwise active compounds on two kinases vary largely. \n",
-    "* For the pair p110a and ErbB2 there are none.\n",
-    "* Also, for p110a and [BRAF, CDK2, LCK, MET and p38a], there are less than 13 commonly tested compounds.\n",
-    "* In contrast, EGFR and ErbB2 have 1170 commonly tested compounds, of which 662 were active on both."
+    "Note that the total number of tested compounds as well as the number of active compounds on two kinases vary largely.\n",
+    "\n",
+    "* For the p110a-ErbB2 pair, there are none.\n",
+    "* For p110a and [BRAF, CDK2, LCK, MET and p38a], there are less than $13$ commonly tested compounds.\n",
+    "* In contrast, the EGFR-ErbB2 pair has $1170$ commonly tested compounds, of which $662$ were active on both."
    ]
   },
   {
@@ -2028,7 +2029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2051,7 +2052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -2223,7 +2224,7 @@
        "p38a   0.050000  0.763816  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2242,215 +2243,215 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679c5_row8_col6 {\n",
+       "#T_fb555_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_679c5_\">\n",
+       "<table id=\"T_fb555_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2467,121 +2468,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_679c5_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_679c5_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_679c5_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_679c5_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_679c5_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_679c5_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_679c5_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_679c5_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_679c5_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_679c5_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_679c5_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_679c5_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_679c5_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_679c5_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_679c5_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_679c5_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_679c5_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_679c5_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_679c5_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_679c5_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_679c5_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_679c5_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_679c5_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_679c5_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_679c5_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_679c5_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_679c5_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_679c5_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_679c5_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_679c5_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_679c5_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_679c5_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_679c5_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_679c5_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_679c5_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_679c5_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_679c5_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_679c5_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_679c5_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_679c5_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_679c5_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_679c5_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_679c5_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_679c5_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_679c5_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_679c5_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_679c5_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_679c5_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_679c5_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_679c5_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_679c5_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_679c5_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_679c5_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_679c5_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_679c5_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_679c5_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_679c5_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_679c5_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_679c5_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_679c5_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_679c5_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_679c5_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_679c5_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_679c5_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_679c5_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_679c5_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_679c5_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_679c5_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_679c5_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_679c5_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_679c5_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_679c5_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679c5_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_679c5_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_679c5_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_679c5_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_679c5_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_679c5_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_679c5_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_679c5_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_679c5_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_679c5_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_fb555_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_fb555_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_fb555_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_fb555_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_fb555_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_fb555_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_fb555_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_fb555_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_fb555_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_fb555_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_fb555_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_fb555_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_fb555_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_fb555_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_fb555_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_fb555_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_fb555_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_fb555_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_fb555_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_fb555_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_fb555_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_fb555_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_fb555_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_fb555_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_fb555_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_fb555_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_fb555_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_fb555_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_fb555_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_fb555_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_fb555_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_fb555_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_fb555_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_fb555_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_fb555_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_fb555_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_fb555_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_fb555_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_fb555_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_fb555_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_fb555_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_fb555_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_fb555_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_fb555_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_fb555_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_fb555_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_fb555_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_fb555_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_fb555_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_fb555_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_fb555_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_fb555_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_fb555_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_fb555_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_fb555_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_fb555_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_fb555_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_fb555_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_fb555_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_fb555_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_fb555_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_fb555_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_fb555_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_fb555_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_fb555_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_fb555_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_fb555_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_fb555_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_fb555_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_fb555_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_fb555_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_fb555_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_fb555_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fb555_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_fb555_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_fb555_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_fb555_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_fb555_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_fb555_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_fb555_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_fb555_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_fb555_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_fb555_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f3b9d44fc10>"
+       "<pandas.io.formats.style.Styler at 0x7fd71054fa30>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2605,219 +2606,219 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_be23a_row0_col0, #T_be23a_row1_col1, #T_be23a_row2_col2, #T_be23a_row3_col5, #T_be23a_row4_col3, #T_be23a_row4_col4, #T_be23a_row6_col6, #T_be23a_row7_col7, #T_be23a_row8_col8 {\n",
+       "#T_55a79_row0_col0, #T_55a79_row1_col1, #T_55a79_row2_col2, #T_55a79_row3_col5, #T_55a79_row4_col3, #T_55a79_row4_col4, #T_55a79_row6_col6, #T_55a79_row7_col7, #T_55a79_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row0_col1 {\n",
+       "#T_55a79_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row0_col2 {\n",
+       "#T_55a79_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row0_col3, #T_be23a_row7_col0 {\n",
+       "#T_55a79_row0_col3, #T_55a79_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row0_col4, #T_be23a_row3_col0, #T_be23a_row4_col2, #T_be23a_row5_col2 {\n",
+       "#T_55a79_row0_col4, #T_55a79_row3_col0, #T_55a79_row4_col2, #T_55a79_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row0_col5 {\n",
+       "#T_55a79_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row0_col6, #T_be23a_row4_col1 {\n",
+       "#T_55a79_row0_col6, #T_55a79_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row0_col7, #T_be23a_row3_col2 {\n",
+       "#T_55a79_row0_col7, #T_55a79_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row0_col8 {\n",
+       "#T_55a79_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col0 {\n",
+       "#T_55a79_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row1_col2, #T_be23a_row2_col0, #T_be23a_row2_col1, #T_be23a_row2_col3, #T_be23a_row2_col6, #T_be23a_row2_col7, #T_be23a_row2_col8, #T_be23a_row4_col5, #T_be23a_row5_col4, #T_be23a_row6_col2, #T_be23a_row7_col2, #T_be23a_row8_col2 {\n",
+       "#T_55a79_row1_col2, #T_55a79_row2_col0, #T_55a79_row2_col1, #T_55a79_row2_col3, #T_55a79_row2_col6, #T_55a79_row2_col7, #T_55a79_row2_col8, #T_55a79_row4_col5, #T_55a79_row5_col4, #T_55a79_row6_col2, #T_55a79_row7_col2, #T_55a79_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col3 {\n",
+       "#T_55a79_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col4 {\n",
+       "#T_55a79_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col5 {\n",
+       "#T_55a79_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col6 {\n",
+       "#T_55a79_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col7 {\n",
+       "#T_55a79_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row1_col8 {\n",
+       "#T_55a79_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row2_col4 {\n",
+       "#T_55a79_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row2_col5 {\n",
+       "#T_55a79_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row3_col1 {\n",
+       "#T_55a79_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row3_col3 {\n",
+       "#T_55a79_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row3_col4, #T_be23a_row4_col8 {\n",
+       "#T_55a79_row3_col4, #T_55a79_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row3_col6 {\n",
+       "#T_55a79_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row3_col7 {\n",
+       "#T_55a79_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row3_col8 {\n",
+       "#T_55a79_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row4_col0 {\n",
+       "#T_55a79_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row4_col6 {\n",
+       "#T_55a79_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row4_col7, #T_be23a_row5_col8 {\n",
+       "#T_55a79_row4_col7, #T_55a79_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row5_col0 {\n",
+       "#T_55a79_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row5_col1 {\n",
+       "#T_55a79_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row5_col3 {\n",
+       "#T_55a79_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row5_col5 {\n",
+       "#T_55a79_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row5_col6 {\n",
+       "#T_55a79_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row5_col7 {\n",
+       "#T_55a79_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col0 {\n",
+       "#T_55a79_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col1 {\n",
+       "#T_55a79_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col3 {\n",
+       "#T_55a79_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col4 {\n",
+       "#T_55a79_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row6_col5, #T_be23a_row7_col8, #T_be23a_row8_col5, #T_be23a_row8_col7 {\n",
+       "#T_55a79_row6_col5, #T_55a79_row7_col8, #T_55a79_row8_col5, #T_55a79_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col7 {\n",
+       "#T_55a79_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row6_col8 {\n",
+       "#T_55a79_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row7_col1 {\n",
+       "#T_55a79_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row7_col3 {\n",
+       "#T_55a79_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row7_col4 {\n",
+       "#T_55a79_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row7_col5 {\n",
+       "#T_55a79_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row7_col6 {\n",
+       "#T_55a79_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row8_col0 {\n",
+       "#T_55a79_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row8_col1 {\n",
+       "#T_55a79_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be23a_row8_col3 {\n",
+       "#T_55a79_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row8_col4 {\n",
+       "#T_55a79_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be23a_row8_col6 {\n",
+       "#T_55a79_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_be23a_\">\n",
+       "<table id=\"T_55a79_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2834,121 +2835,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_be23a_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_be23a_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_be23a_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_be23a_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_be23a_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_be23a_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_be23a_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_be23a_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_be23a_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_be23a_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_be23a_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_be23a_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_be23a_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_be23a_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_be23a_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_be23a_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_be23a_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_be23a_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_be23a_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_be23a_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_be23a_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_be23a_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_be23a_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_be23a_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_be23a_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_be23a_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_be23a_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_be23a_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_be23a_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_be23a_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_be23a_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_be23a_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_be23a_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_be23a_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_be23a_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_be23a_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_be23a_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_be23a_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_be23a_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_be23a_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_be23a_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_be23a_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_be23a_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_be23a_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_be23a_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_be23a_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_be23a_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_be23a_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_be23a_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_be23a_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_be23a_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_be23a_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_be23a_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_be23a_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_be23a_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_be23a_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_be23a_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_be23a_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_be23a_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_be23a_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_be23a_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_be23a_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_be23a_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_be23a_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_be23a_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_be23a_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be23a_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_be23a_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_be23a_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_be23a_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_be23a_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_be23a_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_be23a_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_be23a_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_be23a_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_be23a_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_55a79_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_55a79_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_55a79_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_55a79_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_55a79_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_55a79_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_55a79_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_55a79_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_55a79_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_55a79_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_55a79_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_55a79_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_55a79_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_55a79_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_55a79_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_55a79_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_55a79_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_55a79_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_55a79_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_55a79_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_55a79_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_55a79_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_55a79_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_55a79_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_55a79_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_55a79_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_55a79_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_55a79_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_55a79_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_55a79_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_55a79_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_55a79_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_55a79_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_55a79_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_55a79_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_55a79_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_55a79_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_55a79_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_55a79_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_55a79_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_55a79_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_55a79_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_55a79_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_55a79_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_55a79_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_55a79_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_55a79_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_55a79_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_55a79_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_55a79_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_55a79_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_55a79_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_55a79_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_55a79_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_55a79_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_55a79_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_55a79_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_55a79_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_55a79_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_55a79_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_55a79_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_55a79_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_55a79_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_55a79_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_55a79_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_55a79_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_55a79_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_55a79_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_55a79_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_55a79_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_55a79_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_55a79_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_55a79_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_55a79_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_55a79_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_55a79_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_55a79_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f3b9d3c5f70>"
+       "<pandas.io.formats.style.Styler at 0x7fd71054fe80>"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2968,7 +2969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2991,7 +2992,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3013,7 +3014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3029,7 +3030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3038,211 +3039,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
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-       "#T_b7ffd_row0_col0, #T_b7ffd_row1_col1, #T_b7ffd_row2_col2, #T_b7ffd_row3_col3, #T_b7ffd_row4_col4, #T_b7ffd_row5_col5, #T_b7ffd_row6_col6, #T_b7ffd_row7_col7, #T_b7ffd_row8_col8 {\n",
+       "#T_13b50_row0_col0, #T_13b50_row1_col1, #T_13b50_row2_col2, #T_13b50_row3_col3, #T_13b50_row4_col4, #T_13b50_row5_col5, #T_13b50_row6_col6, #T_13b50_row7_col7, #T_13b50_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row0_col1 {\n",
+       "#T_13b50_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row0_col2 {\n",
+       "#T_13b50_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col3 {\n",
+       "#T_13b50_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col4 {\n",
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        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col5 {\n",
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        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col6 {\n",
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        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col7 {\n",
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        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row0_col8 {\n",
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        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col0 {\n",
+       "#T_13b50_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
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-       "#T_b7ffd_row1_col2, #T_b7ffd_row2_col0, #T_b7ffd_row2_col1, #T_b7ffd_row2_col3, #T_b7ffd_row2_col6, #T_b7ffd_row2_col7, #T_b7ffd_row2_col8, #T_b7ffd_row4_col5, #T_b7ffd_row5_col4, #T_b7ffd_row6_col2, #T_b7ffd_row7_col2, #T_b7ffd_row8_col2 {\n",
+       "#T_13b50_row1_col2, #T_13b50_row2_col0, #T_13b50_row2_col1, #T_13b50_row2_col3, #T_13b50_row2_col6, #T_13b50_row2_col7, #T_13b50_row2_col8, #T_13b50_row4_col5, #T_13b50_row5_col4, #T_13b50_row6_col2, #T_13b50_row7_col2, #T_13b50_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col3 {\n",
+       "#T_13b50_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col4, #T_b7ffd_row3_col2, #T_b7ffd_row6_col0 {\n",
+       "#T_13b50_row1_col4, #T_13b50_row3_col2, #T_13b50_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col5 {\n",
+       "#T_13b50_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col6, #T_b7ffd_row6_col1 {\n",
+       "#T_13b50_row1_col6, #T_13b50_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col7, #T_b7ffd_row6_col5, #T_b7ffd_row7_col1, #T_b7ffd_row8_col5 {\n",
+       "#T_13b50_row1_col7, #T_13b50_row6_col5, #T_13b50_row7_col1, #T_13b50_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row1_col8, #T_b7ffd_row8_col1 {\n",
+       "#T_13b50_row1_col8, #T_13b50_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row2_col4, #T_b7ffd_row2_col5 {\n",
+       "#T_13b50_row2_col4, #T_13b50_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row3_col0 {\n",
+       "#T_13b50_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row3_col1 {\n",
+       "#T_13b50_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row3_col4 {\n",
+       "#T_13b50_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row3_col5 {\n",
+       "#T_13b50_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row3_col6 {\n",
+       "#T_13b50_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row3_col7 {\n",
+       "#T_13b50_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row3_col8 {\n",
+       "#T_13b50_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row4_col0 {\n",
+       "#T_13b50_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row4_col1 {\n",
+       "#T_13b50_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row4_col2, #T_b7ffd_row5_col2 {\n",
+       "#T_13b50_row4_col2, #T_13b50_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row4_col3 {\n",
+       "#T_13b50_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row4_col6 {\n",
+       "#T_13b50_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row4_col7 {\n",
+       "#T_13b50_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row4_col8 {\n",
+       "#T_13b50_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row5_col0 {\n",
+       "#T_13b50_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row5_col1 {\n",
+       "#T_13b50_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row5_col3 {\n",
+       "#T_13b50_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row5_col6, #T_b7ffd_row5_col8 {\n",
+       "#T_13b50_row5_col6, #T_13b50_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row5_col7 {\n",
+       "#T_13b50_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row6_col3 {\n",
+       "#T_13b50_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row6_col4 {\n",
+       "#T_13b50_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b7ffd_row6_col7, #T_b7ffd_row7_col6 {\n",
+       "#T_13b50_row6_col7, #T_13b50_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row6_col8, #T_b7ffd_row8_col6 {\n",
+       "#T_13b50_row6_col8, #T_13b50_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row7_col0 {\n",
+       "#T_13b50_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row7_col3 {\n",
+       "#T_13b50_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row7_col4 {\n",
+       "#T_13b50_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row7_col5 {\n",
+       "#T_13b50_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row7_col8, #T_b7ffd_row8_col7 {\n",
+       "#T_13b50_row7_col8, #T_13b50_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row8_col0 {\n",
+       "#T_13b50_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row8_col3 {\n",
+       "#T_13b50_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b7ffd_row8_col4 {\n",
+       "#T_13b50_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_b7ffd_\">\n",
+       "<table id=\"T_13b50_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3259,121 +3260,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_b7ffd_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_b7ffd_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_b7ffd_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_b7ffd_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_b7ffd_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_b7ffd_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_b7ffd_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_b7ffd_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_b7ffd_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_b7ffd_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_b7ffd_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_b7ffd_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_b7ffd_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_b7ffd_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_b7ffd_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_b7ffd_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_b7ffd_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_b7ffd_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_b7ffd_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_b7ffd_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_b7ffd_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_b7ffd_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_b7ffd_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_b7ffd_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_b7ffd_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_b7ffd_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_b7ffd_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_b7ffd_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_b7ffd_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_b7ffd_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_b7ffd_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_b7ffd_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_b7ffd_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_b7ffd_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_b7ffd_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_b7ffd_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_b7ffd_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_b7ffd_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_b7ffd_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_b7ffd_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_b7ffd_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_b7ffd_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_b7ffd_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_b7ffd_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_b7ffd_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_b7ffd_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_b7ffd_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_b7ffd_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_b7ffd_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_b7ffd_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_b7ffd_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_b7ffd_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_b7ffd_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_b7ffd_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_b7ffd_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_b7ffd_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_b7ffd_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_b7ffd_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_b7ffd_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b7ffd_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_b7ffd_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_b7ffd_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_b7ffd_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_b7ffd_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_b7ffd_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_b7ffd_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_b7ffd_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_b7ffd_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_b7ffd_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_13b50_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_13b50_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_13b50_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_13b50_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_13b50_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_13b50_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_13b50_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_13b50_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_13b50_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_13b50_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_13b50_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_13b50_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_13b50_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_13b50_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_13b50_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_13b50_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_13b50_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_13b50_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_13b50_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_13b50_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_13b50_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_13b50_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_13b50_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_13b50_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_13b50_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_13b50_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_13b50_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_13b50_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_13b50_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_13b50_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_13b50_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_13b50_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_13b50_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_13b50_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_13b50_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_13b50_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_13b50_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_13b50_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_13b50_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_13b50_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_13b50_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_13b50_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_13b50_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_13b50_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_13b50_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_13b50_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_13b50_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_13b50_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_13b50_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_13b50_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_13b50_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_13b50_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_13b50_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_13b50_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_13b50_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_13b50_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_13b50_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_13b50_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_13b50_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_13b50_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_13b50_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_13b50_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_13b50_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_13b50_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_13b50_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_13b50_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_13b50_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_13b50_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_13b50_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_13b50_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f3b9d3dd100>"
+       "<pandas.io.formats.style.Styler at 0x7fd7107cd4f0>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3391,7 +3392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3404,10 +3405,10 @@
    "source": [
     "## Discussion\n",
     "\n",
-    "In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can quantified using the ratio of active compounds over measured compounds.\n",
+    "In this talktorial, we investigate how activity data can be used as a measure of similarity between kinases. The fraction of compounds tested as actives over the total number of measured compounds is a way of accessing the similarity. Moreover, using the same rationale, the promiscuity of a kinase can be quantified using the ratio of active compounds over measured compounds.\n",
     "\n",
-    "When working with these data, we have to keep in mind that some kinases have much higher coverage w.r.t. how many compounds were tested against them, leading to an imbalance in information content.\n",
-    "This cannot be inferred from the calculated fraction, i.e., the pairs EGFR-KDR and EGFR-p38a both have a profile similarity of 0.35, whereas the first was calculated based on 893 tested compounds, the latter on 52 only.\n",
+    "When working with these data, we have to keep in mind that some kinases have much higher coverage with respect to the number of compounds that were tested against them, leading to an imbalance in information content.\n",
+    "This cannot be inferred from the calculated fraction. For example, the pairs EGFR-KDR and EGFR-p38a both have a profile similarity of $0.35$. However, the first was calculated based on $893$ tested compounds, whereas the latter on $52$ only.\n",
     "\n",
     "The kinase distance matrix above will be reloaded in __Talktorial T028__, where we compare kinase similarities from different perspectives, including the ligand profile perspective we have talked about in this talktorial."
    ]
@@ -3422,13 +3423,6 @@
     "2. Can promiscuity be fairly compared between two kinases if one has been tested against many compounds whereas the other only against very few? \n",
     "3. Using the similarity described in this talktorial, what does it mean that two kinases have a similarity of $0$, as is the case for p110a and LCK?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From 6fd6e2eb35b710faaf24de28cb69b3efa56394e0 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 6 Oct 2021 10:29:47 +0200
Subject: [PATCH 132/158] :pencil2: fix typos.

---
 .../talktorial.ipynb                          | 173 +-----------------
 1 file changed, 10 insertions(+), 163 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 6727de2a..43da6a19 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -558,7 +558,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -825,7 +825,7 @@
    "source": [
     "def heatmap(score_df, ax=None, title=\"\"):\n",
     "    \"\"\"\n",
-    "    Generate a heatmap from a distance matrix.\n",
+    "    Generate a heatmap from a matrix.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",
@@ -880,28 +880,16 @@
    "metadata": {},
    "source": [
     "Note for ease of interpretability, we show below:\n",
-    "* Heatmaps based on the **similarity** matrix (higher value ~ more similar)\n",
-    "* Dendrograms calculated on the **distance** matrix, but again the neighborhood describes the similarity between the kinases"
+    "\n",
+    "* Heatmaps based on the **similarity** matrix (the higher the value, the higher the similarity).\n",
+    "* Dendrograms calculated based on the **distance** matrix, where clusters describe the similarity between kinases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 12 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "n_perspectives = len(kinase_distance_matrices_normalized)\n",
     "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*5, 10))\n",
@@ -922,143 +910,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>kinase</th>\n",
-       "      <th>kinase_klifs</th>\n",
-       "      <th>uniprot_id</th>\n",
-       "      <th>group</th>\n",
-       "      <th>full_kinase_name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>EGFR</td>\n",
-       "      <td>EGFR</td>\n",
-       "      <td>P00533</td>\n",
-       "      <td>TK</td>\n",
-       "      <td>Epidermal growth factor receptor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ErbB2</td>\n",
-       "      <td>ErbB2</td>\n",
-       "      <td>P04626</td>\n",
-       "      <td>TK</td>\n",
-       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>PI3K</td>\n",
-       "      <td>p110a</td>\n",
-       "      <td>P42336</td>\n",
-       "      <td>Atypical</td>\n",
-       "      <td>Phosphatidylinositol-3-kinase</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>VEGFR2</td>\n",
-       "      <td>KDR</td>\n",
-       "      <td>P35968</td>\n",
-       "      <td>TK</td>\n",
-       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BRAF</td>\n",
-       "      <td>BRAF</td>\n",
-       "      <td>P15056</td>\n",
-       "      <td>TKL</td>\n",
-       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>CDK2</td>\n",
-       "      <td>CDK2</td>\n",
-       "      <td>P24941</td>\n",
-       "      <td>CMGC</td>\n",
-       "      <td>Cyclic-dependent kinase 2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>LCK</td>\n",
-       "      <td>LCK</td>\n",
-       "      <td>P06239</td>\n",
-       "      <td>TK</td>\n",
-       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>MET</td>\n",
-       "      <td>MET</td>\n",
-       "      <td>P08581</td>\n",
-       "      <td>TK</td>\n",
-       "      <td>Mesenchymal-epithelial transition factor</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>p38a</td>\n",
-       "      <td>p38a</td>\n",
-       "      <td>Q16539</td>\n",
-       "      <td>CMGC</td>\n",
-       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   kinase kinase_klifs uniprot_id     group  \\\n",
-       "0    EGFR         EGFR     P00533        TK   \n",
-       "1   ErbB2        ErbB2     P04626        TK   \n",
-       "2    PI3K        p110a     P42336  Atypical   \n",
-       "3  VEGFR2          KDR     P35968        TK   \n",
-       "4    BRAF         BRAF     P15056       TKL   \n",
-       "5    CDK2         CDK2     P24941      CMGC   \n",
-       "6     LCK          LCK     P06239        TK   \n",
-       "7     MET          MET     P08581        TK   \n",
-       "8    p38a         p38a     Q16539      CMGC   \n",
-       "\n",
-       "                                   full_kinase_name  \n",
-       "0                  Epidermal growth factor receptor  \n",
-       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
-       "2                     Phosphatidylinositol-3-kinase  \n",
-       "3     Vascular endothelial growth factor receptor 2  \n",
-       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
-       "5                         Cyclic-dependent kinase 2  \n",
-       "6       Lymphocyte-specific protein tyrosine kinase  \n",
-       "7          Mesenchymal-epithelial transition factor  \n",
-       "8        p38 mitogen activated protein kinase alpha  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
     "kinase_selection_df"
@@ -1078,7 +932,7 @@
     "- When taking into account the pocket structure (physicochemical and spatial properties) with the _kissim_ approach, similar observations can be made: \n",
     "  - The typical kinases are grouped together, while the atypical kinase p110a is a singleton. \n",
     "  - Within the typical kinase grouping, EGFR and ErbB2 are the most similar (but not as prominent as in the _sequence_ approach).\n",
-    "  - But kinases from different groups, e.g. CDK2 (CMGC) and BRAF (TKL), cluster together.\n",
+    "  - Kinases from different groups, e.g. CDK2 (CMGC) and BRAF (TKL), cluster together.\n",
     "- The _ifp_ method:\n",
     "  - BRAF and the atypical kinase p110a form a singleton each.\n",
     "  - Surprisingly, ErbB2 forms a singleton (=not grouped with EGFR), but we have to keep in mind that we have the least data points ($4$) for ErbB2.\n",
@@ -1139,13 +993,6 @@
     "4. Could the different methods be applied to other proteins in general?\n",
     "5. What is the difference between the distances that we show in the heatmap and the dendrogram? "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

From c6bb9ccc207a72e90254d8947efef07eb967502e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 6 Oct 2021 14:17:49 +0200
Subject: [PATCH 133/158] T026: fix typo.

---
 .../talktorials/T026_kinase_similarity_ifp/talktorial.ipynb     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index f4b34c76..5c8ae658 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -963,7 +963,7 @@
     "\n",
     "The Jaccard distance is a common distance measure in case of binary fingerprints. \n",
     "\n",
-    "$d_J(A,B) = 1 - J(A,B) = \\frac{\\mid A \\mid \\cup \\mid B \\mid - \\mid A \\mid \\cap \\mid B \\mid}{\\mid A \\mid + \\mid B \\mid}$"
+    "$d_J(A,B) = 1 - J(A,B) = \\frac{\\mid A \\cup B \\mid - \\mid A \\cap B \\mid}{\\mid A \\cup B \\mid}$"
    ]
   },
   {

From e239902380a1b8a43ef8d853b1a6acc89a5afd2b Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 7 Oct 2021 11:02:06 +0200
Subject: [PATCH 134/158] =?UTF-8?q?=E2=9C=A8T028:=20add=20distance=20matri?=
 =?UTF-8?q?x=20ttheory=20+=20practical?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../talktorial.ipynb                          | 505 +++++++++++++++---
 1 file changed, 426 insertions(+), 79 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 43da6a19..797b5a92 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -34,7 +34,8 @@
     "### Contents in *Theory*\n",
     "\n",
     "* Kinase dataset\n",
-    "* Kinase similarity descriptor (considering 4 different methods)"
+    "* Kinase similarity descriptor (considering 4 different methods)\n",
+    "* Distance matrix conditions"
    ]
   },
   {
@@ -44,6 +45,7 @@
     "### Contents in *Practical*\n",
     "\n",
     "* Load kinase similarity and distance matrices\n",
+    "* Distance matrix conditions\n",
     "* Visualize similarity for example perspective\n",
     "  * Visualize kinase distance matrix as heatmap\n",
     "  * Visualize similarity as dendrogram\n",
@@ -62,7 +64,10 @@
     "### References\n",
     "\n",
     "* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) \n",
-    "* Clustering and dendrograms with `scipy`: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html"
+    "* Clustering and dendrograms with `scipy`: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html\n",
+    "* Distance matrix:\n",
+    "    * https://en.wikipedia.org/wiki/Distance_matrix\n",
+    "    * https://arxiv.org/pdf/1710.10655.pdf"
    ]
   },
   {
@@ -100,6 +105,21 @@
     "Please refer to the talktorials for more details on each method."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Distance matrix conditions\n",
+    "\n",
+    "A square, real matrix $M \\in \\mathbb{R}^{n \\times n}$ is a distance matrix if $M$ satisfies the following conditions (following the definition of distance) :\n",
+    "\n",
+    "1. a) Positivity: $M[x, y] \\geq 0$ for all $x, y$ and b) Null condition: $M[x, y] = 0 \\iff x = y.$\n",
+    "2. Symmetry: $M[x, y] = M[y, x]$ for all $x, y.$\n",
+    "3. Triangular inequality: $M[x, z] \\leq M[x, y] + M[y, z] $ for all $x, y, z.$\n",
+    "\n",
+    "_Technical note_: In some cases, the null condition can be relaxed and only the following has to be satisfied: $M[x, x] = 0, \\forall x.$ In this case, the term is known as semi-metric. We will use this condition in this notebook."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -189,129 +209,309 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We now make a few checks to verify matrix distance conditions."
+    "### Distance matrix conditions\n",
+    "\n",
+    "We now create functions to verify the distance conditions."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check that the matrices have the same dimensions."
+    "We check the dimension of the matrix: it should be 2D array, and also be square."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape of sequence matrix: (9, 9)\n",
-      "Shape of kissim matrix: (9, 9)\n",
-      "Shape of ifp matrix: (9, 9)\n",
-      "Shape of ligand-profile matrix: (9, 9)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
-    "    print(f\"Shape of {descriptor} matrix: {similarity_df.shape}\")"
+    "def check_dimensionality(matrix):\n",
+    "    \"\"\"\n",
+    "    Checks whether the input is a 2D array (matrix) and square.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the condition should be checked.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    bool :\n",
+    "        True if the condition is met, False otherwise.\n",
+    "    \"\"\"\n",
+    "    if len(matrix.shape) != 2:\n",
+    "        raise ValueError(f\"The input is not a matrix, but an array of shape {matrix.shape}.\")\n",
+    "    elif matrix.shape[0] != matrix.shape[1]:\n",
+    "        raise ValueError(f\"The input is not a square matrix. Failing.\")\n",
+    "    else:\n",
+    "        return True"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check that the matrices are symmetric."
+    "We check that all values of the matrix are positive."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Symmetry of sequence matrix: True\n",
-      "Symmetry of kissim matrix: True\n",
-      "Symmetry of ifp matrix: True\n",
-      "Symmetry of ligand-profile matrix: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
-    "    print(f\"Symmetry of {descriptor} matrix: \"\n",
-    "          f\"{(similarity_df.values == similarity_df.values.transpose()).all()}\")"
+    "def check_positivity(matrix):\n",
+    "    \"\"\"\n",
+    "    Checks whether all values of a matrix are positive.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the condition should be checked.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    bool :\n",
+    "        True if the condition is met, False otherwise.\n",
+    "    \"\"\"\n",
+    "    return (matrix >= 0).all()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check that all values are positive."
+    "We check that the diagonal values of the matrix are zero."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Symmetry of sequence matrix: True\n",
-      "Symmetry of kissim matrix: True\n",
-      "Symmetry of ifp matrix: True\n",
-      "Symmetry of ligand-profile matrix: True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
-    "    print(f\"Symmetry of {descriptor} matrix: \"\n",
-    "          f\"{(similarity_df.values >= 0).all()}\")"
+    "def check_null_diagonal(matrix):\n",
+    "    \"\"\"\n",
+    "    Checks whether the diagonal entries of a matrix are all zero.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the condition should be checked.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    bool :\n",
+    "        True if the condition is met, False otherwise.\n",
+    "    \"\"\"\n",
+    "    return (np.diagonal(matrix) == 0).all()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally, we check that the diagonal values are zero."
+    "We check that the matrix is symmetric."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_symmetry(matrix):\n",
+    "    \"\"\"\n",
+    "    Checks whether a matrix M is symmetric, i.e. M = M^T.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the condition should be checked.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    bool :\n",
+    "        True if the condition is met, False otherwise.\n",
+    "    \"\"\"\n",
+    "    return (matrix == matrix.transpose()).all()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we check the triangular inequality condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_triangular_inequality(matrix, tolerance=0.11):\n",
+    "    \"\"\"\n",
+    "    Checks whether the triangular inequality is met for a matrix,\n",
+    "    i.e. the shorest distance between two points is the straight line.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the condition should be checked.\n",
+    "    tolerance : float\n",
+    "        The accepted tolerance for approximation.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    bool :\n",
+    "        True if the condition is met, False otherwise.\n",
+    "    \"\"\"\n",
+    "    triang_inequality = None\n",
+    "    for i in range(matrix.shape[0]):\n",
+    "        for j in range(matrix.shape[0]):\n",
+    "            for k in range(matrix.shape[0]):\n",
+    "                if (matrix[i, j] <= matrix[i, k] + matrix[k, j] + tolerance):\n",
+    "                    triang_inequality = True\n",
+    "                else:\n",
+    "                    return False\n",
+    "    return triang_inequality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now create a function that combines all these conditions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def distance_condition(matrix):\n",
+    "    \"\"\"\n",
+    "    Checks if all conditions for a matrix are met.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    matrix : np.array\n",
+    "        The matrix for which the conditions should be checked.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    None\n",
+    "    \"\"\"\n",
+    "\n",
+    "    dimensionality = check_dimensionality(matrix)\n",
+    "    if dimensionality:\n",
+    "        print(f\"{'Dimensionality' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "    else:\n",
+    "        print(f\"{'Dimensionality' : <30}\"\n",
+    "              f\"{'Not Satisfied'}\")\n",
+    "\n",
+    "    positivity = check_positivity(matrix)\n",
+    "    if positivity:\n",
+    "        print(f\"{'Postivity' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "    else:\n",
+    "        print(f\"{'Postivity' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "\n",
+    "    diag = check_null_diagonal(matrix)\n",
+    "    if diag:\n",
+    "        print(f\"{'Null diagonal' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "    else:\n",
+    "        print(f\"{'Null diagonal' : <30}\"\n",
+    "              f\"{'Not Satisfied'}\")\n",
+    "\n",
+    "    symmetry = check_symmetry(matrix)\n",
+    "    if symmetry:\n",
+    "        print(f\"{'Symmetry' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "    else:\n",
+    "        print(f\"{'Symmetry' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "\n",
+    "    triang_inequ = check_triangular_inequality(matrix)\n",
+    "    if triang_inequ:\n",
+    "        print(f\"{'Triangular inequality' : <30}\"\n",
+    "              f\"{'Satisfied'}\")\n",
+    "    else:\n",
+    "        print(f\"{'Triangular inequality' : <30}\"\n",
+    "              f\"{'Not Satisfied'}\")\n",
+    "    print(\"\\n\")\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Null diagonal of sequence matrix: True\n",
-      "Null diagonal of kissim matrix: True\n",
-      "Null diagonal of ifp matrix: True\n",
-      "Null diagonal of ligand-profile matrix: True\n"
+      "sequence\n",
+      "==========\n",
+      "Dimensionality                Satisfied\n",
+      "Postivity                     Satisfied\n",
+      "Null diagonal                 Satisfied\n",
+      "Symmetry                      Satisfied\n",
+      "Triangular inequality         Satisfied\n",
+      "\n",
+      "\n",
+      "kissim\n",
+      "==========\n",
+      "Dimensionality                Satisfied\n",
+      "Postivity                     Satisfied\n",
+      "Null diagonal                 Satisfied\n",
+      "Symmetry                      Satisfied\n",
+      "Triangular inequality         Satisfied\n",
+      "\n",
+      "\n",
+      "ifp\n",
+      "==========\n",
+      "Dimensionality                Satisfied\n",
+      "Postivity                     Satisfied\n",
+      "Null diagonal                 Satisfied\n",
+      "Symmetry                      Satisfied\n",
+      "Triangular inequality         Satisfied\n",
+      "\n",
+      "\n",
+      "ligand-profile\n",
+      "==========\n",
+      "Dimensionality                Satisfied\n",
+      "Postivity                     Satisfied\n",
+      "Null diagonal                 Satisfied\n",
+      "Symmetry                      Satisfied\n",
+      "Triangular inequality         Not Satisfied\n",
+      "\n",
+      "\n"
      ]
     }
    ],
    "source": [
     "for descriptor, similarity_df in kinase_distance_matrices.items():\n",
-    "    print(f\"Null diagonal of {descriptor} matrix: \"\n",
-    "          f\"{(np.diagonal(similarity_df) == 0).all()}\")"
+    "    print(f\"{descriptor}\")\n",
+    "    print(\"==========\")\n",
+    "    x = distance_condition(similarity_df.values)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "If one of the checks (dimension, symmetry, positivity, or diagonal) fails, the rest of the notebook will not run properly and the matrix generation from the previous notebooks should be investigated."
+    "If one of the following checks (dimensionality, positivity, null diagonal, or symmetry) fails, the rest of the notebook will not run properly and the matrix generation from the previous notebooks should be investigated.\n",
+    "\n",
+    "The triangular inequality does not have to be satisfied in order for the notebook to run, as is the case for ligand-profile. The reason for its failing is due to the function that describes the (dis)similarity between two kinases, which is not a distance (see __Talktorial T027__)."
    ]
   },
   {
@@ -325,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -349,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -530,7 +730,7 @@
        "p38a   0.370645  0.000000  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -553,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -598,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -612,7 +812,7 @@
        "       0.318747, 0.343975, 0.276907, 0.309121, 0.337419, 0.370645])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -632,7 +832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -648,7 +848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -664,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -713,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -739,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -760,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -769,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -798,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {
     "tags": []
    },
@@ -819,7 +1019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -848,7 +1048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -887,9 +1087,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 12 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "n_perspectives = len(kinase_distance_matrices_normalized)\n",
     "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*5, 10))\n",
@@ -910,9 +1123,143 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>kinase</th>\n",
+       "      <th>kinase_klifs</th>\n",
+       "      <th>uniprot_id</th>\n",
+       "      <th>group</th>\n",
+       "      <th>full_kinase_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>EGFR</td>\n",
+       "      <td>P00533</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Epidermal growth factor receptor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>ErbB2</td>\n",
+       "      <td>P04626</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Erythroblastic leukemia viral oncogene homolog 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PI3K</td>\n",
+       "      <td>p110a</td>\n",
+       "      <td>P42336</td>\n",
+       "      <td>Atypical</td>\n",
+       "      <td>Phosphatidylinositol-3-kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>VEGFR2</td>\n",
+       "      <td>KDR</td>\n",
+       "      <td>P35968</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Vascular endothelial growth factor receptor 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>BRAF</td>\n",
+       "      <td>P15056</td>\n",
+       "      <td>TKL</td>\n",
+       "      <td>Rapidly accelerated fibrosarcoma isoform B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>CDK2</td>\n",
+       "      <td>P24941</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>Cyclic-dependent kinase 2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>LCK</td>\n",
+       "      <td>LCK</td>\n",
+       "      <td>P06239</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Lymphocyte-specific protein tyrosine kinase</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MET</td>\n",
+       "      <td>MET</td>\n",
+       "      <td>P08581</td>\n",
+       "      <td>TK</td>\n",
+       "      <td>Mesenchymal-epithelial transition factor</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>p38a</td>\n",
+       "      <td>p38a</td>\n",
+       "      <td>Q16539</td>\n",
+       "      <td>CMGC</td>\n",
+       "      <td>p38 mitogen activated protein kinase alpha</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   kinase kinase_klifs uniprot_id     group  \\\n",
+       "0    EGFR         EGFR     P00533        TK   \n",
+       "1   ErbB2        ErbB2     P04626        TK   \n",
+       "2    PI3K        p110a     P42336  Atypical   \n",
+       "3  VEGFR2          KDR     P35968        TK   \n",
+       "4    BRAF         BRAF     P15056       TKL   \n",
+       "5    CDK2         CDK2     P24941      CMGC   \n",
+       "6     LCK          LCK     P06239        TK   \n",
+       "7     MET          MET     P08581        TK   \n",
+       "8    p38a         p38a     Q16539      CMGC   \n",
+       "\n",
+       "                                   full_kinase_name  \n",
+       "0                  Epidermal growth factor receptor  \n",
+       "1  Erythroblastic leukemia viral oncogene homolog 2  \n",
+       "2                     Phosphatidylinositol-3-kinase  \n",
+       "3     Vascular endothelial growth factor receptor 2  \n",
+       "4        Rapidly accelerated fibrosarcoma isoform B  \n",
+       "5                         Cyclic-dependent kinase 2  \n",
+       "6       Lymphocyte-specific protein tyrosine kinase  \n",
+       "7          Mesenchymal-epithelial transition factor  \n",
+       "8        p38 mitogen activated protein kinase alpha  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/data/kinase_selection.csv\")\n",
     "kinase_selection_df"

From 51f44a5de591bffcda984248fa40ce0a4f612768 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 7 Oct 2021 13:45:35 +0200
Subject: [PATCH 135/158] T028: :pencil2: fix typo

---
 .../talktorial.ipynb                                            | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 797b5a92..225aa0a8 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -355,7 +355,7 @@
     "def check_triangular_inequality(matrix, tolerance=0.11):\n",
     "    \"\"\"\n",
     "    Checks whether the triangular inequality is met for a matrix,\n",
-    "    i.e. the shorest distance between two points is the straight line.\n",
+    "    i.e. the shortest distance between two points is the straight line.\n",
     "\n",
     "    Parameters\n",
     "    ----------\n",

From 39713c4dbe7e10585d5806c80c8075e320c09edf Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 8 Nov 2021 11:46:42 +0100
Subject: [PATCH 136/158] Add kinase similarity talktorials to docs.

---
 docs/all_talktorials.rst                                    | 6 ++++++
 docs/talktorials/T023_what_is_a_kinase.nblink               | 1 +
 docs/talktorials/T024_kinase_similarity_sequence.nblink     | 1 +
 docs/talktorials/T025_kinase_similarity_kissim.nblink       | 1 +
 docs/talktorials/T026_kinase_similarity_ifp.nblink          | 1 +
 .../T027_kinase_similarity_ligand_profile.nblink            | 1 +
 .../T028_kinase_similarity_compare_perspectives.nblink      | 1 +
 7 files changed, 12 insertions(+)
 create mode 100644 docs/talktorials/T023_what_is_a_kinase.nblink
 create mode 100644 docs/talktorials/T024_kinase_similarity_sequence.nblink
 create mode 100644 docs/talktorials/T025_kinase_similarity_kissim.nblink
 create mode 100644 docs/talktorials/T026_kinase_similarity_ifp.nblink
 create mode 100644 docs/talktorials/T027_kinase_similarity_ligand_profile.nblink
 create mode 100644 docs/talktorials/T028_kinase_similarity_compare_perspectives.nblink

diff --git a/docs/all_talktorials.rst b/docs/all_talktorials.rst
index 7e458651..e94ee6a2 100644
--- a/docs/all_talktorials.rst
+++ b/docs/all_talktorials.rst
@@ -27,3 +27,9 @@ This is the complete list of talktorials available for online reading. Take into
    talktorials/T020_md_analysis.nblink
    talktorials/T021_one_hot_encoding.nblink
    talktorials/T022_ligand_based_screening_neural_network.nblink
+   talktorials/T023_what_is_a_kinase.nblink
+   talktorials/T024_kinase_similarity_sequence.nblink
+   talktorials/T025_kinase_similarity_kissim.nblink
+   talktorials/T026_kinase_similarity_ifp.nblink
+   talktorials/T027_kinase_similarity_ligand_profile.nblink
+   talktorials/T028_kinase_similarity_compare_perspectives.nblink
diff --git a/docs/talktorials/T023_what_is_a_kinase.nblink b/docs/talktorials/T023_what_is_a_kinase.nblink
new file mode 100644
index 00000000..a2b96c56
--- /dev/null
+++ b/docs/talktorials/T023_what_is_a_kinase.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T023_what_is_a_kinase/images"]}
\ No newline at end of file
diff --git a/docs/talktorials/T024_kinase_similarity_sequence.nblink b/docs/talktorials/T024_kinase_similarity_sequence.nblink
new file mode 100644
index 00000000..745c384a
--- /dev/null
+++ b/docs/talktorials/T024_kinase_similarity_sequence.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T024_kinase_similarity_sequence/images"]}
\ No newline at end of file
diff --git a/docs/talktorials/T025_kinase_similarity_kissim.nblink b/docs/talktorials/T025_kinase_similarity_kissim.nblink
new file mode 100644
index 00000000..14ea04dc
--- /dev/null
+++ b/docs/talktorials/T025_kinase_similarity_kissim.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T025_kinase_similarity_kissim/images"]}
\ No newline at end of file
diff --git a/docs/talktorials/T026_kinase_similarity_ifp.nblink b/docs/talktorials/T026_kinase_similarity_ifp.nblink
new file mode 100644
index 00000000..49496882
--- /dev/null
+++ b/docs/talktorials/T026_kinase_similarity_ifp.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T026_kinase_similarity_ifp/images"]}
\ No newline at end of file
diff --git a/docs/talktorials/T027_kinase_similarity_ligand_profile.nblink b/docs/talktorials/T027_kinase_similarity_ligand_profile.nblink
new file mode 100644
index 00000000..ec16281e
--- /dev/null
+++ b/docs/talktorials/T027_kinase_similarity_ligand_profile.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/images"]}
\ No newline at end of file
diff --git a/docs/talktorials/T028_kinase_similarity_compare_perspectives.nblink b/docs/talktorials/T028_kinase_similarity_compare_perspectives.nblink
new file mode 100644
index 00000000..ad86c604
--- /dev/null
+++ b/docs/talktorials/T028_kinase_similarity_compare_perspectives.nblink
@@ -0,0 +1 @@
+{"path": "../../teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/images"]}
\ No newline at end of file

From 3b9dc47b296b5d43822054ce6eab1b3455260606 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 8 Nov 2021 15:03:45 +0100
Subject: [PATCH 137/158] Small textual edits.

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 43 ++++++++-----------
 1 file changed, 18 insertions(+), 25 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index a3d7e29c..8e24eb61 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -20,7 +20,7 @@
     "## Aim of this talktorial\n",
     "\n",
     "In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available?\n",
-    "Finally, we select a set of kinases which will be analyzed in the forthcoming talktorials **T024**-**T028** w.r.t. their similarity, with the goal of generating a broad insight into potential off-target effects."
+    "Finally, we select a set of kinases which will be analyzed in the forthcoming talktorials **T024**-**T028** with respect to their similarity, the goal being to gain insight into potential off-target effects."
    ]
   },
   {
@@ -89,15 +89,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating, and thereby activating, themselves or other proteins and are among the most frequently mutated proteins in tumors.\n",
+    "Kinases are established drug targets to combat cancer and inflammatory diseases ([<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)). They are involved in most aspects of cell life by phosphorylating, and thereby activating, either themselves or other proteins. They are among the most frequently mutated proteins in tumors.\n",
     "\n",
-    "As of September 2021, $5782$ X-ray structures of human kinases have been resolved (see [KLIFS](https://klifs.net/) database) and $67$ FDA approved small molecule protein kinase inhibitors are on the market (see [this list](http://www.brimr.org/PKI/PKIs.htm)). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
+    "As of September 2021, $5782$ X-ray structures of human kinases have been resolved (see the [KLIFS](https://klifs.net/) database) and $67$ FDA-approved small molecule protein kinase inhibitors are on the market (see [the list](http://www.brimr.org/PKI/PKIs.htm) compiled by the Blue Ridge Institute for Medical Research). Most of the approved drugs bind in the ATP-binding pocket and intermediate surroundings.\n",
     "\n",
     "Despite of decades of kinase research, there are still many open challenges:\n",
     "\n",
     "- A large fraction of the kinome is un-/underexplored.\n",
     "- Many kinase inhibitors are promiscuous binders causing off-target effects or enabling polypharmacology.\n",
-    "- There are occurrences of drug resistances due to mutations."
+    "- There are occurrences of drug resistance due to mutations."
    ]
   },
   {
@@ -106,7 +106,7 @@
    "source": [
     "#### The human kinome \n",
     "\n",
-    "The human kinome consists of over $500$ protein kinases (this number may vary depending on the data resource, see [overview on kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
+    "The human kinome consists of over $500$ protein kinases, but this number may vary depending on the data resource (see [overview on Kinodata](https://github.com/openkinome/kinodata/blob/master/human-kinases/human_kinases.ipynb)).\n",
     "\n",
     "As reported in [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762), Manning _et al._ clustered the human protein kinases based on their sequence similarity into eight major groups (AGC, CAMK, CK1, CMGC, RGC, STE, TK, TKL) and one \"Other\" group for unassigned kinases, as well as atypical kinases. The kinase clustering is visualized as the Manning kinome tree. The kinase resource [KinMap](http://www.kinhub.org/kinmap/) enables mapping of kinase data onto that tree, e.g. the number of X-ray structures per kinase as shown in Figure 1.\n",
     "\n",
@@ -129,10 +129,10 @@
     "\n",
     "Kinase sequences and structures are highly conserved. Important regions in the kinase pocket include (see Figure 2):\n",
     "\n",
-    "- Hinge region: Forms key hydrogen bonds to ligands \n",
-    "- DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state\n",
-    "- αC-helix: Forms in the αC-in conformation a salt bridge between highly conserved lysine and glutamine\n",
-    "- Glycine-rich (G-rich) loop: Stabilizes ATP binding\n",
+    "- Hinge region: Forms key hydrogen bonds to ligands.\n",
+    "- DFG motif: Flips between phenylalanine (F) and aspartate (D), driving the active and inactive state.\n",
+    "- αC-helix: Forms in the αC-in conformation a salt bridge between highly conserved lysine (K) and glutamine (Q).\n",
+    "- Glycine-rich (G-rich) loop: Stabilizes ATP binding.\n",
     "\n",
     "![Kinase structure with key motifs](images/T023_kinase_structure.png)\n",
     "\n",
@@ -160,7 +160,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily and quickly looked up in any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
+    "The KLIFS database ([<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895), [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)) fetches all kinase structures deposited in the structural database PDB ([<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451), [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)) and processes them as follows: All multi-chain structures in the PDB are split into monomers and aligned to each other with a special focus on a pre-defined binding site of $85$ residues (Figure 1). For example, this means that the conserved gatekeeper (GK) residue at KLIFS position $45$ can be easily queried for any of the over $10,000$ monomeric kinase structures in KLIFS. \n",
     "\n",
     "![KLIFS binding site](https://klifs.net/images/faq/colors.png)\n",
     "\n",
@@ -243,9 +243,9 @@
    "source": [
     "### Kinase similarity: Off-targets and promiscuous binding\n",
     "\n",
-    "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also with other targets (off-targets). This promiscuous binding can cause mild to severe side effects.\n",
+    "As described before, kinases are highly conserved, especially in their binding site. This high similarity is a challenge in drug design because ligands may form similar binding modes not only with their designated target (on-target) but also with other targets (off-targets). Such promiscuous binding can cause mild to severe side effects.\n",
     "\n",
-    "Predicting such side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
+    "Predicting these side effects is non-trivial since some off-targets are not obvious. For example, the EGFR inhibitor Erlotinib shows affinities to other kinases in the highly sequentially-similar TK kinase group. However, it also strongly affects the off-targets GAK, LOK, and SLK, which are in more remote kinase groups (Figure 4). \n",
     "\n",
     "![Erlotinib profiling data from Karaman dataset (KinMap)](images/kinmap_erlotinib_karaman.png)\n",
     "\n",
@@ -257,7 +257,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the following __Talktorials T024-027__, we will assess kinase similarity from different perspectives, which we compare with each other in __Talktorial T028__:\n",
+    "In the following four talktorials, namely __Talktorials T024-027__, we will assess kinase similarity from different perspectives, which we compare with each other in __Talktorial T028__:\n",
     "\n",
     "- __Talktorial T024__: Kinase similarity based on KLIFS pocket sequence\n",
     "- __Talktorial T025__: Kinase similarity based on KiSSim pocket structure\n",
@@ -505,10 +505,10 @@
    "source": [
     "### KinMap data\n",
     "\n",
-    "There are some KinMap trees shown in this notebook. Please find in the appendix the code to generate the KinMap CSV files to be uploaded to KinMap:\n",
+    "There are some KinMap trees shown in this notebook. The code below generates the KinMap CSV files to be uploaded to KinMap:\n",
     "http://www.kinhub.org/kinmap.\n",
     "\n",
-    "_Note_: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 xxx.svg xxx.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
+    "_Note_: PNG downloads do not seem to work anymore, thus download as SVG and convert to PNG in your terminal (Linux) via `convert -density 25 my_kinmap_figure.svg my_kinmap_figure.png` (SVG cannot be included in Jupyter notebooks out-of-the-box)."
    ]
   },
   {
@@ -554,7 +554,7 @@
    "source": [
     "#### Number of PDB structures per kinase\n",
     "\n",
-    "Generate data for number of structures per kinase in the KinMap format to be mapped onto the kinome tree."
+    "Generate the number of structures per kinase in the KinMap format to be mapped onto the kinome tree."
    ]
   },
   {
@@ -591,7 +591,7 @@
    "source": [
     "#### Number of ChEMBL bioactivities per kinase\n",
     "\n",
-    "Generate data for number of ChEMBL bioactivities per kinase in the KinMap format to be mapped onto the kinome tree."
+    "Generate the number of ChEMBL bioactivities per kinase in the KinMap format to be mapped onto the kinome tree."
    ]
   },
   {
@@ -707,13 +707,6 @@
     "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
     "viewer.viewer"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -732,7 +725,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.8.10"
   },
   "toc-autonumbering": true,
   "widgets": {

From b630e6e09845ba93540c1533a7d77c3c0db2286e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 8 Nov 2021 16:04:08 +0100
Subject: [PATCH 138/158] T024: Textual edits.

---
 .../talktorial.ipynb                          | 88 ++++++++++---------
 1 file changed, 46 insertions(+), 42 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index c3d08bf1..5ea9e5b6 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -19,9 +19,10 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS' API is used to retrieve the $85$ residues of the pocket sequence for each kinase. \n",
+    "In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS API is used to retrieve the $85$ residues of the pocket sequence for each kinase. \n",
     "\n",
     "Two similarity measures are implemented:\n",
+    "\n",
     "   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.\n",
     "   2. Sequence similarity, i.e., the similarity which is based on a substitution matrix, thus, reflecting similarities between amino acids."
    ]
@@ -111,7 +112,7 @@
    "source": [
     "In this talktorial, the KLIFS pocket sequence is used for two main reasons:\n",
     "\n",
-    "1. The sequence is of fixed length (it contains $85$ residues), which makes computation for pairwise similarity between two sequences easy.\n",
+    "1. The sequence is of fixed length (it contains $85$ residues), which makes computation for pairwise similarity between two sequences straightforward.\n",
     "\n",
     "2. The binding pocket is where the action takes place. Why consider the full kinase sequence when an $85$ residues sequence contains most relevant information?"
    ]
@@ -123,10 +124,10 @@
     "![sequence_logo](images/sequence_logo.png)\n",
     "\n",
     "_Figure 1:_\n",
-    "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion. For example, the sequence logo easily visualizes the conserved G-rich loop (position 4-9) and the DFG motif (position 81-83), see more details in __Talktorial T023__.\n",
+    "The sequence logo shows amino acid binding motifs and sequence profiles such as amino acid depletion. For example, the sequence logo easily visualizes the conserved G-rich loop (position 4-9) and the DFG motif (position 81-83), see __Talktorial T023__ for more details.\n",
     "\n",
     "**Note for reproducibility:**\n",
-    "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to $85$ and _Page size_ to $1200 \\times 480$."
+    "The figure is generated using the Seq2Logo tool available at http://www.cbs.dtu.dk/biotools/Seq2Logo/. The input is the $85$ residues KLIFS binding pocket for the query kinases. All parameters are the the default ones. For the graphical layout, the entry _Stacks Per Line_ is set to $85$ and _Page size_ to $1200 \\times 480.$"
    ]
   },
   {
@@ -142,9 +143,9 @@
    "source": [
     "#### Identity score\n",
     "A simple way of assessing the similarity between two sequences is to use the so-called identity score.\n",
-    "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If they are, the entry is set to $1$, and $0$ otherwise.\n",
+    "First, a match vector is created: it checks whether for each position the characters from the two sequences are identical. If they are, the entry is set to $1,$ and $0$ otherwise.\n",
     "\n",
-    "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which in the case of KLIFS pocket sequence is $85$."
+    "The identity score is computed by summing the elements in the match vector and normalizing the entry by the length of the sequence, which is $85$ in the case of KLIFS pocket sequence."
    ]
   },
   {
@@ -171,7 +172,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "and let $M = len(K_i) = len(K_j)$."
+    "and let $M = len(K_i) = len(K_j)$ for two kinases $K_i$ and $K_j$."
    ]
   },
   {
@@ -181,10 +182,10 @@
     "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n}^{M} I (K_i[n], K_j[n]),\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n=1}^{M} I (K_i[n], K_j[n]),\n",
     "$$\n",
     "\n",
-    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$."
+    "where $K_i[n]$ represents the amino acid at position $n$ of the sequence of kinase $i$."
    ]
   },
   {
@@ -202,7 +203,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The BLOSUM substitution matrix $SM$ is defined as below (the full matrix will be displayed in the practical part):"
+    "The BLOSUM substitution matrix $SM$ is defined as below (the full matrix will be displayed in the _Practical_ part):"
    ]
   },
   {
@@ -256,10 +257,10 @@
     "We use the following as similarity between kinases $K_i$ and $K_j$:\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n}^{M} SM''\\big(K_i[n], K_j[n]\\big),\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{1}{M} \\sum_{n=1}^{M} SM''\\big(K_i[n], K_j[n]\\big),\n",
     "$$\n",
     "\n",
-    "where $K_i[n]$ represents the amino acid at position $n$ of kinase $i$ and $M = len(K_i) = len(K_j)$."
+    "where $K_i[n]$ represents the amino acid at position $n$ of the sequence of kinase $i$ and $M = len(K_i) = len(K_j)$."
    ]
   },
   {
@@ -273,7 +274,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. A similarity matrix is not, by definition, a distance matrix. For example, the diagonal elements are not zero. However, if all entries of the are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix $DM$ using $$ DM = 1-SM.$$"
+    "In order to apply some clustering algorithm to assess the similarity between kinases, it is necessary to start with a distance matrix. A similarity matrix is not, by definition, a distance matrix. For example, the diagonal elements are not zero. For now, we map the similarity matrix $SM$ to a distance matrix $DM$ using\n",
+    "\n",
+    "$$ DM = 1-SM.$$\n",
+    "\n",
+    "See __Talktorial T028__ for more details."
    ]
   },
   {
@@ -480,7 +485,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We use KLIFS' API to retrieve the $85$-long pocket sequence for each kinase."
+    "We use the KLIFS API to retrieve the $85$-long pocket sequence for each kinase."
    ]
   },
   {
@@ -496,7 +501,7 @@
     "    Parameters\n",
     "    ----------\n",
     "    klifs_kinase_name : str\n",
-    "        The KLIFS' name of the kinase of interest.\n",
+    "        The KLIFS name of the kinase of interest.\n",
     "\n",
     "    Returns\n",
     "    -------\n",
@@ -524,33 +529,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'EGFR': 'KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA',\n",
-       " 'ErbB2': 'KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA',\n",
-       " 'p110a': 'CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF',\n",
-       " 'KDR': 'KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA',\n",
-       " 'BRAF': 'QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA',\n",
-       " 'CDK2': 'EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA',\n",
-       " 'LCK': 'ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA',\n",
-       " 'MET': 'EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA',\n",
-       " 'p38a': 'SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA'}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EGFR      KVLGSGAFGTVYKVAIKELEILDEAYVMASVDPHVCRLLGIQLITQLMPFGCLLDYVREYLEDRRLVHRDLAARNVLVITDFGLA\n",
+      "ErbB2     KVLGSGAFGTVYKVAIKVLEILDEAYVMAGVGPYVSRLLGIQLVTQLMPYGCLLDHVREYLEDVRLVHRDLAARNVLVITDFGLA\n",
+      "p110a     CRIMSSAKRPLWLIIFKNGDLRQDMLTLQIIRLRMLPYGCLVGLIEVVRSHTIMQIQCKATFI--LGIGDRHNSNIMVHIDFGHF\n",
+      "KDR       KPLGRGAFGQVIEVAVKMLALMSELKILIHIGLNVVNLLGAMVIVEFCKFGNLSTYLRSFLASRKCIHRDLAARNILLICDFGLA\n",
+      "BRAF      QRIGSGSFGTVYKVAVKMLAFKNEVGVLRKTRVNILLFMGYAIVTQWCEGSSLYHHLHIYLHAKSIIHRDLKSNNIFLIGDFGLA\n",
+      "CDK2      EKIGEGTYGVVYKVALKKITAIREISLLKELNPNIVKLLDVYLVFEFLH-QDLKKFMDAFCHSHRVLHRDLKPQNLLILADFGLA\n",
+      "LCK       ERLGAGQFGEVWMVAVKSLAFLAEANLMKQLQQRLVRLYAVYIITEYMENGSLVDFLKTFIEERNYIHRDLRAANILVIADFGLA\n",
+      "MET       EVIGRGHFGCVYHCAVKSLQFLTEGIIMKDFSPNVLSLLGILVVLPYMKHGDLRNFIRNYLASKKFVHRDLAARNCMLVADFGLA\n",
+      "p38a      SPVGSGAYGSVCAVAVKKLRTYRELRLLKHMKENVIGLLDVYLVTHLMG-ADLNNIVKCYIHSADIIHRDLKPSNLAVILDFGLA\n"
+     ]
     }
    ],
    "source": [
     "kinase_sequences_dict = {}\n",
     "for kinase in kinase_selection_df[\"kinase_klifs\"]:\n",
     "    kinase_sequences_dict[kinase] = klifs_pocket_sequence(kinase)\n",
-    "kinase_sequences_dict\n",
+    "\n",
+    "for name, sequence in kinase_sequences_dict.items():\n",
+    "    print(f'{name:10}{sequence}')\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -572,7 +576,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Given two kinases, we create functions which account for identity or substitution similarity, as described in the theory."
+    "Given two kinases, we create functions which account for identity or substitution similarity, as described in the _Theory_ part."
    ]
   },
   {
@@ -704,7 +708,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's perform that translation-rescaling step we have discussed in the _Theory_ part of this talktorial."
+    "Let's perform the translation-rescaling step we discussed in the _Theory_ part."
    ]
   },
   {
@@ -1552,8 +1556,10 @@
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
+    "    Parameters\n",
+    "    ----------\n",
     "    sequence_1, sequence_2 : str\n",
-    "        The two sequence strings for comparison.\n",
+    "        The two sequences of strings for comparison.\n",
     "\n",
     "    type_ : str\n",
     "        The type of metric to compute the similarity.\n",
@@ -1572,8 +1578,7 @@
     "    sequence_2 = sequence_2.replace(\"-\", \"*\")\n",
     "\n",
     "    if len(sequence_1) != len(sequence_1):\n",
-    "        print(\"Mismatch in sequence lengths.\")\n",
-    "        return None\n",
+    "        raise ValueError(f\"Mismatch in sequence lengths.\")\n",
     "    else:\n",
     "        seq_array1 = np.array(list(sequence_1))\n",
     "        seq_array2 = np.array(list(sequence_2))\n",
@@ -1585,8 +1590,7 @@
     "            match_score_array = substitution_score(seq_array1, seq_array2)\n",
     "            similarity_normed = sum(match_score_array)/len(sequence_1)\n",
     "        else:\n",
-    "            print(f\"Type {type_} not defined.\")\n",
-    "            return None\n",
+    "            raise ValueError(f\"Type {type_} not defined.\")\n",
     "\n",
     "        return similarity_normed"
    ]
@@ -2805,7 +2809,7 @@
    "source": [
     "When we compare the matrices calculated based on the identity and substitution score, the overall pattern is similar, while the values are generally higher using the substitution score.\n",
     "\n",
-    "_Note_: For all downstream analysis, we will consider only the kinase similarity matrix calculated based on the substitution matrix."
+    "_Note_: For all downstream analysis, we will only consider the kinase similarity matrix calculated based on the substitution matrix."
    ]
   },
   {
@@ -2845,7 +2849,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix:"
+    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ is mapped to a distance matrix:"
    ]
   },
   {

From d2ab6ba063d7f252cb890db9613f4a463f1e1fcd Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Fri, 12 Nov 2021 10:38:08 +0100
Subject: [PATCH 139/158] T025: minor text edits.

---
 .../T025_kinase_similarity_kissim/talktorial.ipynb | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 8813b252..9f877136 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -107,7 +107,7 @@
    "source": [
     "### Kinase similarity descriptor: Kinase pockets (KiSSim fingerprint)\n",
     "\n",
-    "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potentially off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
+    "Side effects often occur when a drug binds not only to its designated target (on-target) but also to other targets (off-targets) that share similar binding sites to form similar protein-ligand interaction patterns. Many binding site comparison tools have been proposed to predict similarities and potential off-targets (see [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites) and [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)). \n",
     "\n",
     "Here we use the novel [KiSSim](https://kissim.readthedocs.io/en/latest/) (*Ki*nase *S*tructure *Sim*ilarity) fingerprint, which is based on the KLIFS pocket definition and alignment of $85$ pocket residues (see more details in __Talktorial T023__)."
    ]
@@ -116,7 +116,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The KiSSim fingerprint encodes each of the $85$ residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 1). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties include each residue's pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase. See more details on structural motifs in kinases in __Talktorial T023__.\n",
+    "The KiSSim fingerprint encodes each of the $85$ residues in the KLIFS binding site with respect to physicochemical and spatial properties (Figure 1). Since all KLIFS pockets are aligned across the structurally covered kinome, we can compare the KiSSim fingerprints bit by bit. Physicochemical properties of each residue include pharmacophoric features, size, side chain orientation, and solvent exposure. Spatial properties describe the distance of each residue to defined important regions in the kinase pocket, for example the hinge region, which forms crucial hydrogen bonds to ligands, or the DFG region, whose conformation determines the activity state of the kinase. See more details on structural motifs in kinases in __Talktorial T023__.\n",
     "\n",
     "![KiSSim fingerprint](https://raw.githubusercontent.com/volkamerlab/kissim/main/docs/_static/kissim_toc.png)\n",
     "\n",
@@ -132,7 +132,7 @@
     "### Fetching KLIFS data with `opencadd.databases.klifs`\n",
     "\n",
     "`opencadd` is a Python library for structural cheminformatics developed by the [Volkamer lab](https://volkamerlab.org/) at the Charité in Berlin. \n",
-    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS interaction fingerprint (IFP) as a `pandas` DataFrame.\n",
+    "This library is a growing collection of modules that help facilitate and standardize common tasks in structural bioinformatics and cheminformatics. Today, we will use the module `opencadd.databases.klifs`, which allows us to fetch the KLIFS structures as a `pandas` DataFrame.\n",
     "\n",
     "- GitHub repository: https://github.com/volkamerlab/opencadd\n",
     "- Documentation: https://opencadd.readthedocs.io\n",
@@ -691,7 +691,7 @@
     "- Resolution: $\\le 3$ Angström\n",
     "- Quality score*: $\\ge 6$\n",
     "\n",
-    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms. A higher score indicates a better structure quality."
+    "\\* The KLIFS quality score takes into account the quality of the alignment, as well as the number of missing residues and atoms. A higher score indicates a better structure quality."
    ]
   },
   {
@@ -823,7 +823,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot our results."
+    "Let's plot the results."
    ]
   },
   {
@@ -926,7 +926,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprint generation, if any structures cannot be encoded."
+    "You might notice that we have slightly fewer fingerprints than structures. This can happen during the `kissim` fingerprint generation, if a structure cannot be encoded."
    ]
   },
   {
@@ -1155,7 +1155,7 @@
    "source": [
     "### Compare structures\n",
     "\n",
-    "Let's make a pairwise comparison of the KiSSim fingerprints. We use `sklearn`'s `nan_euclidean_distances` method, which calculates the Euclidean distance between all pairwise vectors. \n",
+    "Let's make a pairwise comparison of the KiSSim fingerprints. We use the `nan_euclidean_distances` method of `sklearn`, which calculates the Euclidean distance between all pairwise vectors. \n",
     "\n",
     "If two vectors have NaN values (which can happen if residues are not resolved in a structure), the following procedure is applied:\n",
     "\n",

From cf71b4dcbec23a3780f7836e9fecf86416355752 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Fri, 12 Nov 2021 11:43:26 +0100
Subject: [PATCH 140/158] T026: minor text edits + add label to boxplot.

---
 .../talktorial.ipynb                          | 309 +++++++++---------
 1 file changed, 157 insertions(+), 152 deletions(-)

diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 5c8ae658..5b08a524 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -440,7 +440,7 @@
     "- Resolution: $\\le 3$ Angström\n",
     "- Quality score*: $\\ge 6$\n",
     "\n",
-    "\\* The KLIFS quality score takes into account the quality of the alignment and the number of missing residues and atoms. A higher score indicates a better structure quality."
+    "\\* The KLIFS quality score takes into account the quality of the alignment, as well as the number of missing residues and atoms. A higher score indicates a better structure quality."
    ]
   },
   {
@@ -810,7 +810,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's plot our results."
+    "Let's plot the results."
    ]
   },
   {
@@ -863,7 +863,7 @@
    "source": [
     "#### Prepare IFPs as `numpy` array\n",
     "\n",
-    "KLIFS deposits the IFP as a string of 0's and 1's. We have to prepare our IFPs as an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
+    "KLIFS deposits the IFP as a string of 0's and 1's. We have to convert the IFPs to an array of boolean vectors (required by `scipy.spatial.distance` to be able to use the Jaccard distance). Each row in this array refers to one IFP, each columns to one of the IFP's features."
    ]
   },
   {
@@ -961,9 +961,11 @@
    "source": [
     "#### Calculate pairwise Jaccard distances\n",
     "\n",
-    "The Jaccard distance is a common distance measure in case of binary fingerprints. \n",
+    "The Jaccard distance, defined below, is often used in case of binary fingerprints: \n",
     "\n",
-    "$d_J(A,B) = 1 - J(A,B) = \\frac{\\mid A \\cup B \\mid - \\mid A \\cap B \\mid}{\\mid A \\cup B \\mid}$"
+    "$$\n",
+    "d_J(A,B) = 1 - J(A,B) = \\frac{\\mid A \\cup B \\mid - \\mid A \\cap B \\mid}{\\mid A \\cup B \\mid}.\n",
+    "$$"
    ]
   },
   {
@@ -1243,7 +1245,7 @@
    "outputs": [
     {
      "data": {
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1262,6 +1264,9 @@
     "D_condensed = distance.squareform(D)\n",
     "# Plot pairwise distances\n",
     "plt.boxplot(D_condensed)\n",
+    "plt.xticks([1], [\"EGFR\"])\n",
+    "plt.ylabel(\"Distance between structures\")\n",
+    "# plt.xlabel(\"EGFR\")\n",
     "plt.show()\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -1320,220 +1325,220 @@
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-       "#T_47595_row6_col8 {\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_47595_row7_col1 {\n",
+       "#T_4197b_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_47595_row8_col1 {\n",
+       "#T_4197b_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_47595_row8_col4 {\n",
+       "#T_4197b_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_47595_row8_col6 {\n",
+       "#T_4197b_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_47595_row8_col7 {\n",
+       "#T_4197b_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_47595_\">\n",
+       "<table id=\"T_4197b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1550,118 +1555,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_47595_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_47595_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_47595_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_47595_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_47595_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_47595_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_47595_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_47595_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_47595_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_4197b_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_4197b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_4197b_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_4197b_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_4197b_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_4197b_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_4197b_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_4197b_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_4197b_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_47595_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_47595_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_47595_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_47595_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_47595_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_47595_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_47595_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_47595_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_47595_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_4197b_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_4197b_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_4197b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_4197b_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_4197b_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_4197b_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_4197b_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_4197b_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_4197b_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_47595_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_47595_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_47595_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_47595_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_47595_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_47595_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_47595_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_47595_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_47595_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_4197b_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_4197b_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_4197b_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_4197b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_4197b_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_4197b_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_4197b_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_4197b_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_4197b_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_47595_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_47595_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_47595_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_47595_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_47595_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_47595_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_47595_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_47595_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_47595_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_4197b_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_4197b_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_4197b_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_4197b_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_4197b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_4197b_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_4197b_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_4197b_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_4197b_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_47595_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_47595_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_47595_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_47595_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_47595_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_47595_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_47595_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_47595_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_47595_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_4197b_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_4197b_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_4197b_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_4197b_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_4197b_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_4197b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_4197b_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_4197b_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_4197b_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_47595_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_47595_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_47595_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_47595_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_47595_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_47595_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_47595_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_47595_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_47595_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_4197b_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_4197b_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_4197b_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_4197b_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_4197b_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_4197b_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_4197b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_4197b_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_4197b_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_47595_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_47595_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_47595_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_47595_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_47595_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_47595_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_47595_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_47595_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_47595_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_4197b_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_4197b_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_4197b_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_4197b_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_4197b_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_4197b_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_4197b_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_4197b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_4197b_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_47595_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_47595_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_47595_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_47595_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_47595_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_47595_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_47595_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_47595_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_47595_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_4197b_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_4197b_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_4197b_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_4197b_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_4197b_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_4197b_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_4197b_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_4197b_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_4197b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_4197b_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_47595_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_47595_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_47595_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_47595_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_47595_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_47595_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_47595_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_47595_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_47595_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_47595_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_4197b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_4197b_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_4197b_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_4197b_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_4197b_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_4197b_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_4197b_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_4197b_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_4197b_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_4197b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc0219a15b0>"
+       "<pandas.io.formats.style.Styler at 0x7f9e85618df0>"
       ]
      },
      "execution_count": 23,
@@ -1725,7 +1730,7 @@
    "source": [
     "## Quiz\n",
     "\n",
-    "1. What are the advantages and disadvantages of describing kinase similarity with interaction fingerprints\n",
+    "1. What are the advantages and disadvantages of describing kinase similarity with interaction fingerprints?\n",
     "2. Why is the structural coverage between kinases so different?\n",
     "3. At one point, we are mapping structure pairs to kinase pairs. We use here the minimum structure distance. What other mapping strategies can you think of? What advantages and disadvantages could they have?"
    ]

From 51130bfd3832a2ad2ba7d2d0338898444cf6f8a2 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Fri, 12 Nov 2021 12:42:08 +0100
Subject: [PATCH 141/158] T027: fix typos.

---
 .../talktorial.ipynb                             | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 2718dad2..f34202ef 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "## Aim of this talktorial\n",
     "\n",
-    "The aim of this talktorial is to investigate kinase similarity through ligand profiling data. In the context of drug design, the following assumption can be made: if a compound was tested as active on two different kinases, it is suspected that these two kinases may have some degree of similarity. The concept of kinase promiscuity is also covered."
+    "The aim of this talktorial is to investigate kinase similarity through ligand profiling data. In the context of drug design, the following assumption is often made: if a compound was tested active on two different kinases, it is suspected that these two kinases may have some degree of similarity. We will use this assumption in this talktorial. The concept of kinase promiscuity is also covered."
    ]
   },
   {
@@ -101,7 +101,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human kinases. Luckily, a curated version of ChEMBL29 is already freely available in the openkinome organization, see https://github.com/openkinome/kinodata.\n",
+    "In order to measure kinase similarity through ligand profiling data, bioactivity data is retrieved from the well-known [ChEMBL](https://www.ebi.ac.uk/chembl/) database and the query focuses on human kinases. Luckily, a curated version of ChEMBL29 is already freely available through the Openkinome organization, see https://github.com/openkinome/kinodata.\n",
     "For more details on querying the ChEMBL database, please refer to __Talktorial T001__."
    ]
   },
@@ -114,7 +114,7 @@
     "![Manning tree with number of ChEMBL activities per kinase (KinMap)](images/kinmap_n_activities_per_kinase.png)\n",
     "\n",
     "\n",
-    "*Figure 3:* \n",
+    "*Figure 1:* \n",
     "Number of ChEMBL29 bioactivities per kinase - as collected in [kinodata](https://github.com/openkinome/kinodata) - mapped onto the Manning kinome tree using [KinMap](http://www.kinhub.org/kinmap/). The figure has been generated in __Talktorial T023__."
    ]
   },
@@ -141,10 +141,10 @@
    "source": [
     "#### Kinase similarity\n",
     "\n",
-    "We use the following metric as similarity between two kinases $K_i$ and $K_j$:\n",
+    "We use the following metric as similarity between two kinases $K_i$ and $K_j$ :\n",
     "\n",
     "$$\n",
-    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested as actives on both } K_i \\text{ and } K_j}\n",
+    "\\text{similarity}(K_i, K_j) = \\frac{\\#\\text{ of compounds that were tested active on both } K_i \\text{ and } K_j}\n",
     "{\\#\\text{ of compounds that were tested on both } K_i \\text{ and } K_j}.\n",
     "$$\n",
     "\n",
@@ -377,7 +377,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We retrieve a pre-curated version of a kinase subset of ChEMBL29 freely available at openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
+    "We retrieve a pre-curated version of a kinase subset of ChEMBL29 freely available at Openkinome, see https://github.com/openkinome/kinodata/releases/tag/v0.3."
    ]
   },
   {
@@ -1012,7 +1012,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this example, the first molecule is gefitinib, a known FDA approved drug against EGFR."
+    "In this example, the first molecule is gefitinib, a known FDA-approved drug against EGFR."
    ]
   },
   {
@@ -2987,7 +2987,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Since all entries are between $0$ and $1$, the similarity matrix $SM$ can be converted to a distance matrix:"
+    "The similarity matrix $SM$ is converted to a pseudo-distance matrix (all entries of the similarity matrix are between $0$ and $1$):"
    ]
   },
   {

From 1ae5783b4ae86a6d44d715039b1ffe2122b66907 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Fri, 12 Nov 2021 18:18:44 +0100
Subject: [PATCH 142/158] T028: html rendering latex fix.

---
 .../talktorial.ipynb                             | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 225aa0a8..c0dd799c 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -49,7 +49,7 @@
     "* Visualize similarity for example perspective\n",
     "  * Visualize kinase distance matrix as heatmap\n",
     "  * Visualize similarity as dendrogram\n",
-    "* Visualize similarities from different perspectives\n",
+    "* Visualize similarities from the four different perspectives\n",
     "  * Preprocess distance matrices\n",
     "    * Normalize matrices\n",
     "    * Define kinase order\n",
@@ -67,7 +67,7 @@
     "* Clustering and dendrograms with `scipy`: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html\n",
     "* Distance matrix:\n",
     "    * https://en.wikipedia.org/wiki/Distance_matrix\n",
-    "    * https://arxiv.org/pdf/1710.10655.pdf"
+    "    * Gilbert, A. C. and Jain, L. \"If it ain't broke, don't fix it: Sparse metric repair.\" _2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)_. IEEE, 2017. https://arxiv.org/pdf/1710.10655.pdf"
    ]
   },
   {
@@ -107,15 +107,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Distance matrix conditions\n",
     "\n",
     "A square, real matrix $M \\in \\mathbb{R}^{n \\times n}$ is a distance matrix if $M$ satisfies the following conditions (following the definition of distance) :\n",
     "\n",
-    "1. a) Positivity: $M[x, y] \\geq 0$ for all $x, y$ and b) Null condition: $M[x, y] = 0 \\iff x = y.$\n",
+    "1. a. Positivity: $M[x, y] \\geq 0$ for all $x, y$ and b. Null condition: $M[x, y] = 0 \\iff x = y.$   \n",
     "2. Symmetry: $M[x, y] = M[y, x]$ for all $x, y.$\n",
-    "3. Triangular inequality: $M[x, z] \\leq M[x, y] + M[y, z] $ for all $x, y, z.$\n",
+    "3. Triangular inequality: $M[x, z] \\leq M[x, y] + M[y, z]$ for all $x, y, z.$\n",
     "\n",
     "_Technical note_: In some cases, the null condition can be relaxed and only the following has to be satisfied: $M[x, x] = 0, \\forall x.$ In this case, the term is known as semi-metric. We will use this condition in this notebook."
    ]
@@ -218,7 +220,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We check the dimension of the matrix: it should be 2D array, and also be square."
+    "We check the dimension of the matrix: it should be a 2D, square array."
    ]
   },
   {
@@ -892,7 +894,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Visualize similarities from different perspectives"
+    "### Visualize similarities from the four different perspectives"
    ]
   },
   {

From 957933a4470d00857772319e6a91738a43edd76c Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 17 Nov 2021 09:38:30 +0100
Subject: [PATCH 143/158] T023-T028: Update READMEs.

---
 .../T023_what_is_a_kinase/README.md           | 89 ++++++------------
 .../T024_kinase_similarity_sequence/README.md | 92 +++++++-----------
 .../T025_kinase_similarity_kissim/README.md   | 94 +++++++------------
 .../T026_kinase_similarity_ifp/README.md      | 92 +++++++-----------
 .../README.md                                 | 87 ++++++-----------
 .../README.md                                 | 88 ++++++-----------
 6 files changed, 189 insertions(+), 353 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/README.md b/teachopencadd/talktorials/T023_what_is_a_kinase/README.md
index a38d14a4..aebbbab0 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/README.md
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/README.md
@@ -1,78 +1,49 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T023 · What is a kinase?
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
+In this talktorial, we will talk about kinases: why are they important in life and drug design, what do they look like, and what data resources are available?
+Finally, we select a set of kinases which will be analyzed in the forthcoming talktorials **T024**-**T028** with respect to their similarity, the goal being to gain insight into potential off-target effects.
 
 
 ### Contents in *Theory*
 
-_Add Table of Contents (TOC) for Theory section._
-
-* ChEMBL database
-* Compound activity measures
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
-
-</div>
+- Kinases in a nutshell
+    - The human kinome
+    - Kinase structures and important motifs
+- Kinase resources
+    - Kinase structures and related information
+    - Bioactivity data
+- Kinase similarity: Off-targets and promiscuous binding
+- Kinase dataset compilation
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+- Define the kinases of interest
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+- Kinases as drug targets: [<i>Nat. Rev. Drug Discov.</i> (2021), <b>20(7)</b>, 551-569](https://doi.org/10.1038/s41573-021-00195-4)
+- Sequence-based kinase clustering: Manning et al. [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)
+- KinMap: 
+  - Paper: [<i>BMC Bioinformatics</i> (2017), <b>18</b>, 16](https://dx.doi.org/10.1186%2Fs12859-016-1433-7)
+  - Website: http://www.kinhub.org/kinmap/
+- KLIFS
+  - KLIFS URL: https://klifs.net/
+  - KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)
+  - KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)
+- Bioactivity data
+  - Karaman et al. dataset: [<i>Nature Biotechnology</i> (2008), <b>26</b>, 127-132](https://doi.org/10.1038/nbt1358)
+  - Davis et al. dataset: [<i>Nature Biotechnology</i> (2011), <b>29</b>, 1046-1051](https://doi.org/10.1038/nbt.1990)
+  - KIBA dataset: [<i>J. Chem. Inf. Model.</i> (2014), <b>54(3)</b>, 753-743](https://doi.org/10.1021/ci400709d)
+  - PKIS dataset: [<i>PLOS ONE</i> (2017), <b>12</b>, 1-20](https://doi.org/10.1371/journal.pone.0181585)
+- Kinase selection: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629)
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md b/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
index a38d14a4..ab726e88 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/README.md
@@ -1,78 +1,54 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T024 · Kinase similarity: Sequence
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
-
-
-### Contents in *Theory*
+In this talktorial, we investigate sequence similarity for kinases of interest. KLIFS API is used to retrieve the $85$ residues of the pocket sequence for each kinase. 
 
-_Add Table of Contents (TOC) for Theory section._
+Two similarity measures are implemented:
 
-* ChEMBL database
-* Compound activity measures
+   1. Sequence identity, i.e., the similarity which is based on character-wise discrepancy.
+   2. Sequence similarity, i.e., the similarity which is based on a substitution matrix, thus, reflecting similarities between amino acids.
 
 
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+### Contents in *Theory*
 
-</div>
+* Kinase dataset
+* Kinase similarity descriptor: Sequence
+    * Identity score
+    * Substitution score
+* From similarity matrix to distance matrix
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+* Define the kinases of interest
+* Retrieve sequences from KLIFS
+* Sequence similarity
+    * Identity score
+    * Substitution score
+* Kinase similarity
+  * Visualize similarity as kinase matrix
+  * Save kinase similarity matrix
+* Kinase distance matrix
+  * Save kinase distance matrix
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) 
+* KLIFS
+  * KLIFS URL: https://klifs.net/
+  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)
+* Sequence-based kinase clustering: Manning _et al._ [<i>Science</i> (2002), <b>298(5600)</b>, 1912-1934](https://doi.org/10.1126/science.1075762)
+* Substitution matrix: [<i>PNAS</i> (1992), <b>89(22)<b>, 10915-10919](https://doi.org/10.1073/pnas.89.22.10915)
+* Biotite
+    * Documentation: https://www.biotite-python.org/index.html
+    * [Blosum matrix](https://www.biotite-python.org/examples/gallery/sequence/blosum_dendrogram.html?highlight=blosum)
+* Sequence logo: http://www.cbs.dtu.dk/biotools/Seq2Logo/    
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md b/teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md
index a38d14a4..fb7c6825 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/README.md
@@ -1,78 +1,54 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T025 · Kinase similarity: Kinase pocket (KiSSim fingerprint)
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
+We will assess the similarity between a set of kinases from a structural point of view using the [KiSSim](https://kissim.readthedocs.io/en/latest/) fingerprint. This fingerprint describes the physicochemical and spatial properties in structurally resolved kinases.
 
 
 ### Contents in *Theory*
 
-_Add Table of Contents (TOC) for Theory section._
-
-* ChEMBL database
-* Compound activity measures
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
-
-</div>
+* Kinase dataset
+* Kinase similarity descriptor: Kinase pockets (KiSSim fingerprint)
+* Fetching KLIFS data with `opencadd.databases.klifs`
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+* Define the kinases of interest
+* Retrieve and preprocess data
+    * Set up a remote KLIFS session
+    * Fetch all structures describing these kinases
+    * Filter structures
+* Show kinase coverage
+* Load KiSSim fingerprints
+* Compare structures
+* Map structure to kinase distance matrix
+* Save kinase distance matrix
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) 
+* Protein Data Bank
+  * PDB URL: http://www.rcsb.org/
+  * PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)
+* KLIFS
+  * KLIFS URL: https://klifs.net/
+  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)
+  * KLIFS binding site definition: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)
+ * Binding site comparison reviews: 
+   * [<i>Curr. Comput. Aided Drug Des. </i> (2008), <b>4</b>, 209-20](https://www.eurekaselect.com/67606/article/how-measure-similarity-between-protein-ligand-binding-sites)
+    * [<i>J. Med. Chem. </i> (2016), <b>9</b>, 4121-51](https://pubs.acs.org/doi/10.1021/acs.jmedchem.6b00078)
+* KiSSim: Kinase Structural Similarity
+  * GitHub repository: https://github.com/volkamerlab/kissim
+  * Documentation: https://kissim.readthedocs.io
+* `opencadd`, a Python library for structural cheminformatics
+  * GitHub repository: https://github.com/volkamerlab/opencadd
+  * Documentation: https://opencadd.readthedocs.io
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md b/teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md
index a38d14a4..06ad40d0 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/README.md
@@ -1,78 +1,52 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T026 · Kinase similarity: Interaction fingerprints
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
+We will assess the similarity between a set of kinases based on detected protein-ligand interactions in available complex structures. The [KLIFS](https://klifs.net/) interaction fingerprint (IFP), which describes the interactions seen in a structurally resolved kinase-ligand complex, will be used in this exercise.  
 
 
 ### Contents in *Theory*
 
-_Add Table of Contents (TOC) for Theory section._
-
-* ChEMBL database
-* Compound activity measures
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
-
-</div>
+* Kinase dataset
+* Kinase similarity descriptor: KLIFS interaction fingerprint
+* Fetching KLIFS data with `opencadd.databases.klifs`
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+* Define the kinases of interest
+* Retrieve and preprocess data
+    * Set up a remote KLIFS session
+    * Fetch all structures describing these kinases
+    * Filter structures
+    * Fetch the structures' IFPs (if available)
+    * Merge structural and IFP data
+* Show kinase coverage
+* Compare structures
+    * Prepare IFPs as `numpy` array
+    * Calculate pairwise Jaccard distances
+* Map structure to kinase distance matrix
+* Save kinase distance matrix
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) 
+* Protein Data Bank
+  * PDB URL: http://www.rcsb.org/
+  * PDB database: [<i>Acta Cryst.</i> (2002), <b>D58</b>, 899-907](https://doi.org/10.1107/S0907444902003451) and [<i>Structure</i> (2012), <b>20(3)</b>, 391-396](https://doi.org/10.1016/j.str.2012.01.010)
+* KLIFS
+  * KLIFS URL: https://klifs.net/
+  * KLIFS database: [<i>Nucleic Acid Res.</i> (2020), <b>49(D1)</b>, D562-D569](https://doi.org/10.1093/nar/gkaa895)
+  * KLIFS binding site definition and interaction fingerprint calculation: [<i>J. Med. Chem.</i> (2014), <b>57(2)</b>, 249-277](https://doi.org/10.1021/jm400378w)
+* Interaction fingerprint (IFP): [<i>J. Chem. Inf. Model.</i> (2007), <b>71(1)</b>, 195-207](https://doi.org/10.1021/ci600342e)
+* `opencadd`, a Python library for structural cheminformatics
+  * GitHub repository: https://github.com/volkamerlab/opencadd
+  * Documentation: https://opencadd.readthedocs.io
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
index a38d14a4..064a1fa3 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/README.md
@@ -1,78 +1,47 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T027 · Kinase similarity: Ligand profile
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
+The aim of this talktorial is to investigate kinase similarity through ligand profiling data. In the context of drug design, the following assumption is often made: if a compound was tested active on two different kinases, it is suspected that these two kinases may have some degree of similarity. We will use this assumption in this talktorial. The concept of kinase promiscuity is also covered.
 
 
 ### Contents in *Theory*
 
-_Add Table of Contents (TOC) for Theory section._
-
-* ChEMBL database
-* Compound activity measures
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
-
-</div>
+* Kinase dataset
+* Bioactivity data
+* Kinase similarity descriptor: Ligand profile
+    * Kinase similarity
+    * Kinase promiscuity
+* From similarity matrix to distance matrix
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+* Define the kinases of interest
+* Retrieve the data
+* Preprocess the data
+    * Hit or non-hit
+* Kinase promiscuity
+* Kinase similarity
+    * Visualize similarity as kinase matrix
+    * Save kinase similarity matrix
+* Kinase distance matrix
+    * Save kinase distance matrix
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) 
+* ChEMBL database
+  * Website: https://www.ebi.ac.uk/chembl/
+  * Paper: [<i>Nucleic Acid Research</i> (2017), <b>45(D1)</b>, D945-D954](https://doi.org/10.1093/nar/gkw1074) 
+* KinMap
+  * Website: http://www.kinhub.org/kinmap/
+  * Paper: [<i>BMC Bioinformatics</i> (2017), <b>18(1)</b>, 16](https://doi.org/10.1186/s12859-016-1433-7) 
diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md
index a38d14a4..f1475646 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/README.md
@@ -1,78 +1,48 @@
-<div class="alert alert-block alert-info">
-
-<b>Thank you for contributing to TeachOpenCADD!</b>
-
-</div>
-
-
-<div class="alert alert-block alert-info">
-
-<b>Set up your PR</b>: Please check out our <a href="https://github.com/volkamerlab/teachopencadd/issues/41">issue</a> on how to set up a PR for new talktorials, including standard checks and TODOs.
-
-</div>
-
-
-# T000 · Talktorial topic title
+# T028 · Kinase similarity: Compare different perspectives
 
 Authors:
 
-- First and last name, year(s) of contribution, lab, institution
-- First and last name, year(s) of contribution, lab, institution
-
-
-*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*
-
-
-<div class="alert alert-block alert-info">
-
-<b>Cross-referencing talktorials</b>: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: <b>Talktorial T000</b>.
-
-</div>
+- Talia B. Kimber, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Dominique Sydow, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
+- Andrea Volkamer, 2021, [Volkamer lab, Charité](https://volkamerlab.org/)
 
 
 ## Aim of this talktorial
 
-Add a short summary of this talktorial's content.
+We will compare different perspectives on kinase similarity, which were discussed in detail in previous notebooks:
 
-
-### Contents in *Theory*
-
-_Add Table of Contents (TOC) for Theory section._
-
-* ChEMBL database
-* Compound activity measures
+* **Talktorial T024**: Kinase pocket sequences (KLIFS pocket sequences)
+* **Talktorial T025**: Kinase pocket structures (KiSSim fingerprint based on KLIFS pocket residues)
+* **Talktorial T026**: Kinase-ligand interaction profiles (KLIFS IFPs based on KLIFS pocket residues)
+* **Talktorial T027**: Ligand profiling data (using ChEMBL29 bioactivity data)
 
 
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Theory</i> section.
+### Contents in *Theory*
 
-</div>
+* Kinase dataset
+* Kinase similarity descriptor (considering 4 different methods)
+* Distance matrix conditions
 
 
 ### Contents in *Practical*
 
-_Add Table of Contents (TOC) for Practical section._
-
-* Connect to ChEMBL database
-* Load and draw molecules
-
-
-<div class="alert alert-block alert-info">
-
-<b>Sync TOC with section titles</b>: These points should refer to the headlines of your <i>Practical</i> section.
-
-</div>
+* Load kinase similarity and distance matrices
+* Distance matrix conditions
+* Visualize similarity for example perspective
+  * Visualize kinase distance matrix as heatmap
+  * Visualize similarity as dendrogram
+* Visualize similarities from the four different perspectives
+  * Preprocess distance matrices
+    * Normalize matrices
+    * Define kinase order
+  * Visualize kinase similarities
+  * Analysis of results
 
 
 ### References
 
-* Paper 
-* Tutorial links
-* Other useful resources
-
-*We suggest the following citation style:*
-* Keyword describing resource: <i>Journal</i> (year), <b>volume</b>, pages (link to resource) 
-
-*Example:*
-* ChEMBL web services: [<i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) 
+* Kinase dataset: [<i>Molecules</i> (2021), <b>26(3)</b>, 629](https://www.mdpi.com/1420-3049/26/3/629) 
+* Clustering and dendrograms with `scipy`: https://docs.scipy.org/doc/scipy/reference/cluster.hierarchy.html
+* Distance matrix:
+    * https://en.wikipedia.org/wiki/Distance_matrix
+    * Gilbert, A. C. and Jain, L. "If it ain't broke, don't fix it: Sparse metric repair." _2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton)_. IEEE, 2017. https://arxiv.org/pdf/1710.10655.pdf

From 687c0d9c48cc06d48dc787db092b4f3737bb63a4 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 17 Nov 2021 09:48:46 +0100
Subject: [PATCH 144/158] T023-T028: run black on notebooks.

---
 .../T023_what_is_a_kinase/talktorial.ipynb    |   6 +-
 .../talktorial.ipynb                          | 168 +++++++++---------
 .../talktorial.ipynb                          |  16 +-
 .../talktorial.ipynb                          |   2 +-
 .../talktorial.ipynb                          |  56 +++---
 5 files changed, 117 insertions(+), 131 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 8e24eb61..9a57904e 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -699,11 +699,7 @@
     "\n",
     "# Show pocket\n",
     "viewer = PocketViewer()\n",
-    "viewer.add_pocket(\n",
-    "    pocket,\n",
-    "    ligand_expo_id=\"ATP\",\n",
-    "    show_pocket_center=False\n",
-    ")\n",
+    "viewer.add_pocket(pocket, ligand_expo_id=\"ATP\", show_pocket_center=False)\n",
     "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
     "viewer.viewer"
    ]
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 5ea9e5b6..e6841136 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -468,8 +468,11 @@
     }
    ],
    "source": [
-    "kinase_selection_df = pd.read_csv(HERE / \"../T023_what_is_a_kinase/\\\n",
-    "data/kinase_selection.csv\")\n",
+    "kinase_selection_df = pd.read_csv(\n",
+    "    HERE\n",
+    "    / \"../T023_what_is_a_kinase/\\\n",
+    "data/kinase_selection.csv\"\n",
+    ")\n",
     "kinase_selection_df\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -509,14 +512,14 @@
     "        The 85 residues pocket sequence from KLIFS,\n",
     "        if the kinase name is valid, None otherwise.\n",
     "    \"\"\"\n",
-    "    response = requests.get(f\"https://klifs.net/api/\"\n",
-    "                            f\"kinase_ID?kinase_name={klifs_kinase_name}\"\n",
-    "                            f\"&species=HUMAN\")\n",
+    "    response = requests.get(\n",
+    "        f\"https://klifs.net/api/\" f\"kinase_ID?kinase_name={klifs_kinase_name}\" f\"&species=HUMAN\"\n",
+    "    )\n",
     "\n",
     "    if response.status_code == 200:\n",
-    "        return response.json()[0]['pocket']\n",
+    "        return response.json()[0][\"pocket\"]\n",
     "    else:\n",
-    "        print(f'KLIFS failed for kinase {klifs_kinase_name}')\n",
+    "        print(f\"KLIFS failed for kinase {klifs_kinase_name}\")\n",
     "        return None"
    ]
   },
@@ -554,7 +557,7 @@
     "    kinase_sequences_dict[kinase] = klifs_pocket_sequence(kinase)\n",
     "\n",
     "for name, sequence in kinase_sequences_dict.items():\n",
-    "    print(f'{name:10}{sequence}')\n",
+    "    print(f\"{name:10}{sequence}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -612,10 +615,7 @@
     "        0 otherwise.\n",
     "    \"\"\"\n",
     "    # True is the character is the same, False otherwise\n",
-    "    return np.compare_chararrays(sequence1,\n",
-    "                                 sequence2,\n",
-    "                                 cmp=\"==\",\n",
-    "                                 rstrip=True)"
+    "    return np.compare_chararrays(sequence1, sequence2, cmp=\"==\", rstrip=True)"
    ]
   },
   {
@@ -718,7 +718,7 @@
    "outputs": [],
    "source": [
     "def _translate_rescale_substitution_matrix(\n",
-    "    substitution_matrix=align.SubstitutionMatrix.std_protein_matrix()\n",
+    "    substitution_matrix=align.SubstitutionMatrix.std_protein_matrix(),\n",
     "):\n",
     "    \"\"\"\n",
     "    Translate and rescale substitution matrix.\n",
@@ -746,23 +746,24 @@
     "    normalized_score_matrix = np.zeros(score_matrix.shape)\n",
     "    for i in range(score_matrix.shape[0]):\n",
     "        for j in range(score_matrix.shape[0]):\n",
-    "            normalized_score_matrix[i, j] = (translated_matrix[i, j] /\n",
-    "                                             np.sqrt(translated_matrix[i, i] *\n",
-    "                                                     translated_matrix[j, j]))\n",
+    "            normalized_score_matrix[i, j] = translated_matrix[i, j] / np.sqrt(\n",
+    "                translated_matrix[i, i] * translated_matrix[j, j]\n",
+    "            )\n",
     "\n",
     "    # Create DataFrame from matrix with letters as index/column names\n",
     "    normalized_score_matrix = pd.DataFrame(\n",
     "        normalized_score_matrix,\n",
     "        columns=substitution_matrix.get_alphabet1(),\n",
-    "        index=substitution_matrix.get_alphabet1()\n",
+    "        index=substitution_matrix.get_alphabet1(),\n",
     "    )\n",
-    "    \n",
+    "\n",
     "    # Check for symmetry\n",
-    "    symmetric = (normalized_score_matrix.values ==\n",
-    "                 normalized_score_matrix.values.transpose()).all()\n",
+    "    symmetric = (\n",
+    "        normalized_score_matrix.values == normalized_score_matrix.values.transpose()\n",
+    "    ).all()\n",
     "    if not symmetric:\n",
     "        raise ValueError(f\"Translated/rescaled matrix is not symmetric.\")\n",
-    "    \n",
+    "\n",
     "    return normalized_score_matrix"
    ]
   },
@@ -1492,10 +1493,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def substitution_score(sequence1,\n",
-    "                       sequence2,\n",
-    "                       substitution_matrix=align.\n",
-    "                       SubstitutionMatrix.std_protein_matrix()):\n",
+    "def substitution_score(\n",
+    "    sequence1, sequence2, substitution_matrix=align.SubstitutionMatrix.std_protein_matrix()\n",
+    "):\n",
     "    \"\"\"\n",
     "    Retrieve the match score given the substitution matrix.\n",
     "\n",
@@ -1516,15 +1516,11 @@
     "        The vector of match score\n",
     "        using the normalized substitution matrix.\n",
     "    \"\"\"\n",
-    "    substitution_matrix_df = _translate_rescale_substitution_matrix(\n",
-    "        substitution_matrix\n",
-    "    )\n",
+    "    substitution_matrix_df = _translate_rescale_substitution_matrix(substitution_matrix)\n",
     "\n",
     "    match_score_array = np.zeros(len(sequence1))\n",
-    "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1,\n",
-    "                                                             sequence2)):\n",
-    "        match_score_array[i] = substitution_matrix_df.loc[character_seq1,\n",
-    "                                                          character_seq2]\n",
+    "    for i, (character_seq1, character_seq2) in enumerate(zip(sequence1, sequence2)):\n",
+    "        match_score_array[i] = substitution_matrix_df.loc[character_seq1, character_seq2]\n",
     "    return match_score_array"
    ]
   },
@@ -1550,9 +1546,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sequence_similarity(sequence_1,\n",
-    "                        sequence_2,\n",
-    "                        type_=\"identity\"):\n",
+    "def sequence_similarity(sequence_1, sequence_2, type_=\"identity\"):\n",
     "    \"\"\"\n",
     "    Compares two sequences using a given metric.\n",
     "\n",
@@ -1585,10 +1579,10 @@
     "\n",
     "        if type_ == \"identity\":\n",
     "            is_match_array = identity_score(seq_array1, seq_array2)\n",
-    "            similarity_normed = sum(is_match_array)/len(sequence_1)\n",
+    "            similarity_normed = sum(is_match_array) / len(sequence_1)\n",
     "        elif type_ == \"substitution\":\n",
     "            match_score_array = substitution_score(seq_array1, seq_array2)\n",
-    "            similarity_normed = sum(match_score_array)/len(sequence_1)\n",
+    "            similarity_normed = sum(match_score_array) / len(sequence_1)\n",
     "        else:\n",
     "            raise ValueError(f\"Type {type_} not defined.\")\n",
     "\n",
@@ -1620,7 +1614,7 @@
    ],
    "source": [
     "print(\"The sequences are:\\n\")\n",
-    "for key in ('EGFR', 'MET'):\n",
+    "for key in (\"EGFR\", \"MET\"):\n",
     "    print(f\"{key:5s}: {kinase_sequences_dict[key]}\")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
@@ -1639,12 +1633,14 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
-    "                                              kinase_sequences_dict[\"MET\"],\n",
-    "                                              \"identity\")\n",
+    "egfr_met_seq_similarity = sequence_similarity(\n",
+    "    kinase_sequences_dict[\"EGFR\"], kinase_sequences_dict[\"MET\"], \"identity\"\n",
+    ")\n",
     "\n",
-    "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{egfr_met_seq_similarity:.2f} using identity.\")\n",
+    "print(\n",
+    "    f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
+    "    f\"{egfr_met_seq_similarity:.2f} using identity.\"\n",
+    ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1662,11 +1658,13 @@
     }
    ],
    "source": [
-    "egfr_met_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
-    "                                              kinase_sequences_dict[\"MET\"],\n",
-    "                                              \"substitution\")\n",
-    "print(f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
-    "      f\"{egfr_met_seq_similarity:.2f} using substitution.\")\n",
+    "egfr_met_seq_similarity = sequence_similarity(\n",
+    "    kinase_sequences_dict[\"EGFR\"], kinase_sequences_dict[\"MET\"], \"substitution\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Pocket sequence similarity between EGFR and MET kinases: \"\n",
+    "    f\"{egfr_met_seq_similarity:.2f} using substitution.\"\n",
+    ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1691,11 +1689,13 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
-    "                                          kinase_sequences_dict[\"EGFR\"],\n",
-    "                                          type_=\"identity\")\n",
-    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{egfr_seq_similarity:.2f} using identity.\")\n",
+    "egfr_seq_similarity = sequence_similarity(\n",
+    "    kinase_sequences_dict[\"EGFR\"], kinase_sequences_dict[\"EGFR\"], type_=\"identity\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Pocket sequence similarity between EGFR itself: \"\n",
+    "    f\"{egfr_seq_similarity:.2f} using identity.\"\n",
+    ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1713,11 +1713,13 @@
     }
    ],
    "source": [
-    "egfr_seq_similarity = sequence_similarity(kinase_sequences_dict[\"EGFR\"],\n",
-    "                                          kinase_sequences_dict[\"EGFR\"],\n",
-    "                                          type_=\"substitution\")\n",
-    "print(f\"Pocket sequence similarity between EGFR itself: \"\n",
-    "      f\"{egfr_seq_similarity:.2f} using substitution.\")\n",
+    "egfr_seq_similarity = sequence_similarity(\n",
+    "    kinase_sequences_dict[\"EGFR\"], kinase_sequences_dict[\"EGFR\"], type_=\"substitution\"\n",
+    ")\n",
+    "print(\n",
+    "    f\"Pocket sequence similarity between EGFR itself: \"\n",
+    "    f\"{egfr_seq_similarity:.2f} using substitution.\"\n",
+    ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -1744,7 +1746,7 @@
     "def pairwise_kinase_similarities(kinase_sequence_dictionary, similarity_measure):\n",
     "    \"\"\"\n",
     "    Calculate pairwise similarities between a set of kinases.\n",
-    "    \n",
+    "\n",
     "    Parameters\n",
     "    ----------\n",
     "    kinase_sequence_dictionary : dict\n",
@@ -1752,35 +1754,39 @@
     "        and the kinase sequence as value.\n",
     "    similarity_measure : str\n",
     "        Select similarity measure: \"identity\" or \"substitution\".\n",
-    "        \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
     "    pd.DataFrame\n",
     "        All-against-all similarities between input kinases.\n",
     "    \"\"\"\n",
     "    # Initialize matrix with 0\n",
-    "    kinase_similarity_matrix = np.zeros((len(kinase_sequence_dictionary),\n",
-    "                                         len(kinase_sequence_dictionary)))\n",
+    "    kinase_similarity_matrix = np.zeros(\n",
+    "        (len(kinase_sequence_dictionary), len(kinase_sequence_dictionary))\n",
+    "    )\n",
     "    # Iterate over matrix and fill with similarity values\n",
     "    for i, kinase_sequence1 in enumerate(kinase_sequence_dictionary.values()):\n",
     "        for j, kinase_sequence2 in enumerate(kinase_sequence_dictionary.values()):\n",
-    "            kinase_similarity_matrix[i, j] = sequence_similarity(kinase_sequence1,\n",
-    "                                                                 kinase_sequence2,\n",
-    "                                                                 similarity_measure)\n",
+    "            kinase_similarity_matrix[i, j] = sequence_similarity(\n",
+    "                kinase_sequence1, kinase_sequence2, similarity_measure\n",
+    "            )\n",
     "\n",
     "    # Cast matrix to DataFrame\n",
-    "    kinase_similarity_matrix_df = pd.DataFrame(data=kinase_similarity_matrix,\n",
-    "                                               index=kinase_sequences_dict.keys(),\n",
-    "                                               columns=kinase_sequences_dict.keys())\n",
+    "    kinase_similarity_matrix_df = pd.DataFrame(\n",
+    "        data=kinase_similarity_matrix,\n",
+    "        index=kinase_sequences_dict.keys(),\n",
+    "        columns=kinase_sequences_dict.keys(),\n",
+    "    )\n",
     "    kinase_similarity_matrix_df.index.name = None\n",
     "    kinase_similarity_matrix_df.columns.name = None\n",
     "\n",
     "    # Check for symmetry\n",
-    "    symmetric = (kinase_similarity_matrix_df.values ==\n",
-    "                 kinase_similarity_matrix_df.values.transpose()).all()\n",
+    "    symmetric = (\n",
+    "        kinase_similarity_matrix_df.values == kinase_similarity_matrix_df.values.transpose()\n",
+    "    ).all()\n",
     "    if not symmetric:\n",
     "        raise ValueError(f\"Matrix is not symmetric.\")\n",
-    "    \n",
+    "\n",
     "    return kinase_similarity_matrix_df"
    ]
   },
@@ -2270,9 +2276,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix_identity_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_similarity_matrix_identity_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -2798,9 +2802,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_similarity_matrix_substitution_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_similarity_matrix_substitution_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -2866,9 +2868,11 @@
     }
    ],
    "source": [
-    "print(f\"The values of the similarity matrix lie between: \"\n",
-    "      f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
-    "      f\" and {kinase_similarity_matrix_df.max().max():.2f}\")\n",
+    "print(\n",
+    "    f\"The values of the similarity matrix lie between: \"\n",
+    "    f\"{kinase_similarity_matrix_df.min().min():.2f}\"\n",
+    "    f\" and {kinase_similarity_matrix_df.max().max():.2f}\"\n",
+    ")\n",
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
@@ -3208,9 +3212,7 @@
     }
    ],
    "source": [
-    "kinase_distance_matrix_df.style.\\\n",
-    "    background_gradient(cmap=cm).\\\n",
-    "    format(\"{:.3f}\")"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 5b08a524..2f55d1cf 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -416,12 +416,16 @@
     "# Get all available structures for these kinases\n",
     "structures_df = klifs_session.structures.by_kinase_name(kinase_names=kinase_names)\n",
     "# Keep only relevant columns\n",
-    "structures_df = structures_df[[\"structure.klifs_id\",\n",
-    "                               \"kinase.klifs_name\",\n",
-    "                               \"species.klifs\",\n",
-    "                               \"structure.dfg\",\n",
-    "                               \"structure.resolution\",\n",
-    "                               \"structure.qualityscore\"]]\n",
+    "structures_df = structures_df[\n",
+    "    [\n",
+    "        \"structure.klifs_id\",\n",
+    "        \"kinase.klifs_name\",\n",
+    "        \"species.klifs\",\n",
+    "        \"structure.dfg\",\n",
+    "        \"structure.resolution\",\n",
+    "        \"structure.qualityscore\",\n",
+    "    ]\n",
+    "]\n",
     "print(f\"Number of structures: {len(structures_df)}\")\n",
     "print(\"Kinases:\", *structures_df[\"kinase.klifs_name\"].unique())\n",
     "# NBVAL_CHECK_OUTPUT"
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index f34202ef..0b9a4dd8 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -1003,7 +1003,7 @@
    ],
    "source": [
     "mols = []\n",
-    "for entry in measured_compounds.most_common()[0:5]:    \n",
+    "for entry in measured_compounds.most_common()[0:5]:\n",
     "    mols.append(Chem.MolFromSmiles(entry[0]))\n",
     "Draw.MolsToGridImage(mols, molsPerRow=5)"
    ]
diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index c0dd799c..1b075b65 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -179,7 +179,7 @@
     "    \"sequence\": \"T024_kinase_similarity_sequence\",\n",
     "    \"kissim\": \"T025_kinase_similarity_kissim\",\n",
     "    \"ifp\": \"T026_kinase_similarity_ifp\",\n",
-    "    \"ligand-profile\": \"T027_kinase_similarity_ligand_profile\"\n",
+    "    \"ligand-profile\": \"T027_kinase_similarity_ligand_profile\",\n",
     "}\n",
     "\n",
     "# Distance matrices (for dendrogram)\n",
@@ -375,7 +375,7 @@
     "    for i in range(matrix.shape[0]):\n",
     "        for j in range(matrix.shape[0]):\n",
     "            for k in range(matrix.shape[0]):\n",
-    "                if (matrix[i, j] <= matrix[i, k] + matrix[k, j] + tolerance):\n",
+    "                if matrix[i, j] <= matrix[i, k] + matrix[k, j] + tolerance:\n",
     "                    triang_inequality = True\n",
     "                else:\n",
     "                    return False\n",
@@ -411,43 +411,33 @@
     "\n",
     "    dimensionality = check_dimensionality(matrix)\n",
     "    if dimensionality:\n",
-    "        print(f\"{'Dimensionality' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Dimensionality' : <30}\" f\"{'Satisfied'}\")\n",
     "    else:\n",
-    "        print(f\"{'Dimensionality' : <30}\"\n",
-    "              f\"{'Not Satisfied'}\")\n",
+    "        print(f\"{'Dimensionality' : <30}\" f\"{'Not Satisfied'}\")\n",
     "\n",
     "    positivity = check_positivity(matrix)\n",
     "    if positivity:\n",
-    "        print(f\"{'Postivity' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Postivity' : <30}\" f\"{'Satisfied'}\")\n",
     "    else:\n",
-    "        print(f\"{'Postivity' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Postivity' : <30}\" f\"{'Satisfied'}\")\n",
     "\n",
     "    diag = check_null_diagonal(matrix)\n",
     "    if diag:\n",
-    "        print(f\"{'Null diagonal' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Null diagonal' : <30}\" f\"{'Satisfied'}\")\n",
     "    else:\n",
-    "        print(f\"{'Null diagonal' : <30}\"\n",
-    "              f\"{'Not Satisfied'}\")\n",
+    "        print(f\"{'Null diagonal' : <30}\" f\"{'Not Satisfied'}\")\n",
     "\n",
     "    symmetry = check_symmetry(matrix)\n",
     "    if symmetry:\n",
-    "        print(f\"{'Symmetry' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Symmetry' : <30}\" f\"{'Satisfied'}\")\n",
     "    else:\n",
-    "        print(f\"{'Symmetry' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Symmetry' : <30}\" f\"{'Satisfied'}\")\n",
     "\n",
     "    triang_inequ = check_triangular_inequality(matrix)\n",
     "    if triang_inequ:\n",
-    "        print(f\"{'Triangular inequality' : <30}\"\n",
-    "              f\"{'Satisfied'}\")\n",
+    "        print(f\"{'Triangular inequality' : <30}\" f\"{'Satisfied'}\")\n",
     "    else:\n",
-    "        print(f\"{'Triangular inequality' : <30}\"\n",
-    "              f\"{'Not Satisfied'}\")\n",
+    "        print(f\"{'Triangular inequality' : <30}\" f\"{'Not Satisfied'}\")\n",
     "    print(\"\\n\")\n",
     "    return None"
    ]
@@ -776,8 +766,9 @@
     "    kinase_distance_matrix,\n",
     "    linewidths=0,\n",
     "    annot=True,\n",
-    "    square=True, cbar_kws={'label': 'Kinase distance'},\n",
-    "    cmap='viridis',\n",
+    "    square=True,\n",
+    "    cbar_kws={\"label\": \"Kinase distance\"},\n",
+    "    cmap=\"viridis\",\n",
     ")\n",
     "plt.show()"
    ]
@@ -992,9 +983,9 @@
     "    pd.DataFrame\n",
     "        Kinase distance matrix with sorted columns/rows.\n",
     "    \"\"\"\n",
-    "    kinase_distance_matrix_df = kinase_distance_matrix_df \\\n",
-    "        .reindex(kinase_names, axis=1) \\\n",
-    "        .reindex(kinase_names, axis=0)\n",
+    "    kinase_distance_matrix_df = kinase_distance_matrix_df.reindex(kinase_names, axis=1).reindex(\n",
+    "        kinase_names, axis=0\n",
+    "    )\n",
     "    return kinase_distance_matrix_df"
    ]
   },
@@ -1038,14 +1029,7 @@
     "    title : str\n",
     "        Plot title.\n",
     "    \"\"\"\n",
-    "    sns.heatmap(\n",
-    "        score_df,\n",
-    "        linewidths=0,\n",
-    "        annot=True,\n",
-    "        square=True,\n",
-    "        cmap='viridis',\n",
-    "        ax=ax\n",
-    "    )"
+    "    sns.heatmap(score_df, linewidths=0, annot=True, square=True, cmap=\"viridis\", ax=ax)"
    ]
   },
   {
@@ -1107,7 +1091,7 @@
    ],
    "source": [
     "n_perspectives = len(kinase_distance_matrices_normalized)\n",
-    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives*5, 10))\n",
+    "fig, axes = plt.subplots(2, n_perspectives, figsize=(n_perspectives * 5, 10))\n",
     "for i, (perspective, distance_matrix) in enumerate(kinase_distance_matrices_normalized.items()):\n",
     "    # heatmap based on similarity matrix\n",
     "    similarity_matrix = 1 - distance_matrix\n",

From 5483fc59780878cf53918e28e1ab62cdfc21153e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Wed, 17 Nov 2021 13:12:04 +0100
Subject: [PATCH 145/158] Rerun notebooks top to bottom

---
 .../T023_what_is_a_kinase/talktorial.ipynb    |    4 +-
 .../talktorial.ipynb                          | 2196 ++++++++---------
 .../talktorial.ipynb                          |  281 ++-
 .../talktorial.ipynb                          |  290 +--
 .../talktorial.ipynb                          | 1044 ++++----
 5 files changed, 1907 insertions(+), 1908 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 9a57904e..6d243cf8 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -667,7 +667,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c842ece1bf864f258dc9c457898ca680",
+       "model_id": "0697136546e0480fb93fdfee71b5cf26",
        "version_major": 2,
        "version_minor": 0
       },
@@ -679,7 +679,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "25bb28b90afc440cb2bdc16e9cb55a88",
+       "model_id": "4ca1cad46abb4a6c8fb09f94ccacd1aa",
        "version_major": 2,
        "version_minor": 0
       },
diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index e6841136..0a982a8e 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -513,7 +513,7 @@
     "        if the kinase name is valid, None otherwise.\n",
     "    \"\"\"\n",
     "    response = requests.get(\n",
-    "        f\"https://klifs.net/api/\" f\"kinase_ID?kinase_name={klifs_kinase_name}\" f\"&species=HUMAN\"\n",
+    "        f\"https://klifs.net/api/kinase_ID?kinase_name={klifs_kinase_name}&species=HUMAN\"\n",
     "    )\n",
     "\n",
     "    if response.status_code == 200:\n",
@@ -532,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -784,7 +784,7 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_aa646_\">\n",
+       "<table id=\"T_dc1d7_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -816,658 +816,658 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
-       "      <td id=\"T_aa646_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_aa646_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_aa646_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_aa646_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_aa646_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_aa646_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_aa646_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_aa646_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_aa646_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_aa646_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_aa646_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_aa646_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_aa646_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_aa646_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_aa646_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_aa646_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_aa646_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_aa646_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_aa646_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_aa646_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_aa646_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_aa646_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_aa646_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_aa646_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_aa646_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_aa646_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_aa646_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_aa646_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_aa646_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_aa646_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_aa646_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_aa646_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_aa646_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_aa646_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_aa646_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_aa646_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_aa646_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_aa646_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_aa646_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_aa646_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_aa646_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_aa646_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_aa646_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_aa646_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_aa646_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_aa646_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_aa646_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_aa646_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_aa646_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_aa646_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_aa646_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_aa646_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_aa646_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_aa646_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_aa646_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_aa646_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_aa646_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_aa646_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_aa646_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_aa646_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_aa646_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_aa646_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_aa646_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_aa646_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_aa646_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_aa646_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_aa646_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_aa646_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_aa646_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_aa646_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_aa646_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_aa646_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_aa646_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_aa646_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_aa646_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_aa646_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_aa646_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_aa646_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_aa646_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_aa646_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_aa646_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_aa646_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_aa646_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_aa646_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_aa646_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_aa646_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_aa646_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_aa646_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_aa646_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_aa646_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_aa646_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_aa646_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_aa646_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_aa646_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_aa646_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_aa646_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_aa646_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_aa646_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_aa646_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_aa646_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_aa646_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_aa646_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_aa646_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_aa646_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_aa646_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_aa646_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_aa646_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_aa646_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_aa646_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_aa646_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_aa646_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_aa646_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_aa646_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_aa646_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_dc1d7_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_dc1d7_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_dc1d7_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_dc1d7_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_dc1d7_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_dc1d7_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_dc1d7_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_dc1d7_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_dc1d7_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_dc1d7_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_dc1d7_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_dc1d7_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_dc1d7_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_dc1d7_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_dc1d7_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_dc1d7_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_dc1d7_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_dc1d7_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_dc1d7_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_dc1d7_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_dc1d7_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_dc1d7_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_dc1d7_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_dc1d7_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_dc1d7_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_dc1d7_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_dc1d7_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_dc1d7_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_dc1d7_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_dc1d7_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_dc1d7_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_dc1d7_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_dc1d7_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_dc1d7_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_dc1d7_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_dc1d7_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_dc1d7_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_dc1d7_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_dc1d7_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_dc1d7_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_dc1d7_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_dc1d7_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_dc1d7_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_dc1d7_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_dc1d7_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_dc1d7_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_dc1d7_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_dc1d7_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_dc1d7_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_dc1d7_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_dc1d7_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_dc1d7_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_dc1d7_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_dc1d7_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_dc1d7_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_dc1d7_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_dc1d7_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_dc1d7_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_dc1d7_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_dc1d7_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_dc1d7_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_dc1d7_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_dc1d7_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_dc1d7_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_dc1d7_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_dc1d7_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_dc1d7_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_dc1d7_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_dc1d7_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_dc1d7_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_dc1d7_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_dc1d7_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_dc1d7_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_dc1d7_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_dc1d7_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_dc1d7_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_dc1d7_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_dc1d7_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_dc1d7_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_dc1d7_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_dc1d7_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_dc1d7_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_dc1d7_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_dc1d7_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_dc1d7_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_dc1d7_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_dc1d7_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_dc1d7_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_dc1d7_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_dc1d7_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_dc1d7_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_dc1d7_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_dc1d7_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_dc1d7_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_dc1d7_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_dc1d7_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_dc1d7_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_dc1d7_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_dc1d7_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_dc1d7_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_dc1d7_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_dc1d7_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_dc1d7_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_dc1d7_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_dc1d7_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_dc1d7_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_dc1d7_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_dc1d7_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_dc1d7_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_dc1d7_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_dc1d7_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_dc1d7_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_dc1d7_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_dc1d7_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_dc1d7_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fdd39651fa0>"
+       "<pandas.io.formats.style.Styler at 0x7fc6da71f160>"
       ]
      },
      "execution_count": 10,
@@ -2000,144 +2000,144 @@
      "data": {
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-       "#T_cb06b_row0_col0, #T_cb06b_row1_col1, #T_cb06b_row2_col2, #T_cb06b_row3_col3, #T_cb06b_row4_col4, #T_cb06b_row5_col5, #T_cb06b_row6_col6, #T_cb06b_row7_col7, #T_cb06b_row8_col8 {\n",
+       "#T_c5eb7_row0_col0, #T_c5eb7_row1_col1, #T_c5eb7_row2_col2, #T_c5eb7_row3_col3, #T_c5eb7_row4_col4, #T_c5eb7_row5_col5, #T_c5eb7_row6_col6, #T_c5eb7_row7_col7, #T_c5eb7_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_cb06b_row0_col1, #T_cb06b_row1_col0 {\n",
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        "  background-color: #1c8e1c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_cb06b_row0_col2, #T_cb06b_row1_col2 {\n",
+       "#T_c5eb7_row0_col2, #T_c5eb7_row1_col2 {\n",
        "  background-color: #e8f2e8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row0_col3, #T_cb06b_row7_col3 {\n",
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-       "#T_cb06b_row0_col4, #T_cb06b_row3_col5, #T_cb06b_row7_col4, #T_cb06b_row8_col4 {\n",
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        "  background-color: #add5ad;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row0_col5 {\n",
+       "#T_c5eb7_row0_col5 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row0_col6 {\n",
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        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row0_col7 {\n",
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-       "#T_cb06b_row0_col8, #T_cb06b_row3_col8, #T_cb06b_row4_col6, #T_cb06b_row6_col8, #T_cb06b_row8_col6 {\n",
+       "#T_c5eb7_row0_col8, #T_c5eb7_row3_col8, #T_c5eb7_row4_col6, #T_c5eb7_row6_col8, #T_c5eb7_row8_col6 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row1_col3, #T_cb06b_row6_col3 {\n",
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+       "#T_c5eb7_row1_col4, #T_c5eb7_row3_col4, #T_c5eb7_row4_col3, #T_c5eb7_row5_col7, #T_c5eb7_row7_col5, #T_c5eb7_row8_col7 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row1_col5, #T_cb06b_row4_col5 {\n",
+       "#T_c5eb7_row1_col5, #T_c5eb7_row4_col5 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row1_col6, #T_cb06b_row6_col7 {\n",
+       "#T_c5eb7_row1_col6, #T_c5eb7_row6_col7 {\n",
        "  background-color: #9dcd9d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row1_col7, #T_cb06b_row3_col7, #T_cb06b_row8_col5 {\n",
+       "#T_c5eb7_row1_col7, #T_c5eb7_row3_col7, #T_c5eb7_row8_col5 {\n",
        "  background-color: #8bc48b;\n",
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-       "#T_cb06b_row1_col8, #T_cb06b_row4_col7, #T_cb06b_row6_col5, #T_cb06b_row7_col6 {\n",
+       "#T_c5eb7_row1_col8, #T_c5eb7_row4_col7, #T_c5eb7_row6_col5, #T_c5eb7_row7_col6 {\n",
        "  background-color: #a4d0a4;\n",
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        "}\n",
-       "#T_cb06b_row2_col0, #T_cb06b_row2_col1, #T_cb06b_row2_col3, #T_cb06b_row2_col4, #T_cb06b_row2_col5, #T_cb06b_row2_col6, #T_cb06b_row2_col7, #T_cb06b_row2_col8, #T_cb06b_row5_col2, #T_cb06b_row7_col2 {\n",
+       "#T_c5eb7_row2_col0, #T_c5eb7_row2_col1, #T_c5eb7_row2_col3, #T_c5eb7_row2_col4, #T_c5eb7_row2_col5, #T_c5eb7_row2_col6, #T_c5eb7_row2_col7, #T_c5eb7_row2_col8, #T_c5eb7_row5_col2, #T_c5eb7_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row3_col0, #T_cb06b_row7_col1 {\n",
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        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row3_col1 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row3_col2, #T_cb06b_row4_col2 {\n",
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        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row3_col6 {\n",
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        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row4_col0 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row4_col1, #T_cb06b_row8_col1 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row4_col8, #T_cb06b_row5_col6 {\n",
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        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row5_col0 {\n",
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        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row5_col1 {\n",
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        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row5_col3 {\n",
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        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row5_col4 {\n",
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        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row5_col8, #T_cb06b_row7_col0 {\n",
+       "#T_c5eb7_row5_col8, #T_c5eb7_row7_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row6_col0 {\n",
+       "#T_c5eb7_row6_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row6_col1 {\n",
+       "#T_c5eb7_row6_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row6_col2, #T_cb06b_row8_col2 {\n",
+       "#T_c5eb7_row6_col2, #T_c5eb7_row8_col2 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row6_col4, #T_cb06b_row8_col3 {\n",
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        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row7_col8 {\n",
+       "#T_c5eb7_row7_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_cb06b_row8_col0 {\n",
+       "#T_c5eb7_row8_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_cb06b_\">\n",
+       "<table id=\"T_c5eb7_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2154,118 +2154,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_cb06b_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_cb06b_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_cb06b_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_cb06b_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_cb06b_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_cb06b_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_cb06b_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_cb06b_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_cb06b_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_cb06b_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_cb06b_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_cb06b_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_cb06b_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_cb06b_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_cb06b_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_cb06b_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_cb06b_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_cb06b_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_cb06b_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_cb06b_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_cb06b_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_cb06b_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_cb06b_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_cb06b_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_cb06b_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_cb06b_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_cb06b_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_cb06b_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_cb06b_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_cb06b_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_cb06b_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_cb06b_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_cb06b_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_cb06b_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_cb06b_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_cb06b_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_cb06b_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_cb06b_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_cb06b_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_cb06b_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_cb06b_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_cb06b_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_cb06b_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_cb06b_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_cb06b_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_cb06b_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_cb06b_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_cb06b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_cb06b_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_cb06b_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_cb06b_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_cb06b_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_cb06b_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_cb06b_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_cb06b_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_cb06b_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_cb06b_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_c5eb7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_c5eb7_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_c5eb7_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_c5eb7_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_c5eb7_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_c5eb7_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_c5eb7_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_c5eb7_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_c5eb7_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_c5eb7_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_c5eb7_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_c5eb7_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_c5eb7_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_c5eb7_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_c5eb7_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_c5eb7_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_c5eb7_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_c5eb7_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_c5eb7_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_c5eb7_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_c5eb7_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_c5eb7_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_c5eb7_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_c5eb7_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_c5eb7_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_c5eb7_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_c5eb7_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_c5eb7_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_c5eb7_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_c5eb7_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_c5eb7_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_c5eb7_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_c5eb7_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_c5eb7_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_c5eb7_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_c5eb7_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_c5eb7_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_c5eb7_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_c5eb7_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_c5eb7_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_c5eb7_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_c5eb7_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_c5eb7_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_c5eb7_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_c5eb7_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_c5eb7_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_c5eb7_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_c5eb7_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_c5eb7_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c5eb7_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_c5eb7_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_c5eb7_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_c5eb7_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_c5eb7_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_c5eb7_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_c5eb7_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_c5eb7_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_c5eb7_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fdd39a41220>"
+       "<pandas.io.formats.style.Styler at 0x7fc6daa37550>"
       ]
      },
      "execution_count": 20,
@@ -2482,188 +2482,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_ef15b_row0_col0, #T_ef15b_row1_col1, #T_ef15b_row2_col2, #T_ef15b_row3_col3, #T_ef15b_row4_col4, #T_ef15b_row5_col5, #T_ef15b_row6_col6, #T_ef15b_row7_col7, #T_ef15b_row8_col8 {\n",
+       "#T_fe743_row0_col0, #T_fe743_row1_col1, #T_fe743_row2_col2, #T_fe743_row3_col3, #T_fe743_row4_col4, #T_fe743_row5_col5, #T_fe743_row6_col6, #T_fe743_row7_col7, #T_fe743_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row0_col1 {\n",
+       "#T_fe743_row0_col1 {\n",
        "  background-color: #178b17;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row0_col2 {\n",
+       "#T_fe743_row0_col2 {\n",
        "  background-color: #deedde;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row0_col3 {\n",
+       "#T_fe743_row0_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row0_col4, #T_ef15b_row0_col5, #T_ef15b_row1_col4, #T_ef15b_row3_col8, #T_ef15b_row4_col5, #T_ef15b_row8_col7 {\n",
+       "#T_fe743_row0_col4, #T_fe743_row0_col5, #T_fe743_row1_col4, #T_fe743_row3_col8, #T_fe743_row4_col5, #T_fe743_row8_col7 {\n",
        "  background-color: #8fc68f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row0_col6 {\n",
+       "#T_fe743_row0_col6 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row0_col7 {\n",
+       "#T_fe743_row0_col7 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row0_col8, #T_ef15b_row8_col1 {\n",
+       "#T_fe743_row0_col8, #T_fe743_row8_col1 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row1_col0 {\n",
+       "#T_fe743_row1_col0 {\n",
        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row1_col2 {\n",
+       "#T_fe743_row1_col2 {\n",
        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row1_col3 {\n",
+       "#T_fe743_row1_col3 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row1_col5, #T_ef15b_row1_col8, #T_ef15b_row7_col4, #T_ef15b_row8_col4 {\n",
+       "#T_fe743_row1_col5, #T_fe743_row1_col8, #T_fe743_row7_col4, #T_fe743_row8_col4 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row1_col6 {\n",
+       "#T_fe743_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row1_col7 {\n",
+       "#T_fe743_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row2_col0, #T_ef15b_row2_col1, #T_ef15b_row2_col3, #T_ef15b_row2_col4, #T_ef15b_row2_col5, #T_ef15b_row2_col6, #T_ef15b_row2_col7, #T_ef15b_row2_col8, #T_ef15b_row7_col2 {\n",
+       "#T_fe743_row2_col0, #T_fe743_row2_col1, #T_fe743_row2_col3, #T_fe743_row2_col4, #T_fe743_row2_col5, #T_fe743_row2_col6, #T_fe743_row2_col7, #T_fe743_row2_col8, #T_fe743_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row3_col0, #T_ef15b_row7_col3 {\n",
+       "#T_fe743_row3_col0, #T_fe743_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row3_col1, #T_ef15b_row6_col7 {\n",
+       "#T_fe743_row3_col1, #T_fe743_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row3_col2, #T_ef15b_row5_col2 {\n",
+       "#T_fe743_row3_col2, #T_fe743_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row3_col4 {\n",
+       "#T_fe743_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row3_col5, #T_ef15b_row4_col0 {\n",
+       "#T_fe743_row3_col5, #T_fe743_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row3_col6, #T_ef15b_row4_col3, #T_ef15b_row5_col7 {\n",
+       "#T_fe743_row3_col6, #T_fe743_row4_col3, #T_fe743_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row3_col7 {\n",
+       "#T_fe743_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row4_col1 {\n",
+       "#T_fe743_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row4_col2 {\n",
+       "#T_fe743_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row4_col6, #T_ef15b_row4_col7, #T_ef15b_row7_col5 {\n",
+       "#T_fe743_row4_col6, #T_fe743_row4_col7, #T_fe743_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row4_col8 {\n",
+       "#T_fe743_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col0, #T_ef15b_row8_col6 {\n",
+       "#T_fe743_row5_col0, #T_fe743_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col1 {\n",
+       "#T_fe743_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col3, #T_ef15b_row8_col3 {\n",
+       "#T_fe743_row5_col3, #T_fe743_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col4 {\n",
+       "#T_fe743_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col6, #T_ef15b_row6_col4 {\n",
+       "#T_fe743_row5_col6, #T_fe743_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row5_col8 {\n",
+       "#T_fe743_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row6_col0 {\n",
+       "#T_fe743_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row6_col1 {\n",
+       "#T_fe743_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row6_col2 {\n",
+       "#T_fe743_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row6_col3 {\n",
+       "#T_fe743_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row6_col5 {\n",
+       "#T_fe743_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row6_col8 {\n",
+       "#T_fe743_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row7_col0 {\n",
+       "#T_fe743_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ef15b_row7_col1 {\n",
+       "#T_fe743_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row7_col6 {\n",
+       "#T_fe743_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row7_col8 {\n",
+       "#T_fe743_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row8_col0 {\n",
+       "#T_fe743_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row8_col2 {\n",
+       "#T_fe743_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ef15b_row8_col5 {\n",
+       "#T_fe743_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_ef15b_\">\n",
+       "<table id=\"T_fe743_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2680,118 +2680,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_ef15b_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_ef15b_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_ef15b_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_ef15b_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_ef15b_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_ef15b_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_ef15b_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_ef15b_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_ef15b_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_ef15b_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_ef15b_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_ef15b_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_ef15b_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_ef15b_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_ef15b_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_ef15b_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_ef15b_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_ef15b_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_ef15b_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_ef15b_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_ef15b_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_ef15b_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_ef15b_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_ef15b_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_ef15b_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_ef15b_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_ef15b_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_ef15b_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_ef15b_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_ef15b_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_ef15b_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_ef15b_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_ef15b_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_ef15b_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_ef15b_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_ef15b_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_ef15b_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_ef15b_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_ef15b_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_ef15b_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_ef15b_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_ef15b_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_ef15b_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_ef15b_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_ef15b_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_ef15b_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_ef15b_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_ef15b_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_ef15b_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_ef15b_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_ef15b_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_ef15b_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_ef15b_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_ef15b_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_ef15b_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_ef15b_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_ef15b_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_ef15b_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_ef15b_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_ef15b_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_ef15b_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_ef15b_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_ef15b_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_ef15b_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_ef15b_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_ef15b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_ef15b_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_ef15b_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_ef15b_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_ef15b_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_ef15b_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_ef15b_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_ef15b_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_ef15b_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_ef15b_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_fe743_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_fe743_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_fe743_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_fe743_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_fe743_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_fe743_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_fe743_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_fe743_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_fe743_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_fe743_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_fe743_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_fe743_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_fe743_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_fe743_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_fe743_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_fe743_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_fe743_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_fe743_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_fe743_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_fe743_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_fe743_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_fe743_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_fe743_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_fe743_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_fe743_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_fe743_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_fe743_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_fe743_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_fe743_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_fe743_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_fe743_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_fe743_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_fe743_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_fe743_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_fe743_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_fe743_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_fe743_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_fe743_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_fe743_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_fe743_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_fe743_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_fe743_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_fe743_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_fe743_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_fe743_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_fe743_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_fe743_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_fe743_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_fe743_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_fe743_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_fe743_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_fe743_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_fe743_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_fe743_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_fe743_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_fe743_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_fe743_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_fe743_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_fe743_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_fe743_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_fe743_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_fe743_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_fe743_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_fe743_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_fe743_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_fe743_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_fe743_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_fe743_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_fe743_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_fe743_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_fe743_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_fe743_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_fe743_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_fe743_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_fe743_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_fe743_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fdd39b0c3a0>"
+       "<pandas.io.formats.style.Styler at 0x7fc6daad1c10>"
       ]
      },
      "execution_count": 22,
@@ -2894,188 +2894,188 @@
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-       "#T_93a1c_row1_col6 {\n",
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-       "#T_93a1c_row1_col7 {\n",
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-       "#T_93a1c_row3_col0, #T_93a1c_row7_col3 {\n",
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-       "#T_93a1c_row3_col1, #T_93a1c_row6_col7 {\n",
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-       "#T_93a1c_row3_col2, #T_93a1c_row5_col2 {\n",
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        "  background-color: #0b850b;\n",
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-       "#T_93a1c_row3_col7 {\n",
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        "  background-color: #7cbd7c;\n",
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-       "#T_93a1c_row3_col8 {\n",
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-       "#T_93a1c_row4_col2 {\n",
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-       "#T_93a1c_row4_col6, #T_93a1c_row4_col7, #T_93a1c_row7_col5 {\n",
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-       "#T_93a1c_row4_col8, #T_93a1c_row5_col1 {\n",
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-       "#T_93a1c_row5_col3, #T_93a1c_row8_col3 {\n",
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        "  background-color: #62b062;\n",
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-       "#T_93a1c_row5_col8 {\n",
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        "  background-color: #78bb78;\n",
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-       "#T_93a1c_row6_col0 {\n",
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        "  background-color: #75b975;\n",
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-       "#T_93a1c_row6_col2 {\n",
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        "  background-color: #168b16;\n",
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-       "#T_93a1c_row6_col3 {\n",
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-       "#T_93a1c_row6_col5 {\n",
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        "}\n",
-       "#T_93a1c_row6_col8 {\n",
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        "  background-color: #60af60;\n",
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        "}\n",
-       "#T_93a1c_row7_col0 {\n",
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        "  background-color: #74b974;\n",
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        "}\n",
-       "#T_93a1c_row7_col1 {\n",
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        "  background-color: #72b872;\n",
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        "}\n",
-       "#T_93a1c_row7_col6 {\n",
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        "  background-color: #66b266;\n",
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-       "#T_93a1c_row7_col8 {\n",
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        "  background-color: #52a852;\n",
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-       "#T_93a1c_row8_col0 {\n",
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        "}\n",
-       "#T_93a1c_row8_col2 {\n",
+       "#T_4a3ee_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93a1c_row8_col5 {\n",
+       "#T_4a3ee_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_93a1c_\">\n",
+       "<table id=\"T_4a3ee_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3092,118 +3092,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_93a1c_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_93a1c_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_93a1c_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_93a1c_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_93a1c_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_93a1c_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_93a1c_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_93a1c_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_93a1c_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_93a1c_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_93a1c_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_93a1c_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_93a1c_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_93a1c_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_93a1c_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_93a1c_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_93a1c_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_93a1c_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_93a1c_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_93a1c_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_93a1c_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_93a1c_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_93a1c_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_93a1c_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_93a1c_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_93a1c_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_93a1c_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_93a1c_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_93a1c_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_93a1c_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_93a1c_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_93a1c_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_93a1c_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_93a1c_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_93a1c_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_93a1c_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_93a1c_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_93a1c_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_93a1c_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_93a1c_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_93a1c_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_93a1c_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_93a1c_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_93a1c_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_93a1c_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_93a1c_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_93a1c_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_93a1c_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_93a1c_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_93a1c_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_93a1c_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_93a1c_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_93a1c_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_93a1c_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_93a1c_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_93a1c_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_93a1c_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_93a1c_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_93a1c_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_93a1c_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_93a1c_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_93a1c_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_93a1c_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_93a1c_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_93a1c_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93a1c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_93a1c_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_93a1c_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_93a1c_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_93a1c_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_93a1c_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_93a1c_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_93a1c_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_93a1c_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_93a1c_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_4a3ee_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_4a3ee_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_4a3ee_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_4a3ee_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_4a3ee_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_4a3ee_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_4a3ee_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_4a3ee_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_4a3ee_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_4a3ee_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_4a3ee_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_4a3ee_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_4a3ee_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_4a3ee_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_4a3ee_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_4a3ee_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_4a3ee_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_4a3ee_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_4a3ee_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_4a3ee_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_4a3ee_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_4a3ee_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_4a3ee_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_4a3ee_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_4a3ee_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_4a3ee_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_4a3ee_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_4a3ee_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_4a3ee_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_4a3ee_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_4a3ee_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_4a3ee_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_4a3ee_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_4a3ee_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_4a3ee_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_4a3ee_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_4a3ee_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_4a3ee_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_4a3ee_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_4a3ee_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_4a3ee_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_4a3ee_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_4a3ee_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_4a3ee_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_4a3ee_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_4a3ee_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_4a3ee_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_4a3ee_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_4a3ee_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_4a3ee_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_4a3ee_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_4a3ee_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_4a3ee_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_4a3ee_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_4a3ee_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_4a3ee_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_4a3ee_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_4a3ee_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_4a3ee_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_4a3ee_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_4a3ee_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_4a3ee_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_4a3ee_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_4a3ee_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_4a3ee_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_4a3ee_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4a3ee_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_4a3ee_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_4a3ee_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_4a3ee_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_4a3ee_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_4a3ee_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_4a3ee_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_4a3ee_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_4a3ee_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_4a3ee_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fdd39a8f1c0>"
+       "<pandas.io.formats.style.Styler at 0x7fc6daa47250>"
       ]
      },
      "execution_count": 27,
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 9f877136..2e52b014 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -828,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1447,7 +1447,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1475,188 +1475,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_83d37_row0_col0, #T_83d37_row1_col1, #T_83d37_row2_col2, #T_83d37_row3_col3, #T_83d37_row4_col4, #T_83d37_row5_col5, #T_83d37_row6_col6, #T_83d37_row7_col7, #T_83d37_row8_col8 {\n",
+       "#T_c8953_row0_col0, #T_c8953_row1_col1, #T_c8953_row2_col2, #T_c8953_row3_col3, #T_c8953_row4_col4, #T_c8953_row5_col5, #T_c8953_row6_col6, #T_c8953_row7_col7, #T_c8953_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row0_col1, #T_83d37_row5_col4 {\n",
+       "#T_c8953_row0_col1, #T_c8953_row5_col4 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row0_col2, #T_83d37_row4_col1, #T_83d37_row6_col0 {\n",
+       "#T_c8953_row0_col2, #T_c8953_row4_col1, #T_c8953_row6_col0 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row0_col3, #T_83d37_row2_col0, #T_83d37_row4_col6 {\n",
+       "#T_c8953_row0_col3, #T_c8953_row2_col0, #T_c8953_row4_col6 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row0_col4, #T_83d37_row0_col6 {\n",
+       "#T_c8953_row0_col4, #T_c8953_row0_col6 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row0_col5 {\n",
+       "#T_c8953_row0_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row0_col7 {\n",
+       "#T_c8953_row0_col7 {\n",
        "  background-color: #148a14;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row0_col8 {\n",
+       "#T_c8953_row0_col8 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row1_col0, #T_83d37_row3_col2, #T_83d37_row5_col2 {\n",
+       "#T_c8953_row1_col0, #T_c8953_row3_col2, #T_c8953_row5_col2 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row1_col2 {\n",
+       "#T_c8953_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row1_col3 {\n",
+       "#T_c8953_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row1_col4 {\n",
+       "#T_c8953_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row1_col5 {\n",
+       "#T_c8953_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row1_col6 {\n",
+       "#T_c8953_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row1_col7 {\n",
+       "#T_c8953_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row1_col8 {\n",
+       "#T_c8953_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row2_col1, #T_83d37_row4_col5, #T_83d37_row6_col5, #T_83d37_row8_col4 {\n",
+       "#T_c8953_row2_col1, #T_c8953_row4_col5, #T_c8953_row6_col5, #T_c8953_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row2_col3 {\n",
+       "#T_c8953_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row2_col4 {\n",
+       "#T_c8953_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row2_col5 {\n",
+       "#T_c8953_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row2_col6 {\n",
+       "#T_c8953_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row2_col7 {\n",
+       "#T_c8953_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row2_col8, #T_83d37_row4_col3, #T_83d37_row8_col0, #T_83d37_row8_col5 {\n",
+       "#T_c8953_row2_col8, #T_c8953_row4_col3, #T_c8953_row8_col0, #T_c8953_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col0 {\n",
+       "#T_c8953_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col1, #T_83d37_row3_col6, #T_83d37_row5_col0, #T_83d37_row6_col8 {\n",
+       "#T_c8953_row3_col1, #T_c8953_row3_col6, #T_c8953_row5_col0, #T_c8953_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col4, #T_83d37_row5_col3 {\n",
+       "#T_c8953_row3_col4, #T_c8953_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col5 {\n",
+       "#T_c8953_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col7, #T_83d37_row7_col0, #T_83d37_row7_col1, #T_83d37_row7_col2, #T_83d37_row7_col3, #T_83d37_row7_col4, #T_83d37_row7_col5, #T_83d37_row7_col6, #T_83d37_row7_col8 {\n",
+       "#T_c8953_row3_col7, #T_c8953_row7_col0, #T_c8953_row7_col1, #T_c8953_row7_col2, #T_c8953_row7_col3, #T_c8953_row7_col4, #T_c8953_row7_col5, #T_c8953_row7_col6, #T_c8953_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row3_col8 {\n",
+       "#T_c8953_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row4_col0 {\n",
+       "#T_c8953_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row4_col2 {\n",
+       "#T_c8953_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row4_col7 {\n",
+       "#T_c8953_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row4_col8 {\n",
+       "#T_c8953_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row5_col1 {\n",
+       "#T_c8953_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row5_col6, #T_83d37_row6_col2 {\n",
+       "#T_c8953_row5_col6, #T_c8953_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_83d37_row5_col7 {\n",
+       "#T_c8953_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row5_col8 {\n",
+       "#T_c8953_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row6_col1, #T_83d37_row6_col4 {\n",
+       "#T_c8953_row6_col1, #T_c8953_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row6_col3 {\n",
+       "#T_c8953_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row6_col7 {\n",
+       "#T_c8953_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row8_col1 {\n",
+       "#T_c8953_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row8_col2 {\n",
+       "#T_c8953_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row8_col3 {\n",
+       "#T_c8953_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row8_col6 {\n",
+       "#T_c8953_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_83d37_row8_col7 {\n",
+       "#T_c8953_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_83d37_\">\n",
+       "<table id=\"T_c8953_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1673,118 +1673,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_83d37_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_83d37_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_83d37_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_83d37_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_83d37_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_83d37_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_83d37_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_83d37_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_c8953_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_c8953_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_c8953_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_c8953_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_c8953_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_c8953_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_c8953_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_c8953_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_c8953_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_83d37_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_83d37_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_83d37_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_83d37_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_83d37_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_83d37_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_83d37_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_83d37_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_c8953_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_c8953_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_c8953_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_c8953_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_c8953_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_c8953_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_c8953_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_c8953_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_c8953_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_83d37_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_83d37_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_83d37_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_83d37_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_83d37_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_83d37_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_83d37_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_83d37_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_c8953_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_c8953_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_c8953_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_c8953_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_c8953_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_c8953_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_c8953_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_c8953_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_c8953_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_83d37_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_83d37_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_83d37_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_83d37_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_83d37_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_83d37_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_83d37_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_83d37_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_c8953_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_c8953_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_c8953_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_c8953_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_c8953_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_c8953_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_c8953_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_c8953_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_c8953_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_83d37_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_83d37_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_83d37_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_83d37_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_83d37_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_83d37_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_83d37_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_83d37_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_c8953_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_c8953_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_c8953_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_c8953_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_c8953_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_c8953_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_c8953_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_c8953_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_c8953_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_83d37_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_83d37_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_83d37_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_83d37_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_83d37_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_83d37_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_83d37_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_83d37_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_c8953_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_c8953_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_c8953_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_c8953_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_c8953_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_c8953_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_c8953_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_c8953_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_c8953_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_83d37_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_83d37_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_83d37_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_83d37_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_83d37_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_83d37_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_83d37_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_83d37_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_c8953_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_c8953_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_c8953_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_c8953_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_c8953_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_c8953_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_c8953_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_c8953_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_c8953_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_83d37_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_83d37_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_83d37_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_83d37_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_83d37_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_83d37_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_83d37_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_83d37_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_83d37_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_c8953_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_c8953_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_c8953_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_c8953_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_c8953_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_c8953_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_c8953_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_c8953_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_c8953_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_c8953_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_83d37_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_83d37_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_83d37_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_83d37_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_83d37_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_83d37_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_83d37_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_83d37_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_83d37_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_83d37_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_c8953_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_c8953_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_c8953_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_c8953_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_c8953_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_c8953_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_c8953_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_c8953_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_c8953_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_c8953_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f3ad8e3a520>"
+       "<pandas.io.formats.style.Styler at 0x7fbe2349b2b0>"
       ]
      },
      "execution_count": 21,
@@ -1795,8 +1795,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")\n",
-    "# NBVAL_CHECK_OUTPUT"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 2f55d1cf..6dd72b0d 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -1329,220 +1329,220 @@
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        "  color: #000000;\n",
        "}\n",
-       "#T_4197b_row5_col6 {\n",
+       "#T_7f610_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row5_col7 {\n",
+       "#T_7f610_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row5_col8 {\n",
+       "#T_7f610_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4197b_row6_col0 {\n",
+       "#T_7f610_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row6_col1 {\n",
+       "#T_7f610_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4197b_row6_col2, #T_4197b_row6_col5 {\n",
+       "#T_7f610_row6_col2, #T_7f610_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row6_col3 {\n",
+       "#T_7f610_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row6_col7 {\n",
+       "#T_7f610_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row6_col8 {\n",
+       "#T_7f610_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row7_col1 {\n",
+       "#T_7f610_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row8_col1 {\n",
+       "#T_7f610_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row8_col4 {\n",
+       "#T_7f610_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_4197b_row8_col6 {\n",
+       "#T_7f610_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_4197b_row8_col7 {\n",
+       "#T_7f610_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_4197b_\">\n",
+       "<table id=\"T_7f610_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1559,118 +1559,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_4197b_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_4197b_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_4197b_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_4197b_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_4197b_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_4197b_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_4197b_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_4197b_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_7f610_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_7f610_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_7f610_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_7f610_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_7f610_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_7f610_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_7f610_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_7f610_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_7f610_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_4197b_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_4197b_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_4197b_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_4197b_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_4197b_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_4197b_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_4197b_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_4197b_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_7f610_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_7f610_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_7f610_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_7f610_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_7f610_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_7f610_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_7f610_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_7f610_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_7f610_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_4197b_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_4197b_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_4197b_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_4197b_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_4197b_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_4197b_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_4197b_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_4197b_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_7f610_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_7f610_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_7f610_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_7f610_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_7f610_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_7f610_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_7f610_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_7f610_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_7f610_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_4197b_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_4197b_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_4197b_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_4197b_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_4197b_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_4197b_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_4197b_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_4197b_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_7f610_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_7f610_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_7f610_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_7f610_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_7f610_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_7f610_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_7f610_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_7f610_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_7f610_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_4197b_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_4197b_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_4197b_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_4197b_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_4197b_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_4197b_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_4197b_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_4197b_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_7f610_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_7f610_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_7f610_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_7f610_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_7f610_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_7f610_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_7f610_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_7f610_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_7f610_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_4197b_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_4197b_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_4197b_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_4197b_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_4197b_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_4197b_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_4197b_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_4197b_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_7f610_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_7f610_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_7f610_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_7f610_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_7f610_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_7f610_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_7f610_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_7f610_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_7f610_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_4197b_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_4197b_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_4197b_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_4197b_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_4197b_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_4197b_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_4197b_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_4197b_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_7f610_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_7f610_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_7f610_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_7f610_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_7f610_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_7f610_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_7f610_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_7f610_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_7f610_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_4197b_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_4197b_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_4197b_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_4197b_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_4197b_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_4197b_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_4197b_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_4197b_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_4197b_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_7f610_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_7f610_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_7f610_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_7f610_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_7f610_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_7f610_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_7f610_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_7f610_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_7f610_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_7f610_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_4197b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_4197b_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_4197b_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_4197b_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_4197b_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_4197b_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_4197b_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_4197b_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_4197b_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_4197b_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_7f610_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_7f610_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_7f610_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_7f610_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_7f610_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_7f610_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_7f610_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_7f610_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_7f610_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_7f610_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9e85618df0>"
+       "<pandas.io.formats.style.Styler at 0x7f9db0a0a820>"
       ]
      },
      "execution_count": 23,
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 0b9a4dd8..1dd6f42c 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -627,7 +627,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKi', 'pIC50', 'pKd'}\n",
+      "Activities: {'pKd', 'pKi', 'pIC50'}\n",
       "Units: {'nM'}\n"
      ]
     }
@@ -1541,46 +1541,46 @@
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        "}\n",
-       "#T_d0ee5_row1_col2 {\n",
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        "  background-color: #dcdcec;\n",
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        "}\n",
-       "#T_d0ee5_row2_col2 {\n",
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        "  background-color: #bdbedc;\n",
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-       "#T_d0ee5_row3_col2 {\n",
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        "  background-color: #8c88bf;\n",
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-       "#T_d0ee5_row4_col2 {\n",
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-       "#T_d0ee5_row5_col2 {\n",
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        "}\n",
-       "#T_d0ee5_row6_col2 {\n",
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-       "#T_d0ee5_row7_col2 {\n",
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        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d0ee5_row8_col2 {\n",
+       "#T_ab7c4_row8_col2 {\n",
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        "</style>\n",
-       "<table id=\"T_d0ee5_\">\n",
+       "<table id=\"T_ab7c4_\">\n",
        "  <thead>\n",
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        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1591,64 +1591,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d0ee5_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
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-       "      <td id=\"T_d0ee5_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_ab7c4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_ab7c4_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_ab7c4_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_ab7c4_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d0ee5_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_d0ee5_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_d0ee5_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
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+       "      <td id=\"T_ab7c4_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_ab7c4_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_ab7c4_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
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-       "      <td id=\"T_d0ee5_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
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+       "      <td id=\"T_ab7c4_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_ab7c4_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_d0ee5_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_d0ee5_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_d0ee5_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_ab7c4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
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+       "      <td id=\"T_ab7c4_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_d0ee5_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_d0ee5_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_d0ee5_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_ab7c4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_ab7c4_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_ab7c4_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_d0ee5_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_d0ee5_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_d0ee5_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_ab7c4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_ab7c4_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_ab7c4_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_ab7c4_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_d0ee5_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_d0ee5_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_d0ee5_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_ab7c4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_ab7c4_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_ab7c4_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_ab7c4_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_d0ee5_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_d0ee5_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_d0ee5_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_ab7c4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_ab7c4_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_ab7c4_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_ab7c4_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d0ee5_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d0ee5_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_d0ee5_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_d0ee5_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_ab7c4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_ab7c4_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_ab7c4_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_ab7c4_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd70fcb2e80>"
+       "<pandas.io.formats.style.Styler at 0x7fef067b5880>"
       ]
      },
      "execution_count": 24,
@@ -2250,208 +2250,208 @@
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-       "#T_fb555_row5_col7 {\n",
+       "#T_644e0_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col0 {\n",
+       "#T_644e0_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col1 {\n",
+       "#T_644e0_row6_col1 {\n",
        "  background-color: #bcdcbc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col3 {\n",
+       "#T_644e0_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col4 {\n",
+       "#T_644e0_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row6_col5, #T_fb555_row7_col8, #T_fb555_row8_col5, #T_fb555_row8_col7 {\n",
+       "#T_644e0_row6_col5, #T_644e0_row7_col8, #T_644e0_row8_col5, #T_644e0_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col7 {\n",
+       "#T_644e0_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row6_col8 {\n",
+       "#T_644e0_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row7_col3 {\n",
+       "#T_644e0_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row7_col4 {\n",
+       "#T_644e0_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row7_col5 {\n",
+       "#T_644e0_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row7_col6 {\n",
+       "#T_644e0_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row8_col0 {\n",
+       "#T_644e0_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row8_col1 {\n",
+       "#T_644e0_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fb555_row8_col3 {\n",
+       "#T_644e0_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row8_col4 {\n",
+       "#T_644e0_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fb555_row8_col6 {\n",
+       "#T_644e0_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_fb555_\">\n",
+       "<table id=\"T_644e0_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2468,118 +2468,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_fb555_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_fb555_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_fb555_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_fb555_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_fb555_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_fb555_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_fb555_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_fb555_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_fb555_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_fb555_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_fb555_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_fb555_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_fb555_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_fb555_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_fb555_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_fb555_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_fb555_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_fb555_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_fb555_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_fb555_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_fb555_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_fb555_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_fb555_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_fb555_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_fb555_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_fb555_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_fb555_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_fb555_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_fb555_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_fb555_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_fb555_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_fb555_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_fb555_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_fb555_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_fb555_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_fb555_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_fb555_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_fb555_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_fb555_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_fb555_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_fb555_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_fb555_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_fb555_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_fb555_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_fb555_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_fb555_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_fb555_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_fb555_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_fb555_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_fb555_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_fb555_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_fb555_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_fb555_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_fb555_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_fb555_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_fb555_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_fb555_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_fb555_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_fb555_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_fb555_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_fb555_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_fb555_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_fb555_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_fb555_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_fb555_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_fb555_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_fb555_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_fb555_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_fb555_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_fb555_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_fb555_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_fb555_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fb555_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_fb555_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_fb555_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_fb555_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_fb555_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_fb555_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_fb555_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_fb555_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_fb555_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_fb555_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_644e0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_644e0_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_644e0_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_644e0_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_644e0_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_644e0_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_644e0_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_644e0_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_644e0_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_644e0_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_644e0_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_644e0_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_644e0_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_644e0_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_644e0_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_644e0_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_644e0_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_644e0_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_644e0_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_644e0_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_644e0_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_644e0_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_644e0_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_644e0_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_644e0_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_644e0_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_644e0_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_644e0_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_644e0_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_644e0_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_644e0_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_644e0_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_644e0_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_644e0_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_644e0_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_644e0_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_644e0_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_644e0_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_644e0_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_644e0_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_644e0_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_644e0_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_644e0_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_644e0_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_644e0_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_644e0_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_644e0_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_644e0_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_644e0_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_644e0_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_644e0_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_644e0_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_644e0_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_644e0_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_644e0_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_644e0_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_644e0_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_644e0_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_644e0_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_644e0_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_644e0_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_644e0_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_644e0_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_644e0_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_644e0_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_644e0_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_644e0_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_644e0_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_644e0_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_644e0_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_644e0_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_644e0_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_644e0_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_644e0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_644e0_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_644e0_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_644e0_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_644e0_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_644e0_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_644e0_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_644e0_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_644e0_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_644e0_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd71054fa30>"
+       "<pandas.io.formats.style.Styler at 0x7fef06a87b20>"
       ]
      },
      "execution_count": 30,
@@ -2613,212 +2613,212 @@
      "data": {
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        "  background-color: #008000;\n",
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-       "#T_55a79_row0_col1 {\n",
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-       "#T_55a79_row0_col2 {\n",
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-       "#T_55a79_row0_col3, #T_55a79_row7_col0 {\n",
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-       "#T_55a79_row0_col5 {\n",
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-       "#T_55a79_row0_col7, #T_55a79_row3_col2 {\n",
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-       "#T_55a79_row0_col8 {\n",
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-       "#T_55a79_row1_col0 {\n",
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-       "#T_55a79_row1_col2, #T_55a79_row2_col0, #T_55a79_row2_col1, #T_55a79_row2_col3, #T_55a79_row2_col6, #T_55a79_row2_col7, #T_55a79_row2_col8, #T_55a79_row4_col5, #T_55a79_row5_col4, #T_55a79_row6_col2, #T_55a79_row7_col2, #T_55a79_row8_col2 {\n",
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-       "#T_55a79_row1_col3 {\n",
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        "  background-color: #92c892;\n",
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-       "#T_55a79_row1_col4 {\n",
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        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
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-       "#T_55a79_row1_col5 {\n",
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        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
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-       "#T_55a79_row1_col6 {\n",
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        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row1_col7 {\n",
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        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row1_col8 {\n",
+       "#T_be6ff_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row2_col4 {\n",
+       "#T_be6ff_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row2_col5 {\n",
+       "#T_be6ff_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row3_col1 {\n",
+       "#T_be6ff_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row3_col3 {\n",
+       "#T_be6ff_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row3_col4, #T_55a79_row4_col8 {\n",
+       "#T_be6ff_row3_col4, #T_be6ff_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row3_col6 {\n",
+       "#T_be6ff_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row3_col7 {\n",
+       "#T_be6ff_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row3_col8 {\n",
+       "#T_be6ff_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row4_col0 {\n",
+       "#T_be6ff_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row4_col6 {\n",
+       "#T_be6ff_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row4_col7, #T_55a79_row5_col8 {\n",
+       "#T_be6ff_row4_col7, #T_be6ff_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row5_col0 {\n",
+       "#T_be6ff_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row5_col1 {\n",
+       "#T_be6ff_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row5_col3 {\n",
+       "#T_be6ff_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row5_col5 {\n",
+       "#T_be6ff_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row5_col6 {\n",
+       "#T_be6ff_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row5_col7 {\n",
+       "#T_be6ff_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col0 {\n",
+       "#T_be6ff_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col1 {\n",
+       "#T_be6ff_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col3 {\n",
+       "#T_be6ff_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col4 {\n",
+       "#T_be6ff_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row6_col5, #T_55a79_row7_col8, #T_55a79_row8_col5, #T_55a79_row8_col7 {\n",
+       "#T_be6ff_row6_col5, #T_be6ff_row7_col8, #T_be6ff_row8_col5, #T_be6ff_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col7 {\n",
+       "#T_be6ff_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row6_col8 {\n",
+       "#T_be6ff_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row7_col1 {\n",
+       "#T_be6ff_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row7_col3 {\n",
+       "#T_be6ff_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row7_col4 {\n",
+       "#T_be6ff_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row7_col5 {\n",
+       "#T_be6ff_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row7_col6 {\n",
+       "#T_be6ff_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row8_col0 {\n",
+       "#T_be6ff_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row8_col1 {\n",
+       "#T_be6ff_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_55a79_row8_col3 {\n",
+       "#T_be6ff_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row8_col4 {\n",
+       "#T_be6ff_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_55a79_row8_col6 {\n",
+       "#T_be6ff_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_55a79_\">\n",
+       "<table id=\"T_be6ff_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2835,118 +2835,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_55a79_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_55a79_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_55a79_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_55a79_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_55a79_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_55a79_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_55a79_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_55a79_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_55a79_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_55a79_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_55a79_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_55a79_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_55a79_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_55a79_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_55a79_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_55a79_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_55a79_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_55a79_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_55a79_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_55a79_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_55a79_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_55a79_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_55a79_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_55a79_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_55a79_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_55a79_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_55a79_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_55a79_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_55a79_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_55a79_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_55a79_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_55a79_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_55a79_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_55a79_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_55a79_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_55a79_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_55a79_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_55a79_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_55a79_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_55a79_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_55a79_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_55a79_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_55a79_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_55a79_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_55a79_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_55a79_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_55a79_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_55a79_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_55a79_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_55a79_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_55a79_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_55a79_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_55a79_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_55a79_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_55a79_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_55a79_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_55a79_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_55a79_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_55a79_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_55a79_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_55a79_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_55a79_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_55a79_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_55a79_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_55a79_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_55a79_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_55a79_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_55a79_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_55a79_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_55a79_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_55a79_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_55a79_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_55a79_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_55a79_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_55a79_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_55a79_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_be6ff_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_be6ff_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_be6ff_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_be6ff_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_be6ff_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_be6ff_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_be6ff_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_be6ff_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_be6ff_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_be6ff_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_be6ff_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_be6ff_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_be6ff_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_be6ff_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_be6ff_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_be6ff_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_be6ff_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_be6ff_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_be6ff_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_be6ff_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_be6ff_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_be6ff_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_be6ff_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_be6ff_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_be6ff_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_be6ff_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_be6ff_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_be6ff_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_be6ff_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_be6ff_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_be6ff_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_be6ff_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_be6ff_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_be6ff_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_be6ff_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_be6ff_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_be6ff_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_be6ff_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_be6ff_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_be6ff_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_be6ff_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_be6ff_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_be6ff_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_be6ff_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_be6ff_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_be6ff_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_be6ff_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_be6ff_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_be6ff_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_be6ff_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_be6ff_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_be6ff_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_be6ff_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_be6ff_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_be6ff_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_be6ff_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_be6ff_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_be6ff_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_be6ff_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_be6ff_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_be6ff_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_be6ff_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_be6ff_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_be6ff_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_be6ff_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_be6ff_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_be6ff_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_be6ff_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_be6ff_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_be6ff_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_be6ff_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_be6ff_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_be6ff_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_be6ff_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_be6ff_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_be6ff_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_be6ff_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd71054fe80>"
+       "<pandas.io.formats.style.Styler at 0x7fef06ae2d90>"
       ]
      },
      "execution_count": 31,
@@ -3046,204 +3046,204 @@
      "data": {
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-       "#T_13b50_row0_col0, #T_13b50_row1_col1, #T_13b50_row2_col2, #T_13b50_row3_col3, #T_13b50_row4_col4, #T_13b50_row5_col5, #T_13b50_row6_col6, #T_13b50_row7_col7, #T_13b50_row8_col8 {\n",
+       "#T_d2e97_row0_col0, #T_d2e97_row1_col1, #T_d2e97_row2_col2, #T_d2e97_row3_col3, #T_d2e97_row4_col4, #T_d2e97_row5_col5, #T_d2e97_row6_col6, #T_d2e97_row7_col7, #T_d2e97_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row0_col1 {\n",
+       "#T_d2e97_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row0_col2 {\n",
+       "#T_d2e97_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col3 {\n",
+       "#T_d2e97_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col4 {\n",
+       "#T_d2e97_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col5 {\n",
+       "#T_d2e97_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col6 {\n",
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        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col7 {\n",
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        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row0_col8 {\n",
+       "#T_d2e97_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
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-       "#T_13b50_row1_col0 {\n",
+       "#T_d2e97_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
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-       "#T_13b50_row1_col2, #T_13b50_row2_col0, #T_13b50_row2_col1, #T_13b50_row2_col3, #T_13b50_row2_col6, #T_13b50_row2_col7, #T_13b50_row2_col8, #T_13b50_row4_col5, #T_13b50_row5_col4, #T_13b50_row6_col2, #T_13b50_row7_col2, #T_13b50_row8_col2 {\n",
+       "#T_d2e97_row1_col2, #T_d2e97_row2_col0, #T_d2e97_row2_col1, #T_d2e97_row2_col3, #T_d2e97_row2_col6, #T_d2e97_row2_col7, #T_d2e97_row2_col8, #T_d2e97_row4_col5, #T_d2e97_row5_col4, #T_d2e97_row6_col2, #T_d2e97_row7_col2, #T_d2e97_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row1_col3 {\n",
+       "#T_d2e97_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
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        "}\n",
-       "#T_13b50_row1_col4, #T_13b50_row3_col2, #T_13b50_row6_col0 {\n",
+       "#T_d2e97_row1_col4, #T_d2e97_row3_col2, #T_d2e97_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row1_col5 {\n",
+       "#T_d2e97_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row1_col6, #T_13b50_row6_col1 {\n",
+       "#T_d2e97_row1_col6, #T_d2e97_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row1_col7, #T_13b50_row6_col5, #T_13b50_row7_col1, #T_13b50_row8_col5 {\n",
+       "#T_d2e97_row1_col7, #T_d2e97_row6_col5, #T_d2e97_row7_col1, #T_d2e97_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row1_col8, #T_13b50_row8_col1 {\n",
+       "#T_d2e97_row1_col8, #T_d2e97_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row2_col4, #T_13b50_row2_col5 {\n",
+       "#T_d2e97_row2_col4, #T_d2e97_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row3_col0 {\n",
+       "#T_d2e97_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row3_col1 {\n",
+       "#T_d2e97_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row3_col4 {\n",
+       "#T_d2e97_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row3_col5 {\n",
+       "#T_d2e97_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row3_col6 {\n",
+       "#T_d2e97_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row3_col7 {\n",
+       "#T_d2e97_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row3_col8 {\n",
+       "#T_d2e97_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row4_col0 {\n",
+       "#T_d2e97_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row4_col1 {\n",
+       "#T_d2e97_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row4_col2, #T_13b50_row5_col2 {\n",
+       "#T_d2e97_row4_col2, #T_d2e97_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row4_col3 {\n",
+       "#T_d2e97_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row4_col6 {\n",
+       "#T_d2e97_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row4_col7 {\n",
+       "#T_d2e97_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row4_col8 {\n",
+       "#T_d2e97_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row5_col0 {\n",
+       "#T_d2e97_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row5_col1 {\n",
+       "#T_d2e97_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row5_col3 {\n",
+       "#T_d2e97_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row5_col6, #T_13b50_row5_col8 {\n",
+       "#T_d2e97_row5_col6, #T_d2e97_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row5_col7 {\n",
+       "#T_d2e97_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row6_col3 {\n",
+       "#T_d2e97_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row6_col4 {\n",
+       "#T_d2e97_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_13b50_row6_col7, #T_13b50_row7_col6 {\n",
+       "#T_d2e97_row6_col7, #T_d2e97_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row6_col8, #T_13b50_row8_col6 {\n",
+       "#T_d2e97_row6_col8, #T_d2e97_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row7_col0 {\n",
+       "#T_d2e97_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row7_col3 {\n",
+       "#T_d2e97_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row7_col4 {\n",
+       "#T_d2e97_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row7_col5 {\n",
+       "#T_d2e97_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row7_col8, #T_13b50_row8_col7 {\n",
+       "#T_d2e97_row7_col8, #T_d2e97_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row8_col0 {\n",
+       "#T_d2e97_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row8_col3 {\n",
+       "#T_d2e97_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_13b50_row8_col4 {\n",
+       "#T_d2e97_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_13b50_\">\n",
+       "<table id=\"T_d2e97_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3260,118 +3260,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_13b50_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_13b50_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_13b50_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_13b50_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_13b50_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_13b50_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_13b50_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_13b50_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_13b50_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_13b50_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_13b50_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_13b50_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_13b50_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_13b50_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_13b50_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_13b50_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_13b50_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_13b50_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_13b50_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_13b50_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_13b50_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_13b50_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_13b50_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_13b50_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_13b50_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_13b50_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_13b50_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_13b50_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_13b50_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_13b50_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_13b50_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_13b50_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_13b50_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_13b50_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_13b50_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_13b50_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_13b50_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_13b50_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_13b50_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_13b50_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_13b50_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_13b50_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_13b50_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_13b50_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_13b50_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_13b50_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_13b50_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_13b50_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_13b50_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_13b50_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_13b50_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_13b50_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_13b50_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_13b50_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_13b50_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_13b50_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_13b50_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_13b50_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_13b50_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_13b50_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_13b50_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_13b50_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_13b50_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_13b50_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_13b50_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_13b50_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_13b50_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_13b50_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_13b50_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_d2e97_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_d2e97_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_d2e97_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_d2e97_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_d2e97_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_d2e97_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_d2e97_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_d2e97_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_d2e97_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_d2e97_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_d2e97_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_d2e97_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_d2e97_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_d2e97_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_d2e97_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_d2e97_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_d2e97_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_d2e97_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_d2e97_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_d2e97_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_d2e97_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_d2e97_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_d2e97_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_d2e97_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_d2e97_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_d2e97_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_d2e97_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_d2e97_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_d2e97_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_d2e97_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_d2e97_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_d2e97_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_d2e97_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_d2e97_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_d2e97_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_d2e97_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_d2e97_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_d2e97_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_d2e97_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_d2e97_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_d2e97_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_d2e97_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_d2e97_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_d2e97_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_d2e97_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_d2e97_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_d2e97_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_d2e97_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_d2e97_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_d2e97_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_d2e97_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_d2e97_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_d2e97_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_d2e97_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_d2e97_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_d2e97_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_d2e97_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_d2e97_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_d2e97_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_d2e97_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d2e97_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d2e97_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_d2e97_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_d2e97_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_d2e97_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_d2e97_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_d2e97_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_d2e97_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_d2e97_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_d2e97_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fd7107cd4f0>"
+       "<pandas.io.formats.style.Styler at 0x7fef06dc5190>"
       ]
      },
      "execution_count": 36,

From b7ffcc713e407c61c66609a0eb4c52b18e523dc2 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 18 Nov 2021 08:39:44 +0100
Subject: [PATCH 146/158] Add sorting when printing for CI nbval output.

---
 .../talktorial.ipynb                          | 1049 ++++++++---------
 1 file changed, 524 insertions(+), 525 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 1dd6f42c..7d52c8ae 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -627,17 +627,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Activities: {'pKd', 'pKi', 'pIC50'}\n",
+      "Activities: ['pIC50', 'pKd', 'pKi']\n",
       "Units: {'nM'}\n"
      ]
     }
    ],
    "source": [
     "print(\n",
-    "    f\"Activities: {set(data['activities.standard_type'])}\\n\"\n",
+    "    f\"Activities: {sorted(set(data['activities.standard_type']))}\\n\"\n",
     "    f\"Units: {set(data['activities.standard_units'])}\"\n",
-    ")\n",
-    "# NBVAL_CHECK_OUTPUT"
+    ")"
    ]
   },
   {
@@ -1541,46 +1540,46 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_ab7c4_row0_col2 {\n",
+       "#T_56c17_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ab7c4_row1_col2 {\n",
+       "#T_56c17_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ab7c4_row2_col2 {\n",
+       "#T_56c17_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ab7c4_row3_col2 {\n",
+       "#T_56c17_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ab7c4_row4_col2 {\n",
+       "#T_56c17_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_ab7c4_row5_col2 {\n",
+       "#T_56c17_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_ab7c4_row6_col2 {\n",
+       "#T_56c17_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ab7c4_row7_col2 {\n",
+       "#T_56c17_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ab7c4_row8_col2 {\n",
+       "#T_56c17_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_ab7c4_\">\n",
+       "<table id=\"T_56c17_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1591,64 +1590,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_ab7c4_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_ab7c4_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_ab7c4_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_56c17_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_56c17_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_56c17_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_56c17_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_ab7c4_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_ab7c4_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_ab7c4_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_56c17_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_56c17_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_56c17_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_56c17_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_ab7c4_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_ab7c4_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_ab7c4_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_56c17_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_56c17_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_56c17_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_56c17_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_ab7c4_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_ab7c4_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_ab7c4_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_56c17_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_56c17_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_56c17_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_56c17_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_ab7c4_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_ab7c4_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_ab7c4_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_56c17_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_56c17_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_56c17_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_56c17_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_ab7c4_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_ab7c4_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_ab7c4_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_56c17_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_56c17_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_56c17_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_56c17_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_ab7c4_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_ab7c4_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_ab7c4_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_56c17_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_56c17_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_56c17_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_56c17_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_ab7c4_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_ab7c4_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_ab7c4_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_56c17_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_56c17_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_56c17_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_56c17_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ab7c4_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_ab7c4_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_ab7c4_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_ab7c4_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_56c17_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_56c17_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_56c17_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_56c17_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef067b5880>"
+       "<pandas.io.formats.style.Styler at 0x7fbf0ca752e0>"
       ]
      },
      "execution_count": 24,
@@ -2250,208 +2249,208 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_644e0_row0_col0, #T_644e0_row1_col1, #T_644e0_row2_col2, #T_644e0_row3_col5, #T_644e0_row4_col3, #T_644e0_row4_col4, #T_644e0_row6_col6, #T_644e0_row7_col7, #T_644e0_row8_col8 {\n",
+       "#T_bea2b_row0_col0, #T_bea2b_row1_col1, #T_bea2b_row2_col2, #T_bea2b_row3_col5, #T_bea2b_row4_col3, #T_bea2b_row4_col4, #T_bea2b_row6_col6, #T_bea2b_row7_col7, #T_bea2b_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row0_col1, #T_644e0_row3_col4, #T_644e0_row4_col8 {\n",
+       "#T_bea2b_row0_col1, #T_bea2b_row3_col4, #T_bea2b_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row0_col2 {\n",
+       "#T_bea2b_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row0_col3, #T_644e0_row7_col0 {\n",
+       "#T_bea2b_row0_col3, #T_bea2b_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row0_col4, #T_644e0_row3_col0, #T_644e0_row4_col2, #T_644e0_row5_col2 {\n",
+       "#T_bea2b_row0_col4, #T_bea2b_row3_col0, #T_bea2b_row4_col2, #T_bea2b_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row0_col5 {\n",
+       "#T_bea2b_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row0_col6 {\n",
+       "#T_bea2b_row0_col6 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row0_col7, #T_644e0_row3_col2 {\n",
+       "#T_bea2b_row0_col7, #T_bea2b_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row0_col8 {\n",
+       "#T_bea2b_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col0 {\n",
+       "#T_bea2b_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row1_col2, #T_644e0_row2_col1 {\n",
+       "#T_bea2b_row1_col2, #T_bea2b_row2_col1 {\n",
        "  background-color: #000000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row1_col3, #T_644e0_row4_col1 {\n",
+       "#T_bea2b_row1_col3, #T_bea2b_row4_col1 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col4 {\n",
+       "#T_bea2b_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col5 {\n",
+       "#T_bea2b_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col6 {\n",
+       "#T_bea2b_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col7 {\n",
+       "#T_bea2b_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row1_col8 {\n",
+       "#T_bea2b_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row2_col0, #T_644e0_row2_col3, #T_644e0_row2_col6, #T_644e0_row2_col7, #T_644e0_row2_col8, #T_644e0_row4_col5, #T_644e0_row5_col4, #T_644e0_row6_col2, #T_644e0_row7_col1, #T_644e0_row7_col2, #T_644e0_row8_col2 {\n",
+       "#T_bea2b_row2_col0, #T_bea2b_row2_col3, #T_bea2b_row2_col6, #T_bea2b_row2_col7, #T_bea2b_row2_col8, #T_bea2b_row4_col5, #T_bea2b_row5_col4, #T_bea2b_row6_col2, #T_bea2b_row7_col1, #T_bea2b_row7_col2, #T_bea2b_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row2_col4 {\n",
+       "#T_bea2b_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row2_col5 {\n",
+       "#T_bea2b_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row3_col1 {\n",
+       "#T_bea2b_row3_col1 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row3_col3 {\n",
+       "#T_bea2b_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row3_col6 {\n",
+       "#T_bea2b_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row3_col7 {\n",
+       "#T_bea2b_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row3_col8 {\n",
+       "#T_bea2b_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row4_col0 {\n",
+       "#T_bea2b_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row4_col6 {\n",
+       "#T_bea2b_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row4_col7, #T_644e0_row5_col8 {\n",
+       "#T_bea2b_row4_col7, #T_bea2b_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row5_col0 {\n",
+       "#T_bea2b_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row5_col1 {\n",
+       "#T_bea2b_row5_col1 {\n",
        "  background-color: #bddcbd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row5_col3 {\n",
+       "#T_bea2b_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row5_col5 {\n",
+       "#T_bea2b_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row5_col6 {\n",
+       "#T_bea2b_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row5_col7 {\n",
+       "#T_bea2b_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col0 {\n",
+       "#T_bea2b_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col1 {\n",
+       "#T_bea2b_row6_col1 {\n",
        "  background-color: #bcdcbc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col3 {\n",
+       "#T_bea2b_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col4 {\n",
+       "#T_bea2b_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row6_col5, #T_644e0_row7_col8, #T_644e0_row8_col5, #T_644e0_row8_col7 {\n",
+       "#T_bea2b_row6_col5, #T_bea2b_row7_col8, #T_bea2b_row8_col5, #T_bea2b_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col7 {\n",
+       "#T_bea2b_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row6_col8 {\n",
+       "#T_bea2b_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row7_col3 {\n",
+       "#T_bea2b_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row7_col4 {\n",
+       "#T_bea2b_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row7_col5 {\n",
+       "#T_bea2b_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row7_col6 {\n",
+       "#T_bea2b_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row8_col0 {\n",
+       "#T_bea2b_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row8_col1 {\n",
+       "#T_bea2b_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_644e0_row8_col3 {\n",
+       "#T_bea2b_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row8_col4 {\n",
+       "#T_bea2b_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_644e0_row8_col6 {\n",
+       "#T_bea2b_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_644e0_\">\n",
+       "<table id=\"T_bea2b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2468,118 +2467,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_644e0_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_644e0_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_644e0_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_644e0_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_644e0_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_644e0_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_644e0_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_644e0_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_644e0_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_644e0_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_644e0_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_644e0_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_644e0_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_644e0_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_644e0_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_644e0_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_644e0_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_644e0_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_644e0_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_644e0_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_644e0_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_644e0_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_644e0_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_644e0_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_644e0_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_644e0_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_644e0_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_644e0_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_644e0_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_644e0_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_644e0_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_644e0_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_644e0_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_644e0_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_644e0_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_644e0_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_644e0_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_644e0_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_644e0_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_644e0_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_644e0_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_644e0_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_644e0_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_644e0_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_644e0_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_644e0_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_644e0_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_644e0_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_644e0_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_644e0_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_644e0_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_644e0_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_644e0_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_644e0_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_644e0_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_644e0_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_644e0_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_644e0_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_644e0_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_644e0_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_644e0_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_644e0_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_644e0_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_644e0_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_644e0_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_644e0_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_644e0_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_644e0_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_644e0_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_644e0_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_644e0_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_644e0_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_644e0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_644e0_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_644e0_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_644e0_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_644e0_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_644e0_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_644e0_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_644e0_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_644e0_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_644e0_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_bea2b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_bea2b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_bea2b_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_bea2b_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_bea2b_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_bea2b_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_bea2b_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_bea2b_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_bea2b_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_bea2b_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_bea2b_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_bea2b_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_bea2b_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_bea2b_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_bea2b_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_bea2b_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_bea2b_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_bea2b_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_bea2b_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_bea2b_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_bea2b_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_bea2b_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_bea2b_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_bea2b_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_bea2b_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_bea2b_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_bea2b_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_bea2b_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_bea2b_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_bea2b_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_bea2b_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_bea2b_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_bea2b_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_bea2b_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_bea2b_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_bea2b_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_bea2b_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_bea2b_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_bea2b_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_bea2b_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_bea2b_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_bea2b_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_bea2b_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_bea2b_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_bea2b_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_bea2b_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_bea2b_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_bea2b_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_bea2b_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_bea2b_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_bea2b_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_bea2b_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_bea2b_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_bea2b_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_bea2b_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_bea2b_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_bea2b_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_bea2b_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_bea2b_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_bea2b_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_bea2b_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_bea2b_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_bea2b_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_bea2b_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_bea2b_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_bea2b_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_bea2b_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_bea2b_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_bea2b_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_bea2b_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_bea2b_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_bea2b_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_bea2b_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bea2b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_bea2b_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_bea2b_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_bea2b_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_bea2b_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_bea2b_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_bea2b_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_bea2b_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_bea2b_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_bea2b_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef06a87b20>"
+       "<pandas.io.formats.style.Styler at 0x7fbf0cb038e0>"
       ]
      },
      "execution_count": 30,
@@ -2613,212 +2612,212 @@
      "data": {
       "text/html": [
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-       "#T_be6ff_row0_col0, #T_be6ff_row1_col1, #T_be6ff_row2_col2, #T_be6ff_row3_col5, #T_be6ff_row4_col3, #T_be6ff_row4_col4, #T_be6ff_row6_col6, #T_be6ff_row7_col7, #T_be6ff_row8_col8 {\n",
+       "#T_5f495_row0_col0, #T_5f495_row1_col1, #T_5f495_row2_col2, #T_5f495_row3_col5, #T_5f495_row4_col3, #T_5f495_row4_col4, #T_5f495_row6_col6, #T_5f495_row7_col7, #T_5f495_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row0_col1 {\n",
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        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row0_col2 {\n",
+       "#T_5f495_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row0_col3, #T_be6ff_row7_col0 {\n",
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        "  background-color: #a6d2a6;\n",
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-       "#T_be6ff_row0_col4, #T_be6ff_row3_col0, #T_be6ff_row4_col2, #T_be6ff_row5_col2 {\n",
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        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
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-       "#T_be6ff_row0_col5 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row0_col6, #T_be6ff_row4_col1 {\n",
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        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row0_col7, #T_be6ff_row3_col2 {\n",
+       "#T_5f495_row0_col7, #T_5f495_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row0_col8 {\n",
+       "#T_5f495_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col0 {\n",
+       "#T_5f495_row1_col0 {\n",
        "  background-color: #128912;\n",
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-       "#T_be6ff_row1_col2, #T_be6ff_row2_col0, #T_be6ff_row2_col1, #T_be6ff_row2_col3, #T_be6ff_row2_col6, #T_be6ff_row2_col7, #T_be6ff_row2_col8, #T_be6ff_row4_col5, #T_be6ff_row5_col4, #T_be6ff_row6_col2, #T_be6ff_row7_col2, #T_be6ff_row8_col2 {\n",
+       "#T_5f495_row1_col2, #T_5f495_row2_col0, #T_5f495_row2_col1, #T_5f495_row2_col3, #T_5f495_row2_col6, #T_5f495_row2_col7, #T_5f495_row2_col8, #T_5f495_row4_col5, #T_5f495_row5_col4, #T_5f495_row6_col2, #T_5f495_row7_col2, #T_5f495_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col3 {\n",
+       "#T_5f495_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col4 {\n",
+       "#T_5f495_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col5 {\n",
+       "#T_5f495_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col6 {\n",
+       "#T_5f495_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col7 {\n",
+       "#T_5f495_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row1_col8 {\n",
+       "#T_5f495_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row2_col4 {\n",
+       "#T_5f495_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row2_col5 {\n",
+       "#T_5f495_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row3_col1 {\n",
+       "#T_5f495_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row3_col3 {\n",
+       "#T_5f495_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row3_col4, #T_be6ff_row4_col8 {\n",
+       "#T_5f495_row3_col4, #T_5f495_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row3_col6 {\n",
+       "#T_5f495_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row3_col7 {\n",
+       "#T_5f495_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row3_col8 {\n",
+       "#T_5f495_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row4_col0 {\n",
+       "#T_5f495_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row4_col6 {\n",
+       "#T_5f495_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row4_col7, #T_be6ff_row5_col8 {\n",
+       "#T_5f495_row4_col7, #T_5f495_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row5_col0 {\n",
+       "#T_5f495_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row5_col1 {\n",
+       "#T_5f495_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row5_col3 {\n",
+       "#T_5f495_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row5_col5 {\n",
+       "#T_5f495_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row5_col6 {\n",
+       "#T_5f495_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row5_col7 {\n",
+       "#T_5f495_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col0 {\n",
+       "#T_5f495_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col1 {\n",
+       "#T_5f495_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col3 {\n",
+       "#T_5f495_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col4 {\n",
+       "#T_5f495_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row6_col5, #T_be6ff_row7_col8, #T_be6ff_row8_col5, #T_be6ff_row8_col7 {\n",
+       "#T_5f495_row6_col5, #T_5f495_row7_col8, #T_5f495_row8_col5, #T_5f495_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col7 {\n",
+       "#T_5f495_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row6_col8 {\n",
+       "#T_5f495_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row7_col1 {\n",
+       "#T_5f495_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row7_col3 {\n",
+       "#T_5f495_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row7_col4 {\n",
+       "#T_5f495_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row7_col5 {\n",
+       "#T_5f495_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row7_col6 {\n",
+       "#T_5f495_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row8_col0 {\n",
+       "#T_5f495_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row8_col1 {\n",
+       "#T_5f495_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_be6ff_row8_col3 {\n",
+       "#T_5f495_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row8_col4 {\n",
+       "#T_5f495_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_be6ff_row8_col6 {\n",
+       "#T_5f495_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_be6ff_\">\n",
+       "<table id=\"T_5f495_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2835,118 +2834,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_be6ff_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_be6ff_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_be6ff_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_be6ff_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_be6ff_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_be6ff_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_be6ff_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_be6ff_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_be6ff_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_be6ff_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_be6ff_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_be6ff_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_be6ff_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_be6ff_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_be6ff_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_be6ff_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_be6ff_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_be6ff_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_be6ff_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_be6ff_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_be6ff_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_be6ff_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_be6ff_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_be6ff_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_be6ff_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_be6ff_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_be6ff_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_be6ff_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_be6ff_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_be6ff_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_be6ff_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_be6ff_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_be6ff_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_be6ff_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_be6ff_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_be6ff_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_be6ff_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_be6ff_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_be6ff_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_be6ff_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_be6ff_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_be6ff_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_be6ff_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_be6ff_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_be6ff_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_be6ff_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_be6ff_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_be6ff_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_be6ff_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_be6ff_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_be6ff_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_be6ff_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_be6ff_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_be6ff_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_be6ff_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_be6ff_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_be6ff_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_be6ff_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_be6ff_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_be6ff_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_be6ff_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_be6ff_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_be6ff_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_be6ff_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_be6ff_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_be6ff_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_be6ff_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_be6ff_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_be6ff_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_be6ff_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_be6ff_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_be6ff_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_be6ff_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_be6ff_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_be6ff_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_be6ff_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_5f495_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_5f495_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_5f495_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_5f495_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_5f495_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_5f495_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_5f495_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_5f495_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_5f495_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_5f495_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_5f495_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_5f495_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_5f495_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_5f495_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_5f495_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_5f495_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_5f495_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_5f495_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_5f495_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_5f495_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_5f495_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_5f495_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_5f495_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_5f495_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_5f495_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_5f495_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_5f495_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_5f495_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_5f495_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_5f495_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_5f495_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_5f495_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_5f495_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_5f495_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_5f495_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_5f495_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_5f495_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_5f495_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_5f495_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_5f495_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_5f495_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_5f495_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_5f495_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_5f495_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_5f495_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_5f495_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_5f495_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_5f495_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_5f495_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_5f495_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_5f495_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_5f495_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_5f495_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_5f495_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_5f495_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_5f495_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_5f495_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_5f495_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_5f495_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_5f495_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_5f495_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_5f495_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_5f495_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_5f495_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_5f495_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_5f495_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_5f495_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5f495_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_5f495_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_5f495_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_5f495_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_5f495_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_5f495_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_5f495_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_5f495_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_5f495_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_5f495_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef06ae2d90>"
+       "<pandas.io.formats.style.Styler at 0x7fbf0cb03c70>"
       ]
      },
      "execution_count": 31,
@@ -3046,204 +3045,204 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_d2e97_row0_col0, #T_d2e97_row1_col1, #T_d2e97_row2_col2, #T_d2e97_row3_col3, #T_d2e97_row4_col4, #T_d2e97_row5_col5, #T_d2e97_row6_col6, #T_d2e97_row7_col7, #T_d2e97_row8_col8 {\n",
+       "#T_b484c_row0_col0, #T_b484c_row1_col1, #T_b484c_row2_col2, #T_b484c_row3_col3, #T_b484c_row4_col4, #T_b484c_row5_col5, #T_b484c_row6_col6, #T_b484c_row7_col7, #T_b484c_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row0_col1 {\n",
+       "#T_b484c_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row0_col2 {\n",
+       "#T_b484c_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col3 {\n",
+       "#T_b484c_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col4 {\n",
+       "#T_b484c_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col5 {\n",
+       "#T_b484c_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col6 {\n",
+       "#T_b484c_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col7 {\n",
+       "#T_b484c_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row0_col8 {\n",
+       "#T_b484c_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col0 {\n",
+       "#T_b484c_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row1_col2, #T_d2e97_row2_col0, #T_d2e97_row2_col1, #T_d2e97_row2_col3, #T_d2e97_row2_col6, #T_d2e97_row2_col7, #T_d2e97_row2_col8, #T_d2e97_row4_col5, #T_d2e97_row5_col4, #T_d2e97_row6_col2, #T_d2e97_row7_col2, #T_d2e97_row8_col2 {\n",
+       "#T_b484c_row1_col2, #T_b484c_row2_col0, #T_b484c_row2_col1, #T_b484c_row2_col3, #T_b484c_row2_col6, #T_b484c_row2_col7, #T_b484c_row2_col8, #T_b484c_row4_col5, #T_b484c_row5_col4, #T_b484c_row6_col2, #T_b484c_row7_col2, #T_b484c_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col3 {\n",
+       "#T_b484c_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col4, #T_d2e97_row3_col2, #T_d2e97_row6_col0 {\n",
+       "#T_b484c_row1_col4, #T_b484c_row3_col2, #T_b484c_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col5 {\n",
+       "#T_b484c_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col6, #T_d2e97_row6_col1 {\n",
+       "#T_b484c_row1_col6, #T_b484c_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col7, #T_d2e97_row6_col5, #T_d2e97_row7_col1, #T_d2e97_row8_col5 {\n",
+       "#T_b484c_row1_col7, #T_b484c_row6_col5, #T_b484c_row7_col1, #T_b484c_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row1_col8, #T_d2e97_row8_col1 {\n",
+       "#T_b484c_row1_col8, #T_b484c_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row2_col4, #T_d2e97_row2_col5 {\n",
+       "#T_b484c_row2_col4, #T_b484c_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row3_col0 {\n",
+       "#T_b484c_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row3_col1 {\n",
+       "#T_b484c_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row3_col4 {\n",
+       "#T_b484c_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row3_col5 {\n",
+       "#T_b484c_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row3_col6 {\n",
+       "#T_b484c_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row3_col7 {\n",
+       "#T_b484c_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row3_col8 {\n",
+       "#T_b484c_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row4_col0 {\n",
+       "#T_b484c_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row4_col1 {\n",
+       "#T_b484c_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row4_col2, #T_d2e97_row5_col2 {\n",
+       "#T_b484c_row4_col2, #T_b484c_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row4_col3 {\n",
+       "#T_b484c_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row4_col6 {\n",
+       "#T_b484c_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row4_col7 {\n",
+       "#T_b484c_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row4_col8 {\n",
+       "#T_b484c_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row5_col0 {\n",
+       "#T_b484c_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row5_col1 {\n",
+       "#T_b484c_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row5_col3 {\n",
+       "#T_b484c_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row5_col6, #T_d2e97_row5_col8 {\n",
+       "#T_b484c_row5_col6, #T_b484c_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row5_col7 {\n",
+       "#T_b484c_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row6_col3 {\n",
+       "#T_b484c_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row6_col4 {\n",
+       "#T_b484c_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d2e97_row6_col7, #T_d2e97_row7_col6 {\n",
+       "#T_b484c_row6_col7, #T_b484c_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row6_col8, #T_d2e97_row8_col6 {\n",
+       "#T_b484c_row6_col8, #T_b484c_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row7_col0 {\n",
+       "#T_b484c_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row7_col3 {\n",
+       "#T_b484c_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row7_col4 {\n",
+       "#T_b484c_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row7_col5 {\n",
+       "#T_b484c_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row7_col8, #T_d2e97_row8_col7 {\n",
+       "#T_b484c_row7_col8, #T_b484c_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row8_col0 {\n",
+       "#T_b484c_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row8_col3 {\n",
+       "#T_b484c_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d2e97_row8_col4 {\n",
+       "#T_b484c_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d2e97_\">\n",
+       "<table id=\"T_b484c_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3260,118 +3259,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_d2e97_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_d2e97_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_d2e97_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_d2e97_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_d2e97_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_d2e97_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_d2e97_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_d2e97_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_d2e97_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_d2e97_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_d2e97_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_d2e97_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_d2e97_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_d2e97_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_d2e97_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_d2e97_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_d2e97_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_d2e97_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_d2e97_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_d2e97_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_d2e97_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_d2e97_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_d2e97_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_d2e97_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_d2e97_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_d2e97_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_d2e97_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_d2e97_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_d2e97_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_d2e97_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_d2e97_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_d2e97_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_d2e97_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_d2e97_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_d2e97_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_d2e97_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_d2e97_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_d2e97_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_d2e97_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_d2e97_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_d2e97_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_d2e97_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_d2e97_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_d2e97_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_d2e97_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_d2e97_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_d2e97_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_d2e97_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_d2e97_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_d2e97_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_d2e97_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_d2e97_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_d2e97_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_d2e97_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_d2e97_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_d2e97_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_d2e97_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_d2e97_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_d2e97_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_d2e97_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d2e97_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_d2e97_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_d2e97_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_d2e97_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_d2e97_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_d2e97_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_d2e97_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_d2e97_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_d2e97_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_b484c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_b484c_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_b484c_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_b484c_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_b484c_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_b484c_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_b484c_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_b484c_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_b484c_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_b484c_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_b484c_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_b484c_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_b484c_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_b484c_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_b484c_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_b484c_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_b484c_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_b484c_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_b484c_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_b484c_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_b484c_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_b484c_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_b484c_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_b484c_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_b484c_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_b484c_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_b484c_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_b484c_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_b484c_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_b484c_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_b484c_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_b484c_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_b484c_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_b484c_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_b484c_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_b484c_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_b484c_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_b484c_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_b484c_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_b484c_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_b484c_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_b484c_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_b484c_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_b484c_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_b484c_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_b484c_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_b484c_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_b484c_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_b484c_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_b484c_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_b484c_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_b484c_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_b484c_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_b484c_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_b484c_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_b484c_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_b484c_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_b484c_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_b484c_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_b484c_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b484c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_b484c_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_b484c_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_b484c_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_b484c_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_b484c_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_b484c_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_b484c_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_b484c_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_b484c_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef06dc5190>"
+       "<pandas.io.formats.style.Styler at 0x7fbf0cc27af0>"
       ]
      },
      "execution_count": 36,

From d4b1b00b66b4f622ac152d7272b79edf502f47df Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 18 Nov 2021 08:40:16 +0100
Subject: [PATCH 147/158] Remove one nbval_output for CI.

---
 .../T024_kinase_similarity_sequence/talktorial.ipynb           | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 0a982a8e..7cfd8819 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -1476,8 +1476,7 @@
     }
    ],
    "source": [
-    "_translate_rescale_substitution_matrix().style.format(\"{:.2f}\")\n",
-    "# NBVAL_CHECK_OUTPUT"
+    "_translate_rescale_substitution_matrix().style.format(\"{:.2f}\")"
    ]
   },
   {

From 0a005c21198e3cfd8cebbbbeb761457bebe42e83 Mon Sep 17 00:00:00 2001
From: dominiquesydow <dominique.sydow@charite.de>
Date: Thu, 18 Nov 2021 13:42:19 +0100
Subject: [PATCH 148/158] Empty commit


From f23ee4e8f8d94aece5af97fce34c8dbb3efcf495 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Thu, 18 Nov 2021 15:36:22 +0100
Subject: [PATCH 149/158] Trigger action.


From a78674dc5f86a8d6526d9f9ab66cfe9f771b675e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 23 Nov 2021 16:18:06 +0100
Subject: [PATCH 150/158] trigger action


From 00082d88288e5fceeecfd930682d4d3929c2a31e Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 29 Nov 2021 12:25:39 +0100
Subject: [PATCH 151/158] Add kinase similarity collection + change caption
 location.

---
 docs/talktorials.rst | 25 ++++++++++++++++++++++---
 1 file changed, 22 insertions(+), 3 deletions(-)

diff --git a/docs/talktorials.rst b/docs/talktorials.rst
index 9e34cb50..82a65449 100644
--- a/docs/talktorials.rst
+++ b/docs/talktorials.rst
@@ -25,9 +25,10 @@ Talktorials as presented in the original `JCIM 2019 article <https://jcheminf.bi
 Ligand-based cheminformatics
 ----------------------------
 
+The basis for computer-aided drug discovery
+
 .. nbgallery::
    :name: ligandbased
-   :caption: The basis for computer-aided drug discovery
 
    talktorials/T001_query_chembl.nblink
    talktorials/T002_compound_adme.nblink
@@ -44,9 +45,10 @@ Ligand-based cheminformatics
 Structural biology
 ------------------
 
+3D models, structural similarity, protein-ligand interaction analysis and other methods
+
 .. nbgallery::
    :name: structuralmodeling
-   :caption: 3D models, structural similarity, protein-ligand interaction analysis and other methods
 
    talktorials/T008_query_pdb.nblink
    talktorials/T009_compound_ensemble_pharmacophores.nblink
@@ -63,9 +65,10 @@ Structural biology
 Online APIs/servers
 -------------------
 
+Online API/servers for structural bioinformatics and cheminformatics
+
 .. nbgallery::
    :name: onlineapi
-   :caption: Online API/servers for structural bioinformatics and cheminformatics
 
    talktorials/T001_query_chembl.nblink
    talktorials/T008_query_pdb.nblink
@@ -73,3 +76,19 @@ Online APIs/servers
    talktorials/T012_query_klifs.nblink
    talktorials/T013_query_pubchem.nblink
 
+
+Kinase similarity
+-------------------
+
+Kinase similarity comparison using various methods
+
+.. nbgallery::
+   :name: kinasesimilarity
+
+   talktorials/T023_what_is_a_kinase.nblink
+   talktorials/T024_kinase_similarity_sequence.nblink
+   talktorials/T025_kinase_similarity_kissim.nblink
+   talktorials/T026_kinase_similarity_ifp.nblink
+   talktorials/T027_kinase_similarity_ligand_profile.nblink
+   talktorials/T028_kinase_similarity_compare_perspectives.nblink
+

From bd0095747bfd2b06d99734e8cb3240f5631668b3 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 29 Nov 2021 14:35:03 +0100
Subject: [PATCH 152/158] Add viewer for thumbnail.

---
 .../T023_what_is_a_kinase/talktorial.ipynb    | 42 +++++++++++++++++--
 1 file changed, 39 insertions(+), 3 deletions(-)

diff --git a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
index 6d243cf8..0c46cf1f 100644
--- a/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
+++ b/teachopencadd/talktorials/T023_what_is_a_kinase/talktorial.ipynb
@@ -662,12 +662,14 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0697136546e0480fb93fdfee71b5cf26",
+       "model_id": "035844e0cdf24bedb2d914734b9800e2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -679,7 +681,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ca1cad46abb4a6c8fb09f94ccacd1aa",
+       "model_id": "342dcb48952b4aa192e9befc6f7ba5a3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -703,6 +705,40 @@
     "viewer.viewer.add_ball_and_stick(selection=\"ATP\")\n",
     "viewer.viewer"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "viewer.viewer.render_image(trim=True, factor=2);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "viewer.viewer._display_image()"
+   ]
   }
  ],
  "metadata": {

From b0afb4f8e5535f59d528f2aca8ca9ac4ea30e97b Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 29 Nov 2021 14:35:28 +0100
Subject: [PATCH 153/158] Add tag for thumbnail.

---
 .../talktorial.ipynb                                        | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
index 1b075b65..62ba0882 100644
--- a/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
+++ b/teachopencadd/talktorials/T028_kinase_similarity_compare_perspectives/talktorial.ipynb
@@ -858,7 +858,11 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {

From c8aa8aa0e255cc1730b37860ddb02724e646beab Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 29 Nov 2021 15:11:33 +0100
Subject: [PATCH 154/158] Add df as img for thumbnail.

---
 .../data/kinase_distance_matrix_df_styler.png |  Bin 0 -> 84604 bytes
 .../talktorial.ipynb                          | 2242 ++++++++---------
 2 files changed, 984 insertions(+), 1258 deletions(-)
 create mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png
new file mode 100644
index 0000000000000000000000000000000000000000..0ace8e58fe308e0b4540eddae1e7624012dc586a
GIT binary patch
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diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index 7cfd8819..f7bdad90 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -301,7 +301,9 @@
     "import numpy as np\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
-    "import biotite.sequence.align as align"
+    "import biotite.sequence.align as align\n",
+    "from PIL import Image\n",
+    "import dataframe_image as dfi"
    ]
   },
   {
@@ -784,7 +786,7 @@
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-       "      <td id=\"T_dc1d7_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_dc1d7_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_dc1d7_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_dc1d7_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_dc1d7_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_dc1d7_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_dc1d7_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_dc1d7_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_dc1d7_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_dc1d7_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_dc1d7_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_dc1d7_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_dc1d7_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_dc1d7_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_dc1d7_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_dc1d7_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_dc1d7_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_dc1d7_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_dc1d7_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_dc1d7_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_dc1d7_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_dc1d7_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_dc1d7_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_dc1d7_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_dc1d7_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_dc1d7_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_dc1d7_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_dc1d7_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_dc1d7_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_dc1d7_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_dc1d7_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_dc1d7_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_dc1d7_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_dc1d7_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_dc1d7_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_dc1d7_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_dc1d7_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_dc1d7_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_dc1d7_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_dc1d7_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_dc1d7_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_dc1d7_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_dc1d7_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_dc1d7_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_dc1d7_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_dc1d7_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_dc1d7_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_dc1d7_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_dc1d7_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_dc1d7_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_dc1d7_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_dc1d7_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_dc1d7_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_dc1d7_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_dc1d7_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_dc1d7_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_dc1d7_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_dc1d7_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_dc1d7_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_dc1d7_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_dc1d7_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_dc1d7_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_dc1d7_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_dc1d7_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_dc1d7_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_dc1d7_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_dc1d7_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_dc1d7_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_dc1d7_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_dc1d7_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_dc1d7_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_dc1d7_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_dc1d7_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_dc1d7_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_dc1d7_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_dc1d7_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_dc1d7_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_dc1d7_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_dc1d7_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_dc1d7_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_dc1d7_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_dc1d7_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_dc1d7_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_dc1d7_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_dc1d7_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_dc1d7_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_dc1d7_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_dc1d7_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_dc1d7_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_dc1d7_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_dc1d7_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_dc1d7_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_dc1d7_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_dc1d7_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_dc1d7_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_dc1d7_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_dc1d7_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_dc1d7_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_dc1d7_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_dc1d7_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_dc1d7_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_dc1d7_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_dc1d7_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_dc1d7_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_dc1d7_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_dc1d7_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_dc1d7_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_dc1d7_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_dc1d7_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_dc1d7_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_dc1d7_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_dc1d7_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_dc1d7_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_4e799_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_4e799_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_4e799_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_4e799_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_4e799_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_4e799_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_4e799_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_4e799_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_4e799_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_4e799_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_4e799_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_4e799_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_4e799_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_4e799_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_4e799_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_4e799_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_4e799_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_4e799_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_4e799_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_4e799_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_4e799_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_4e799_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_4e799_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_4e799_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_4e799_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_4e799_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_4e799_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_4e799_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_4e799_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_4e799_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_4e799_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_4e799_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_4e799_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_4e799_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_4e799_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_4e799_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_4e799_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_4e799_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_4e799_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_4e799_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_4e799_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_4e799_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_4e799_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_4e799_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_4e799_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_4e799_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_4e799_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_4e799_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_4e799_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_4e799_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_4e799_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_4e799_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_4e799_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_4e799_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_4e799_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_4e799_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_4e799_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_4e799_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_4e799_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_4e799_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_4e799_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_4e799_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_4e799_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_4e799_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_4e799_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_4e799_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_4e799_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_4e799_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_4e799_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_4e799_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_4e799_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_4e799_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_4e799_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_4e799_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_4e799_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_4e799_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_4e799_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_4e799_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_4e799_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_4e799_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_4e799_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_4e799_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_4e799_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_4e799_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_4e799_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_4e799_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_4e799_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_4e799_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_4e799_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_4e799_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_4e799_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_4e799_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_4e799_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_4e799_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_4e799_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_4e799_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_4e799_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_4e799_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_4e799_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_4e799_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_4e799_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_4e799_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_4e799_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_4e799_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_4e799_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_4e799_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_4e799_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_4e799_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_4e799_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_4e799_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_4e799_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_4e799_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_4e799_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_4e799_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_4e799_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc6da71f160>"
+       "<pandas.io.formats.style.Styler at 0x7f9cbbd9d9d0>"
       ]
      },
      "execution_count": 10,
@@ -1999,144 +2001,144 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_c5eb7_row0_col0, #T_c5eb7_row1_col1, #T_c5eb7_row2_col2, #T_c5eb7_row3_col3, #T_c5eb7_row4_col4, #T_c5eb7_row5_col5, #T_c5eb7_row6_col6, #T_c5eb7_row7_col7, #T_c5eb7_row8_col8 {\n",
+       "#T_a7550_row0_col0, #T_a7550_row1_col1, #T_a7550_row2_col2, #T_a7550_row3_col3, #T_a7550_row4_col4, #T_a7550_row5_col5, #T_a7550_row6_col6, #T_a7550_row7_col7, #T_a7550_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c5eb7_row0_col1, #T_c5eb7_row1_col0 {\n",
+       "#T_a7550_row0_col1, #T_a7550_row1_col0 {\n",
        "  background-color: #1c8e1c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c5eb7_row0_col2, #T_c5eb7_row1_col2 {\n",
+       "#T_a7550_row0_col2, #T_a7550_row1_col2 {\n",
        "  background-color: #e8f2e8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col3, #T_c5eb7_row7_col3 {\n",
+       "#T_a7550_row0_col3, #T_a7550_row7_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col4, #T_c5eb7_row3_col5, #T_c5eb7_row7_col4, #T_c5eb7_row8_col4 {\n",
+       "#T_a7550_row0_col4, #T_a7550_row3_col5, #T_a7550_row7_col4, #T_a7550_row8_col4 {\n",
        "  background-color: #add5ad;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col5 {\n",
+       "#T_a7550_row0_col5 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col6 {\n",
+       "#T_a7550_row0_col6 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col7 {\n",
+       "#T_a7550_row0_col7 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row0_col8, #T_c5eb7_row3_col8, #T_c5eb7_row4_col6, #T_c5eb7_row6_col8, #T_c5eb7_row8_col6 {\n",
+       "#T_a7550_row0_col8, #T_a7550_row3_col8, #T_a7550_row4_col6, #T_a7550_row6_col8, #T_a7550_row8_col6 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col3, #T_c5eb7_row6_col3 {\n",
+       "#T_a7550_row1_col3, #T_a7550_row6_col3 {\n",
        "  background-color: #9ccd9c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col4, #T_c5eb7_row3_col4, #T_c5eb7_row4_col3, #T_c5eb7_row5_col7, #T_c5eb7_row7_col5, #T_c5eb7_row8_col7 {\n",
+       "#T_a7550_row1_col4, #T_a7550_row3_col4, #T_a7550_row4_col3, #T_a7550_row5_col7, #T_a7550_row7_col5, #T_a7550_row8_col7 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col5, #T_c5eb7_row4_col5 {\n",
+       "#T_a7550_row1_col5, #T_a7550_row4_col5 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col6, #T_c5eb7_row6_col7 {\n",
+       "#T_a7550_row1_col6, #T_a7550_row6_col7 {\n",
        "  background-color: #9dcd9d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col7, #T_c5eb7_row3_col7, #T_c5eb7_row8_col5 {\n",
+       "#T_a7550_row1_col7, #T_a7550_row3_col7, #T_a7550_row8_col5 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row1_col8, #T_c5eb7_row4_col7, #T_c5eb7_row6_col5, #T_c5eb7_row7_col6 {\n",
+       "#T_a7550_row1_col8, #T_a7550_row4_col7, #T_a7550_row6_col5, #T_a7550_row7_col6 {\n",
        "  background-color: #a4d0a4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row2_col0, #T_c5eb7_row2_col1, #T_c5eb7_row2_col3, #T_c5eb7_row2_col4, #T_c5eb7_row2_col5, #T_c5eb7_row2_col6, #T_c5eb7_row2_col7, #T_c5eb7_row2_col8, #T_c5eb7_row5_col2, #T_c5eb7_row7_col2 {\n",
+       "#T_a7550_row2_col0, #T_a7550_row2_col1, #T_a7550_row2_col3, #T_a7550_row2_col4, #T_a7550_row2_col5, #T_a7550_row2_col6, #T_a7550_row2_col7, #T_a7550_row2_col8, #T_a7550_row5_col2, #T_a7550_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row3_col0, #T_c5eb7_row7_col1 {\n",
+       "#T_a7550_row3_col0, #T_a7550_row7_col1 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row3_col1 {\n",
+       "#T_a7550_row3_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row3_col2, #T_c5eb7_row4_col2 {\n",
+       "#T_a7550_row3_col2, #T_a7550_row4_col2 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row3_col6 {\n",
+       "#T_a7550_row3_col6 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row4_col0 {\n",
+       "#T_a7550_row4_col0 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row4_col1, #T_c5eb7_row8_col1 {\n",
+       "#T_a7550_row4_col1, #T_a7550_row8_col1 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row4_col8, #T_c5eb7_row5_col6 {\n",
+       "#T_a7550_row4_col8, #T_a7550_row5_col6 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row5_col0 {\n",
+       "#T_a7550_row5_col0 {\n",
        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row5_col1 {\n",
+       "#T_a7550_row5_col1 {\n",
        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row5_col3 {\n",
+       "#T_a7550_row5_col3 {\n",
        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row5_col4 {\n",
+       "#T_a7550_row5_col4 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row5_col8, #T_c5eb7_row7_col0 {\n",
+       "#T_a7550_row5_col8, #T_a7550_row7_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row6_col0 {\n",
+       "#T_a7550_row6_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row6_col1 {\n",
+       "#T_a7550_row6_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row6_col2, #T_c5eb7_row8_col2 {\n",
+       "#T_a7550_row6_col2, #T_a7550_row8_col2 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row6_col4, #T_c5eb7_row8_col3 {\n",
+       "#T_a7550_row6_col4, #T_a7550_row8_col3 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row7_col8 {\n",
+       "#T_a7550_row7_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c5eb7_row8_col0 {\n",
+       "#T_a7550_row8_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c5eb7_\">\n",
+       "<table id=\"T_a7550_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2153,118 +2155,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_c5eb7_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_c5eb7_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_c5eb7_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_c5eb7_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_c5eb7_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_c5eb7_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_c5eb7_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_c5eb7_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_c5eb7_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_c5eb7_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_c5eb7_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_c5eb7_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_c5eb7_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_c5eb7_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_c5eb7_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_c5eb7_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_c5eb7_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_c5eb7_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_c5eb7_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_c5eb7_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_c5eb7_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_c5eb7_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_c5eb7_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_c5eb7_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_c5eb7_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_c5eb7_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_c5eb7_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_c5eb7_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_c5eb7_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_c5eb7_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_c5eb7_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_c5eb7_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_c5eb7_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_c5eb7_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_c5eb7_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_c5eb7_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_c5eb7_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_c5eb7_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_c5eb7_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_c5eb7_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_c5eb7_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_c5eb7_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_c5eb7_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_c5eb7_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_c5eb7_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_c5eb7_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_c5eb7_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_c5eb7_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c5eb7_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_c5eb7_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_c5eb7_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_c5eb7_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_c5eb7_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_c5eb7_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_c5eb7_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_c5eb7_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_c5eb7_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_a7550_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_a7550_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_a7550_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_a7550_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_a7550_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_a7550_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_a7550_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_a7550_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_a7550_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_a7550_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_a7550_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_a7550_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_a7550_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_a7550_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_a7550_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_a7550_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_a7550_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_a7550_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_a7550_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_a7550_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_a7550_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_a7550_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_a7550_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_a7550_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_a7550_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_a7550_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_a7550_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_a7550_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_a7550_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_a7550_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_a7550_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_a7550_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_a7550_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_a7550_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_a7550_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_a7550_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_a7550_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_a7550_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_a7550_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_a7550_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_a7550_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_a7550_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_a7550_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_a7550_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_a7550_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_a7550_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_a7550_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_a7550_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a7550_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_a7550_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_a7550_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_a7550_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_a7550_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_a7550_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_a7550_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_a7550_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_a7550_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_a7550_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc6daa37550>"
+       "<pandas.io.formats.style.Styler at 0x7f9cbbe6eac0>"
       ]
      },
      "execution_count": 20,
@@ -2481,188 +2483,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_fe743_row0_col0, #T_fe743_row1_col1, #T_fe743_row2_col2, #T_fe743_row3_col3, #T_fe743_row4_col4, #T_fe743_row5_col5, #T_fe743_row6_col6, #T_fe743_row7_col7, #T_fe743_row8_col8 {\n",
+       "#T_93ba2_row0_col0, #T_93ba2_row1_col1, #T_93ba2_row2_col2, #T_93ba2_row3_col3, #T_93ba2_row4_col4, #T_93ba2_row5_col5, #T_93ba2_row6_col6, #T_93ba2_row7_col7, #T_93ba2_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row0_col1 {\n",
+       "#T_93ba2_row0_col1 {\n",
        "  background-color: #178b17;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row0_col2 {\n",
+       "#T_93ba2_row0_col2 {\n",
        "  background-color: #deedde;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row0_col3 {\n",
+       "#T_93ba2_row0_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row0_col4, #T_fe743_row0_col5, #T_fe743_row1_col4, #T_fe743_row3_col8, #T_fe743_row4_col5, #T_fe743_row8_col7 {\n",
+       "#T_93ba2_row0_col4, #T_93ba2_row0_col5, #T_93ba2_row1_col4, #T_93ba2_row3_col8, #T_93ba2_row4_col5, #T_93ba2_row8_col7 {\n",
        "  background-color: #8fc68f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row0_col6 {\n",
+       "#T_93ba2_row0_col6 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row0_col7 {\n",
+       "#T_93ba2_row0_col7 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row0_col8, #T_fe743_row8_col1 {\n",
+       "#T_93ba2_row0_col8, #T_93ba2_row8_col1 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row1_col0 {\n",
+       "#T_93ba2_row1_col0 {\n",
        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row1_col2 {\n",
+       "#T_93ba2_row1_col2 {\n",
        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row1_col3 {\n",
+       "#T_93ba2_row1_col3 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row1_col5, #T_fe743_row1_col8, #T_fe743_row7_col4, #T_fe743_row8_col4 {\n",
+       "#T_93ba2_row1_col5, #T_93ba2_row1_col8, #T_93ba2_row7_col4, #T_93ba2_row8_col4 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row1_col6 {\n",
+       "#T_93ba2_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row1_col7 {\n",
+       "#T_93ba2_row1_col7 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row2_col0, #T_fe743_row2_col1, #T_fe743_row2_col3, #T_fe743_row2_col4, #T_fe743_row2_col5, #T_fe743_row2_col6, #T_fe743_row2_col7, #T_fe743_row2_col8, #T_fe743_row7_col2 {\n",
+       "#T_93ba2_row2_col0, #T_93ba2_row2_col1, #T_93ba2_row2_col3, #T_93ba2_row2_col4, #T_93ba2_row2_col5, #T_93ba2_row2_col6, #T_93ba2_row2_col7, #T_93ba2_row2_col8, #T_93ba2_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row3_col0, #T_fe743_row7_col3 {\n",
+       "#T_93ba2_row3_col0, #T_93ba2_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row3_col1, #T_fe743_row6_col7 {\n",
+       "#T_93ba2_row3_col1, #T_93ba2_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row3_col2, #T_fe743_row5_col2 {\n",
+       "#T_93ba2_row3_col2, #T_93ba2_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row3_col4 {\n",
+       "#T_93ba2_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row3_col5, #T_fe743_row4_col0 {\n",
+       "#T_93ba2_row3_col5, #T_93ba2_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row3_col6, #T_fe743_row4_col3, #T_fe743_row5_col7 {\n",
+       "#T_93ba2_row3_col6, #T_93ba2_row4_col3, #T_93ba2_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row3_col7 {\n",
+       "#T_93ba2_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row4_col1 {\n",
+       "#T_93ba2_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row4_col2 {\n",
+       "#T_93ba2_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row4_col6, #T_fe743_row4_col7, #T_fe743_row7_col5 {\n",
+       "#T_93ba2_row4_col6, #T_93ba2_row4_col7, #T_93ba2_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row4_col8 {\n",
+       "#T_93ba2_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col0, #T_fe743_row8_col6 {\n",
+       "#T_93ba2_row5_col0, #T_93ba2_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col1 {\n",
+       "#T_93ba2_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col3, #T_fe743_row8_col3 {\n",
+       "#T_93ba2_row5_col3, #T_93ba2_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col4 {\n",
+       "#T_93ba2_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col6, #T_fe743_row6_col4 {\n",
+       "#T_93ba2_row5_col6, #T_93ba2_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row5_col8 {\n",
+       "#T_93ba2_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row6_col0 {\n",
+       "#T_93ba2_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row6_col1 {\n",
+       "#T_93ba2_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row6_col2 {\n",
+       "#T_93ba2_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row6_col3 {\n",
+       "#T_93ba2_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row6_col5 {\n",
+       "#T_93ba2_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row6_col8 {\n",
+       "#T_93ba2_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row7_col0 {\n",
+       "#T_93ba2_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_fe743_row7_col1 {\n",
+       "#T_93ba2_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row7_col6 {\n",
+       "#T_93ba2_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row7_col8 {\n",
+       "#T_93ba2_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row8_col0 {\n",
+       "#T_93ba2_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row8_col2 {\n",
+       "#T_93ba2_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_fe743_row8_col5 {\n",
+       "#T_93ba2_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_fe743_\">\n",
+       "<table id=\"T_93ba2_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2679,118 +2681,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_fe743_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_fe743_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_fe743_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_fe743_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_fe743_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_fe743_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_fe743_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_fe743_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_fe743_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_fe743_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_fe743_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_fe743_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_fe743_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_fe743_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_fe743_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_fe743_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_fe743_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_fe743_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_fe743_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_fe743_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_fe743_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_fe743_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_fe743_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_fe743_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_fe743_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_fe743_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_fe743_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_fe743_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_fe743_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_fe743_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_fe743_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_fe743_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_fe743_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_fe743_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_fe743_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_fe743_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_fe743_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_fe743_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_fe743_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_fe743_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_fe743_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_fe743_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_fe743_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_fe743_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_fe743_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_fe743_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_fe743_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_fe743_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_fe743_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_fe743_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_fe743_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_fe743_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_fe743_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_fe743_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_fe743_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_fe743_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_fe743_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_fe743_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_fe743_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_fe743_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_fe743_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_fe743_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_fe743_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_fe743_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_fe743_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe743_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_fe743_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_fe743_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_fe743_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_fe743_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_fe743_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_fe743_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_fe743_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_fe743_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_fe743_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_93ba2_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_93ba2_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_93ba2_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_93ba2_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_93ba2_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_93ba2_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_93ba2_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_93ba2_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_93ba2_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_93ba2_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_93ba2_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_93ba2_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_93ba2_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_93ba2_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_93ba2_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_93ba2_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_93ba2_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_93ba2_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_93ba2_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_93ba2_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_93ba2_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_93ba2_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_93ba2_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_93ba2_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_93ba2_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_93ba2_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_93ba2_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_93ba2_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_93ba2_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_93ba2_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_93ba2_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_93ba2_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_93ba2_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_93ba2_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_93ba2_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_93ba2_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_93ba2_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_93ba2_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_93ba2_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_93ba2_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_93ba2_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_93ba2_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_93ba2_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_93ba2_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_93ba2_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_93ba2_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_93ba2_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_93ba2_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_93ba2_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_93ba2_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_93ba2_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_93ba2_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_93ba2_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_93ba2_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_93ba2_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_93ba2_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_93ba2_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_93ba2_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_93ba2_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_93ba2_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_93ba2_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_93ba2_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_93ba2_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_93ba2_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_93ba2_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_93ba2_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_93ba2_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_93ba2_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_93ba2_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_93ba2_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_93ba2_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_93ba2_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_93ba2_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_93ba2_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_93ba2_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_93ba2_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc6daad1c10>"
+       "<pandas.io.formats.style.Styler at 0x7f9cbbdfb4f0>"
       ]
      },
      "execution_count": 22,
@@ -2888,330 +2890,54 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
+    "    cmap=cm\n",
+    ").format(\"{:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We export the matrix with colors as an image to use as thumbnail for the website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[1129/150221.846804:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/150221.847725:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
+      "[1129/150221.848869:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/150222.386688:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmp9qqnr1ji/temp.png.\n"
+     ]
+    },
     {
      "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
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-       "  background-color: #74b974;\n",
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-       "#T_4a3ee_row7_col1 {\n",
-       "  background-color: #72b872;\n",
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-       "#T_4a3ee_row7_col6 {\n",
-       "  background-color: #66b266;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_4a3ee_row7_col8 {\n",
-       "  background-color: #52a852;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_4a3ee_row8_col0 {\n",
-       "  background-color: #59ac59;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_4a3ee_row8_col2 {\n",
-       "  background-color: #0e870e;\n",
-       "  color: #f1f1f1;\n",
-       "}\n",
-       "#T_4a3ee_row8_col5 {\n",
-       "  background-color: #79bc79;\n",
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-       "</style>\n",
-       "<table id=\"T_4a3ee_\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
-       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
-       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
-       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
-       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
-       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
-       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
-       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
-       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_4a3ee_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_4a3ee_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_4a3ee_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_4a3ee_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_4a3ee_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_4a3ee_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_4a3ee_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_4a3ee_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_4a3ee_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_4a3ee_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_4a3ee_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_4a3ee_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_4a3ee_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_4a3ee_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_4a3ee_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_4a3ee_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_4a3ee_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_4a3ee_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_4a3ee_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_4a3ee_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_4a3ee_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_4a3ee_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_4a3ee_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_4a3ee_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_4a3ee_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_4a3ee_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_4a3ee_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_4a3ee_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_4a3ee_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_4a3ee_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_4a3ee_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_4a3ee_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_4a3ee_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_4a3ee_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_4a3ee_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_4a3ee_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_4a3ee_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_4a3ee_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_4a3ee_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_4a3ee_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_4a3ee_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_4a3ee_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_4a3ee_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_4a3ee_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_4a3ee_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_4a3ee_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_4a3ee_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_4a3ee_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_4a3ee_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_4a3ee_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_4a3ee_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_4a3ee_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_4a3ee_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_4a3ee_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_4a3ee_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_4a3ee_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_4a3ee_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_4a3ee_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_4a3ee_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_4a3ee_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_4a3ee_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_4a3ee_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_4a3ee_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_4a3ee_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_4a3ee_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4a3ee_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_4a3ee_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_4a3ee_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_4a3ee_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_4a3ee_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_4a3ee_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_4a3ee_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_4a3ee_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_4a3ee_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_4a3ee_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
+      "image/png": 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\n",
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fc6daa47250>"
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=518x303 at 0x7F9CBC2189A0>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "Image.open(f\"{DATA}/kinase_distance_matrix_df_styler.png\")"
    ]
   },
   {
@@ -3223,7 +2949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [

From 8c7f1d822135415ef5b3dcd8239c02618dad814d Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Mon, 29 Nov 2021 16:00:00 +0100
Subject: [PATCH 155/158] Open thumbnail matrix img with matplotlib.

---
 .../talktorial.ipynb                          | 2245 ++++++++++-------
 .../data/kinase_distance_matrix_df_styler.png |  Bin 0 -> 91861 bytes
 .../talktorial.ipynb                          |  332 ++-
 .../data/kinase_distance_matrix_df_styler.png |  Bin 0 -> 85081 bytes
 .../talktorial.ipynb                          |  352 +--
 .../data/kinase_distance_matrix_df_styler.png |  Bin 0 -> 84551 bytes
 .../talktorial.ipynb                          | 1108 ++++----
 7 files changed, 2267 insertions(+), 1770 deletions(-)
 create mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_df_styler.png
 create mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_df_styler.png
 create mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_df_styler.png

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index f7bdad90..e9595f3d 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -300,9 +300,9 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
+    "import matplotlib.image as mpimg\n",
     "import matplotlib.pyplot as plt\n",
     "import biotite.sequence.align as align\n",
-    "from PIL import Image\n",
     "import dataframe_image as dfi"
    ]
   },
@@ -786,7 +786,7 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_4e799_\">\n",
+       "<table id=\"T_f07d8_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -818,658 +818,658 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
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-       "      <td id=\"T_4e799_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_4e799_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_4e799_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_4e799_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_4e799_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_4e799_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_4e799_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_4e799_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_4e799_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_4e799_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_4e799_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_4e799_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_4e799_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_4e799_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_4e799_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_4e799_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_4e799_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_4e799_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_4e799_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_4e799_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_4e799_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_4e799_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_4e799_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_4e799_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_4e799_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_4e799_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_4e799_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_4e799_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_4e799_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_4e799_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_4e799_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_4e799_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_4e799_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_4e799_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_4e799_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_4e799_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_4e799_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_4e799_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_4e799_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_4e799_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_4e799_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_4e799_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_4e799_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_4e799_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_4e799_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_4e799_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_4e799_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_4e799_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_4e799_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_4e799_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_4e799_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_4e799_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_4e799_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_4e799_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_4e799_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_4e799_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_4e799_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_4e799_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_4e799_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_4e799_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_4e799_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_4e799_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_4e799_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_4e799_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_4e799_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_4e799_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_4e799_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_4e799_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_4e799_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_4e799_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_4e799_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_4e799_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_4e799_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_4e799_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_4e799_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_4e799_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_4e799_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_4e799_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_4e799_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_4e799_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_4e799_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_4e799_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_4e799_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_4e799_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_4e799_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_4e799_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_4e799_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_4e799_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_4e799_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_4e799_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_4e799_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_4e799_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_4e799_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_4e799_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_4e799_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_4e799_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_4e799_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_4e799_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_4e799_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_4e799_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_4e799_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_4e799_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_4e799_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_4e799_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_4e799_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_4e799_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_4e799_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_4e799_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_4e799_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_4e799_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_4e799_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_4e799_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_4e799_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_4e799_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_f07d8_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_f07d8_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_f07d8_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_f07d8_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_f07d8_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_f07d8_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_f07d8_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_f07d8_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_f07d8_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_f07d8_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_f07d8_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_f07d8_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_f07d8_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_f07d8_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_f07d8_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_f07d8_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_f07d8_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_f07d8_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_f07d8_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_f07d8_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_f07d8_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_f07d8_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_f07d8_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_f07d8_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_f07d8_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_f07d8_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_f07d8_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_f07d8_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_f07d8_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_f07d8_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_f07d8_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_f07d8_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_f07d8_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_f07d8_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_f07d8_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_f07d8_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_f07d8_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_f07d8_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_f07d8_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_f07d8_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_f07d8_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_f07d8_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_f07d8_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_f07d8_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_f07d8_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_f07d8_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_f07d8_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_f07d8_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_f07d8_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_f07d8_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_f07d8_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_f07d8_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_f07d8_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_f07d8_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_f07d8_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_f07d8_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_f07d8_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_f07d8_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_f07d8_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_f07d8_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_f07d8_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_f07d8_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_f07d8_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_f07d8_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_f07d8_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_f07d8_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_f07d8_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_f07d8_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_f07d8_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_f07d8_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_f07d8_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_f07d8_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_f07d8_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_f07d8_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_f07d8_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_f07d8_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_f07d8_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_f07d8_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_f07d8_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_f07d8_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_f07d8_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_f07d8_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_f07d8_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_f07d8_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_f07d8_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_f07d8_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_f07d8_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_f07d8_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_f07d8_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_f07d8_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_f07d8_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_f07d8_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_f07d8_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_f07d8_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_f07d8_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_f07d8_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_f07d8_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_f07d8_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_f07d8_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_f07d8_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_f07d8_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_f07d8_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_f07d8_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_f07d8_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_f07d8_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_f07d8_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_f07d8_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_f07d8_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_f07d8_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_f07d8_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_f07d8_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_f07d8_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_f07d8_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_f07d8_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_f07d8_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cbbd9d9d0>"
+       "<pandas.io.formats.style.Styler at 0x7f8414d8de20>"
       ]
      },
      "execution_count": 10,
@@ -2001,144 +2001,144 @@
      "data": {
       "text/html": [
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+       "#T_3271c_row0_col0, #T_3271c_row1_col1, #T_3271c_row2_col2, #T_3271c_row3_col3, #T_3271c_row4_col4, #T_3271c_row5_col5, #T_3271c_row6_col6, #T_3271c_row7_col7, #T_3271c_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7550_row0_col1, #T_a7550_row1_col0 {\n",
+       "#T_3271c_row0_col1, #T_3271c_row1_col0 {\n",
        "  background-color: #1c8e1c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_a7550_row0_col2, #T_a7550_row1_col2 {\n",
+       "#T_3271c_row0_col2, #T_3271c_row1_col2 {\n",
        "  background-color: #e8f2e8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col3, #T_a7550_row7_col3 {\n",
+       "#T_3271c_row0_col3, #T_3271c_row7_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col4, #T_a7550_row3_col5, #T_a7550_row7_col4, #T_a7550_row8_col4 {\n",
+       "#T_3271c_row0_col4, #T_3271c_row3_col5, #T_3271c_row7_col4, #T_3271c_row8_col4 {\n",
        "  background-color: #add5ad;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col5 {\n",
+       "#T_3271c_row0_col5 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col6 {\n",
+       "#T_3271c_row0_col6 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col7 {\n",
+       "#T_3271c_row0_col7 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row0_col8, #T_a7550_row3_col8, #T_a7550_row4_col6, #T_a7550_row6_col8, #T_a7550_row8_col6 {\n",
+       "#T_3271c_row0_col8, #T_3271c_row3_col8, #T_3271c_row4_col6, #T_3271c_row6_col8, #T_3271c_row8_col6 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col3, #T_a7550_row6_col3 {\n",
+       "#T_3271c_row1_col3, #T_3271c_row6_col3 {\n",
        "  background-color: #9ccd9c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col4, #T_a7550_row3_col4, #T_a7550_row4_col3, #T_a7550_row5_col7, #T_a7550_row7_col5, #T_a7550_row8_col7 {\n",
+       "#T_3271c_row1_col4, #T_3271c_row3_col4, #T_3271c_row4_col3, #T_3271c_row5_col7, #T_3271c_row7_col5, #T_3271c_row8_col7 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col5, #T_a7550_row4_col5 {\n",
+       "#T_3271c_row1_col5, #T_3271c_row4_col5 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col6, #T_a7550_row6_col7 {\n",
+       "#T_3271c_row1_col6, #T_3271c_row6_col7 {\n",
        "  background-color: #9dcd9d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col7, #T_a7550_row3_col7, #T_a7550_row8_col5 {\n",
+       "#T_3271c_row1_col7, #T_3271c_row3_col7, #T_3271c_row8_col5 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row1_col8, #T_a7550_row4_col7, #T_a7550_row6_col5, #T_a7550_row7_col6 {\n",
+       "#T_3271c_row1_col8, #T_3271c_row4_col7, #T_3271c_row6_col5, #T_3271c_row7_col6 {\n",
        "  background-color: #a4d0a4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row2_col0, #T_a7550_row2_col1, #T_a7550_row2_col3, #T_a7550_row2_col4, #T_a7550_row2_col5, #T_a7550_row2_col6, #T_a7550_row2_col7, #T_a7550_row2_col8, #T_a7550_row5_col2, #T_a7550_row7_col2 {\n",
+       "#T_3271c_row2_col0, #T_3271c_row2_col1, #T_3271c_row2_col3, #T_3271c_row2_col4, #T_3271c_row2_col5, #T_3271c_row2_col6, #T_3271c_row2_col7, #T_3271c_row2_col8, #T_3271c_row5_col2, #T_3271c_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row3_col0, #T_a7550_row7_col1 {\n",
+       "#T_3271c_row3_col0, #T_3271c_row7_col1 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row3_col1 {\n",
+       "#T_3271c_row3_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row3_col2, #T_a7550_row4_col2 {\n",
+       "#T_3271c_row3_col2, #T_3271c_row4_col2 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row3_col6 {\n",
+       "#T_3271c_row3_col6 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row4_col0 {\n",
+       "#T_3271c_row4_col0 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row4_col1, #T_a7550_row8_col1 {\n",
+       "#T_3271c_row4_col1, #T_3271c_row8_col1 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row4_col8, #T_a7550_row5_col6 {\n",
+       "#T_3271c_row4_col8, #T_3271c_row5_col6 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row5_col0 {\n",
+       "#T_3271c_row5_col0 {\n",
        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row5_col1 {\n",
+       "#T_3271c_row5_col1 {\n",
        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row5_col3 {\n",
+       "#T_3271c_row5_col3 {\n",
        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row5_col4 {\n",
+       "#T_3271c_row5_col4 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row5_col8, #T_a7550_row7_col0 {\n",
+       "#T_3271c_row5_col8, #T_3271c_row7_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row6_col0 {\n",
+       "#T_3271c_row6_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row6_col1 {\n",
+       "#T_3271c_row6_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row6_col2, #T_a7550_row8_col2 {\n",
+       "#T_3271c_row6_col2, #T_3271c_row8_col2 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row6_col4, #T_a7550_row8_col3 {\n",
+       "#T_3271c_row6_col4, #T_3271c_row8_col3 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row7_col8 {\n",
+       "#T_3271c_row7_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_a7550_row8_col0 {\n",
+       "#T_3271c_row8_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_a7550_\">\n",
+       "<table id=\"T_3271c_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2155,118 +2155,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_a7550_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_a7550_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_a7550_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_a7550_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_a7550_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_a7550_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_a7550_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_a7550_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_a7550_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_a7550_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_a7550_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_a7550_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_a7550_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_a7550_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_a7550_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_a7550_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_a7550_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_a7550_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_a7550_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_a7550_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_a7550_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_a7550_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_a7550_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_a7550_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_a7550_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_a7550_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_a7550_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_a7550_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_a7550_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_a7550_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_a7550_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_a7550_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_a7550_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_a7550_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_a7550_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_a7550_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_a7550_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_a7550_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_a7550_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_a7550_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_a7550_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_a7550_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_a7550_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_a7550_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_a7550_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_a7550_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_a7550_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_a7550_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_a7550_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_a7550_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_a7550_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_a7550_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_a7550_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_a7550_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_a7550_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_a7550_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_a7550_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_3271c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_3271c_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_3271c_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_3271c_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_3271c_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_3271c_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_3271c_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_3271c_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_3271c_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_3271c_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_3271c_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_3271c_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_3271c_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_3271c_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_3271c_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_3271c_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_3271c_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_3271c_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_3271c_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_3271c_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_3271c_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_3271c_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_3271c_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_3271c_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_3271c_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_3271c_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_3271c_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_3271c_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_3271c_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_3271c_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_3271c_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_3271c_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_3271c_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_3271c_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_3271c_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_3271c_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_3271c_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_3271c_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_3271c_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_3271c_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_3271c_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_3271c_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_3271c_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_3271c_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_3271c_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_3271c_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_3271c_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_3271c_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_3271c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_3271c_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_3271c_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_3271c_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_3271c_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_3271c_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_3271c_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_3271c_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_3271c_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_3271c_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cbbe6eac0>"
+       "<pandas.io.formats.style.Styler at 0x7f8414e43880>"
       ]
      },
      "execution_count": 20,
@@ -2483,188 +2483,188 @@
      "data": {
       "text/html": [
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-       "#T_93ba2_row0_col0, #T_93ba2_row1_col1, #T_93ba2_row2_col2, #T_93ba2_row3_col3, #T_93ba2_row4_col4, #T_93ba2_row5_col5, #T_93ba2_row6_col6, #T_93ba2_row7_col7, #T_93ba2_row8_col8 {\n",
+       "#T_2433d_row0_col0, #T_2433d_row1_col1, #T_2433d_row2_col2, #T_2433d_row3_col3, #T_2433d_row4_col4, #T_2433d_row5_col5, #T_2433d_row6_col6, #T_2433d_row7_col7, #T_2433d_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row0_col1 {\n",
+       "#T_2433d_row0_col1 {\n",
        "  background-color: #178b17;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row0_col2 {\n",
+       "#T_2433d_row0_col2 {\n",
        "  background-color: #deedde;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row0_col3 {\n",
+       "#T_2433d_row0_col3 {\n",
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-       "#T_93ba2_row0_col4, #T_93ba2_row0_col5, #T_93ba2_row1_col4, #T_93ba2_row3_col8, #T_93ba2_row4_col5, #T_93ba2_row8_col7 {\n",
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        "  background-color: #8fc68f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row0_col6 {\n",
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        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row0_col7 {\n",
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        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row0_col8, #T_93ba2_row8_col1 {\n",
+       "#T_2433d_row0_col8, #T_2433d_row8_col1 {\n",
        "  background-color: #92c892;\n",
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        "}\n",
-       "#T_93ba2_row1_col0 {\n",
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        "  background-color: #188c18;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row1_col2 {\n",
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        "  background-color: #e3efe3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row1_col3 {\n",
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        "  background-color: #79bc79;\n",
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-       "#T_93ba2_row1_col5, #T_93ba2_row1_col8, #T_93ba2_row7_col4, #T_93ba2_row8_col4 {\n",
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        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row1_col6 {\n",
+       "#T_2433d_row1_col6 {\n",
        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row1_col7 {\n",
+       "#T_2433d_row1_col7 {\n",
        "  background-color: #75b975;\n",
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-       "#T_93ba2_row2_col0, #T_93ba2_row2_col1, #T_93ba2_row2_col3, #T_93ba2_row2_col4, #T_93ba2_row2_col5, #T_93ba2_row2_col6, #T_93ba2_row2_col7, #T_93ba2_row2_col8, #T_93ba2_row7_col2 {\n",
+       "#T_2433d_row2_col0, #T_2433d_row2_col1, #T_2433d_row2_col3, #T_2433d_row2_col4, #T_2433d_row2_col5, #T_2433d_row2_col6, #T_2433d_row2_col7, #T_2433d_row2_col8, #T_2433d_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row3_col0, #T_93ba2_row7_col3 {\n",
+       "#T_2433d_row3_col0, #T_2433d_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row3_col1, #T_93ba2_row6_col7 {\n",
+       "#T_2433d_row3_col1, #T_2433d_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row3_col2, #T_93ba2_row5_col2 {\n",
+       "#T_2433d_row3_col2, #T_2433d_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row3_col4 {\n",
+       "#T_2433d_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row3_col5, #T_93ba2_row4_col0 {\n",
+       "#T_2433d_row3_col5, #T_2433d_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row3_col6, #T_93ba2_row4_col3, #T_93ba2_row5_col7 {\n",
+       "#T_2433d_row3_col6, #T_2433d_row4_col3, #T_2433d_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row3_col7 {\n",
+       "#T_2433d_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row4_col1 {\n",
+       "#T_2433d_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row4_col2 {\n",
+       "#T_2433d_row4_col2 {\n",
        "  background-color: #daebda;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row4_col6, #T_93ba2_row4_col7, #T_93ba2_row7_col5 {\n",
+       "#T_2433d_row4_col6, #T_2433d_row4_col7, #T_2433d_row7_col5 {\n",
        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row4_col8 {\n",
+       "#T_2433d_row4_col8 {\n",
        "  background-color: #95c995;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col0, #T_93ba2_row8_col6 {\n",
+       "#T_2433d_row5_col0, #T_2433d_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col1 {\n",
+       "#T_2433d_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col3, #T_93ba2_row8_col3 {\n",
+       "#T_2433d_row5_col3, #T_2433d_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col4 {\n",
+       "#T_2433d_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col6, #T_93ba2_row6_col4 {\n",
+       "#T_2433d_row5_col6, #T_2433d_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row5_col8 {\n",
+       "#T_2433d_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row6_col0 {\n",
+       "#T_2433d_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row6_col1 {\n",
+       "#T_2433d_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row6_col2 {\n",
+       "#T_2433d_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row6_col3 {\n",
+       "#T_2433d_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row6_col5 {\n",
+       "#T_2433d_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row6_col8 {\n",
+       "#T_2433d_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row7_col0 {\n",
+       "#T_2433d_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_93ba2_row7_col1 {\n",
+       "#T_2433d_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row7_col6 {\n",
+       "#T_2433d_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row7_col8 {\n",
+       "#T_2433d_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row8_col0 {\n",
+       "#T_2433d_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row8_col2 {\n",
+       "#T_2433d_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_93ba2_row8_col5 {\n",
+       "#T_2433d_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_93ba2_\">\n",
+       "<table id=\"T_2433d_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2681,118 +2681,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_93ba2_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_93ba2_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_93ba2_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_93ba2_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_93ba2_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_93ba2_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_93ba2_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_93ba2_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_93ba2_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_93ba2_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_93ba2_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_93ba2_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_93ba2_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_93ba2_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_93ba2_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_93ba2_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_93ba2_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_93ba2_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_93ba2_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_93ba2_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_93ba2_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_93ba2_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_93ba2_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_93ba2_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_93ba2_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_93ba2_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_93ba2_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_93ba2_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_93ba2_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_93ba2_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_93ba2_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_93ba2_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_93ba2_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_93ba2_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_93ba2_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_93ba2_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_93ba2_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_93ba2_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_93ba2_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_93ba2_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_93ba2_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_93ba2_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_93ba2_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_93ba2_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_93ba2_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_93ba2_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_93ba2_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_93ba2_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_93ba2_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_93ba2_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_93ba2_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_93ba2_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_93ba2_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_93ba2_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_93ba2_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_93ba2_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_93ba2_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_93ba2_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_93ba2_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_93ba2_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_93ba2_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_93ba2_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_93ba2_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_93ba2_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_93ba2_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_93ba2_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_93ba2_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_93ba2_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_93ba2_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_93ba2_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_93ba2_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_93ba2_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_93ba2_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_93ba2_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_93ba2_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_2433d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_2433d_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_2433d_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_2433d_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_2433d_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_2433d_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_2433d_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_2433d_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_2433d_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_2433d_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_2433d_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_2433d_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_2433d_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_2433d_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_2433d_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_2433d_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_2433d_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_2433d_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_2433d_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_2433d_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_2433d_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_2433d_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_2433d_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_2433d_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_2433d_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_2433d_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_2433d_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_2433d_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_2433d_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_2433d_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_2433d_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_2433d_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_2433d_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_2433d_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_2433d_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_2433d_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_2433d_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_2433d_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_2433d_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_2433d_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_2433d_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_2433d_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_2433d_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_2433d_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_2433d_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_2433d_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_2433d_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_2433d_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_2433d_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_2433d_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_2433d_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_2433d_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_2433d_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_2433d_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_2433d_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_2433d_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_2433d_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_2433d_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_2433d_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_2433d_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_2433d_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_2433d_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_2433d_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_2433d_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_2433d_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_2433d_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_2433d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_2433d_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_2433d_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_2433d_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_2433d_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_2433d_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_2433d_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_2433d_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_2433d_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_2433d_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9cbbdfb4f0>"
+       "<pandas.io.formats.style.Styler at 0x7f840ffb5bb0>"
       ]
      },
      "execution_count": 22,
@@ -2890,11 +2890,333 @@
    "cell_type": "code",
    "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_29a76_row0_col0, #T_29a76_row1_col1, #T_29a76_row2_col2, #T_29a76_row3_col3, #T_29a76_row4_col4, #T_29a76_row5_col5, #T_29a76_row6_col6, #T_29a76_row7_col7, #T_29a76_row8_col8 {\n",
+       "  background-color: #ebf3eb;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_29a76_row0_col1 {\n",
+       "  background-color: #d4e8d4;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_29a76_row0_col2 {\n",
+       "  background-color: #0d860d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row0_col3 {\n",
+       "  background-color: #78bb78;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row0_col4, #T_29a76_row0_col5, #T_29a76_row1_col4, #T_29a76_row4_col5, #T_29a76_row8_col7 {\n",
+       "  background-color: #5bad5b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row0_col6 {\n",
+       "  background-color: #6fb76f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row0_col7 {\n",
+       "  background-color: #7bbc7b;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_29a76_row0_col8, #T_29a76_row8_col1 {\n",
+       "  background-color: #58ab58;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row1_col0 {\n",
+       "  background-color: #d3e7d3;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_29a76_row1_col2 {\n",
+       "  background-color: #078407;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row1_col3 {\n",
+       "  background-color: #71b771;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row1_col5, #T_29a76_row1_col8, #T_29a76_row7_col4, #T_29a76_row8_col4 {\n",
+       "  background-color: #54a954;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row1_col6 {\n",
+       "  background-color: #64b164;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row1_col7 {\n",
+       "  background-color: #76ba76;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row2_col0, #T_29a76_row2_col1, #T_29a76_row2_col3, #T_29a76_row2_col4, #T_29a76_row2_col5, #T_29a76_row2_col6, #T_29a76_row2_col7, #T_29a76_row2_col8, #T_29a76_row7_col2 {\n",
+       "  background-color: #008000;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col0, #T_29a76_row7_col3 {\n",
+       "  background-color: #77ba77;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col1, #T_29a76_row6_col7 {\n",
+       "  background-color: #73b873;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col2, #T_29a76_row5_col2 {\n",
+       "  background-color: #0b850b;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col4 {\n",
+       "  background-color: #61b061;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col5, #T_29a76_row4_col0 {\n",
+       "  background-color: #5dae5d;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col6, #T_29a76_row4_col3, #T_29a76_row5_col7 {\n",
+       "  background-color: #65b265;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row3_col7 {\n",
+       "  background-color: #7cbd7c;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "#T_29a76_row3_col8 {\n",
+       "  background-color: #5cad5c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row4_col1 {\n",
+       "  background-color: #61af61;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row4_col2 {\n",
+       "  background-color: #118811;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row4_col6, #T_29a76_row4_col7, #T_29a76_row7_col5 {\n",
+       "  background-color: #5fae5f;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row4_col8, #T_29a76_row5_col1 {\n",
+       "  background-color: #56aa56;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row5_col0, #T_29a76_row8_col6 {\n",
+       "  background-color: #5aac5a;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row5_col3, #T_29a76_row8_col3 {\n",
+       "  background-color: #5eae5e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row5_col4 {\n",
+       "  background-color: #57ab57;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row5_col6, #T_29a76_row6_col4 {\n",
+       "  background-color: #62b062;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row5_col8 {\n",
+       "  background-color: #78bb78;\n",
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+       "}\n",
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+       "  background-color: #75b975;\n",
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+       "  background-color: #6db56d;\n",
+       "  color: #f1f1f1;\n",
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+       "#T_29a76_row6_col2 {\n",
+       "  background-color: #168b16;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row6_col3 {\n",
+       "  background-color: #6cb56c;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row6_col5 {\n",
+       "  background-color: #69b369;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row6_col8 {\n",
+       "  background-color: #60af60;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row7_col0 {\n",
+       "  background-color: #74b974;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row7_col1 {\n",
+       "  background-color: #72b872;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row7_col6 {\n",
+       "  background-color: #66b266;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row7_col8 {\n",
+       "  background-color: #52a852;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row8_col0 {\n",
+       "  background-color: #59ac59;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row8_col2 {\n",
+       "  background-color: #0e870e;\n",
+       "  color: #f1f1f1;\n",
+       "}\n",
+       "#T_29a76_row8_col5 {\n",
+       "  background-color: #79bc79;\n",
+       "  color: #000000;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_29a76_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >EGFR</th>\n",
+       "      <th class=\"col_heading level0 col1\" >ErbB2</th>\n",
+       "      <th class=\"col_heading level0 col2\" >p110a</th>\n",
+       "      <th class=\"col_heading level0 col3\" >KDR</th>\n",
+       "      <th class=\"col_heading level0 col4\" >BRAF</th>\n",
+       "      <th class=\"col_heading level0 col5\" >CDK2</th>\n",
+       "      <th class=\"col_heading level0 col6\" >LCK</th>\n",
+       "      <th class=\"col_heading level0 col7\" >MET</th>\n",
+       "      <th class=\"col_heading level0 col8\" >p38a</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_29a76_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_29a76_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_29a76_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_29a76_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_29a76_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_29a76_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_29a76_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_29a76_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_29a76_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_29a76_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_29a76_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_29a76_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_29a76_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_29a76_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_29a76_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_29a76_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_29a76_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_29a76_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_29a76_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_29a76_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_29a76_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_29a76_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_29a76_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_29a76_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_29a76_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_29a76_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_29a76_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_29a76_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_29a76_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_29a76_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_29a76_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_29a76_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_29a76_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_29a76_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_29a76_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_29a76_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_29a76_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_29a76_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_29a76_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_29a76_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_29a76_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_29a76_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_29a76_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_29a76_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_29a76_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_29a76_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_29a76_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_29a76_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_29a76_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_29a76_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_29a76_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_29a76_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_29a76_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_29a76_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_29a76_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_29a76_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_29a76_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_29a76_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_29a76_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_29a76_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_29a76_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_29a76_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_29a76_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_29a76_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_29a76_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_29a76_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_29a76_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_29a76_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_29a76_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_29a76_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_29a76_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_29a76_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_29a76_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_29a76_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_29a76_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7f8414ebe400>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
     "    cmap=cm\n",
-    ").format(\"{:.3f}\")"
+    ").format(\"{:.3f}\")\n",
+    "kinase_distance_matrix_df_styler"
    ]
   },
   {
@@ -2917,27 +3239,38 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[1129/150221.846804:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/150221.847725:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
-      "[1129/150221.848869:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/150222.386688:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmp9qqnr1ji/temp.png.\n"
+      "[1129/155748.371060:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155748.372200:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
+      "[1129/155748.373300:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155748.966480:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmpkm8yq0yn/temp.png.\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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xRpxOJ998802PpjlOxel0up1nTEyM+9W//e958+bxzTffAK6FrI8//php06bxn//8h927d/Nz8rXEx8eTmprK66+/TnBwMAMHDuxwPCwszH0vv//+++775nyg0+kIDAwkKioKURTJzMzkwQcfRKfT8d5776FQKHjxxRfZtWsXjz/+OB988AGBgYF8+eWX7Nixg5UrV7J79+6fbcf/xPRCbm4ur732GuB6Zb35ppsZOXIk77//Pg0NDZjNZoqLi/H39wdwv0a0j67anYBWq6Vfv34EBQXx2WefkZeXh8Ph6HZOt53ly5eTlpYGQGpKKm1tbWdEHSxevBi9Xs+ePXuYMmUKnp6eAHzzzTcEBgaydetWfHx8KCkpYeHChVitVgIDA/nss8+YOXMmo0aN6pHjXbp0KWFhYaxfv57U1NQejRIVCgXDhg1j5cqV/P3vf+eHH35g4sSJ5OXnkZ+fzzPPPMPKlSs5ePBgr268qKgo5s+fz4EDB3j11Vf573//iyzLGAwG3nzzTbRaLT/88ANjx451OxCNRkNSUhKBgYF8/PHHlJWV0dra2uvVZJvNxoYNG6ipqWHnzp2oVCr3jezr60v//v0JCQnhpZdeIjMzk+LiYjQaDY8++igBAQEsX76cw4cPk5OTA4C/vz/R0dE9fvhpNBpuvfVWHnnkEf7617/i5+fHV199xdSpU92LM7t37+avf/0rR48epaamhvnz57ujKUaNGsU111zDxo0beeGFF+jXrx/PPPMMBQUFTJ48mbVr11JSUsIVV1zRI3sqKipYuHAher0etVrNVVddRUxMDE8++SS7du5i1+5dlJWVkZqaSnV1NaIoMnXqVKKjo7n00kt59tlnWbFiRa/m1k9FoVBwxRVXsGXLFoYMGeK+B9tpaGhw38uCIHDLzbcwaPCgcyrrdOrq6vjoo4/c90JyUjILn19IbW0t0dHRNDY2Mn78eEpKSjh8+DAApaWlDB48mH379hEeHs7VV1/Nhg0b3JEv58Jv2umqVCoGDhyI0+nkwIEDgGse6IYbbuCf//wnb7zxBnv37kWlUvHEE09QVVVFQEAAY8aMca/eAkiS5Ha2AN7e3owfP56GhgaMRmO3T1tvb2/GjRtHQ0OD+/VYFEUiIyOx2WwolUqUSiWjR4/Gz8+Pb775htTUVP70pz9RXFzMhAkTOH78OOBa1Pvb3/6GVqslJCQEWZbZv38/AElJSYwaNapHdePh4cHHH39MQnwCjz76KHq9HqfTSUpKChaLpcO0SWpqKuC6Ia699lpycnLYsWMHqampPPHEE8iyzLRp01i0aBEREREMGDCgR9MuSqWSoUOHEhMTgyAI3H333ZSXl3Pw4EEGDBjAkCFDKCkpQavVctNNN3H33XcTGBhIUlISsiyjUqlQKBSuKYbMzF6PdH19fRk+fDh1dXVs3rwZDw8PXn31VcaOHcuIESNISkpyhwdeeuml2O12mpubWbBgAXfffTcKhYLY2FieeuopDAYD48ePd7+G94bbb78dSZL45ptvyM7O5uqrr+avf/0rWq2WoUOHkp+fz+HDhwkJCeG9995j3rx5HDx4kPHjx+Pl5UV4eDgLFy5k0aJFfPfddwQEBDB8+HDS09Pd0TndOV1BEEhISCA5OZnMzEzANfJ76KGH+Pjjj3nppZfYvGUznp6e/Otf/2LMmDHk5+czfvx4lEolQ4YM4amnnmLlypVs377d/SbXUxQKBSNHjiQqKoqRI0dy+eWXc+2116LVahk9ejQ+Pj4kJydTWlrqvpdFUWTOnDkAhIaGMnbs2HOaYmrHy8uLnJwcampqeOCBB7ju+uvwD/Dno48+YufOndx22208+uijrFu3js8//5y8vDzmzp3rvu6vvvqKTz75hHHjxp3TYmI7Qjcjlos6Trc7ZFl2xYkKIhqt5leLc+3OJrPZjEKhOKfY0+5oaGhg+PDhPPjgg9xzzz0olcoexxu30x7/rNFo3KP89hjKcwlZ68PV7larFYvF4o57vZhwOp0YDIYz4sT/V/j000958skn2b17N0FBQWg0P/kDm82G1WrtECve3t/1er37M6PRiEql6hBn3wVn/cL/tNP9/xGz2cx3333H4EGDGTR40EV3c/fRx4Ug60QWe/bu4eqrr3ZP6f3C9DndPvroo49fkbM63f+J6IU++uijj98KfU63jz766ONXpM/p9tFHH338inS5rL1r1y4WLVr0a9nSLU753GPjzicO+eftyjlfWO02GtoaLrQZAEiihEapuWDlO2XHzwra/19DEMSzTyr+8oVzAUvHKcsX9B594i+PM23StLMe79Lpjho16owdI78k7aEZZ8Msm3Fy0vHK/DRVffpPTm1v+Syfne23p55DPvPfdqed2rbas9r4a5JReYKHlz9+oc0AINw3jIHJgy9Y+Y1tjbRZ27r/4v8neGk8UUq9z1VxPhAQ0KnOX1au3mK0GKlprblg5Ssiuq73bgM4z1fOyp7SVYiT4BQQ2r3lqV/r6qHa2bFz/e1Jx3uxhGEJQp8tP5UvXDR1cbFwoevjgpV/Ed0XndE3p9tHH3308SvS53T76KOPPn5F+pxuH3300cevSJ/T7aOPPvr4FelVJpT2rO7l5eUdPvf09CQ4OBi73U5FRQUlJSVEREQQERGBQqGgqamJuro69/clSSIgIICamhp3tIJGoyEgIKBDIore0C4OKSCg1XUUMmxXDzC1mVBr1O4yLBYLJpMJlUrlFpG0WqzulIxanfacFhLbk9pYzFa0Wg0qtaqDLS6lCzOy0+kSPlRI2G12twCfKIqoNWrX99pMgIBWp+l14hpwrSRrlBpUCiVmmwWLvWNSaI1Cg3TyGu1OBw6nA5Wkor0JZBnabG0oJSVapQan7MRkM+Nw9iwkRxREVKISQRCxOqxnhPIoRSVKUYnNacPmdF2/QlSgklQ4nHasDhvyyTASAQGdUofJbup1+KBKUqGUlNgcNqyOjtnKJEFEo9AgAxa7pYONGoUGURBps7UhCRJKSXGyXmQsjt7n91WKStQKFXanA4vd4r42lx0Saskljmi2m3HIDlf9nYxCkGXctmsUakRRwmK3YHeemSu5MwQEVJIKSZCwOq1n/E4SJNQKNQ6nA5vThlN2IgkSKsmVAMfisHT4TEY+4xrOhizLyE4Zq8mKIAgo1UoESeh4X9gc2Cw2FCoFCpWrnh1WBzazFYVa6fpMAIfVjt1qR1IqUKh7njTKVfdq7E77GXa76lmFQlRgsVuwOW0ICKgVard9VofLdld7uFbVLXZrj67/VHp9Fx86dIgFCxbg7++PRuOKy5w1axYPP/ww7777Lp9//jk+Pj60trZy88038/DDD/PJJ5/w8ssvExoaiiiKeHp6cv/99/PQQw/h5+eHp6cnDQ0N9OvXj//85z+9Fr2zWCx8+emXbN28FUlSMHTYEP5w3x/copBVlVW8+PcXMba61A7+cO8fiIiM4I1X36ChocGVdvC+u4mOiebVl16lqaEJu8PO/Q/ez9gJY3tbRWRnZPP+v94HXOkn73v0fqJiIgFXNqdvP/2Wvdv3IggCg4YP5vZ7b+PLD77k8N7DePv6EBEdzrW3Xcvyr5eTeSwTp9PJyPEjWXDTAtSa3uWTnRg/jvvG3k2brQ2H7OTZ9c9T3lQBgIfag0UL/oWAgMlmYn/ZQXJqcrl52A0oJSU+Gh9Ukorbv/0Dd4/9HQkB8eiUOjbkbuLT/V9ic9i6LFshKpgUOZFYH1dqx/KWcjYVb3E7jjifWKZGTcbmtKGUVPyQv442Wxvzky6Dkw52a/E2TtRnAZDgG89lCXP5MuMranoRthflHcWNA67H4rCgllQszlxKfmOB28Y5CZcwIKg/sgylLWUsyVyKxWHBQ+XBbYNvoc5Yx3eZS5gSM5mxkWMw2ow0m5v5Ov1bTDZTj+0I1Adyy6CbQAYPlZ7lWd9zvMYlT6SSVFyVegVRXlGoFWoyazP5PnsVQ0OGMDdxDs3WFsx2M/9N+5qhIYOZGDUBs8OC2Wbi02Nf0GrtXrInNSCFGTHTabObkHGyIvt76k2uOG+9Usfs+EsI0AUiCSJZ9dnsLz/AzLgZBOmDUEkqMmszOVB5kLkJl+Kp8kCj0HC46gi7Snd363hkWebYqiOUpJUAEN4vgqFXDENSuLLYNVU0sf+bPdisdnDKjLh6FHp/D3Z+vB2b1Yogiwy7cjgqvZojKw5habOgUCoYftUIAuODunW8vhofbh96G0pBgU6lZ03uWg6UHwRcDndi9ATGR44DZFosBj47+gXRPlFcP+A6msxN2Jw2vjj2X+J8Y7ky9QoaTA04nQ4+P/Zf6k313db9qfTa6ban/bv33nsZMWIE4MqOv3LlSj755BP+9re/MWLECNauXcuGDRuYN28edrtrFPfaa6/h7e2NUql065rddtttzJs3jw0bNvD000+TmZnZa6dbXFTMhnUbeOm1l3A4nTz56P8x57I5JCUnAfDD6h/w9vHm2Rf/zoZ1G/ju68UMHDIQvYeep557ij079/DlZ/9l2IihaDVannzzSXbv2M1XX37F0BFD3Q+XnrJq8SqGjR7G3Kvm8t8P/8vmtZu4/b7bAWhubGbHxh088Pgf8fbx4qUnX2LKrMkUFRQx9+q5JKQk4uvnQ2VZJUcPHuOJF57AYrbwj7+9ytjJY4lNiO2VLTcPvYFNeVtYmbGahyc9xJUDLuftnf8BQCUp0Sv1vLPnXcqbK6kyVGO2WsiuzUWv0vHMjP/jx+wNhHuHEewRxNPr/k5qUAp3jryVZWnfU2/semOGh1LPwMABfJ+7klZrK1enLCDSM4L8pgIkQWJgYH/SatPJqs9iTvxsEnwTKG0pRS1pWJ23htFho0jyS+REfRZ+Gl/Gho9BRv4pbLAHiILI6PCRFDcX80PeOsZHjmdSzESKmopxyA78tL5EekXy2bEvCNAFcNPAG/gh7wesJivjo8YR4RVBo6kRhagg3i+OnSU7KW4updVqwGzrXrPuVEaGDafB1MjK7FUMDxvKuMhxZNfnYHVY0Sm0hHqEsuTEUvy1/kyLnYKX2pNE/wQOVh4is/YERlsbDtnBwKABrMxZTb2pgVsH3USkVwQn6rK6LFun1DE6fDQ7y3ZR0FTA5YmXkeSXxJ7yvYDrweSj8eW7zMWEeIRwWeJcSppLifeNY0nWMgJ0AQwLHorR3oZeqWN59vf4aX2ZFjONzLoTNJi67guG6haKDhcy+Q9Tcdqd7PhkG0mTkvEKcuWqLj1Wgn90AP1nDSR9XRoFe/MJ7ReG7HQy9a7pHF19hLLjpdhtdgJiAug3YwB5u3PI3JTBhCh/98j4bAwNHYLFZuHLzP/SL6gf4yPHcbw6HbPdjFJUMjCoPz/krqOurZ47htxKtE8kSf6JpFWncajyMG22NprMTcT5xpJTl8uOkh2Y7CaazE09bv92zjmJ+fLly9m5cycAd9xxBwcOHECv13PjjTdSUlJCVFQUd955pzs7vtPpZOnSpajVagYMGODW+yosLGT//v3uzOxxcXGA68nY09eGpsYm9Ho94RHh2Ow2PDw8MBqM7nOUFJeQlJyEv78fCYnxrF25hrLSMlJSU/Dz86PfgH589flX5OXkkZCUQEBgAAOHDmLxN4sxthp77XQrSiuYPGsyvv6+xCfFc+zQT3pKDXUNaHVaomIj8fTyxNvHm4LcAvJO5FFXXYepzcSw0cOYOH0iVrMFpUKBQ2GnrLgMQ4uhV3YAaJVasmtzqDbUUtJUSqR3hPtYkFcAUd6R3DXq9/hqffn00Od8fXgx1S013DjsWmpaa1l2/HusdisPr3ycqcmTuGvk79ldvJdmUwsdd5mciShI2JxW6k31tFqN2J129EpXf3DIDraV7KTt5E3spfIiz5pPs6UFjULD7LhZ+Gn9WFewHkmQmBAxnvymfPfve4pSUuKn9eNw1RGazM0UNhUyKHgAkijhcDioMdbyn0Pvk+SfyCXxs8hvLMBobSMlIJkY72iO1xxHlmUUooIY72givCJAhpKWEr5M+wqzveeON8wzjLyGfBpMDWTX5TIoaBA6pQ6rw0qTpZk3973F8LBhzIqbQWVrFc3mFuJ8YtEp9QwPHU6TuYlPjn7Gd5lLqGurJyUgGQ+VB43mxm7LVksqfDQ+5DcW0GxpprillACdPwICMjKFTUVUGCppsRoYHDyIRnMTdaY6qlqrmZ94GSpJRVZDDk3mJlSS+uRUkIBeqcNX49ut0zU2GpGUCnwj/LCZbaj0atoajW6nmzghCUkp4bQ7qS+pJ2JgJIFxgez71sD2j7bSUtPC+NsmUp5RisVkQaVVYjPbaKlpxmFzdOt0wzzDKGkppbatjpz6HMaEj8ZT5YHZbsbisLDowHtEeUeyoN+VyMgUNRVzaeIcAvWB9Avsh8lu4v2DHxCkCyLaJ5oYn2isDivvHfqg22s/nXN2umFhYURFRQGuOV1ZllEoFMiyzMGDB3nhhRdoampi4cKF7t80NDSgUqkwGAzuudzt27dz8OBBKisriY+Pd+e67PW8roD7/hcEocPrzumB87Isg7Pz16H2OVxBBvnkf71FEDtu4jh1l11nO+4khcQDjz9AbEIcddW1vP7c68y+Yg6JKYksfNxVf+cr4fmppdcbG3jkhyfIrs5lcvwELk2ZzZK0FXirvZieMJVPD3+JxW5FISqQRImCuiJ2FO9idMRI/HS+1LT25BX/FJtPu/YWawsRnhHMjJlGjbGGjLpM4nxisTvtZNSdIME3niivSDSSGg+1B2m1xxkaNASdUtfj6+20xk6pR1EQUYpKGkwNZNadYFTYSKK8I7ksaR6bCjcT7xeHXqFHEGDJiWVUt1aDIHD74FtJCUjmaFXPBQoF4dR+0bEuXHYoqGqtIqM2kyS/REI9Q1idu5YGUyM2h5WbB93IwKAB7C/fz9jIMUyKmsDOkl3UttV1UlrnNfFTH+r4wGwf8V2aMJsoryjW5a9DI2kI1AdyvOY4WoWWcM9wjlYdxeKwcF2/axAQUIrKHs7pgqgQz/zwJBoPDY3lDRxZcRi1Xk38mHgqT1Sg89IRNyaBkqPFVGSWEzM8jr1f7ebHf67DarQgKMSuNzi5r144/YMO/UApKWmxtHCsOo3J0ZNI8EtgXf56DGYDJruJGwZcz8jwkWwt3oYhtxWrw8p1/a9hcMggthRu7d6AUzgnpysIAjfffDPTpk1z//vIkSOsWbOGzZs3u6WiFy5c6BZEVCgULFq0yK1TlpXleh164oknuP766/nkk094+OGHKSgocH+np2i1Wpoam7BZbdgddloNre7RqSzL+Pn7UVVZ5VKCbWpGo9Xg4+tDWWkZsixTW1OLSq0iMCiQqsoqHA4HFeUVqNVqdNqe3+DtePt4U1tTiyzL1NXU4+nl6Xa2Xj5etBpaaTW0IkkSLc0GkMFithAcGoRao8LD04O2ViOTZ0/Bw0OPQqHgnVcXueeoe4OAgL/WNaLx0njRav1ptBztE4XZYqWypYoTtVlcmjIbnUpLuHc4Ed7hHCt16b0NCR/EpPjx/HPb23x98DvmJM7CQ+VBDV07XRnZtVAnKFxORVK6F8sAYr1jmBU7gz3l+0ivy8DutBPvG0d2fTYHKg5QbaxmctQkfNTeBGoDuCxhLj4aHyZFTaQ4vaRHi2k2px2DtZVQD9eUlZ/WjxZzi/u30d5RjIkYzXcZS9hWtM01wvWNwV/nx/zky/BUeyIKImm1x1FLahpMjZjsJmwOG56q3iXDbjQ14q/zQxREQj1Csdgt7pFykD6QabFTWZG1ku+zVvHUpCeI8ApHFCTyGwswWAy0Wo14a7yZGjOFEWHDWZmzmvSajB6VbXfaMNna8NP60WxuJkgfRPUpW2V1Sh2XJ83DKct8m/ktDaZG4nziEBDYUeJ6o31o9IPolXoOVR7GbHdtyZ8aPblHr9gavRprmwVLqwWHzYGl1YzGS+O+L5oqGtn+0VYSxiaRMjUVSSlRnVdNeP9wUqak4hngRdrqoyRPTGH0dWNQqJU0VTZRmlbinhfusu7NTfhpfVGICoL1wdiddtpsrm3jGoWGmwbdwMrs1Wwp3EqKfzIRXhE0mBooaSqlydxEk7mJMM9QDBYD+Q0FWBwWKgwV+Gn9elT/p3JOTleWZf7yl7+4pw7GjBnDAw88wPr163nooYcIDg6moaHhjFHd2bZqSpLEyJEjUSgUVFRU9NqeyKhIAoMCefvNt9FqtWg0GvR6PW/98y3GjB3DqNGjeO3l1/j6y6/ZtH4TY8ePJaVfCm++9iZLvlnC5o2bGTt+LMNHDuf5vz3Pd199x7Yt2xg+cjhaXe81mYaNGc66FeuwmK1s37CNOx+4kwO7DpB+NJ0b7rwBHz8flv13GSq1GqVCQXR8DP9+6d801DViamtDFCUCQwL55J1PiE+Op7WlFb8AX4JCg3pty4Hyg1w1cD6+em/GRI3kpa3/4OpBVxDsGUxGbSYPjLmXL49+xdjoMVS31tBiMhARE0p+YwHWkwtldqedybGTaLUYUSvVGKytGG3dS7Bb7BYaTQ1MjpqIxWHB4rBQ01bD/MTLyGnIYUToCBrMDUiixKDAAVS0VlLaUsbEyPEYbUYivSJpsjSxtXgboiCiVWi5KvkKdpTu7HH0gsPpoKSphGmxU2iztTEuYixbireR4BvPxOgJrM39gWifaK7udxWt1lZ81b6kV2dwpPIoANPjpqFX6ChoKOSPo+8nyCMIq92KKIrkNeb3qi3SazK4ZdDNGCyuV/ichjz8tL7MTZzLlqIt9A/sj0N2YLQacTgdVLXWcG3/q4n2iabWWIu/zp8DlQeZHXcJRc1F+Gp8mBA1jrTqdFosLV2WbbS1UdhUyIyY6WR5ZhHtFcWxqmMMDx1GoC6QKmMVEZ4R7C7bQ7xPPOGeZprNzUiCyKSoiQiCQKu1FZPdxPSYqWTVZxGgC6DJ3EyjqfvpDe9QH9Q6DUe/P4wgCShVSlR6DYeWHCCsfzjFh4pQqlWotEqKDhTgE+aLf5Q/x9cfxzPQk7L0MrzDvTE2GcncmEHsyFjy9+SRMD4RSdm9002vyeDeEXdzScIsUgOSqTRUoVNquXnQjfyYtx5/rT8LUq8kuz6bYI9gDlYcYkLUBJICkihtKiHaJ5rlWSsYEzGGlMBkChoLGRQ8iM+PfdHj9m+nS+UIm80mn6o7L8syZWVlLFmypMP32tVeq6qqWLVqFRUVFfTv3x9JkkhKSsJsNrNv3z7+8Ic/uIXl6uvr+fLLL5k5cyapqak0NjayZMkSRowYwbBhwzq1x+Q8GSrUyetEfm4+q75fhSAIzJo9i9j4WDas20BKagrxCfGsX7eetGNphEeEM/eyueg99GzbvI0jh48SFh7KvPnz0Gq17Ny+k0MHDhMUHMiVC67Ey7ujKKXNYaOum9c5k8nEhlUbKC8pJz45nimzplCUX0RJYQnTZk+jpLCEbRu34bA7mDh9IgkpCWQcy2DPtr2oNSomTJtAbEIshXmF7Nm2B4Bps6cRGtFRFDGjMpOHlz/RpS3eGi8u638pfjpf8usK+TF7I8MjhqJVatldtJdZydOJ84+h2WRgbfY6ag11DA4fiFahZX/JQZyyE0EQmBA7jsFhA7HZbewq2kN6VWaHcsJ9Q5mQPO6M8kP0IST5JSCJEvmNhZS2lDI0eDAVrVVEeIa7Q3IASlpKqTZWk+qfQoA2AIvDzPHaDJotLrVmhahgQEB/chpyabN3TG7TVcIblaRkdPhoAnT+1LTVcaTyCF4qT1ICktlWvIMEv3hSA1IBmZyGXHLqc91OPd43HpWkJKsum1jfGPoH9kMQRE7UZZFbn9tl3Z+OUlIyOHgQUV6RGKyt7C7djUpSMTB4IEerjhLqEUqSv0ssM7sum+z6HMK9whkSPAiFqCS/MZ8KQyWDgwehPhnGJQMHKg6eMa/oqfZApeiodeah8mBI8GD0Sj0VhkpO1J8gwjMCL7UX9aZ6Enzj3d+1OqwcrjpClHckMd4xOGWZ3MZcSptLifWNJcY7GofTyeGqwxisZ6416JT6M6b0msoayd+Xh4xM3Mh4vEN9KDxQgH+UP41ljTRU/HQNIQkhhPULI39vPnXFtXj4e5IwNhGVVkVpWgk1+dX4RfoTNzr+jJFuq6WV2tOmvhSigoHBA4nziaHNZnJHXAwLGcqx6jT0Sh2DQwajVWgpbSnlYMUhgvRBDA8dhlpSUdhUxNGqYwR5BDEidDhKSUlhYyHptRlnhE/+eeKfmBQ38dzkek53uj1BlmXsdvsvIrpocpp+yjLWSbnt19LZiFqWZZxOJ6IodogNdDplRFFwJclA6PR7p9ITp3v6uTs7T7uEc/sccrv9AgKCeNr880lOP09PnG47oiCedXTY1bGO3xOQT851n87ZnC64ZxN7NT8u9PL7Pcky1tV1ts/5dVdmT793Pu04lzI7c7qnnq+3bdHb8k93uu3IJ9dSTu3jXeG+L4SOMb3tU9KdldGZ022nu75++vGzXXtXddid0z3vEuyCIKBU/vop5Tp1tKeEFwmC4Fa2PfU3kiR08ln3rys9sef0c5/K6Zsuzjb1cr4eXF11tJ6+qjvPMV+tfMqfPf/N+c+N29V19rS882FXb+0433XR2/Odn/JdXrKnzradzu4LQRB6tHjWGd319dOPn+3af9ZD95x/+RugN/GcffTRxy+I3HcvtvM/7XT76KOPi4Q+n+umz+n20UcfffyKdDmne7Fl43fKzguuTyYjIwoXx7NKEMSLRhdMpvONH7+qAReYi6UtwFUd5zoH/3Nxb7+4gPVxMbXF6XTpdCVJOqeA/F+K0tqyC66DpRAVBOl7Hy/7S+Crqbh4xDoddmz23mfdOl84LxKx0O6SAP1amG3mHmcfO98ICO5saRcCh9N+Qe+L7hx+tyPdi4qL6OF1cdTNxWDDxcFF1DX6uNBc5J3h4nhP7qPH/BKhVH300cevR5/T/Y3RFwbXRx+/bfqc7s/kNz/y/I2b30cfvzX6nO7P5Dc/8vyNm99HH781eq2RZrFYqK6u7vC5Xq/H39/fvbhkt9upqqrC19fXnYlMlmWMRiP19S5pC1EU8fb2Rq/XI4oiTqeT1tZWBEFwf9abxSqn04nVYkUQQKlSnbFX2+FwYLVYUaqUbq0xh93hXmkUJRFRFM/43rksmLXnn7BarKjVaiSF1MEWWZaxWq04nU7UajWiKLrrVsCVgenUz8Al+9PbOmlHISpRKRRY7NYzknMoxJ+u0Sk7cTgdLq0slQqb3YH9pEaZgIBS4drebXf0fHVYQEASJURBxHaK3lk7kiAhiQocTrs7HFAURBSiAofT0eEzlaTC5rCdU9igJEgoJAV2h/2M3wsIKE/qkJ1uo1JSIgoiFrvl5LWIcDJHx7nY4dKMU2GX7WdEF7jsUCDg0pOTkZEEqUObO2UnTtnpynEsSG4ts57i+p2IrZMV/vbcwg7ZpZXXXg/tNpsdZve/T81J0Ju+oBAVyLgiDE7vC+3ln3pNZ+sLp3/WE9r1+uyyo9O6l0QJhajA6rC6y5cE12f2k/2z/XvtnEuESK9zL+zbt4/58+fj5+eHWu3KEDV79mxeffVVd86F6upqpk2bxt/+9jduuukm92+///57HnjgAUJDQ5EkidbWVv7yl79w++2389JLL7FkyRIEQWDBggU888wzPVZssFltrF66mu3rtyFKIkNHDePqW65Go3X9vqGugXf/sYjG+kaUKiU3/v4mQsJCeP3vr6NQSEiSxKz5lxAZHcnn731GQ10jarWK3z34exJTE3tbRWSfyOb1V17HarXi5eXNI//3V6KiXQnfnU4n//30v6z/cT0AEyZO4M6772Tx14tZu3otgiAwdvxYbv/97axesZo1q9YgiiIjR43kjrvu6HUI39iYUTww4T5sDlc+1b+vf5GqFtdD00Ol599XvY5KUmOxm9lXepAvD37NkzMfJco7EqWkZOnxFSxP+57Z/WZy4+DrsTltHKk4xn92f4DZ1nUyJIUgMSFqIsn+iQiCSFFTIZuLtrhTRsb4RDM9Zro79nl17mpAYHb8JS4RSFHBD/k/0mhq4MrUK1CJKpSSii1FW7qVpzmVcM8wbhhwPU7ZiSgILDmxjKKmYsB1U82Mn86Q4CGATH5jId9nr8TqsKJX6rhp0I00tjWy5MQyJsdMYnzkOEz2NprMzXyT/i1tvdBIC9AFcMvgm1BJKjQKDYszlpBZewJwiSZekXo5iX4JqCQ1R6uOsTZ3Lb8f9ju81V5IooS3xoc1OWsw2tq4NHEOVruFFouBz4992Wmmr9NJDUjh0oQ5WB1WLHYLS7KWubOTaRVa5idfRrA+GKWkZF/5fvaW7cNX68vAoAGk+Cfzn0PvIwoi1/RbQIg+GKvTRqWhkqVZy7stW0BgSvQkEvwSccpOSppL2FS02e3cgvRBzImfjSi4HPOavB8wWo1cljTP/aDYWryNFquBS+JmuR3oxqLNFDUVdVu+t8ab2wbfjE6pR6PQ8H32Snf6TkEQGB85lknRkxAEgQZTA58f+5IgfRBXplyOSlJhcVj55MhnxPnGsiD1SposzThlJ58e/Zz6tl9YI81ut9Pc3MwTTzzh1kgLCgrihx9+oKCgAKvVlWu0paWFvXv3smfPHmJiYrjjjjuwWq2YzWYee+wxQkNDeeqpp1ixYgVJSUksW7aMP//5z9TV1fHqq69yyy23kJqa2iObykvL2bh6Iw//7WFkWeb1v7/GhGnjiTmpJ7Z13RYUSgVPvPR/bN+wjdWLV3HpgrlotGpuu/c2VGo1fgF+fPPx1wQGB3HfI/ezcfUGlv13KY8892ivFYG/+fIbBg8ZzDU3XMN777zHmpVrufeP9wDQ3NTMqu9X8cj/PYqPjzdPP/40Kf1SWLl8JU/9/SlEUeS5p5+j/4D+LF+ynGcWPoNSpeTpx55m6oyp9BvQr1e23DHyVnYX7WFF+ioemfxnrhx4Oe/u+gAAlcIl4fLm7neoaKqgtrWOmIAoYn1jeGHzK4yKHMnl/eeRUZXJTUNu4F873sZsN5EYnIgkKoCuna6H2pMRocP5Pud7DNZWrkldQG5DPvmN+UiCxPDQYWTXZ5Nem8G8xEtJ9U/FS+NFfVs9O0t3MSp8JKPCRpJRm4lCVLIm7weGhw5jSMjgHjtdURAZEzGaamM1a3PXMTFqPJOjJ1Ha/BUO2YG/zo8kvyS+Sv8aX40vNw68gU2Fm2g02RgbOYZ43ziOWdJQiBIJfvHsLNlJUXMJbbY2TL3VSAsfgdlm4r9pXzMqYiRTYiaT15CH1WHDQ6UnyT+RxZnL8NP4MiNuGtuKt/NdxmKUkpIRYcNJ8Isnqy6HWwffxM6SXWTWnuD6AdcyOHQQO4t3dVm2TqljUtRE9pbvI7suh6tTryQ1IIVdpbsBV0J7X40v32R8S4JvPKPCR5FRm8GQ4EEMDh4EuN4clZKScM9wNhVuprattkeCmABBHkEk+CWyvmADkigxO24Wh6oOu51+v4BUqoxVHKo8zJToSaQGpLjeSpwO1uSuZUBgf8ZHjqewqYhGcyM7S3cxOmwkA4MGUNpc2u2Id2jIYERB5NOjnzMweABTYiaTWXPCLVY6MnwEGws2UWWs5s4htxPnG8uk6ImUNJdyoPwgsX6xaBRq4v3iyW3IZ1vxNsx2C02mph5d/6mcc5axtWvXcuTIEQBuv/12tmzZwqJFixg0aBCXXXYZABs3bmTixIn84x//wGazERgYiM1mY9myZUiSRE5ODk8++SSRkZE89dRTzJw5k0WLFrmTkPeU5sZmdHodETEROOwOPDw9aG09RSOtqJTElEQCggJISElk99Y9lJeUcyLtBK89+zogc/df7sbhcD11Pb08QRCoKq/CbDKj0/dudFlUVMT0S6YTHBJM/4H9ObT/kPtYTU0NOr2OpOREvH288fX15fDBw4iSyIBBAzCZTPgH+nMiMwuNRkNsvOvB4ePrQ0tLS6+04wD0Sg/SqzIob6qgoKGQWJ8Y9zF/T3+ifaJ4YOw9eKo8+WD/R6zOXIdaUvPI5D8T4R3BV0e/JcI7ghDPEP4y6UE81B5syttyMjVl1xpp0kmNtKrWalqtrdiddjxVHsBJjbTi7bRajaglNXqlB0abSxkBoNXailN24qX2pN5cj7/Wj9nxlxCsD2Zt3toeX79SVOKv8+do1THqTfXkNORyefJlSCenkmqNdSw6+B9ifWKYGjuFoqZC2mwmEvziSfBL4HhN+kmNNCUx3jFEeUeBLJPfWMA36d9hcfQ89WmUdyQ5DXnUGGtIr0mnf2A/dEo9VkcTjeYmXtn5GgNDBjItdip1bfU0mZqQkfHWeDEidDhfp3+LXbYjiQpO1GVRaaikrKWcKK/IbsvWKNT46/zJrD1Bk7mJ/KZCgvVBiIKAU5bJrsuhsLGIgUH9mRg1gfTaTJrNLWws3ExVazUz46afrE8FQbpAZsXNQCWp2FdxgPX5G7pdUPbX+mFzWCluLkar0NJkacZP6+d2ujtKdqBV6hgZNoJo72gOVh4mxT+ZamM1jeZGqoxVDA4eREZtBhaHBdnpxE/rT1VrVY+mNyK9IyloLKSqtQpREBgRNhxPtSeWNgtmu4W39y0ixDOU+SmXISNT3FxMqMc1BOuDGBY6jAZzA+nV6YTog4n1jSHONwaDtZUPD31EYy/FKc/Z6er1ery8XAm+VSoVsizj5eXF559/jlKpZNGiRdx555386U9/oqqqit27dzNnzhxEUSQkJARvb28yMjJYv3499913H/7+/rzxxhssXbqUhx9+mNDQ0G4s+InTHZEgCB30lwQBBEnscCwhJYE/P/NnEpIT2bh2I6sXr2bOVZfyn9fepaSwhMa6BlTqzvORdocgCO6twqIgunPnAjgdHXODCqKAw+Fwz5cKCIiIOJ2OM65JPouuW9f8lGf49HoymFt4ev3fyag6wdSESVzRfz4lzaVIosjBskM0tDUwJnIUqw1r0So0vLPnP4hIPD3jCZanraSwoagHpZ+d+rYGwjzDmJMwm0ZzA+m16dS21TI/+XJu1N2Ar9qHVpuRaK8oLA4rhU1FCIJLiv1YdVqPrr69LU69MUXhVEE9V5232lopaS5hYNBAwjxCuTxpHluKtxHrG4uHQo+MzJq8tVQaKhEQuGngjSQHJJFWfbxHdrjLPVkhzvbcye39QHDNkraYmylqKiLKO5IQz2AqDVWMDh9FbVsdhY1FeGm8XJa39+8eP4R/WvKVkZFPJqd31YPs1hVsNDdT2lJGrE8MKkmFyW46Ob/rwuKwsix7OUVNJQTrA5kRN4MjVUepMdZ0Xuwp5bcbIJ/sk2IHuwUcTgd1bXU0mZuI9z0pUNt++OT/tFha8FZ7MTNxJkpRyZHqoz2KIDp1674T+aREWsfyTTYTBQ0FBEcEEecTh1rScLDyEPvLD3BlyuVMiJrArtLd/Ji/HrPdwnUDrmVwyCC2Fm3vtvxTOWeNtHvvvZeZM2cCrpt51apVhIeHk5iYSGVlpevkCgUKhQJRFLHZbMiyjFKp5J577nErS3z66accPnyYf//73xw7dozXXnuNefPm9SqnrUajoaW5BbvNjsPhoK21DZX6J0UCbx8f6qrrkGUZQ3MLCqUCo6GViKgIAoIDGDh0IAd27cfL24t7/novCoVEeUkFB/ccRK1Rd1Fy5/j4+FBTXePSX6ut7aA+4ePjjaHFgMFgQFIoaG5uJjIqkv1799Pc1IzD4aChoYHQsFBaWlqwWW2IkojBYHDPUfcGQRDw07l0nLw0Xh3m/oI9gqlpqaW4oYTDFUeZkTiNibHjqTHU8c6O9xkQ2o+X5z6P1WGloa2R3YX78FR7UG+qR6Pqvl5kZLQKDYqTC2lnaKT5xjI3YQ77yw9wpPooVocVo9XIhsJNyLKTIF0gIR6hhHuGUdRUxJaiLVQbU5kcNbnH12932jFYDG6NNF+NDy0Wg9sJR3lHMipsJEtPLGd9/kbifGOJ948n2COY+cmX46nyRBRFjtemY3fYqWqtxmw3Y3VY8FZ799gOgCZzM35ujbQQzHbzKRppQUyNmczK7NV8ffxb/j71GSK8IqhvayDZP5mchlwsDgtt1jZkXPPDNcZaPNWe1LR15/BcGmlttjb8tX40mZsI1gdTbaxxP5BT/JOJ8o5kXf56Wq0G7htxL+qTTvdUvFSeNJqbqDZW02RuYlrMNLxUnt06XZOtDUmQ0Kv0qEQlHioPWiw/9cXxEeOobqvmeE06aklF/8ABVLZW4nVSh06lcC0+BumDmJcwl5yGHPaV7z/DvrPXfRM+Gp+TGmlB2J12TKdopF034BrW5PzA2tx1xPnGEe4ZhsluoqixiMLGQqpaqwnzDKWspYzS5lIsDivFTcUE6gN7VP6pnLNG2sMPP4y3t6vTDR8+HEmSznCUn3zyCXl5eezdu5cHH3wQtVqN2WzmvvvuQ6/Xk5OTQ2JiImlpafzwww94eXnx9ttv8/7777Nw4UKGDx/eI3tCwkPw9Pbgu0+/RaPVIEoiOp2Wrz/6ioHDBjFg6AA+eftjNqzawNYft9BvUD+KC4pZ9t9lXHHjlaz//kcGDRuEydjGsv8uY+bcGWxbv43Jsyb1OqG5jMyIUSP4fvn3CKLAhnUbuPv+u9mzaw/Hjx3npttuwsPTg8VfL0aj1YIM02ZOY8OPG/jkg0/Q6fXYrDaGjRjGj2t/5KP3P8LHxwenw0lQcFCvoxeOVh7jiv6XoVGpGR01kte2/5P5A+YR5BFEUXMRd4/8PR8c+JiJseNpMDZwpDyNqbFTuG74AsK9wzBajWRUn6CitZLfjb6NVlsreqWeFlP3CzcWu4VmSzMToiZgtluwOqzUGGu5PGkeOQ15jA4fRb2pHrPDTIp/MlXGKnRKPRMjJ3Cg4gAJfglk1+dgdVgZFT6KEWEjiPWJptXWs3lEcDndspZyJkZNoMnSxKSoSewu3UOsTyzjIsewuXALif6JXJ48j1arEV+NHydqs8ioyQAEpsRMRqfQUtxcwh9H3U+gRyAWuwWFpKSwqbBXbXG8OoNbBt1IfVsdI8JGUNxcjI/GmxsGXs+O4h2MCBvhekicnFqpNdbip/XFX+dHZrZrwc1oM1LfVs+UmMmEeYaSEpDCpv2buy3baG2jsKmIWfEzSas+TpxvHEerjzE8bBhBukBKmksZETock92Et9obk62t05V5paRkQcpVbCnaSqAuAKvDSkVrZbflV7RWIggCk6Im4nA6UUkq2mxtzIidTmlLKWqFmolRE9EoNCT7J9NobqSitZKJkRMYFT6S/gH9KWgqZFDQQBw4aDQ3Eu8bR72pgarWqm5Hu+k1mdwz/A9MjZ3CgKD+1BrrUCs0XDvgWjYVbCLSK5IrUy4no+4EQfogDlYcIrchl9GRoxBEgXi/ePaW7WNM5GhSApLJb8xnSMggvkj7qttrP50u5Xo47e1QlmUKCwv5+OOPO3wpNjaWwMBAysrKuOeeezAYDLz77rv4+PiQnZ1NREQEt956K2VlZSxdutRVsCAQEBDA3LlzKSsrY+PGje7zSZLEzTffTGJix8iBrOpst4LnGXblFrBm2VpEQWDapdOJTYhl09qNJPVLJi4pjk2rN5JxLIOQiFDmXDEHhVLBprWbKMgpIDoumlmXz0KhULBn2x7SjxwnKjaKOVddikrVcYrBlfAmsEvnZzQa+e6r7ygqLKLfgH7Mv2o+2Seyyc3J5cqrryQnO5fV36/GYbczZ94cBg0ZRG5OLsuXrMDpdDD70tkMHjqYgrwCFn+zGKfs5NJ5lzJk2BD36zDA4dKj3Prl77pqP3x1Plw7eAF+Oj9y6/L4Pn01Y6NHo1Pp2JK3lcv7zyMhII4Ws4Gl6Suoaq5h/oB5pAYnY7KZ+TF7PVnVOaSGJDMn+RIAdhbt5kDJoQ6JPcJ8QhiXOPqM8iO8Iugf2A9JkMhpyCG/oYCxEWMoaS4l1icGjeKn0Xt+UwHlLWUMDBpIkD6IFksLe8v3Icsyo8JH4q/1x2w3c7jqyBmaYC0WAxZ75/OraknFxKgJBOhdo8N95fvxVnszMKg/Gwo2keKfTP+g/oBMVn02GTWZ7pFwsn8yKklJek0Gif4JDAoaiCiIpNdmuCMP2pFlucuEN6qTCzaxPq75wE0Fm1Er1IwMG8Hesn1EekfSPzAVQRBIr8ngeHU6vhpfRoWPYFPhFrcT9NP6MSVmMhqFmrzGAg5VHDojFFCn0qGQOo6pvNXejIkYjafSg5KWUo5UHSXONxZfjQ/7Kw4yLGQoMT7RWOwW0mqOU9xcAkCQLpAE/wT2lu1DAAYFDyLWJwabw86hqkNUGDo6XQEBH433GfdIpFcEqQH9AJns+mwqW6voH9iPelMDNcYaRoSOwFvjhcFi4FDlIdpsJkaEDidAH0CLxUBGTQaxPjH4an3d5yw3VJDbkNth+shoMVJr7CippRAVjAgbTrxfPCZbG5sLtyIjMzZiNPvLD+Kh8mBk+Ai0Cg3FzSXsKtmNXqVnQtR4/LS+VBgq2V60g2CPYMZFjkUpKchvKOBw5ZEzHk4PT/ozk+MnnZtGGj9zv1J7fKwoir2OAOiMsznd9rLatZOETnTJzq6RduZnstOJcJaY2J443fbzOBwOJEnq9LtOpxNZljuMpF0LeT991n5Nsix3GqPbE6fbjiSKOJydLzh0dqw9TrgzjbbO+szZnC6cm8aWIAhnlNOVvlVXTrcnv2+f8+tuUaZ9Priz73XndNuRBOmsq+09taN9Pvhs3+vM6Z5aRlf1IMtyt23V1ffO5nTbj8HZ+0JntvVUw6+dzpxuO5Ig4uzCblEQcHRTfnd1353TPe8aaaciCIJ7I8IvTXe5f8+ukXbmZ8J50kjr6to7ewhJotghGOB85jM+m8M92zFnJ5+da47Sc9kq3VlZPzdd3/nTift5e6e7Cm/qqR3ti1Hnwvmph3Nri+76QmfnPZ9pGk93qKciI+PoQb/7OXUPfduALy76tuT20cf/PH1Ot48++vgNci4a0xcHfU63jz76+A0inPLnb4suJ1wvNp0hk81Eq8V4QW1QiAp0Cu0FtaEdi92Ch9rjQpsBgFqhxmq/cFI1docdu+PCyNO0I5+042LA5rBfsPtXEASsDtsFc4i2C9wXfpZcj91ud2e5uhh4bPlTpFX0fAfQL8PFk8xxUPhAtvzxxwttBgDbcrfz8PInLlj5F9sA4f93LqyclcyF7A51wxu6PP7rhBacJ6x2G5YLKH7YK7pOS3BesDvtaJW936V2vmhP+QigkJRnxIr2cZHxK/TJDmVdLPya1033ERp9c7q/FBfLcPgX5OIZ8/fRI/5/ba6L7Lr7nG4fffTRx6/I/7bTvZhecfo4v/S17S/DL1Wvv7Xz/oL8bzvdi+y1oo/zSF/b/jL8UvX6WzvvL8h5dbrt+QZO3bN/6mft/zabzbS2trrzD5wvFJIChdj5Ft72vKrte9sFcP/7VM0nAQGVQtkh/+a5IAqu83Q27+na432y3FNWeRWS4oz98pIoohClc54/ba9/i9lyRn23556wWqzY7XZ3uzmdTiwWS4e2bNd1s9vsZ+Rk6A5JlFCeJQ/Ame0inNEunX3WW0TBpT8mniUfQGftIQoiCunMuldIEspz3N7erjXXWf86tU+6+6Nw+rX/dB6FpOh1XYiieFKHrZt6OO24SqF0a4O1ly2d5V7riq5+5zqv1KFuBOHM+9GltSb1+h511b3irHlgOusDndWzJEou/cRzvCfPS/SCLMsYW43k5uVy//338/rrrzNmzBgMBgP5+fn89a9/5bHHHmPatGn8+OOPvPzyy9hsNqZOncojjzyCj4/PzwoxUUoKrhl2FZelzsXhtLOneB8f7fkM88kEKFqVho+vfQ+r04JTdvLFka/QKXVcN+hqwJW1Kbs+hw/2fsy94+7CW+2FSlLx3IaXOFGV3Wt7UoOTeHzGXxEQMNraeHHDq5Q2lgOuhr1/0h+YGDMBo9VIlaGaJ9c8y00jr2NeyqU4ZAcHyw7zzo73GB41hHvH3o0TJ2kVaby760ParD3X5ALIOJ7BO2++g9FoJDIykr889hf8A/wBMJlMLHpzEWlH0xBFkdt/fztjxo3h3bfe5cjhI6jVaq674TomTp3I0u+Wsm7NOhQKBTMumcHV1119Rga201FKCm4cfi0zk2YgiRLbC3fw8Z7P3REowyIH8/i0RzBajVgdVr449BX+Hn5cPeBKwNUuWXXZHKs6xvS46YiCgI/Gh2pDDb//7r4e10G0fyTPzHwCSZSwO+y8suUNcmvy3cfvmfA7ZibOoNncRK2xjpc2vkZsQDQPTrgfAcipy+Xf29/FYG5Fq9Ly5MxHaGhr4I0tb/eqLYK8Avm/GY/gq/FBpVDzyubXOFrmCoEUBIE7R93CzOQZmGxtFDYW88bWf7Nw9jME6P2RRAl/rT/v7/+YrOpsHpp8PypRTY2xhhc2vEpTW3O35Q+PHsLDk/6E1WGjoa2Blze9Ro3hp8QwL1+2kGjfKEy2No5WpfHezo8w2yyEeAXx99lPs/rEWjblbOXPU/5IckAiGoWGLw5/xarjP3RbtiAI3Db6JuYkzcIhO9hZtJv3d32M/WTEi16t48HJ95EamIyAwJs73qGkoZSnL3kCL40Xsuzk3zvfJb+mgPsm3kVKYBIyMv/Z8yF7Cw90W76vzocnZj5CiEcQaoWGf21/iz2F+93HL0mewT3jf0+TqYlmSwtv7XwXEZEHJt6Lj9qLJnMzz61/icERA/nDqDuwOWwUNZbw2pZ/9SjN6an0Wg04LS2NPXv2AFBZWcmMGTMYNWoU7777LkuWLiEzMxOTyeUY3nnnHZYtW0ZpaSmtra1UVFTw2muvMWzYMCZNmsQjjzzC0KFDGTFiBEuWLKG2tpbk5GQWLFiAj49Pj+1KCIznhsHX8uz65wF4ee7zbMzeQnZNLgBJwQlYHBZe2fYaFpuVGkMtkihxoiqLIK9A7ht3Nz9k/cjwqKHUtzbw+rY3+cvEB7l60JUsrHq5N1UEwO/G3E5eXQGfHfySB8ffxw3DruHVTf8CTqaYixzO4rRlHKtMo9VsRKFQMC/1Ur4+9h0mq5lnZzzJ+qyNPDD+Xj7c9ynVrdUMixyCRqk5w+meGrbV2WefffwZQ4cPZe7lc3nj1TfYsG4D1998PQDHjx3n+LHj/O35v5GTlcOnH36Kw+Hg6JGjLHx5IcfT0vnsk88ICgni+2Xf89xLz2FsNfLi319k0pRJRERGdFkPgfpAbh1xE39f/yL1xnpenvs8B0uOcKDYJV8U4RVBo6WBN7a9hcVmobqlBqVCSXpFBiFewfx50h/5MWsD+0oPsjN/L75ab56c8Tg/ZPU8NlkUBBYMuoKq1moW7X6fG4dex43Dr+WFH//hTsnXLyiVNTlr2Z63E6OlDaWk5E8TH+C7o0vJq89nVPRw9CodBnMr8/rPZlbSTFafWNNjG9q5JHU6IiLPrF/I3H6zuXXETWRUPY3NZkOr0jA0ajCfH/qS3Pp8Ws2tGC1tvL7tTTRKNZMSJzA2agz7Sw7w1ykPcajsCNvydzA6epRbELYrPDR67hx1G2uy1rGjYBfPzHqCSfETWHJ0BQBqpYpAjwD+s/99ShvKaTY1Y7FZUIgSt4+6hWHhQ9mUt5khkQPpH5TK3ze8SJRvBL8beQd7iw9Q29J5Vq92YgIimZcyh1e3vYFSUvLE1EdYfnwllU0ukdRZKdOJ9oripc2vEesfjZfOk6sir0AlqXh63d+Z328ed4+9k/VZm4jxi+Klzf8g1i8G9Unl7+7evCYljsdX48Oz619gUvx4bh95C4fLjmKxWREEgQi/cLYX7WRN5jpMVhNVLdW8NO85ShtLWZS5hmERQ4gOjOCOEbfwycHPKawv4ukZTzAmdiTrM7vPZ3wqvR7pHj16lP/7v/9jwoQJAKxatYr33nuPOZfOISw8jPvu+2kEMnfuXAIDA3nppZcAlz5YXV0dc+bMYcyYMURERHDgwAF27NhBRkYGI0aMYOHCheh0Oq6//voej34DdQEoJSXHyzNRKVTUm+pRn5KndVDIIPoFp/LszKdRiBIvbnqVQyVHMZqNXN5vHvl1BWzK2opSUiEKAjeOvJbU4BRe2vJqb6sHAF+NL1vzt1NSX0ZmdRZjon9KeSiJEkn+SdwxMgAQWJeznre3v0d5cwU3DL4WGZkjVcfw1wcQ4RPBH8bcgafak20FO7B2EqN8ttfEdkqKS7juxusIjwhn4KCBFOQXuI8VFhQSHRtNXEIcnl6efPbxZxw9fJTYuFiiY6KRZZmPDK1UVlTi4+NDQmICra2tqDVqjK3d7wxUSkrMdjMnqrKpM9ZjsVvcCg6CIBDkFcCQ4MG8MPtZbHY7T617lvzaQlraDFySMoOMqhNsyd2BzWGj2djCfRP/QFrFcVZl9lwjTa1UE+YdyobcTZQ3VLK3cD/3TbwLhShhd9rRqbUEewZxdcCVXJF6OftLD7I280fi/GK4efj1eKk92Vd6ALPNwpDIgYyLGcPXR77FU9P7nYADgvtxoOQwxXWlbM7bysipI/DSeFJvb0Cv0jMgqD8x3jGIosiPORt4d+cHlDVW4Kvz5tKk2byx9d9YHBYivMPx0OiZmzKH3Po8VqSt6rZsT7Un0T5RvJDzKpXNVewp2Ud0QJQ7bWF8YCzRvlE8MPZetAot3x1bwleHFjM9eTJKScGe4r3IgChI2GU79a0NeKj1eGk8ifKJ6NbpxvnFYrKZ2V94CE+tB6XNZUT6Rbid7oSY8UT5RfL87L+BAK9t+RcBWn8yqjMpqS8jrSKdywfMJSU0CQ+1By/PfR67084rW17v0VTXgJD+HCs7TkFtEU4cTE+Yiq/eh6qmGlSSisHhA+gXmMqU2EmUNJXy9x9fJCUgmWjfKCbHTaS4qZi0qnQEQaDFbKCmpRa7bKd/SL9f3uk6nU50Oh3/+Mc/0Gg0TJw4kczMTG699VZ0Ol0HRzlw4EBkWXa/hjocDneOWQClUoksy8yaNYvg4GAMBgNtbW00NDT0SoCx45NOxu50dJi7S69K5++bXiCrKptrBl/JbaNu5mjZcXy0PsxJvYS/rnoCpyxjd9oRBGgyNdNoaiTZP5kt7OhtFbmu9WT6PqfsRDzFCTqcdt7a/Q7pFZkEeQbywLh72Va4Aw+NB+Ut5bRajAwKGYiHSo+32pt/Hvs3TW0tPD3zMdaf2MSxc9iR1z6HJUnSaXptTlRKlfsYgM1mc/9/ew7f9pzIcHLe72Su3Z7VQ7uApasuTm3TjJpMnlm/kIL6Qv406X7mps7hrbp30at1zEycwWtb/+XOTxvqE8LE2PG8vu3NXm03FhBc0wonR7WyDApBwU979wW+SfuO/JoifLTePDPr/0irOI6vxpdFe96npKGMZ2Y9wf7iw1w9eD4r0lcT6RWBr84l/dKZusLZkESFu97sDjsiP81lt1nb+M++DzhRlY1GoeHpmU+wI38Xh0uPMStlBjWGWo6WHyfQyx+9Ss+RsqN8mPcp94z9PbNSp/PdoWXd1sOp7WZ32FGIP93+TW3NLNrzHhkVmfQLTuWBifeSVZPL7SNu4fkNr3DnyNtQSypyqnLRKXW8PHchOpUOb403Yg/mdiVRct8FTqcTp+zsUL5eqafOWM+i3e8zM3Ead466jeKGUhxye7vJiIKIl9oTL5UXCze8xLDIIdw15ndkVeZgsHStJqIQJfemBZvDfnKu3GW3Q3awLnsD3x5eSqOpiednP8vM5Gl4qDw4mH+YtSfWcf/4u5kUO4HDZUd4fMojlDeXMzhkECdqeqZK3cGWXv8Cl5PTaDR4eLie9haLpUc3oaenJx4eHhQVFTFgwADKysqYNGkSb775JiqVimnTpvXoVel0zHYLClGBQpJQiEqC9IHYHT/tjpKdMllV2eTXFnKo5CjjYsYiCiLDY4ZQ0lxKcYMrQ/51I66iqK6EZUdX0mRq4uqBV6FVaTBZeye1LcsygTqXdpKn2hOj7adRoZfWk8LaYjIqs8ipzeeWYTcxK3k6PmpvHv7+MZSiitcuf5GogHCaTE1syNqMU3ZSbqhAr+mdKjEyeHp60VDveojV1pyu1+ZDXX0dVquVuto6nE4nYeFhHNh3AKfDSauhFVEU8fT0pKmxCbvdjtVqxWwydzuf6ypexkftjUpSIwoiWqXWrQkmCiIqQcOR0mPUtNayv+wg8X6xKESJmcnTaDY1c6jsiPtcw8KGoFVoOVLaM0HKdmwOG82mFuL8YtnENvw8fKhrq3PnSNUrdTS1tnCs/DjeWi/KWsrw1HnQZG5mY9YWmkxNFDYWkRwST1JAIveO+wO+Gh9EQWRn4R7Wn9jUY1tqjLX4e7o00mL8ojHZTO6k/IIgUNlUQ1ZVDjaHDafTSbBHMCqFkgkx4zhYdog2axvNJgUNpka2Fm5nT+F+JsZOIDkwqduyrQ4rzeZmgrwCqDbUEOcXQ0lTmdsRqUQ1VU01ZFXnYrZZkQSJ/mEphHuH89Lc5wj3CqN/WCoZVSd4d8/7qBQqDFYD942+m6qW7uV6GkyNiKKIt84TnVJPgM6PutafRseN5kaKG4vZW7gfjVLN7wNvB0Em5OSbkValxWQzUWOoxWQ7zIHiQzS1NXPVwPlolJpunW5Nay3+Hv4oJAXRvpHYHHZaT/5GQMBudZBReYImUzOlzaX4qH0wWAwcKD3I/pJDzEjOI9IrkqXHV5BWk47D7sBgM5BXl99luZ1xTk63ubmZF154AUEQ8PT0JDU1tUej0sjISAYMGMAHH3zAjz/+iCzLDB06lO+++47Ro0e7V9pttt4lTilqLKKytYrfj70dk92E1W7FJlt5YNLd7Cs9wKjwkQyJGMR7+z7kykGXs6t4Nw6ng/HR4yioLaTZ1AKAj8aHP028DLVKxezkmTSbWs5p2/Hx6nQuSZ5Bg6WB6YlT+Ozwl0xJnEhqUAqrstbw1MzH+fDAJ3iqPfBUe7AxZwtjI8Zw88gbqGqpItInkrd3/4fhYcO4eeT1FDeVEOIRTJOx+8WSDggwcPAAvl/2PQ6Hg107dvHnR//Mjm07qKyoZMSokSx6axFrV60lPS2dsPAwxk0cx9LvlvL98pWcyMgkIiqC2PhYBEHg26++xWF3oFKpOjjvs2G0Gqltq+O64QtoNjdjsVvIq8/n4akPsrtoLzOSpzCv/2y+TVvCJYkz2JC9GZvDToJfAnXGesxW10KoIAiE+YSQVZfdaXL1rrA57OTXFTJ/wFyKG0u4adj1rDmxjv4hqVza7xK+S1vKPeN+T4CnP3bZTrhnOEdL0zgaeYxbRl7P0co0Ynyi+e/Bb9ictR1BEJjffx4Bnv4cLD7cK1v2Fu7nyRmPkVefyxX9L6eovgQfnTcPTbqf1Rk/8PTMx1m09z1UohKLw0J2XQ7BXsGEeAWzc/duAJqMLVQ0VTC336UoJAVjY0bx1u5F3ZbdZGrmeEUGd4/9HatOrGVs1Fi25b3GjJQpRPpEUlRfzJ8m3s8/d0r0D+5HrbGWzdnbOFjkevA9OOE+jtekU9dazxPTH+HbY4sZEjaIJlML5U1V3ZafVZWDSlJz++hbsDvs+Gh9aTQ2ce+E33O04jgbcjfywLh7mZI8gdnJM6k11pFWeZzfjbqdeYNmc2X/+Wwr3EFmZRZ3j/s9YxNGMztpJofKjnTrcAH2Fh3gtcteIr06nenxU6luqUGtUPHkrEdYcmwFd465jdTQZI5VphHlE8WazHUcqTzK9OSptNgNDArtz4qMVcxOmYkgCBytPMaQ0MF8tOezbss+nS7lemw2m3xqwhtZlvn000959tlnueOOO2hsbGTKlCnMmzcPpVJJfX09r732GnfccQeJiYkIgkBFRQWfffYZc+fOZeDAgZSWlvLhhx/S0tLCJZdcwqRJk1izZg3bt28nIiICg8HAxIkTmTlz5hmqDvM+WMCxis5HOsOjhjAv9VJAZlPuVo6UH+PaIQs4WnGM3Np87hh5C0GeQZQ3V7Dk2HLqWuu5ZvBVVBtq2F6wEwBfrQ/XDbuaSJ9wmtqa+ezQf6lpqe11pXppPLlj9K0EevhTVF/CV4e/Y1j4ECJ9I/ju6FKmJU1mfMxYHE4Ha7J+4HDJMSYmjGN6wlQEAXYV7mVTzlYSguJYMOAKlJKS3cWuz051OsMih/DV7Z92aUtDfQNffvYl5aXlDBk+hAXXLmD/3v1UVVYx/6r5bNu8jS2btuDh4cFNt95EZHQkG3/cyMb1G/H19eW6m64nJjaao4ePsvibxahUKq5ccCWDhg7q8KDdmL2FB77785ntEjmUOamzUEpKNuVuYVf+Xv4w9g52FO6mxdzC1YOuwE/vS1FDCV8e+gaLzcIlKTOwOCxszXVN7YiCyOx+M6hva2Bf4UHXiXuxn16r0nDbiJsJ9wmltLGc744tJdQzlJlJ03hn13uMjxvD9ISp2J12tuZvZ1fBXqL9I7l20AK0Ki0Hyw7zQ+Z6d26J0VEj8dZ5sj6rd3N5WpWG+QPmMSC0H82mFj7c9wlapZYrB8xnybFl9AtJZVL8BARZ4IfsH9lXdJBQrxCuHHgZH+79DKvDNQDw9/DjzpG34qnxJKP6BCvSVvZocBDkFcgtw2/ER+tNRlUmK46vYmT0cCK9I/j2yFKuHHQZg8MGYbQYWZW5lozKnzTgLut/KSVNpRyvzGBywgQmxIzD4XTw6cEvqOiB0wVX9MRlqXORkfkxewPHyo9z1cD55NXlc6Q8jfkD5zIwpD8GcyufH/ovda0N3DryRuIDYqkx1PLd0WXUttayYNAV9A/tR4uphW+PLaGssaLbslUKFZf1n8OQ8MG0Wdv4aP9nOGUH1w2+huXHV+Kn8+XyAXPRKjQcqTjG8rRVBOj9uX7oNQR5BlBQV8h/D31LoFcANw69Hq1Sw67CPWzJ3X6GssT7N77DNUOvOjeNtM6c7tdff83rr7/OmjVr8Pf3d8Wr9TLcy+Fw4HQ63b9tjwFVKpVdaql15XThpL6U0LnUDLjiK0+ddjgbCklxXlLDdVVeZxpkrhjBjvI5guBaFnN20k49cbpwMlbX7kBSdK7XZrfbO+jYdaZt1x6/22776ec5m9N1XZcAgnDWdpEkCUcP2uXn0lU5oiiCLHeo53a5pN6OrrtDcdKOzu48lx09k6iRRKnXSYYEBERJPGs9SJKE0+HsNmlLV3p5XdHen87aFzq5ps7aTRTFc2qX9nWNzuxuj4k+o/xObBIFodN7Erp3ur2eXpg8eTJRUVH4+vqe0/wrcIZcuyAIqNXqnv24ixGOU3Z2uS2wJw7X9b1uHG4PR1ldlddZh+nMfpf4389DEAQUyrM3dQctN7lzfbfO9OR6hAxOZLrKtfeLO9yT7dVVOWfThPslUkb2tl+cjXPJ6iYjd1kPPW2LdhHY3u4P6O76HE7HGeftzKZzfRB2dX2yLHeqX9dZPZ/N4faEXjldQRAIDw8nPDz8nAv82VwM2/4uBht+Kc73tV0MdXUx2PC/SN/W3nPifzv3Qh999NHHRUaf0+2jjz76+BX5TSlHSKLY6yQX/+tY7BeHnJLT6UAldR+7+0vhkB2/yPzruXEx2HFh39EvrFjPT39eCLpLhNOl01UoFGcsqFxIZgycSkJE3IU1QpZxdjLZfiFobmth2ltzLrQZAIyNHc2OP2+8YOUvzVjG4fLexc3+EtjbF4IuMEqFEuECDVBEQcBP53/BHK/JZqK+tf4ClQ5B3gFdHu/So15YcbkzUUpK1IpfejTV9ZKsfBE5XUEQMFh6l+Hol8LmsOGt7X7DxC+FVqlBqTi3aJrzhWvr+sXxJqaUug6//CUREFArVBfMf9id9gvaF7q77oujh1xUXFwPmj766ON/iz6n28dvkp8fvdxHHxeG/2mn23dbnm8unhrtUyLu47fK/7TT7bstzzd9NdpHHz+XXitH2Gw2mpub8fb2RqlUYrFYMBgMaLVa7HY7NpsNQRDQ6/VoNK5E4k6nk5aWFux21/ZapVKJp6dnp3v4e2wLZ7oAURBw7U7sfEQmCdIZ2/ykk0mcT/+FUlRic3bMdtbbcV5n5bUjcHJf/yl77EXhZD6GU0pqD5HryV78Lm35lfIbnMqpunhOpxNJ6pj7of14e16IU3E4HC4trVMa2W63u6NpzqXfiIJ01kVQV92DzCl5LzppIxBO7rs/9/boKmeCyMk8Faedv12/qzf5eztDOGm/4yz2S4J08n6QO/xGIUrYTin7bHaeHdlVv04ZQQCEnzTg4Ke+4HQ4EaWOY0Gn3YmoEN3fb9+eLZx2jp4gnewDZ7uXz5ZnQRSkn1337fQ6HuzgwYPcfPPNfPLJJyQkJPDQQw9RU1PDSy+9xFtvvcWhQ4fQ6XRuDbRHH30Um83GXXfdRWVlJR4eHphMJpKTk3nzzTfPeUvxqdUsCiJDQ4YwKGgATlkmrzGffeX73ZXkrfZiVvxMtAoNdqeDbcXbMVgNzIidjofKA2TYXLyFshaXjlmMdzQz4qbzedqX7sxO7WX2xPEG6gKZnXAJCkHCZDezLv9HmsxNblvHRowhyS8RURDJqs9mf/kBRkeMIskvCVl2klmXxaHKQwwJHszg4EHIyBQ0FrKzdFevG35AaD8eGH8vSlFBtbGG17b+iwZjIwA6lY7n5/wNH403NoeNg2WHya7N4YYh16GUlGgVGmTg9W3/4raRN+Oh0qMUlXhrvLl/+UOUN3ed3cnhcLBuzTpWLluJ1Wpl9tw5LLj2KpQqV/L60pJS3n3rXcpLywkLD+PBhx9Eq9Xy3jvvcSLjBHoPPX/8yx8JjwjnnTffIftENp6entx9/930H9i/xzebv9afBSlXolKosdgtrMpdTVVrlbs9xoSPZmTYSGRksuqy2Fa8naEhQxgZNhIByG3IY0vxVsaEj2ZA0AAkQSK9Jp2txdvO+lDtDG+1N1emzMdb442AwPKsFRQ3u3I5S6LEtJgp9A/sj1JScqjyEFuLXBmsJEFkTuIcvNSeLM/6nqtSriBQH4goiOiVejYWbmJP6d5uy4/xieHS+NkANFmaWJWzBoPVFf2iklTMiptBjHc0giCwq3QPx6rT0Kv0pPgn0z+wP5+nfYEoCMyMm0GsdwySqGB/xX72lu/vqlgAZCfk7swme0c2MjKJY5NInpyCILnasK2xjUPLDtBU1YTWS8uIBaPQemnZ+/Vumiob0XvrGXrFcLxDfDixKYOy9FI0HhoGXDKIwPigbvuCXqXnypT5BOgCkASRldmryW3IA1wPlUHBA5kcMwmlqKSytZKlmcuxOCwICEyPm06EVzjfpi9mbtIcIjwjEAQBD5UHO0t2srlwa7fXfyq9nl4wmUwUFRWRm5vLgw8+SHp6Oo899hgDBw6ksrISf39/HnvsMe666y6WLFnCM888Q3NzMxUVFQwYMIAXX3yRa6+9lm3btrFixYreFt8pQfpARoWNZHvJTnaW7mJE6DD8tH7u40NDhoIMP+T9SG1bHaPDRxLnE0ewPpgf89djdpjpF5CKgIBGoWZC5Hh81T7nPG84KWoiLZYW1uatQ5adjA4f5T7mqfZkcNAgdpXtZl/5fgYHD2ZAcH8GBQ1kfcEG9lccYGzEGIL1QQwNGcK+igPsKNnJ8NOuqafcM/YP5Nbl8dKWf+Cj9mH+gHnuY2pJTbRvFJ8d/oJ/bH+DZekrOFR2lNd3vMkbO/6NyWHiQOlBlzDjrnf4x/Z/UmYoJ6s2h2ZT57l9Tx0h1dfV89lHn3Hb72/n8acfZ8XS5eRk5wAuh7z4m8X4+Pjw8usv4+3jzbdffcvWzVspKynjuZeeY/TYMXz47ods/HEjJUUlPPvC3xk0ZBAfvvdhj69fQGB8xDisThtLTiylydzE5OhJSCdVA/y0fowMG8HavLVsL9nO5OhJxPrEMC12GpuLNrM6by2jwkfRLyCVSVGT2FGyg1W5qxkeNpwYn5hetcWwsKHoVXqWZC6jqLmIGXHT3eoJnipPRoWPYkPhRlcfDhuBl9oTgH6B/ZgUNQEvtSdWu5VNhZtZdmI5GbWZGKwGChoLuy1bo9AwPWYq2Q05rMj5Hj+NH/0D+7mPx/nGEucTx4qclRyrPs6kqIl4a7wYFzGWGbHT8Nf5IQgCgfpA+gX048eCDewt38e4iLF4qrqXLmptaCV9/XFGXDWSEVeMIHNTBm3Nbe7jeXtyEQSBqXdNQ+ejJ2tzJhWZ5RhqDUy9ZwYqvZr8vXmUHCmmtqCWcTdPIG5UguscPdgUMzBoAIG6QJadWM6Juixmxc9EKbrCylSSiqmxUzhceYTlWSuI840jwT8egCT/RKbFTMFL7YXdaWdb0Q6WnVjO0eqjtNnayK3P67bs0zmnOV1Zlnn22WdZvXo1jzzyCJdccok7JjA0NJRLL72UBx54gLlz57Jjxw5aW11JhiVJQqVSYbPZUKvVREVFnUvxZ+Ch9EASJYqbiik3lNNmM6E8RQokszaTjYWbaLa0oFNoMdraqDPVIwoiEyLHE+IRQl1bPQpRYnLUJBotDTRZms7ZHr1KT3FzMdXGasoN5QTpgtzHTDYTq3JXk99YgCRKyLIT2el6XWowNdBgakQhSigkJQ3mRkaHjWJ85HiqjTXnFJPro/HmUNlh8moLyKrNJtn/J5UBX703EV4R3DvmHl6e/QJDwgZjtBjJq8kn1i+aNquJjw98htHSRkFtEZ4qTyK8wnlt2z9ptXaukXbqg6rN6LqpUvulkJSSRGBgIJUVLpUBh8NBQV4BY8aNITQ8lLETxlJUWExmeiYDhwwkKiaKCZPHU11dzbHDxxg4eCAxsdGMGjOKyvLulQraUUkq/HX+ZNRmUtVaRXptBuGe4e5pm0ZTIx8e+RiT3czQkKGUtZRTbazh6/RvyKrLxuF04JDtWBxWwNVGtcZaFIKCEI/gXrVFjE80mbUnKDeUc6w6DS+1J3qlHoBmczNv7nsLu8PO0OAhNJubMVgMhHiEMD5qHFuLtiGfTPlY1VpNg6mBfgGpbC/aTnVrdbdlaxVagvSBHKk6SqWhityGXEI8gt3tld9QwCfHPsNT5cmQ4MEUNxfTYjGwpWgr6/LXu7N6NZmb+O7EYoqai1FJKmxOe4e3wbPRWmtAVEqEJIcSEBuI2kOFse6n/hw7IpbhV41A46XBZrKi8dTgH+VPW5ORtDVHqcmrJjA2kJrCaiwmC/u+3kPOjmy03lr3VEVXxPrEkFOfS0lzKceq09AqNXhpXA81i8PCh4c/Jq8xnzERLjGF8pYKAnT+TI2dwpaibYCMU3ZSY6yhtq2O/gH92Fmyi9KWsm7LPp1zXkhraWnB09OTFStWUF9/5u4PURTx8fHBbDa7NJGcTr7//nuuu+46Xn75ZbRaLUOGDDnX4jsgCAKcTIHo6piODq8bNW21OGQnM2OnE6AL4HDlEVSSElEQMNqMWGxmPFUepAQk46vx4Vj1cWRcr3znSvtcl1OWO4yXrQ4rFYZKBgcPYkLkBI5UHyOnIRerw8o1qQuYGTcDvVKPl9oLvVKH2W6i1dqKh8oDzSlimz1FPqn9Bq4Udaduo7bLdt7Z8y4LN77EtsLt3Dr8ZiRRxEvjyaUps9mQuxGD2fXAVEpKZqfM4mD5Yep6uNtHRu4wby8pJBwOZ/tBAHfKSaVCicNudytTtH8mO2WsNqs72F1SSK45PWfPZtgF4XSNNLmDXpdDdtBma0MSJGwOGx4qD+xOO8XNxQwMHsgNA65nT9leCpsKqWurY37yfG4ddDO+Wl/EXt4+7fOl4NpMIgiiW8tPRsZoNaKQFFgcFpSSEn+dP3MSLiG3IY8Gc6M73yvA4ODBCMDxmvQe14OA6HrII2Nz2lEIP82x25w2THYTSlGJydZGgC4AheCyxWy30N5gbTYTFYZKJkSOY2LkBPaU7Tn5QOoap8OJQqUAAQRRQBBFnI6f5oM9g7xAEDjw3X5a6wzEj0vEUGdAoVIiqSSUOhVNFU1YjVaMjUYGzRuKX7QfR1cdwW7pfspNEiX3fL3daUdAdGukgUvlRDh5TDw5op8VN5OylnJqjDUIiO63o36BqXioPDhceaSzorrlnJ3uK6+8wlNPPcWWLVt477333Is0TqcTm81GSUkJu3fvJiEhAY1GgyiK/P73vycjI4M333yT5uZmtm/ffq7Fd8BVUSKSKLnnuU7Nt+mr8eXqlKuQRIlvMxdTb2ogzieOgsZCNhRsYm3+OvoFpjIqbCQhHiHMT7qMEH0ws+NnoVf2UpcMABndyRGMWqHGesqCnCiITI+dxtDgoazL/5H95fuxO23sKN1JRl0m6bXp1LXVIQkiQboglmd/z9q8H5BEEW+11ykl9AxREPHV+gKuqY12TS4ApaBiX9FBMioz2VqwA0EAT60H0b5RJPonsDN/j/u7Hio9k2InsCWn522mVChxOBxYrVacTifNzc3o9a76FEQBnU5HXa1LJ6uqsgoPTw98/Xzdem011TUolUoCAwOpranF6XTS1NiERqtBEHs29WN32mmzthGoC3DXgdFqpH3pNMwzjFnxMyltKWVlzirsThvhXuFMiZ7M9JiprMv/ka1F2zDbzWwt3s6hysNsL9lJVWsVBmv3MjGnYrAY8NZ4IQoigfpArA6r22EF6AOYnTCL/IYCPj/2JT5qb4aGDCHON5bxkeO4LGkuSX6JjI0cg1pS0y8wlbSa4x0Wt7qrB7PdhLfaNZ8cqAugxdrqXsBK9E1gbPgY0mqOszrvB8I9w1ErzsxxrRSVLEi+khT/ZL7N/I5DPXQ8Kp0Kq8mCzWTFZrZjbbOg9vjp/K11rWx5dyOiUmL6H2fhGehJWVop0UOiGXPDOIZePoyKExWodCoiBkYSkhxC7Mh42hrbsFvtdHdHtFgNeKo9kQQJf61LmslsNwGgllTMT7kMs83M0sxl1JrqGRY6lAT/BEaEDWdB6lXE+sQwKXoiClFBv8BU0mszezTC74xzFqaMjY1l9OjRrFy5kkWLFjF+/HgA9u3bxy233EJDQwNFRUU899xzeHq6hvEajQY/Pz/Gjh2Ln58f5eXlvVL9PRtN5iaMtjbGho/G6rQhn/xvYuR4Sg1lxPm4NL5y6nMI0gXQYjXQam0lMXgwsT4xJPol0GhuYnfpHiRRwlPlwcSoiRyqPHzyKd87aoy1pPqnYLAYSPZP4kjVURL9EgjSB1HQWMCAoP7sK9+PjJMIrwjqTfWMjRhDZu0JPFWeOGQnTeZmbE4bQ4IH02JtQS1pOkRT9LTGcuvzuCR5JmaHiRGRw/hg/8dMS5xCgN4fk6ON6wdeyz93/Ztp8VMwW80YTK0ERwRRZijrcO1+el9ara00mhp7XA96T1cEy49rf8Tb2xtjq5HIqEi+/uJr+g8awMDBA1n9/WpCw0L5Yc0PTJ46mbiEOF5/+XV27djND6vXkpCYwNgJ43jl+ZfZtmU7P65dR1Jy90KM7diddqqN1QwJGUJlayUTIseTXpNOuGcYg4MHc6w6jWEhQzHbLbTZ2vDV+KKRNEyKmsTOsl202dqI9ommvq2OidETyK3PRSm5bpv8xt6JEmbX53BFynzKWsoZEzGK+rZ69Cods+Jncrz6OJOjJ2Nz2mixGBAEgeLmEvKPFSAJEskBSST5JZJV51ILjvGJZl3ejz0uu83WRllLOVNjpnC46giJfgmsyllD/8D++Gl9abG0MDlqIq22Vnw1vtidtg6RHu0uLco7kpSAZDYUbkISJaK8IylrKTtrNEQ73iHeCLJAzvZsBFHAYXWg8dRyYnMmAbGBlBwpQpREogZHYahpwW61ofPTU3ggn4i8SCoyytH56AhKCObExkxq82soPlyIZ5AnSvVP6s5nrfu6bG4ZdDOFjYUMDh5Ei7kFSVBwRcp8DlQcZFDwQJSSiszaTPw1fpyoyeRg+UEUkoIY7xiGhw0lvSYDpagkwS+eL9O+7nHdn06vnW5YWBi33347ERER+Pj48Morr/Dhhx+Sm5vLrFmziI2NBSA5OZlnnnmGSy65hNraWq666ioGDx4MuAQqr7vuOsLCws6L0200N7GteDsDgwYAsLFwM43mJkRRQiWqMFgM1LXVkeiXCEBlayXpteloFRqGBA/CZDezpWgr1cYaAHRKHd4aH8oNFb1anW5ne8kOpkRPpl9gKrkNeRyrTiPGOxpwLWgUNBYQoA0gQBuA2WFme8kO9pcfIMU/BYfsYG3uD5S2lLKhcCOp/imECqFsKdpCVQ/m7k7n3d0fcOeo25iRMJ31ORvZlruDqQlTQIZN2VsJ9Qjl6gFXYbQaeWnLP3A4ndgcNpYfX4n1FKerVqpZfvx7Gtoaely2r68vDz/+MN9+9S1mk9kViRAZTnNzMzarlcuuvAxjq5EvP/uSIUOHcOXVVyIpJK654RqWfbeUgMAA7rrvLnx8fVhw7QKWL1lGcHAwd971u17Vwe7yPehUeoaHDCevIY99Ffvx0figECTKWsr4MX89A4IGIMsyq3PXUtlaSV5jHoHaAAK1AdicNnaU7GRTwWZGhI1AKSpYlbvKvfLfU7LqstlVupuhIUNoNDWxLu9HV44ERJrMTSzLWk6/QNeC7rr89WTX5bgXJi0OC83mZurb6tEpdaRVH6fZ0tLjsu1OO5uLtjAlZjKDgwaxu3QP2fWuOX6FqCC9NgNvtTcDAwdgc9pYnLnUPW/fZG4kozYDp+xELanJacgl0iuSSK8IWiwGqo01OOxdK2YrtSrG3DiOzM0ZAIy5cRxKjQpzqwmHzYFCrUShVZK5xXU8anA0qVNSsZmsHF17BA9fD0YsGIne3wNbm5Wjaw7j4efJ6GvHIKm6d2OFjUVsLtzCwKCBmOxmfszb4J6usdqtfH38W8ZEjGZM+GgOVh5kV+ke95SUwdKKQ3ZQbaxGKSpJr8mgwXTuCXW61EjjYtqCBLy27Z+UNJWc9fi5xLQqRAUOp6PH20p7k/BGISp6FeJ1tjhdAaFT519nqGdr1s5uzyvg0n2znUWGSNnFsZ4yNXEyby547YzP22N0ZVk+I063/bjNZkOpVHaI2bTZ7CgUUgeNNrvdjiRJnSZy+frYN+wvO9CljV21R/t8XXcP2c5jd3+6FkcPZGS66nM9taM7zpbwRqDjHHdntjll58+KQxYQCNAHdDqYap/HPT0W92zIsuyO0z21f8gOJ4LUeZy/0WKk3ti5U1SIirOmARUEAek8xOM+OOEBJsZOOH8aaRcz59JRzlfA8/k4d2f2/9xNEeB6cnblVH+uw+1YltwhgqE7bTVBENwLZx0/U57x2blq8rXTVXv01MnJ/HzdtPNhx7kiI3dZ/i95P0DPnW07giAgKaUzPhMU57bI3dX1ybKMXf5lrx/+x7cB9/Hr05cToY8+uuY36XS7G2dcVHMiP4Nf9TrOKOzXKf1cs4WdOtrsyzh2rsin/f0b4Tdm7un8Jp1ud2Op/5Wx1q96HWcU9uuUfq4j41Pn8vpG1+eKcNrfvxF+Y+aeTpdzuheP5lQ7P38+7bxYcRHYcLEhn9yxc+EQLrjzFQQBB86Lon+cj7nnc0UQBNfbxwWshouhDc5Gl07X4XBgsVwcwocA9cZ6ao21F9QGAbhYxDGNNiNiDzcJ/NLk1efx/JaXLlj5MxOmc2ny7AtWPrh2G7645ZVzDpo/nzgcDpxcuIdgdXPvwxvPFw6no8ebRn4JnN04/G5HuhfTE8Pp/HmhLOeTi0E/7nzEOJ8vXEH9PY8bPd9IooS3xvuClQ8uZeYLPdpu50L3jQt5n3a3UeNCc3EM2fr4bXARPYD76OO3Sp/T7aPnXCSj6j76+C3T53T76KOPPn5F+pxuH3300cevSK810ux2OwaDK9GHIAhotVo0Gg1Op5PW1la3DpokSW4dtPbfmkwmLBYL3t7eiKKIzWbDaDR2SMOo1WrRarXndDGd5S44lbNplnWmh3Y+6EojTRTEk/l/z9TCOluuhZ9lSxe6XJ3RrkXV2XmgZzkG3OfqIl8BnF277vQFka7qsztkWXbprkku3bXTF7wcDofLTlFwL0C154w4NSew7JRxOB1urbZzQTypy9cZ3dWV+3uCK0TuXBasuiq/s2OCq8LOy6L66Zpop9O5RtmZNnV1DV3RWb/qePzMPtZZWd2dpyt63XOOHj3K/fffj16vdycwufHGG5k+fTqPP/44JSUlaLVabDYbSUlJPP3000RERGC323nkkUdIS0vjzTffZNiwYRw5coSnn36atrY29776O++8k5tvvrlXNomCyIiw4e6s72k1x9lZssvdeF5qLxakXomHygOz3cyqnDVujaw431imx05jyYllIMM1/RegklQ4ZSdHKo+yp6x77anTCdAFMD/5cjQKNS0WAytzVtFsdsnbCAhMip5I/8D+SILIgcqD7C3bB7ic/3UDruVE7QkOVR5mRux0UgNTsDnt1Bpr+TF/Pa29zOGaGJjAfWPvQqPQUt5Szls7F9FsdkUZaJVa/m/6owToArA77RypOMqnBz5nTuolzE2eg93pYFnGCrbl7UBGxkOt5/Gpj7Cv5ACrMtd0W7YoiAwPGcqw0GFIgsTx2nR2l+5xd+owj1Bmxs1Aq9BisLbyfc5KREHkkriZ+Gv9ccgO1uSupdVmZF7ipXipvDDbTawr2OBuv55QV1vHO2++Q2FBIbFxsfzhnj8QGh7qfrDs2bWHzz/+HJvNxsxLZnLF1Vdw9PBRvvjkC2xWG1NnTOXKq6/k8KHDfPPlNxhbjUybOY1rb7y2V/kgPFWeXJY0lwBdAE7ZyfKsFZQbXDpzkiAxIXo8g4MGohCV7K84wK6S3SQHJHNp4mxX7l27hW8yviPKO5IZsdMREChsKmJt7g89GjREekcwN/FSJFGi1ljLmpwfMNpcmcSUooIZcdOJ94sHZHaU7CKrNptJMRNJ8k9AQGRX6W4qWyu5KuVKBEFwJ+5ZkfW9W1+wK8ZGjmFE2HBkWeZYdRq7Sna7nZmPxof5KZfhqfLE6rCy7MQKmi3NTI+bSrxPPBaHhY0Fm2g0NzEv8VK8Nd7YnTZW56ylwtC9kohOqWN+8jxCPUKRkVmRvZLCxiL38f5B/ZgeOxWVpKKkuZTlWd/jq/FhbuIcfDQ+NJgaWJy5jFDPEC5LmouISGVrJSuzV2M8JUd1T+j19EJbWxsnTpxg1qxZPPTQQyiVSt5++21aW1spKCggNDSUv/zlL1x66aWsW7fOrYNWUFDAmjVryMrKYtmyZTidToxGI0VFRQwfPpz777+f+++/nxEjRvTWJEI9QpkVP5OtRdvYUrSNGbHTCdb/JKUyPnIcgiCwInslKknFkNDBCAjoFFpmxs8k2jsKhajAQ6UnQBvAhoKNrMpZTXptRq9tAZiTMBuLw8LKnNVoJDWToia6j3moPBgTMZptJdvJqDvB9JjpCIKIgMiYk4KVepUeSZCI8IrgSNVR1uauZXvxjg4JyHvK/ePuodJQzZu73yLcK5zLB8x1H9Mo1KQEJfNd+mLe2vMOq06sISU4mRsGXcenhz5neeb3RPtGIoquBPHX/7/23jM6juPK3356csYAg5xzYCaYwZyzqGRJlmRZztmW5bD2enf/m9f7Wrtee23ZlrMl2VZOJEWJOWeCIImcc8YAM8Dk7n4/DDBMIJIoUfbOo+Pjwy5MV3V11e2qW1X3N/cBitIWE2WInFDeEVoLm7M3cbbtHPsa9lOUtJi0iKBEk1JQUpS8hB5XL2/V7CLeGEth/BwWJM7DrDGzs2YXDq+DtRlrWJO+Cq1Sy1vVOxn0uULiihNBkiRe+OMLSJLMP//7P6PRaPjjs38KBd3v6+3j6R89zQMffYCvPPEVXnnxFc6fOc9P/+en3PfAfTzx7a/z+iuvc/bMWZ7+0dOs37ieb/3tt9j15i4uX7w8iTcB8xLnEmOM4Y3KN+kc6mRz9qaQRppFa2ZV2koONx7lbNs5lqUsxaq3BsOD9taws3o3u2vexi/6WZ+xjnPt5znYeIhlqUuxXBPc/lboVDo2ZW+kydHMm5U7SbYkMyt+Rig925bN9Njp7KzeTXlPBesy15Jjy6YoZTHv1u7jRPMJNmSuQ0Dg7Zo97K3bh9PnpNvVQ697/HCfscYYVqat4FjTMU40n2RNxioidFfLvTS1CIA3q3YiAEWpSyhMmEueLY+3a/ZQ3lNBrCmGRUkL0Kv1vF75Bk7fECvSl09I4WVW3AzSrem8XvkmDQMN3JW7LaSRplVp2ZKzidKuMt6s3MmMmGnk2XLZnrsVv+jn9co36fPYSbTEsz1nK+U9FbxZtZO86FxybNnj5n0jU54jNTQ04Pf7aW5uZvny5aHpltFoJCEhgdraWnQ6HTExMUiSxFtvvUVKSgpZWVmcOHGC1tbgl1GWZYaGhhgYGEClUpGSkjLpskToIlApVJR1l6NWqBnyD6FRXh2BHG08ilalJSsqixhDTChY+bqsdXQOdqBXBmVwLFoL0QYbd+VuR5IlXi57ZUq6ZGaNmfPt52keaKauv4GcqKsvxuV38fNzz2DT28hMzqDH3QOyTLo1nemx06jtrws9U6wxhhhDNIuTFlHccZHDjUcmPaWK0kXyRumbVHRUUd5ZTkF0QSjNoreQaErkU/M+gSzL/Pz0L8m2ZWHSmXh83mNoVVpeuPQSkiQxJ2kWWVGZlHdVTPikkVKhIiCJ1NrrcHgdBCSRSH0U9NcjyiLv1u1DkkUWJi1Eo9TQ5mzHHXBzuasUu9uOgIBP9KFT6nB6nXQMdtLr7iUrMmPCz+/z+aivrWfD5o2kpKWwdMVSXvzji4hi0EXQ2dmJ2+1m+arliAGR5NRkrlwuRRRFCucXYjQZSUpOoq2ljUHnIHPmzSElNYW09DTq6xsoXFA44bJkRGZwpesKjQNNKBVK7s67C6PawIDXwYDXwY9P/4QoQyQLkxbg9DkZ8g2RFZmJWWNiesw0GgYaeL3iDX5X8gf0Kh1LU4sY8g/hl8Y/jGFQ64kzxvJa+ev0uvqo6Kkg3hQfGu3X9dXzm+LfEWWIpCC6gGZHC63ONp6//Cca7I3MjJuBT/Lj9rtpd3aQHplGrCGGX1X8FrffPW7+CeYEPAEPF9svYdAYWOJaTJwxDru7H4AjDUdQK9XkReeRYE7gZOtpcqJy0CjV3JW3Hb/o553ad2l1tFPcUcKAZwCVQonT65qQ2yMrKpOy7nLq7PX4JT95tjwsOjO9rj58AR+/vvA7NEo1K9KWISHTMdhBakQKQz4XH53+IB2uDo432Xmn9l3q+xuJ0FpQCEo848QRHo0pL6S1trZSX18flNJubsbtDlb8rl272LFjB3/7t3+LXq9n7ty59PT0cODAAZKTk9mwYQOVlZVcunQp5CM+fPgwzzzzDL/5zW9wuSY/mlNc438cCV0nXPNog/4hfJKfSH0k/uH/nx47nQRz/LAhE1EIAn7Jz7u1+3it4nV63b2syVg9xQ3mV8Pn3ajXJsoiA94BLFozaoUaURaxGWysy1jDkcajDHgGQr66S11X2FXzNocbgyq1147eJ14SGb8YnHoGZPG6GKsKhcAfip/jv4/+iOL2i3x6wScwa83oVXreqd7LxbYSvrjkc2TY0vjcok/zwsWX6HPZQ7pe4yEMP/+Ij12SJRTX+FIdPgeiLGHRWhAEAZ1KR6uzDafXyfacbaRYkjnecoLS7jLyo/P5yLT7WZy0EMUktOtkScYfCKDRBj/CKpUqGNN1uJ+OaLIpFUoERTB8pNfrQTEcq1VAQKVWBdcqFITCVKo16tD6xUQJvu/gR9Mn+VAolKFnkWQJu8eORWNBPTxgMGvMlHaXsrN6F7tqdpMdmU1BdAH9nn50Kh1GtRG/6EenGn8NREAx7C8NaqT5RB8ahTrk2/aKXhxeB1adNSjbpDHj8rmo66tnScpituVu5WjjUfqH3WSr01dR2l1G7wSDeY/ECZaRkSQRURJDzwng9A3il/xE6IKquza9DZVCiU6p40jjUeweOxuzN9DhbMfld3FPwQ6SzImcazs/oYHISJxgCJ4cHImdO1KmPncfaqUag9qAQHCwYlAbGfAOsLduHwnGeBYkzqe8p5J4Uxwfnfkg7c4OmgY+IGFKQRD4whe+wE9+8hO+/OUvc+rUKSoqKgD4yEc+wqlTp/jhD3+Iw+Hg9OnTlJaWcuHCBc6fP8/3v/99fD5fyMWg1Wr5+te/zu7du3nttdew2WyTLk9ACqAUlKH/mTWmUKBxhaBgcfIiYo2x7K3dx97afSxLKWJx0kKSzEl8bt5nyIjMZHvuNkDgbNtZavpqudhRglFjnJIYJIBxWJZap7peZifBlMCylKWUdpfx3KXnMaqNbMzaQKo1la05m1mYuIDV6atIiUimqreK8u5yrnSV4pf8o2pWjYdCUIROapk0xutGJWJAYm/lfi60XGRv9X4UgoBKqaDR3sSbpbvYU7WXgChSmDyXvJhcvrHya6zMXM5Dcx4kNTJ53LxlOfj8ymEBRI1SHfoYKQUlq9JXYtaYeKtqJ+c7iimIzifaYONjsx7BoNbz+8vP0eJopcXZwp7aPbQPtnGp6zJDt1AiHg2lSonRYKC7M3h83N7Xj0anCWmsjaw/OJ1O/D4//X392Gw2PG5PUNtNlnA6nBhNRpQKJR63J6TVZjIZJ1wOAKfPiVljRhAEbHobPtGHLzCskWawsS5zLeU9Ffz6wm+J0keREZlOZU8VlzqvcLkzqJ1XEFPA+qx1tA928ELpi7Q5W5kVN3PcvIOaYB4sOjMCAjZDNM5rNNKyojJZkDSf860XeK38TTIi07Hqrdw77R7mxM/mpdKXudB+EYAYYzTRhmjKussnvLjmDrhRCAr0aj06tR6jxnid8kZR6hJijLG8W7OPY80nmBU3k4Ak0jjQxIX2Yko6LxFjCOb7iTkfR6PU8OsLv5uQLxkYFnc1DmukRSFKYkiOSqPUsC13C06vk+cu/YleVx/ZUdnBkXlHCWfbzlNvbyDBnMD02AI+OefjVPfW8Jvi303J5Tcl94Isyzz77LMcOnSIc+fOERERQXR0UPhPr9eTkJDAunXr+K//+i86Ojq4cOECUVFR/Od//ic6nY5XXnmFAwcOsHLlSiDokoiKippKUQDodfUy5B9iaVoR/oB/eLVRZnX6KhoHmkg0JbAkeTFvVe0kNzqHHncP79buxaQ1o1Go2ZS1gYsdJcyMm06cIY6d1buZHTcLp9eJdwoaaZ1DXcyInU6vq4eC6AKKO4rJicoh3hRHQ38jazJWIyPjFb0Y1HoudBRT3HER5fAHwukdBFngkZkf5bXyN9CoNAgopjSVaexvYk32aga8DuYmzeH5i39iaUYRNn0kSpWSu/K381/HfsiarFX4xACH6o6yJHUJRemLyYhKR6lQcLrxLF3ObgQEHi18mHZnO71D42ul+SQfPtFHYcJchnxDyMj0uHtYnb6KpoEmsiOzSDIlcqLlJGmWFFqd7SxImI9Bpedw4xH0Kj3RBhsxhhjmxs/lUONhVqauoG7YBTMR1Go1edPy2PfuPvIK8nh7527mzJtLU0MT58+dZ/O2zZjNZl57+TWSkpNoaW5h5uxZ7Ht3H7vf2k1GVgYd7R2kpKZgjbSy79395Obn0tzUTE5uzqTeRVVvNdtzttHQ38DipEXY3XZ0ah0r01dQ2VvJxqz1+EU/A94BlIICt9/NI7Me5njzCdqc7UQboyntKWNpShE6pY4aew1J5iRKOsf3LQ/5hmhxtLImYxWnW8+SH53LW5W7KIjJJ1JnxSv62Ji1HqfPiU0fhSRJJJoTmBM/mzcr38QjeokzxdI12E2KJQWlQkmDvXHCz94y0IJOpWVZahGiLGLVRdDvGWBF2nJaHa1kRKRRGD+XPTXvkBqRgsvvon2wnaLkJWRFZTItpoA+j52FyQvRqrScqTuLUWtERJyQhFRFTxUfm/kIlb1VFCbMxel1ohAEtuVuobj9IkXJQcHPy11XiNRbcQVctDnbmB47jV53HykRKdTaa9mcvYmqvmqq+qpIsiTSMdiBawLulWuZtNGNiYlhx44dCIJAZ2cns2bN4h/+4R/Iz89n9erV5OfnA5CQkMC2bduIjIykubmZr3zlK2zcuBG1Wk1ERASCIOD1etmwYQOpqamTLcZ1dLt6eLtmD4uTFiEj82bVTnrdvczXFdI1pONAwyG2Zm9mbcZaBn1OXqt4k/bhFU+1Qk2sMZaq3mpq+mrZmLWeTVkbcAVcvF29Z0rbUvbW7WNbzlaKUopo6G/gTOsZcqJyMKqNtA+2c6jhMAXRBcjI7K8/SEV3RWgKrlPrGPIOUdFbhVljYlHyQkRJ5K2qt2ifxIr9CD87+QyfXfRp7p1+NyebTrG/6hDrc9di0Zp5vfQtUiJSeLzwMTx+Dz848t/UdNfwp5IXuHvaXfhFP98/9BTN/S009wenUfHmeDqcHRMabTq9Tt6s2sn8hEIUgpKDDYdpc7YzP2E+zTTxds07rEhdxpr0VfS6+zjWfIwlyUuwe/pZkBBcUG1yNHOq9TQ5UdksTVlKj7uHI03jSxSNIAgC99x/Dz1dPTz946fJzsnm7nt30NrSSmNdIwaDgSe+/QS/++XvOHfmHF/82hcpmJ7Pk996kl/+4pecPnmaT372k8yeO5uvPvlVnv3ds5w/e46Pf+rjZOdObhGlvLuCRHMiy1KX4gl4eLtmD2qFGqs2AofHwZuVO5kWE/S576vbT3lPBa9VvM7ipEVkR2ZxsvkkJ5pO0u/upyhlCUnmRC50FFPaVTZu3gEpwL66A2zIWs+ylKWcbT1PeU8F02OmEaGLYF/dAaINNoqSl+CX/Lxc9gp+yU+jvZFp0dMosMkMeAc4UH8It99NeXf5pLbvOX2D7Kp+m4VJC5Blmdcr3sQb8GLWmDBoDOyq3sO23C2syViNy+/iteo3sLvtRGqtrE5fhcvv4o2KN5kWU0C/u5/FSYsAqOqr5rT7zLj9tKavlmNNxylKWYJf9PNG5ZuoFCqsOiuiLPLHy39maWoRGzLXcanzMieaT1HWXc6m7A1szFpPnb2e0y1n0asMGNVGVqUFB4x76/bRMIaE2GiMqZHm9/vlD1OUsX859G/U9zfcMl0pBPdg3mo/qkapmVAEKLVSHfKDjp7P6NpM1yIQ3FJzq608KoUquHd0nIY7lmbVgMtBceOlMX8Pw1I3ShW+wOhl0ajU+MXAdVNFlVKFJInjRkwaIdpsY3ba6NNcpaAAQRj1vQgMl22M+g6VaQyNswdm3s/C5AW3/K0kyQT8ftQa9ajvLhAIPr9KpQotLklisN5HrsFw9C5RQqVW3XQfb8DLd/f8HV5x7D6jUarxS4FRp+Yjuxmufc4R98yN1xSC4pbtS4Fi1Gh4AsKwXt7ovxtZZ3i/AtaM7DSYTB9VK1STihoWkMRb1kvQxTX68ykEBSqF8qa2OJ49uJEnVzzByszl/zc00kRZHHNlfaIh9yZTwbdCRh5z7+REtahuh2aVLMu3NLjAqGmB26ibJsrSLd9LcFFnYvX9XupCoRDQaDW3TL/xsIMgCChVSpRcv2inVCrH1HybCGM972jPOFq7FmVxSgdFrl1YHY3345DQtYx3QGe0Pno7wzSOVfeSLOETbzbGt8MeXEv4GHCYMGHCfICEjW6YMGHCfICEjW6YMGHCfID8xfl077SSxXgBOz5oPizlgDtfljudP/AXL5r418CH/RWMaXRVKtV7XjS4rQh3VgYEghv+fe/zYsNE8fi9SNKHwNAAoijhH2Ox7v3mlcuvTygIz/uJWqnmyWVPXHcE/U7xwqWXqO2b+H7m281E4iG8X+gUemI0U4tUeDswqMfOe0yjKwjCFI/B/hUzRujIDxrpQ1IO4I7XicPruOMfZI1SQ7TBhk49tVOMt7UsKs0d77t3Kn+lQnHdEeMPGmEc4dqwTzdMmDBhPkDCRjdMmA+CiUwEPjwTlzDvI2GjGybMB8FEZtphT97/CcJGN0yYMGE+QKZkdEf0zhoaGujp6QlpnLlcLux2O/39/bjd7tAWnhH9tMHBwdC/HQ4HTqczpEPl8XhC0fynykj801sx2ln09/J34zESr/NWeSgmMLQRGPuZRp+TjqZt9t6fSWBqgzEBYcwYvLeq75E4BLeLsd7reG0n9Hfj3Gc8ZFlGDIjXbW+7dhFSkqSb+sFI3Omb7jPF/jJW+W8Vr+HGthxsvZNvDeO151v1GZWguulXk20fI/E0brW1cCT9/WbSrVoURU6dOsW//uu/0tfXh1ar5Z577uHzn/88P/jBD9izZw9qtRqFQsG8efP42te+hsFg4O///u8xGAz853/+J6+++iq//OUv2b59O/fddx+/+MUvOHnyJJGRkXznO99h7ty5k1r5FASBufFzWJKyBFmWKOm4xKmWU6GA0SaNiXvydxChi2DQN8ju6rfxiX525Acj98uyzOmWM1Tba9iYtZ44Y1woElHnUNdkq4gofRT3FtyNUW2gz93HG5Vv4RhWoBAQWJa6lLkJs1Gg4ETLSZodLWzJ3oRBbUQhCKgVal4oewmb3saKtGUgy5R0XuZY04lRztuPVk9Xr6VHpfL5JZ/BpDHR0N/Iz0/8kkFvMEKYXq3jG6ueIM4YG9SEa7tIdU8N9828B5VChVoRjH/7zZ3fITcuh0/O+ziCIHCg5iC7yt8Z9xy9gMCc+NksTFqAUqHkYkcJp1pOh3YZxJli2ZC5HpPGRL+nnzcq3wrFJ02xJLMtdyu/Kf4dcxJmMydudkiXq98zwHOXnp/w+7BozdyTfzcRWjMDHgc7q3eHJGYEBAoT5rA0pQgZmYsdlzjRfIIlyYuZl1hIQBKxe/rYU/MumZEZLE5ahEqh4mJnCYcbjk5414YsyzidTp7//fNcOHeBmNgYvvS1L5GUnBQMWi9JnDpxipf+/BJul5sd9+5g/ab11FTV8Iff/IHe3l6KlhXx0Y99lMb6Rn7x01/gdDopWlbEAw8/MCEx1wRTApuyNqBRqmkf7GRv3T7cgWBYQqWgZGXacnKjchBliWPNJ6joqQgqrkRmMjN2Ji+VvYxCEFieupzsyCwQBA41HqKqt3pCdVAYP4d5CfNCOoZn286F6s+sMbMleyMR2mBYxV3Vu7F7+gGIN8WzIWMde2rfocsVjIucYklhReoydtXsDgVWHwu/x0/F/jJaSlvQm/TMvbuQiERr6N30NfZS+u4VXAMukqYlUbB+OsWvXcDZ5QAZ/F4/WUuycXY7sLfYg9c8fvJW5pG2MGNS9mpSn2xZlrl06RKPPvooBoOBr33tayxevJj/+I//YM+ePdTW1tLV1cVdd93Fhg0beO211/jMZz5Db28vFRUVlJeX88orr/DEE0+QlpbGY489xp/+9Cf+8Ic/sGHDBtrb2/nOd77D0NDEg1RDsDFtzd3CqZZTnGw5xZbczcSZrqosrEhdhkFjYHf120ToIpiXOI9EcwJJ5kT21u3jndp3qe2vY0nyIuKMceyueRuf5GNLzuZJlWOEHXnbEASBXdVvE6WPYk3G6lCaWWtiXeZqjjefoKqvmrtyt9Hv7udw4xH21e3D4Q2qKehVOu7O287p1rMcaTrOlpxNJFuSJl2Wry3/MoO+QZ458yvyY/Ju0EjTUZg0hzcr3+IXZ3/Jnqp3udJZxu8vPMsfip9DUAp0DnYRa47lmyu+zjvVe3nh0ktkxWRi0o4fwNuqi+Du/Lso6bzEkcajrE5fRbo1DQhGHluRtpwh/xBv1+4hzZrGgqRgOEeNUsPmnE1kRmaiVCio7KniYMMhjjefwKQx0j3c8SaCgMCa9NVYNCbeqtqFWqliXdba0IjOZohifeY6jjYd43z7Be7Ov4sofRSZUZlU26t5p/YdDjceJSCJbMvdSnHHRc63X2Bl2goi9dZJvAk4tP8Ql4ov8a3vfouExAR+/r8/J+APjmDtfXZ+9NSP2Lh5E5/70uf43a9/T0VZBT/+7x+TlZPN177xNfbv3c+Joyf40X/9iOkzp/OVr3+FvXv2cuHshXHz1iq1bM7eSK+7l3fr9pETlX1d8PPsqGzmxs9lX8MBGgea2JS1nki9lTXpq7iv4F7SIlIQBIGMyAwWJs7nSPNRKnoq2J69FbPGPG7+Nr2NNelruNBRTElXCesz1l2n7VaUsgSdSs/e+v0YVAaWJC8BhsU8s7eSbk0LBfHXq/Ssz1hDVmRmSOdsPNrL2mguaWLhA4uISrVx/pVziIHgoCHgC1D8+gViMmNZ+NBiGs430F7aSu6KPGZvn0Pq3FTEgEhUShSZS7KZvX0OafPSkWUZc9z4+nQ3MumR7vHjx2lpaeHNN99k5syZ7Nixg0cffZSYmBhee+01oqOjefDBB0lKSkIUxdCIGODs2bOUlJRQUFDAv/zLvxAREYHVauWb3/wm999/P8XFxbS2tk76ZFGkzopGqaGk4xIapRqX34VGeVVl4XjzSTTt50m2JBGpi6RnqAeL1kKMIZq7crfjFb28Wv46NX21XOkqo2uo6ybJn8mVJ5IzwwoUVb3V5NlyQ2kuv5ufnv05RrWB+QmF9HnseAJeavrqiDXGEmeK4+WyV1EpVOjVBi62lyAIAk6fE61y8soRMcYY3irfxaXWUq60lzIjbgbwIgBmvYkEUzwfm/MIfjHAz049w7mmC5S4rrAyexmyLPP0yV8wK34GMcZo1mevwaKz8E71Xly+8SPmqxRqZGQqeioZ8AywLWcrNr2NOns9oixxoO4QPtHH3MTZqBUqOgc7UQpK1mSsxif6gvpxQL+nn35PPyvTltM11M3hhiMTfn6NUk2sKYYLHRep72/kdOs5tuUGFXElUcLhdfDbi78HYF3mGjqHOlEr1SSY4ki1JDMzZiYlnZc4WH+Yn597BrVSzcq0FXgD3mCw+UlQXlbO/EXzycnLQRRFvv+v/8ng0CBWqxWXy0UgEGDegkKiY6KJjY2hsaGR9rZ2ipYtoWB6AdOnT+fs6bP09vayZt0aUtNTmTlnJpUVlSxdsXTMvA1qA/HGuGCsaVcv5T0VJJjiERCQkWkaaOLZy89jUOlJj0ilY7CTQd8Qx5pP0O/ppyglKBxpVBsZ8DpoHmjB5XexNKWIWGPMdSoQo5FoTsAb8FDccRGDWs/8hHnEGWMZ8AZHqadbz6AUlKRGpGDT2zjTdhaFoGBJ8mJanK1E6YNiqCMj8j63HYN64sodvY29RGfFEJVmQ6VV0XC+Dt+QF32EAaVSwYxNM4lKjUIMSCgUChRKBREJEYj+AFf2XCZ3eR6RqVHBEJu+AKXvXCF7aQ5RqbZJ70eetFXp6enBZDIRGRmsBK1WS2JiIgaD4bq/EwSBpKQk/H5/SPfMbrfj8XioqKigqqoKgE996lM88MAD/OAHP+DgwYN88pOfxGQyTe4hBAXysF9ZloOh6671TQ14B/AFfKRGpCLLMgaNAYfHweHGI+yq3o074GZD1jqaB1roc/exI+8usiIzOTqJYNk3MnJqTZTF6zZLB6QAXUPdxJniidBZcfldaFWaYbdDEX3uPmr6aoOdQZYAORia8YZnmiiyLIUkYfySH9U1PjO1Ss2LV17h6ZO/oKavli8XfQGFIGDUGrlnxg4O1B6ib8iOTqUj2mCjtKuc442n+PzizxJvjp9Q/gEpMPwcwbq49hl63b2IskiyORnFcHzYHFs2GZHpHKg/iCSJjLhKLFoL8xLncb7twqQUNARBgVqhDsW4lWQJtUIV8iv6RD+dQ13Y9FHY9LaQY6a6r4Z3avdxuPEIq9NXkRqRQsdgJ9F6G9H6KHyiF4t2/BHetYh+EZ0ueHBCp9MhiWLILyvLcrCzD/veNRrN1Ri/6uDYSKvT4nIF10rUw2EqNRoNft/4JwEVgoBSoUKUAsOqJZ6gRtqwwXAH3PS5+0gwJaBXG9Aog/d3DItmjtDY30isMZa78+5ie85WLFrLhEabKoUKmWD9ByQRvxgI5QHBD6tf8pFkTgQBjGoD+bY8UiNSOd121SWVZ8sl0ZzIocbDiJMI+ShLUqjOVGolsgySGBzcCUoFcXnxuAc8HP/1UcyxZuLy4pFlma6aLgZ7B0meExTMlWWZzsoOnD1O0hekh2SfJsOke3FeXh5DQ0OcO3cOURQ5ffo0W7Zs4fe//32oUKIo4nA4OH78ODExMSEDvXDhQv70p6D89T/+4z9it9tpamri8ccf5+DBgzz99NM89NBDk/5yBKRAUORPUKAQlJg0xuEOGzTIC5LmE6m3srNyF3vrghpp/d4BDtYfpqKnksMNR0kwxRNniuPxOY8RbbDxm+Lf0TBGwPTxMA1/hbVKLYFrjg3Hm+JYmrKY823n+eWF3xCps5IZmYFFZ2Fm7HSON50ARgyUMmSMzBrTlOKnKgRFaBpn1BivM1guj4uXL77GqYaz7CrfjUIhYDGYSbOmUhCbz8Gqw0Awxmmvq4/nzv+JN668Ra+rF5NuYqMMnUoXFH0cRSNtedpSDGoDL5a+zIWOC8yKm8nK9BUkmOJ5aMYDJJoTuadgByaNkczIDPRqPWXd5ZN6flEScQfcROmDclAGtR6X/6qCbJwxlpVpy6noreL5K39CpVBj09u40B50I5xru4DL7yLeHMfqjJVU9lbxi/O/RqfSkx+dN6myGIwG7HY7sizT29OLWqNGowkaAqVSGRww+P1IUlCXTa/Xo9VqcQ0Fy2u324mOtiEgMGDvRxKDWm3GCWi1iZKIV/RiUpsQEIjURTJ4TT2kR6QxO24Wp1pP81rlG2RFZaEfRfDS6Rvk9co36HX3UtxxkV5XL45xRrkAnoAHhaBAp9SiU2oxqA0M+a+6EecnFGLVWnm75h1OtZ5iQdIC5sTNJsEYxyMzHibWGMuWrE0sTlpIoimBx2Z9jLSINDZmbRi1nDei1qnxONzIkozb6UVQCKg0VwcgHRXtHPvNEeLzE1j08BJUWjWyJNNwpp7EaUlojdpgcHtJprmkicRpiSg1U1vonfSvli5dyqxZs/jOd77D4cOHOXv2LHV1dSxevJhz587R2NjI3/3d3+FyuThw4ACPPfZYSP8sOjqaNWvW8MQTT/Bv//ZvPP3009jtdt5++202b97MuXPnaGpq4rHHHgsZ6onQ6+7DHXCzJHkxPsmHWqFGQmZZ6lJaHK1kWNNZmlLE6xVvkG5NY9A/xJKURdj0Nt6ofIvFyQvpGOpkfmIhVp2VV8tfA4ILYn3u8fWXbqTb1c3M2Bm0D3YwI3YaFzsukR2VRZwpjjZnG9tytyIDnoAXvUrPoG8Ig8pAlD6K9sFOAOxuO06fk6LUxQz5htCpdFPSSGtxtLIqawXdrm7mJs3mhUsvsShtAZF6Kya9kS25m/n+oR+wLGNpsLO7h4hNiKZrqCvkQqiz1zPgHWB55lJ8og+bPgqXd3xdKL/kIyCJzI6bxaB/CEEQ6HH3sCJtOS2OFmbFziLFksLRpmMkmhLoHOrifN0FLDoLZo2Z1ekrudJZik/0E22w0T3UPelA5n7JT+dgF3Pj51Bvr2d52jKq+2qJM8WSH51PY38D6zPX4hU9uAIeIrQWTBojH535IC+WvhRU21Vq8fg9PDTjARQoaHW2oVNprzMaE2HajGn89pe/ZeXqlex8cydp6Wl4PV72v7ufwnmFqFQqjh05RlxcHD09PaRnphOfEM+BvQcIBAJcuXSF7/zdd6isrGTPrj0MDg5SUlzCjnt3jJv3kN9Fu7OdlWkrON12hoLofHZX7yHHlk2ENgJk2Jy9kUHfIDa97ZbH3Q1qA2vT1/Bu/V6SzEl4RS9dE1hsbnW0olfpWJi0EEkWidRHYvf0szBxAR1DneRG5VIYX8ie2neIM8bj8rk40XKSit5KlAolUbpIynsraXG0YNVZUSvVrEoNfgQnEnjdlhZNzYkaumu7aTxXjznajCxD9ZEqEqYlcGlnCYnTEkmdk4JvyINCpUD0i/Q29TD/IwuvKof4RHrqeyi8d964ed6KSRldQRBITk7m2Wef5amnnuL8+fNERUXxzDPPUFhYyMyZM+ns7KS7uxuTycQ//dM/8fGPfxxJkpg/fz4GgwGlUsknP/lJmpqaqKqqwu12s2HDBmRZ5sKFC6SlpTFZiaDuoW7erXmXxcmLkZHZVf02fe4+ilIW4/Q6OVB/kB15d7E9d1tQf6n8ddwBD1uyN3F/wb24/C52V73N8tRlDHoHWZ2+CoBaex376vZPqiwAu6rf5oFp97MpawPtgx0caz7BtOgC4o1xnG05y5HGYyxKWogkSxxsOEyzo4VYQwwXO0pChrXH1cvumrdZnrIMkNldvYe2YV23yfCzE8/w5aVf4BPzH+NS+2X2Vhxkc/5GrHoLL116lRxbNl8p+iJ+0c9TR/4HURLRqbW8U7kX73AU/8a+Zp45/WvunnYXkizx81O/onWgjeDWtFvPSgY8Dt6sfItFSQtRCAoONxyh1dHGspSldA51sat6N2sz1rA9dxtOr5MjjcdCHzmz1ky0wUZFT+Wwi0LmYkfJlGI8HGs6Hhwp5Wymz23nePNJonSRpEekcbjhCPvqD7A4aTGiLLG//iDFHSWolWqWpy4DGd6p20tJ52UMagNz4mczI3Y6p1rPcLmzdFLlWLx0MZXllfzPU/9DZFQkT3zzCYYGh7hw7gJLli7hy098mef/8Dwet4fHP/04WdlZfP7Ln+eZp5/hJ//zEzZu2ciceXOIsEbw0x89TemVUrbetZVp06eNm7df8rO/4SCbsjayLmMNl7uuUNFbyYyY6cSZYnm3di/xpjjWpK8mIAV4o/ItnMM7bgZ9g9TbG5BliUHfIBc6ilmStBiANyvfmtCH0OFzsr/hQGj3wp6ad/AFvMQZ4/AGvLxT9y7bcrayKWsjXtHHm1Vv0THUScNAI0pBSZollbLu8pCfX61QE62PprK3akL5x2THkrkwk/OvnkVr0DLv/vmIvgAdVe3Y0qNBgI6qDjprO1Fr1czcPAtTtBlLfERolwMAAthSbZiizVOOLTGmRhpjHEyUJImhoSG0Wm1oivR+8ze7vktNb+0t00fTlxpBQECj0tyk7qtRavCL/klt/REnEFhFIDid9t5CIkg9vMg0XoNRCUq4QR9rBIfbSVlzxfhlEQQ0KjVe/+hl0ao1+AL+cRcwg5GjZETp5ue3maOYmTJ651cqlAiM/gy3ei+TxS8Fxgx4E9RiU99Sskk93HaulYZRD+vYBa5x66iG/cGjja40Sg0/2PwfYwa8kWUZr9eLWq0eNYJfIBBAkiTU6qv+VlEUCQQCaDSa666JAfGWEkS/OPNLKrorb7oe1AFT3bIeNEoNojS+FJBSUCIj37LOFbfQERy7j4JGqcUn+t5TACWNUoNJe/O6kCzLiH4Rheqq7/z94GNzH2V+cuHt10hTKBSYzZNbSHi/GcuAycijduyJ6qZNluBixa3vPVEtqsA4um8TKoss39LgAmOmXct4+3Kn8rtbvZfbTVCLbaz3cXPbGe3ae9WsEwQhtJg2GjdqtcHoumxT1WoL6oDduh4m2h8mv74QnBWN3UcZV9TzvSAIAqop+mFvJ+FjwGHChPkACAeWGCFsdMNMknAorDBh3gthoxvmesa1qeERS5gw74Ww0Q1zPWGb+pdBeMLxF8uYXuVAIIDP9/4sNE0Fi9ZCjDH6jpZBksdekPkgEWRh1IWXO4FWrcFwB3WpfKJ/ygt9twtBEPj56WduW5S698LS9CK25m25I3n7RB/PFT9/x95HQArQN9h7R/IG8I2zMDxmj5VlORS28cOASqG8o9pHEFz9lbm5Tsbesfr+oFIoUUzhGOL7QfCY6Z0TI1RIIrLizg//2p0dd1ybDIKR9ZIjJh8g6Xbg8Xtue1jOyRCQAu95l8l7QR5nS+mEPskfCmnrDzl3vpuFCRPmL4EJGd0Pw5c7TJgwHw7utPLzXzp33vn0V0q4WYb5sHG7jOVUFCPCXOW2G92wKyLIX1WzDL/SvwrCxvLDwaSVIwKBAA6H4ybNJgieB+/p6aGzszOU7vf7GRwcDP3b5/PhdDrx+Xy33UCPp780qv7TqNduT+McU5PrFmUdS1ftvaCcwoq6UjF8fn64mAphqt12Cu8F4abr73VXwGTfx/j6dFMtx63vKTC6cbzx/d3q78ZjJPTqrfrejWkji+k3LqiPdm0iCOMo7Y32jkbvt1N7L+O1oYnm/16Y9BJjaWkp3/ve9/inf/on5s0LhjeTZZnOzk5+/vOfc+bMGSRJYvbs2Tz55JNUVVXxq1/9ii996UtkZWXx4x//mMbGRp588klmzpw5Tm4TQ0AgOyqbOfGzkWWZip4KSrvLQtMprUrLitTlROqsDPldnGw5yZDfRVHyEmIMMXgDXo41H8cT8LA4eTE2fSQuv5tDjYcZ9E1OHQDArDGxLmMdRo2RPncfhxoPh7S/BARmxc1kRsx0QOBCRzHlPeWoFCpijTHMip3F3vp9iJLIwsQF5ERlIyFjd/dxtOkYQ/7xFRuuJdGSwCfnfxyLzkKdvZ4/nHsOlz8YllGn0vL5xZ8h3hSPJEtc6Srlj8UvAGDSmvjWiq/z0uVXKe+sYEvBRpalLcUv+Xm97E3ONxePO10VEMiz5TI3fi5KhZIrXVco6bwU+l2UPorlKcswa83Y3Xb21e/HJ/rQKDXMjp+Fy+fmSvcVtEoNK9NWEmuMxT38XnrdE98SZFAb2Jy1kQhdBP2efvbXHwwpFgBMiy5gcdIiZGQud12muKOEnKhsFictQiEoKO0u41LXZe7NvxuNUhMSsTzefIKK3puDytwKvUrHstRlJJji8Ype3q3bi93dH6qrWXEzmBk7A4WgorijmMtdV1AKSvRqPZuzNnK46ShdQ12kW9NYlrwUQRCo6avhTNu5cWMhyLJMe1s7v/7Fr2lvb6doaRH3PXAfekNwm58kSRw+eJg3XnkDvUHP/Q/cz+zC2Rw/cpw3X3sTQRB4+LFHmDVnJscOH+ONV99AoVTw8KMPU7igcEJGMD86j9mxs5CQKesuo6y7PNQW9Co9K9NWYNVZGfIPcbDhEJ6Ah1XpK7Hpbbj8Lk40n8QreilKXkKkPpJB3yCHG49MqI9qlVpWpC0nNSIVv+RnT827dA11htLTItJYnrYMnUpHQ38DB+oPkmRJYnX6KjRKDQ39DRxvPsGq9JXEG+MRhKAySklnCadbzoyb/7VM2oT39/dz5MgR7HZ76JrX6+Wb3/wmr7/+Otu2bWPr1q3s2rWLf//3f6erq4uTJ09SXV3Nd7/7XV5++WXuvvtupk0bPxzdRIk22FiTsYrqvmqqeqtYm7GGaMPV/byLEhcSb4zjdOsZVAoVS5OLyLflMT16GqdbT2PWmihMmMvSlCJi9DZOt55Fq9SyOm3VlMqzPnM9erWeky2niDfFszhpUSjNoDawKm0VpT3ldA51silrAwICy1OX8cC0j5AdlY0CBQpBQWZkJi3OVs60nuVS1xU8UwgM89WlX0KhUPDilZeYlziXLQWbQmk6lZ6i9MUcbjzCi6Uvc6T+KBD8SH1+8adZnr6UKH0kC1Lm8Vjho7xa9jrF7Rf51soniTXFjJu3RWthe+526vvrKem8xKq0lSRbkoHg6KFoWAfrRMtJcqKyKYyfi0Vr4Z78u9mctYnIYYmWRUmLSLWkcqLlJEqFks3Zm26Z540ICKxMXUGMMYbDTUeI0EawOn1laPQSpY9kfeY6SjovUdlXxd15O8iMzOTuvB1U9FZwruM8m7I2kGRO5GzbOc60naXPY8eoMYaEEyfKzNiZ5NlyONFyEkmW2JS5MTSCtWjNbMhcT3lPJY0DjaxKW4lFa2FG7HQem/0oM2Kno1fpMGvMbMvZSoOjkeLOiyxLXUpqRMq4eXu9Xn7yw59gi7bxpa9+icMHDnP44OHQqLamuoaf/+/PeejRh1i8dAlPff8pis8X85Mf/ZR7P3IvG7ds5If/339z6eIlfvI/P2Hb3dvYuGkjT33/KXq6e8bN36qLYHXaKmrstdTZ61iXsQ6T5moksIVJC7DqrJxtO0ukLpKFSQtYkDifNEtaSLpnXcYailKWEKWP5ETLSfQqHSvSlk9odlgQk8/chDmcaDmByz/EjrztoS2OWqWGbblb6Rrq4nTrGRYmLSTXlsOOvLtCwdqXJC8mxhhDWXc5p9vO0OxoIVIfSa9r8vG2b8u4uauri/379/Pwww/z+c9/ni984Qu88MILPP744wB4PB6eeuopdu7cyde+9jW2b9+OSqW6bdP4CG0EGqWWK12lVPVV4xW9aK7Zz9vkaGJ/w0FaHK0M+YYQFIrg11GAzMhMTFozTt8gEboI2gbbaRpoon2onQid5TpJkYli1Vmp6qum1l5Lrb2W1IjUUJpX9PLn0hfoHOzEqrPiHP5KF3dc5FDj4VCUJbPGjE0fRZ4tj5VpK0g0JUxpISTeFMexhuOcayrmcscVZsfPCqWZtEZiDLHcXbCDx+c+FjJyS9IWYdKaaBsMxu81a82IksjF1hLONxeTYErAoDaMmt+1BMMgQnlPBVe6riAhBwNkExx5nWk9y4GGg0TqrAiCArvHzqBvkAP1ByjtLgvdp9nZwjt179LY38iA1zHm9PxG1Eo1scYYSjovUdtXx5m2s2RY00Mddcg3xMvlr9Aw0ECMIYY+dx8Or4O99fs4336BVkcbCMGoWlV91TQ7Wsi2ZrG3bh+d14yUJkJmZAaXO69Q01fL+fYLROgsoXoc8rt4/sqfaXO2Y9NH4Ql4cPvd1NpreatyZyjOsl6tC45we2uos9fT7xkgzZo6VrYADDoHqampYev2rRRML2DewnlUV1YjS8E21VjfSHRMNIuWLGJJ0WL8AT/Njc1YLGYKFxSydPlSEAQul1wmKiqKhYsWsnbjWhQKBS1NLePmH29KwCv6KO64SEVPJf3efqKvOeh0uesKe+v2oVKoMWtMdLt6sOqstDhbqLfXU9FTQaIlCYvGQouzlZaBFpoczSSYEibUR7OjsintKqO8u4LTrWcxaowhRRW/FOCNyje50F5MrDEGWZboc9t5rfw1zraewzrcPgc8AzT2N1LVU02yOZlzreeo7bt1qNlbcVuMbl9fH36/n+TkZARBQKlUkpaWRlpaWih+aGdnJ16vl7Nnz47qD34vCIICWQ7q2Y9opF3rq23ob8Tu7qMoZQl5tlzOtZ3HE/AiIBBrjEUtqBAlkeq+GgoT5rImYzWzY2ehVWpDcVZvxa3M4IhEjyhJKK6p5oAUoHOokyRzEjGGoKCfWqmm39NPn9seik+qEBQ0OZo503aGip4K1mWsJdYYO+m6keWroRN9ov+6AwwGrZ49Ve/w3MU/0uPq4atFXyLDls5Dsz7CCxdfwj3shhhpWF9Z+iW+sOQzGNWG4AdznG+AMPy8I88kSVc10mRkulxdiLJIdmQ2KkGJXwogyiJdru7rVBnq7fV0u7pZmrKUmbEzONJ4dMLPrxAUaJSakNESJRG1Uh3yKnpFH63ONuKMcSSbk/BLfpw+J+fbL5AakcrHZz3KxY5LtDhaAZgbP5t+Tz/1U5ByUivVBORg2/cEPCgFZeh9BKQAbc42ki2JxJvi8YgedCotg74hOoe6QjGc+9x9OD0OtuduZUPmOrIiM9FNQK5GkqRgXWiDenx6vR6v14s8/J/f70erDQqfKpXBcvn9/qtxfQVQq1QMDQ6h1mpQKpUoFApUahW+CYQGVQ3HYpZkCVEWCUjXa6T1ufvwiT4yrOmoFGpUgvK6uMWSHPQh1w80MD9hPivSVrAkaTFapeYGn+vojVKj1IRcMCPSQSMHOCRZosXRgkljItOaiSTLqBQqWp1tWLQWMqzpuANujMMfyIKYfGKNMRxvPjGlgdCUje6II12SJBISEjAYDJw7dw6fz4fdbufb3/42X/3qVxFFEa1Wy5NPPsnjjz/O7t27eeONN0KCfLcDSRZRCiMaaQp0av11wZX1Kj078u4iLSKNVypeo3OwkzxbLs2OZl6vfIN36t5lbvwc2p3tHGs6jkJQUD/QwJDfhWec+J63GnPplMGYqZprOhoEpahnxc7kYsdFXih7kShdJCmWm6eHLv8QZ1rPUtJxiQsdxQTkALopqAErBEVoGmdQG647wtw3ZOf355/naN1x3izfiUqh4r4ZO0iLTOOLSz5Hni2Xj897lD53Pz85+TTugIsrnWV0DnYGj3iOM+CUCfrSlMMdSK1QIw7XhUJQUJhQiFap4dWK17jSU8rMmBmjThUNaj335O0gz5bLn0tfoMnRPOHnFyURT8BDhC4CICR7NNJVovU2FiTOp6K3khfKXkKn0pNkSmReQiH35N/Npa4rvFu3F7/kRykomR4znRp77ZSkkzx+D3qVHgEBi8aCXwrgF4P1YdVGUJgwl5LOyzx76TliDbGkRaTddI+AJHKw8QiNjiacvkGq7TXYJyApNWIknU4nMjL2PjsGoyHkn9bpdME0Wcbn8yGKInqDHp/XF+znooTH48FqtTI0OIjP78Pn8+F2u9Hrxzf6voAPQVCgUarRKNVBCaTAVcmnadHTMGoM7KvfP6yXNwtZlkP6ZyqFCkkWqeur41jzcbRKDSVdl3F6nTecPhu9Ubr8LnQqXUgzUJTEkGCrWqGmKGUJPa4enr30HA6fg9nxs1icvJjOoU6eu/Q8fa4+ChPmohSU5EblUN/fMKU2AFMMYi7LMs8++yxHjwZHHBs3buTee+/lxRdfRKPR4HA4eOWVV/jqV7+KWq1Gp9Mxffp07rvvPk6dOsVTTz3FrFmzyMvLuy0uhgHvAF7Ry/SYaQTkAFqFBlmWmRU7k86hLnJtuRjVJvYNL1AZ1Hr8UoBoQzRWrZVYQyxe0UuCOYGsqCyONB1lWcpS7B77hM+PX3sMuN/TT05UDu2DHWRFZlHbX0uyJRmbPgq7p58NWetBCI6yNErtqC8vwZTA/dPu45XyV9EoNSiFW0f7H4uOoU6WphfRMdjJrPgZvFmxk7mJs7HoLMRZYlmdvpL/7+h/sSB5HpIs807VPqq7a1EqlKRaUyjtLMOqj+C+Gffw+wvPkxmVTtdQ97iS2xAc7cvI5EXnMuRzIQgCdk8/8xPm0TnUSWH8XFLMyZxqPU2ULop+T/+oq+oLEhcQbYhmV83b+EQfJo0JxzUKtWPhl/x0u3qYGTODOns9i5MXUW9vwKa3kRmZQddQFxszN+ATfXgDXoxqA2atmU1ZGznZeoqy7nJMGhN+yU+MIQaj2kB1X/Wk3wNAXX89K9OWU2uvY27CHBxeByqFisVJi+gY7OCunG3IsozD60ApKEYNgq8QgqrRjf2NdLt6WJg4n/r+xnHzNplNpGWks/P1nWy7ezunTpzi8U8/TkVZBQMDA6SmpdLR3sGZU2fo6uxCoVCQmJRIX5+dkgslOJxOJEli+uzpPPeH5yg+V4zLFTSaySnJ4+bfPtSBTqVjdtxsJFkiUheJ3d3PzNgZ9Lh6KIjOR6PScLDhIFadFVES6ff2Myt2JimWFPKj8+kY6iDdmkamNYPDjYdZnb6aVmfbhPpFrb2Wu/N2UNZdxtyEubgDbiQklqYUUdNXy8asDRg1Jsq6yzCqjfhEH2syVhNtsFHSWYJFa6a+vx61Uk1BdD4vlb8ybp63YtJG12KxsGDBAlpbW2ltDU655s+fz3e/+11sUTb27d+HIAh8/vOf53Of+xxXrlxhzpw5WK1W0tLS+M53vsNPfvITDh06RG5u7m0xur2uPk63ngntXjjechKH18HCxPkgBKc2oixSlFwEBH28pd2lxBqi2ZC1HkmWONp0lG5XD2mWVNamr8ErejnefHLCZbj2KQ43HglN/wb9g5xrO09OVDaJpgTKu8sp6brEoqRFyLJMcWcx7cO+0xGRPwmZVmcr59svsCZ9DTIShxuP0DkBAcAb+dWZ3/KVoi/whUWfpaG/kX3VB9iStwmr3sqLl15mWmwB31j2BJIs8/Spn3O5vZTL7aXBL3pMLgdqDtHU18z51mI+Nf9xJFniF2d+hd3VP27eTt8g++r3Mzd+DgpBwdn287Q721maXDSsXXeAlWkr2J6zFXfAE5TaHo5r0e/pv2aXhQ6f6BvWi4P2wXberds74To43XqaBFM8d+VsZcjv5nTrWay6CKbHTONc+3lOtpxkZeoKRDnAmbaz9Lh7cQfc5ERmkx2ZhU/08U7tXrRKDZ1DXXQMTs6XO0JlbxUZkRlsyAy2uZ3VuzCoDeTZcqnvr+d48wkWJS5ARuZc+wXq7HVAcJDT5mzDK3qRZJnz7RdYlrKUAgROtJyk1zX+Tg6NRsMnP/MJfvTfP+Jf/9+/MG/BPBYXLebA3gO0t7Xz2S99lkcf/xg//u8fo9Pp+MwXPsvM2TN59OOP8LOf/AxZlvn0Fz5NwbQCPvGZT/CH3/4heO1znyYyanwRWafXyenW08yMnYksyxxtPo5f8ofWOw43HWFT1kY2Zm5ElEX21x/A7rGTaEpkbcYafKKPQ42HGPK5SItIY0PmBtwBN2fazk5oil/TV0tFbxUbszYCMm9V7UKn0pEfnU99fwNvVe1kcfJicm05tDpaOdl8Cru7n2UpRaRb0+gc6uJUy2kAet29DHgGxs5wDMbUSPP7/fJkRCJlWcbtdiNJEkaj8bYfH/7pmadDvrXRGEuL61aMppGmUqhueY/x5E5GCGqkaW4pPzJRjTS1Qh3yg92Ifaif0/Xnxi+LIKBVafD4Ry+LTq3FGxh/33RwiieNqosVa45mbtrsW/4OxtCuU2qH62nq+7a9Ad+Y26Ym8j5g4jJKt0Kj0Izb7rVKLX7JP2o9BtcQRtdguxaFIKBAcZ1+27V8Yt7HyYvJvem6KIr4fH50Ou1N5RxxLSgUCtRqdeia3+8PSt0ML37LyPh9wWsjf3ctHr+H/z72P6O+77HaAgholZqbNNIm20e9AS9DvtGVmrUqLX5x9LpXCkpUStV18lGjXRuPLxd9kaUZS2+/RtpoCIKAwTD+qvbt4ea4XlMJJTeaAb21IZy4UQhqpN36RU20c79XIwDBjnMrgwuMmXYtU43cNK52nTg139hkuF3v43YwdjkmVseSLCMx+fauVCrR60ffYiUIQmgx7dprNwrPCtx8baKM3YZGf0eT66NjM5bxFOWg2Od4194rf8GxF+7EkcbwMcowYcK8N/6CjW6YMGHC/OURNrphwnzoCEcY+msmbHTDhPnQEXZj/TUz5kJa0Ol+53SvbkRQKD4UAZTvpCzNtQgIuNzv/yLURAjoJbTDB0LuBG6fG5dvcsGAbjeCICCr5A9FCMW3yneyv9Z4R/JWK1U8NPvBKUW2ux2UdVXw2pXX7kjewC13lIwwptFVKD5cA+E735SDfBiEB0eQROnmirkDgm2SJN/Rj5EsM+o2oA+2EMGTmh8GpZV2Z8cd6786lY6UiOSp6xm+x/bbNdgdOjZ9Jxhv6+WHx3q8L9z5UfH7zmiN8873+fed/wNv9v8uf+Xt96/c6P6Vv73/w4TfbJi/VP7KjW6YMGHCfLgIG90wYcKE+QCZ9DHgQCCAx+NBoVCg1+sRBAG/34/X60WhUKDT6fB4PNfpJymGtbZuDOeoUqnQam8+Az5VBIQxdzeMli4Iwk2O7/HuM+HyjHLva/OA6xVag/kGr95uFIKANIaDXxCCi1FX/33rsk+Fsep01Hcw6nt5bzUzZhmG735j6kTbzKTKMc7vR80TAUEQblosVAiKSS8gjlcPo+UNN7fVqegLj9iFWy3yiaIYshdwVaPtxmuyJCMohEnbjvH69nj9ZASloESSpSnZiUkZXVmWKS0t5e///u9JSkrie9/7HklJSezcuZM//OEPZGZm8tWvfpX/+I//oKPj6uppYmIiVquVsrKykOFVKpUsW7aML37xi+h0732rUYY1g9lxswCZ8p4KqnqrQxWiVqpZmlxElD6KQZ+T061ncQfcLExcENLdOtl6Cm/Ay4LE+UQbohnyDXKk+VgokPdkMKj0LE9bjlljotfdx8mWk9dJ7eRH5zEtehrIUNJVQq29jgxrOnPj5iIjU9FbEYxIZU1nVmxQ6aGsp5yq3spJv+I4cwyfWPRxovRWKnuqef7sn0NlERBYnLGALfmbkJB4oeQlytur2D5zC0tTl+Dyu3jx4iuUd1aSEZ3K4/M/jlKh4N2qfRytPTEhw5MekcbM2BmoFCrKeyqu0xQza0wsSJxPhDaCfm8/J5pPISMzL6GQeGMcPtHH8ZYTDHgcFMTkkxOZjU/yc6b1DL0TiCE7gk6lY3P2JmwGG72uHvbXH8DhvRqaMj86j6UpRcjIFHdc5GJHCVadlU3ZG9CrDFT0VHCm9SyZkZksSV6EWqnhUuclzraem1Sn06q0LEspIsmShCfg4Z2avSGtNgGBabEFzImfjVJQcq79PGVd5aG0uYlzsOmj2Fd7IJTn/MRCjBoTxxqPj6uRBkFpolWpK9GpdbQ62zjZchKf6A/lMT+hkFxbLn7Jz+nWszQNNDE9ZhozY2cAcKbtLA39jcyJn01uVA6yLHOs+TgtzlsHoRpBlmUuXrjIyy+8DDLcff/dzFswL2QjPG4Pr770KhfOXyAlNYWPPf4xzBYzL/3pJS6cu0BaehoPPvIQlggzr7/yOqdPnCYtPY1HH38UW7RtXOOrUWpYnraMTGsmPsnLrqq36XFdlRlKiUhhVfoKDCoDNfZaDtYf5J78u7HqrEDw43ap6zJnW8+iUWrZmL2Bs61naXO2j/vsNzJp90JPTw9vvfUWzz33HMeOHcPlcvHHP/6R119/nSNHjuB0Otm/fz8Oh4OFCxeycOFCZs2aRUFBAXPmzKGyspLKykpmz55Ndlb2bdnWEm2wsT5zLW3ONpodLWzM3IjNYAulL0pcSFpEGpc6L2HRRLAsZSn5tjzmxs3hUtdlYo0xzE+YR1HKElIsyVzuukyEzsqatNVTKs+ajDXY9DYud10hPSKdBYkLQmkGlYG16WtpHGjE6XeyOWsTFq2FDZnr6RjqoNXZyqbMjWRHZbMxcyONA000OZrZnLWRGMP4umQ38o3VXyPSEMFbFbtYm72KLTM2htJSopL41qqvc67tHLW9tWzK28D8tDk8Pu9R9tbuY8Dbzz9s/C7JkYn8w/rvUdlbyfGmE6zOWUGE3jJu3maNma3Zm+lx9VDf38DKtOUkmhKAYCNelrKUCK2FS12XybBmMC+hkARzPPMS5lLeU0GE1sKKlOWkRqRQlLSEWnsdLt8QBdEFE35+AYF1mWvJjMrgZMtJkizJrM1ce51G2o68u6jqq6ZpoIlHZn6URHMiH53xIG6/h+KOYvKj80iNSOGx2Y/SOdTJla4rbMnZNCGZnGuZHT+LwsS5XGgvRqfSsS1vS6gcFq2Fu/N30DDQSJeri03ZGzFrzcH3FJHC/QX3khmZGbpXqjWVTdkbSYtIndAWRo1SzaasjSgVSi52ljA/YR7TY6aH0rMiM1maupTLXVdwBzxszdlMZmQG23O3UmuvpcXZyl252yhMmMO6jDWU91Zg99q5J29HSFFhLOx9dn7w7z9g0ZJFLCpaxFPff4p+e38o/cC+AxzYd4AHH36Q3p5envnZM+zZtYcD+w7w0Y99lIGBAX7zzK95Z/c7HNh7gMc+8Rh+v5+nf/w0ft/4wYqmxRSwMm0F59vPA/DgjI+EAuZrlVo+Mu1eBn1DXOwsYXX6KvKi8yjrKaek8xLdrm5SrSk4vU5mxM3gK4u+xJqM1aH3M1mmbPF0Oh0HDhygvLycS5cuERt7vZSMwWAgNjaW2NhYcnNzeeSRR/j6179OZmYmWVlZPPHEE9y1464pRyu6FqvWik6l50JHMaXdZfglP9prpEA6Bjs42HiQuv56et29aFQa3AE3MjLxxlh0Kh3ugJtofTSNA83U9NUGhed01lDIv8lg00dR2VtFZW8VNfaa6xQA/JKfNyrfoGmgGa1SizvgJt4UjyzD6dYzXOkupdfTh0Vr4VTrKS52XqRxoAGVQjWl/cFp1jT2Vx3kaPVJiltKWJSyMJS2JGMhroCLwsS5zIyfycG6Q8yJn0Ons5t95Yd4t+IAFq2FmfEzSLDEkxWZybrMNZxuOoPTM74Cq1qhQqlQUjrceJG57mPY4mzhUONRGvob8fg9KAUF/oAfhaAkxhiNQW2k3ztAfnQekiySFZlJpM5KrX3iulRqpZoEczxn285R2lXG0aaj5NtyQx3O7ffwStmrXOm8glFjZMDrwKK1kGZNw6K1sChpIY0DjXQNdbG3bi9HG49RZ69DlKVJ70PNs+Vwof0iV7pKOdlyiih9FEZN8ACDy+/iT5dfoKa3Fr1Kj1/04w14sWgtrMtcw6XOy8jDbgSTxsSm7A1c7iqdcN4GtZFkcxKHGo9Q3l1BRW8FSebEkOugy9XN6xVv0DHUgVFtwOF1BENQin5KOi9zpvUsJrWJdGs6Pa5eKnuquNx1Ba1aR4xx/MFAQ30DCLBl+xZWrlmJwWCktaUtlH655DJLli5hwaIFbNq6ifLSckqKS1iydAnzFsxj7fq1VJZXUl5azqIli5kzbw7b795OdWU1Lvf4h2KmxRZQ0lFCcftFjjQexaK1EKEb0Ujz81bVbo41HsOgNiAjM+B1UN5dQXH7RVIiUjjedJLy7goa7Y28Wv46XVOIbT3ClI3uXXfdxa5du9i3bx9Op5MlS5Zcl37hwgV+9rOf8fOf/5x9+/ZNuYATYUQjjZBGmu86jbQaey1dQ90sSlrI9JjpnGs7z5B/CEFQkG7NGJZw8VJjr2VewlyWpS5lVuxMtCrddQKXk8EvBcPRiZKI8ppq9kt+WpytpFvTSLYkY/f0B6V9BDmkHeUTfXgCboo7LpJiSeGevHu42FlCzyRkx0eQZAn3sDKFV/Rep/mWZEkiKyqTXlcfPtHLt1d8A4veEiq7T/Lhk/yYtCYShnW72pztfHvlN0iwxI+fuSBcp5EmyiKK4U4uyRKXu0rxiV42ZK5Dr9YHXQ9CMC09Ig2TxsiQfwiDyoBZa6HPY0en1rMmfeIzEIWgQKvS4hqWrg+IATSqqzFv3QE3NfZaUq1p5EfnB4VLEYjSRaJSKGl1tLIpayNJlmQONxwlzhTHY7M/RvNAM7V9dRMuB4Bm2IhB0MiqFCpUw8bfL/mps9eRHZVFVmQWg75BtCotazJW0THUSZ29HgClQsGK9OU0D7RQ3TtxBYsRTTD/sJqHy+9Gq7y6nuLwOmh1tlIQnU+iOTEUTtEnXY1jK8kSzf3NJJkTWZexhg0Z67BqI9BOQEbK6/Wi1+lRqVSoVWo0GjUej/v69OHTr3q9nkAggNfjDUnEqzXq0HqS0RgcWWs0GiRJColrjkUwhnEg9OxKQYFquG9LskR1bzVWvZW5CXMQJTHkOiuIyceqs3Km9WzIGDf0N0wqvu6NTCmeriAIFBUVsXv3bn74wx+yZs0aNBpNSEkC4J577uEb3/gGACaT6bYuytyIJEsoBCWCIAx3Ml1oVADBCt+WsxWzxszrVW/Q7mxnRdpyWp2tvF27hwxrBstSini98k1Ot54h2hBNq7MFs8YyqmTK+AihhhjUBbvqb7PqrMQb47jQXkx9fwP35t8zLH0joFaqUSvU6IeVEmbFzqQoZQlXuks523ZuSjFEgxppwdGUXnW9Rtqgb5DTzWf55cnfEmm08l/bv0+0JQqtSjv89/pgWQI+el19/PjIzzCqDSzLLMKiN0P/OJnLMuprhANVCtV1J4UidZFsyFwLwO6at+l29bA+cy2N/Y0caDhIdlQWs2Nn4RV9NPY3cqzpOFmRmaxOXzlmEOtrkWQJb8BLhDaokaZVafEEvKH2aDNEkWZNo6T9Ep2DnXx+/mdIMMXhCXh4o+JNBrwD5EXnEqmzUpgwly05myjtLmNv7f5JL2B5Ah50Kh0CAmathYAUCMXPjdBZyLBmcLY1OCL/0oLPMy9xLvMSCul19zE9ZhqROitbcjYzO342juEZgE0fxZyE2ZxtHTuYvSRL+CU/eqWOAQQsGnNQK264HhJM8Vi0Fo42HafF0cqjMx+h1l6HTqkNLeIpFUr6PHbeqt5FpjWDJmczNkP0dSKit2Jkgd3r9eL1evG4PSGDOpI+otHmdDrRarWYzCYGnYPBeNAeDyq1Gr1ez8BA0A/ucrtBmNjJ2WDda4fr3owoS/jFqxpp8xILudx1hV+c+yVfmP85ZsfOosPZwZz42bQ52+ibxBrCeEx5pJuVlcX8+fMZGBhgy5YtqFTX2+8rV67w3HPP8dxzz/HSSy/R13v7Cn0jDq8Dv+gjz5ZPujUdnUqHLMsURBcQa4hhYeICInVW9jccwO13o1PrhnWarJjUJiK0FvxigGRLEimWZM61n0On0tPv6Z+SoXN4HWRZM0kyJ5FuTad1sI0EUzz50fkh/22uLReL1oJaoaLF2YpKoWJ27CwyIzOIHvbdBn1QxZR3l6MfFtWbLJ1DnSzLKGJ6Uj6zEqdT0nGJWYnTWZZZxKnGM2REppMdm0lh8hzUCjVH644Rb45jXtocitIX4Q14qe2rZcA7wNzkWWTGphOps+Lxjh/zISCLCECmNYOcqKD/fsA7wPSYacQZY1masgSFoORQ4xF8og+9SodfDBClj8SitWBSm5CRaRhoJFJvJdYYQ4I5AUkeX3FjBJ/oo2uwi7nxs0mJSGZpahG1fXXYDFEsSV5MtCGa+wruZXrsNCJ0EejVeuweO52uLvKi84g3JRCljwLggen3U9xRwonmU+hUupAKwkSp7atjbsJsUq2pzE8sxOFxoFQoWJi0gChdFA9M/wjTYwuI1FtRCAoGPA7eqX2Xix0ldA52MuQbor6/gb21+yjpuEybsw2H1zkhg+Dyu+gY7GRJymKSLUnkR+fRMdRJiiWZadEFJJuTuTf/bpLNSaEPVNBQ6cmOymJ6zDTcAQ8yMkXJSyjpuowoBT9onYPjT7VT01Jxu90cPXSUC+cv0NPTQ1xsHCePnaS5qZm8gjyOHzlOTXUNhw8cJj0jnZy8HE6fPE19bT1HDx0lLT2VrJwsTh4/SWNDI+/s2kNmVuaE4sNU9lYxK24W2bZsFiUtwO13EZBEilKWYDNEcf+0+1iWWkSENgKdSocoi1i0ZlIiUrjYfnHc+0+GSY90TSYT06dPJy4ujq1btwJQWFhIdXU1g4ODaLVacnJyaGpq4sUXXwQgPT2dLVu2EKuPJS0tLTgivY3nwnvdvZxrP8/i5KDu2Jm2szh9ToqSi6jtr0Wv1iOgYEXqcgAaBxop6y4n0ZTI1uzNCILA8ZYTdA52kmXNCuo0SQH2teyfUnmONR9ne842NmVtxCt6KW4vJjsqm0RzIntq36HaXsOylKVIshTcldBThVljojChEEmWONd2joAYnJYXROeRb8vFK/rYW79v0l/cZ07+hr9Z/STfWf0tuod62FO2j3tm3kWUIYqfHPsZB2oP8eTyr6JWavjtuT+wr+IQs+Nn840VX0OWZZ4+8QsqOqp4rvhPfKnocwSkAC9eeoVWR9u4eQ/6BjnecpLC+DkIgsCVrjI6BjspSl5CaXcpVm0EKqWadRnB0e6lrstc6rxEvCmODZnrUQgKjjefoMnRTKwhho1ZGxAQONp8bFJ1cKLlFBmRGTw84yG8oo8TzSex6W0UpSzhp2d/xunWM2zP24pfDHChvZiy7gpAYG3mGopkmdLuMnrcPXgDXqZFF5Bry8Hj97CrejdNAxNXJi7vLqcgJp978ncgCAKvlr+GWWNmfuI8Xq94gzOtZ1idvhpJlrjYWcKVztKQosX0mGkM+V1c6SwNTffTrWm4/C4a+5vGzdsn+jjefJwtOVvYkbudFkcrNX21zIqdQYIpnt01e8iKyuKuvO3IsszBhoPU9NVyqvUUa9NXB/tI83GaBppp6G9kQ8ZalIKSvfX7J6S6EWWL4uHHHua3v/otAgKPPPYIWr2WPbv3sHrdalasWsHxI8f5h+/+Ayajib/5u78hNi6Ws6fP8nd/83cYTUae/JsniY2N5dLFy3zv29/DZDLx9W99HbVmfBdgVU8VDf0N3D/tXpSCkpfLXsGoNrA4eREtjhbeqtzJstSlzImfQ7erm9OtZ4LSQQHfTcKfsizT4+qZsothTI00RtkWebv2J05lb+4PDj01ZgNTDetLBSYhvaJVafEH/CFBRAguvoz43m5EluUJbROaiiaXSqFCmIA+1gjdjh72XTk8flkEAa1Gg+cWendqlQpJkhCv2VutVWsIiIHrrqmUKkAmIN68PSk5KonV05aPen+VQomAYlKSOKPpiGmUmqBPcpQ22DPYw6Dv1ot7AsKwa2H0EbpmeOH1WveLUlCiVCgnpcKsV+nHbdsjQpujaqQp1QgQ2so1VVRK1agDG8WwDLrnFgZDp9QSkALXRcpSK9XIN8wuVIISCXnUZ9CpdPzDmu/dtNA4osEGApobDOWIbfC4PWi0mlDZZVnG6/Gi1WlRKBShd+/xeNBqtKPu1T3bfI6fn35m1OcLuu78o26xUyqUaBTq0BrIVPnmyidZlbXy9mmkvdeDDO9nBKapuAJG+1rdyuBOhqlock1V92ncssjyLQ0ugD9wc75e/yi6VOJUNdJEmKSe10S1siaKjHxLg3ure4uyeNOBntvBWOW4HW1vLCRZuqXBBfCMUu+jlWm88IWjMZoG243p1/p5R7s2Yj+mGnJ2LIMqSiLuKegsTpbwMeAwYcK873wY4mB/WAgb3TBhwrzvfBgCu39YCBvdMGHChPkACRvdMGHChPkAGXMhLRAI4Pe/v479yWB3D9Drev/2+04I+cPjn3L6Bm/aH32nkAUJu8t+x/J/L4tstwsBAavR+qGQ63F6nHj8d0Y/zxfw8cyZX90xWSubwcZXl375juQNkBeTO2b6mD1WluX3ZfV2qvgC3vd0/O528H6erJssftGPUvnhmKzIyHfU8IkfwKrzRNCqtB8KbUGnx3nH6kREpLSz7I59fAoT5zI7YdYdyRvAMk4wqDvfOsKECRPm/xBhoxsmTJgwHyBhoxsmTJgwHyBhoxsmTJgwHyC31ehKkoTff/3ZeFEUP7AdEONtwP6gN2iPtZAgDP832vUPuiwTvsd7KNtYv71V2u1e/Z5KGd7r397IiL7XrRZkR9LfTyZbD7ezTY53r4m+85Fwk5NhRG9trLq/MV2WZSRRGvfvJsNt2W8kyzKVlZU8//zz9PX1sWbNGu655x7Ky8t5/vnnGRoaYtmyZezYseMmpQhZlm+LQciKymRR4kJk4GLHRcp7KkJpaoWadZlriDZE0+e2c7TpGKIssiptJRatBZAp6bhEk6OZ1ekrMWnMjGitXewomXRZ9Co967PWYdVF0DHYyeHGI9ftupgWU8Dc+DkAnG49S01fDTZ9FKszVqFRaijtKuNy1xXmxs8lLzoHIFjuxqMM+cePkn8tUYZIHp77IFH6SKp6a3j10huhXQYapYaHCx8g0ZQIQHlPBa9dfpNZidPZmrcZn+TnrbJdVHfXsiJrKcvTl+ETvbxZtouKrqoJ5Z9hzWB+YiEqhYqSzkuUdZeH0iK0FopSiojUW+ka6uZQw+FQ/Amb3saqjJW8VbmT/Og88qPzhjusgMPn4O3qPROuA41Sw7rMtcQYoul29XC48QhDvqsxYLOisihKXjyskVZCWVcZZp2ZdRlrMagNVPZWcqHtAqnWNBYlLUCj1HK56zLFkwj5J8syol+k4Uw9LaXNWKItTN84E61JG0rvbeil5ngVAV+AnOW5xGTGcmlnCR6HG5mgeGjKnFR6G3tx9Q0FNy7KMhmLsojPGz+ovFVnZXX6KoxqA02OJk42nwrF8wWYl1BIQUw+XtHHqZbTtDpamR0/i4LoAiRZ5HjzCVw+N+uz1obehU/0cqzpOJ0TUFLIjspiQeJ8ZDmoRVfZe7UNaZVaVqWvJMYYQ7+nnwP1B8mJyqYgOh+EYG4D3gH21LwLBKWPJFmipOPShOrf5/Oxf+9+jh85TnR0NB/7xMewRdtCdd/d1c3LL7xMW2sbS4qWsGnbJux9dl7680u0trSyaMliNm/bhNPh5NnfPktnZydFy4pYt3HdmPEkRmPSQ4nOzk6Ki4spLi7myJEjtLS04Ha7+dKXvsSlS8EK+Od//mf27t3LN77xDRoaGoiOjuYf//EfOXnyJA6HgxMnTrB3715KSkqGow69N2KNMdxfcB897l46hzr56MwHiTPGhdJXpi0PaR7NjJ3BwqT5JJuTKEyYS7W9hsreKno9vVh1VuYnzqfJ0URlbxUdg51TKs9dedtIt6ZS1l3OnPjZLE9dFkozqg3cP+0+OgY7CEgBHp7xIDqVjkdmPYIkS7Q4W1mSsgS9Ss/chNl4RA+VvVXU99dPKfLU15Z/idTIFE62nGJr3iY25K8NpRnUeu6ato16Rx0nW09S2lVGcmQS31rxJDX2WnrdvWyfvoVFaQv4ytIvcq7tHL3uPv5u3XeINkaPm3eE1sLDMx/E4XPS6mxje+42ki1JQHBEsyJ9BbHGGMp7ylmUtJCFSUEtOZVCxaacDRQlL0alUNLt6qZxoJHWwTbSI9MwaUyTqoO1mWuYETudy11XyLXlsDZjdWhEZdVZuTf/btoH2+lz9/HY7EdJMMfz0PQHUClU1PTVMDd+DmnWND4x5zGG/C4aBxq4K3cbqREpkypHy6UWqo5UkL04B4/Dw8U3LoTUcT0ON6eeO0FUchQJ+YmcfPYEQ72DxOXGkTgjCXO0ia6aTjR6DbFZsaFrvQ09qNTKcfNWK9Vsz91KhM5CeW8FK9NWMivu6raq7Kgs1meto7qvBqWg4N78HeREZbM9dxv1/fUoBSVbc7aAAA0DjTQ5monQWYg3x48aJOdGIrQW7p92H31uO3aPnY/OeAiz5qrG2PK0ZeTasqnoqSDflseKtOX0uHupH2igfbCNNGsaWqWWGGMMH535IA/PeIgUS/KE6774fDHP/fY51m1Yh8/n48f//WMCw4Ge/L6g1tqgc5BNWzfz5z/+mSOHjvDTH/2Unq4e1m9cz0t/epGTx07y0x/9FOfgIBs2beDPz/2ZMyfPTHrEOyGjO3JTWZbZv38/Dz30EJ/97Gf57Gc/yze/+U3a29v51Kc+xd//3d+Tm5t7nfy6RqPBbDbj9/sRBIEf/ehHfPGLX+Rf/uVfeOSRRzh37tx73vsabYjBojVzsP4gp1pOE5BE9OqrCsOVvVW8Wv4ava5eEIKRxaw6KxaNmTmxs8iJysYb8GFQGzCqDcyInkaeLQ9PYPJKwAAJ5gSKO0o413aeK51XKIjJD6WJssgLpS9xuesKkizhE32kRqQSpbciyRIZEelc7ChGq9Ji00eRaExgdtwsdErdlKKQZUVmsrdqP3srDlLcWsKSlEWhNL1GR5Q+kkVJC1mRtgJ/wM/y9KUoFUrSItJIMieyv+YQVp0VSZLYV3mIdyr2kWFNx6wd3/BplFo0Ki1nWs5wtPEYCkFBvCk4IpNlmbKuMt6s3IlX9CEh4fK7EBBYkDifSJ2V3mF5onZnB6dbzuLyDTHoG5z0KDfZksSZ1jNcaC/mUMNhpsUUhDTS/KKPXdW7Q3Isbr8Lq85KZmQmgiBQEJ1PRU8FXUNdHGs6zsH6Q1zuLEVCQq+eXKSrzuoOEqYnkjQrmZwVuXQ3dON3Bz+kCpWSvJX5ZCwOjlqlgIjoF0koSCRpRhL2FjsFa6YTnRlDfH4CybNS6G/rJ3tZLrb08T+AJo2JjMh09tS8y4W2Yq50l5ISkRKa7ve57bxe8QY1fbUICLgDHnpcvbxQ+hJ19jokZAJiAIfHwanm01T1VBNjiGZn5S4GPAPj5p9qTSUg+tlff4CTLacY8A4Qb746MKrureG1ijfpc9tRCgoG/YO0Olo53XKGgCji8Dh4p3YvDs8AJ5tPTUofDqCkuIQ5hXNYsXoF937kXmqqa3E4HAB4fV5qa2rZsn0Ly1YsZd78eZRdLqPsShmbt29m5ZqVzFswj2NHjlFVWcU9993NitUrWLhkISXFk58JT8joXjv993q99PX18cMf/pDf/e53nDhxgvPnz/PRj34Ul9vF888/j0ajITo6mszMTI4fP86LL76I1WpFo9EQFxfHww8/zOc//3m6urq4fPnypAt9I0pBEfSxMBx/M+ANdSqAZkcLg74hVqQtx6g24BV9eANeKnuruNJdSrw5gc3ZG7HozFT2VnG5u5R4Uxzbc7dP2Z814k7wS4HryuIJeKnsqWRG7AymxUyjfbADmyEKq86KzWDDK3q5O/9uEkzx9Lh6qeitotfVywPT7w8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       "text/plain": [
-       "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=518x303 at 0x7F9CBC2189A0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
     "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "Image.open(f\"{DATA}/kinase_distance_matrix_df_styler.png\")"
+    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "plt.imshow(matrix_styler_img)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
    ]
   },
   {
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_df_styler.png b/teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_df_styler.png
new file mode 100644
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diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 2e52b014..4ea372fa 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -159,8 +159,10 @@
     "import pandas as pd\n",
     "from sklearn.metrics import pairwise\n",
     "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
     "import seaborn as sns\n",
-    "from opencadd.databases.klifs import setup_remote"
+    "from opencadd.databases.klifs import setup_remote\n",
+    "import dataframe_image as dfi"
    ]
   },
   {
@@ -1475,188 +1477,188 @@
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+       "#T_95e80_row0_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row0_col7 {\n",
+       "#T_95e80_row0_col7 {\n",
        "  background-color: #148a14;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row0_col8 {\n",
+       "#T_95e80_row0_col8 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row1_col0, #T_c8953_row3_col2, #T_c8953_row5_col2 {\n",
+       "#T_95e80_row1_col0, #T_95e80_row3_col2, #T_95e80_row5_col2 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row1_col2 {\n",
+       "#T_95e80_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row1_col3 {\n",
+       "#T_95e80_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row1_col4 {\n",
+       "#T_95e80_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row1_col5 {\n",
+       "#T_95e80_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row1_col6 {\n",
+       "#T_95e80_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row1_col7 {\n",
+       "#T_95e80_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row1_col8 {\n",
+       "#T_95e80_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row2_col1, #T_c8953_row4_col5, #T_c8953_row6_col5, #T_c8953_row8_col4 {\n",
+       "#T_95e80_row2_col1, #T_95e80_row4_col5, #T_95e80_row6_col5, #T_95e80_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row2_col3 {\n",
+       "#T_95e80_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row2_col4 {\n",
+       "#T_95e80_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row2_col5 {\n",
+       "#T_95e80_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row2_col6 {\n",
+       "#T_95e80_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row2_col7 {\n",
+       "#T_95e80_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row2_col8, #T_c8953_row4_col3, #T_c8953_row8_col0, #T_c8953_row8_col5 {\n",
+       "#T_95e80_row2_col8, #T_95e80_row4_col3, #T_95e80_row8_col0, #T_95e80_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col0 {\n",
+       "#T_95e80_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col1, #T_c8953_row3_col6, #T_c8953_row5_col0, #T_c8953_row6_col8 {\n",
+       "#T_95e80_row3_col1, #T_95e80_row3_col6, #T_95e80_row5_col0, #T_95e80_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col4, #T_c8953_row5_col3 {\n",
+       "#T_95e80_row3_col4, #T_95e80_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col5 {\n",
+       "#T_95e80_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col7, #T_c8953_row7_col0, #T_c8953_row7_col1, #T_c8953_row7_col2, #T_c8953_row7_col3, #T_c8953_row7_col4, #T_c8953_row7_col5, #T_c8953_row7_col6, #T_c8953_row7_col8 {\n",
+       "#T_95e80_row3_col7, #T_95e80_row7_col0, #T_95e80_row7_col1, #T_95e80_row7_col2, #T_95e80_row7_col3, #T_95e80_row7_col4, #T_95e80_row7_col5, #T_95e80_row7_col6, #T_95e80_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row3_col8 {\n",
+       "#T_95e80_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row4_col0 {\n",
+       "#T_95e80_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row4_col2 {\n",
+       "#T_95e80_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row4_col7 {\n",
+       "#T_95e80_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row4_col8 {\n",
+       "#T_95e80_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row5_col1 {\n",
+       "#T_95e80_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row5_col6, #T_c8953_row6_col2 {\n",
+       "#T_95e80_row5_col6, #T_95e80_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_c8953_row5_col7 {\n",
+       "#T_95e80_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row5_col8 {\n",
+       "#T_95e80_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row6_col1, #T_c8953_row6_col4 {\n",
+       "#T_95e80_row6_col1, #T_95e80_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row6_col3 {\n",
+       "#T_95e80_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row6_col7 {\n",
+       "#T_95e80_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row8_col1 {\n",
+       "#T_95e80_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row8_col2 {\n",
+       "#T_95e80_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row8_col3 {\n",
+       "#T_95e80_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row8_col6 {\n",
+       "#T_95e80_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_c8953_row8_col7 {\n",
+       "#T_95e80_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_c8953_\">\n",
+       "<table id=\"T_95e80_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1673,118 +1675,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_c8953_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_c8953_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_c8953_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_c8953_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_c8953_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_c8953_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_c8953_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_c8953_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_95e80_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_95e80_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_95e80_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_95e80_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_95e80_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_95e80_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_95e80_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_95e80_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_95e80_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_c8953_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_c8953_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_c8953_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_c8953_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_c8953_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_c8953_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_c8953_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_c8953_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_95e80_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_95e80_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_95e80_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_95e80_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_95e80_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_95e80_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_95e80_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_95e80_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_95e80_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_c8953_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_c8953_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_c8953_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_c8953_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_c8953_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_c8953_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_c8953_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_c8953_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_95e80_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_95e80_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_95e80_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_95e80_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_95e80_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_95e80_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_95e80_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_95e80_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_95e80_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_c8953_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_c8953_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_c8953_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_c8953_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_c8953_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_c8953_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_c8953_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_c8953_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_95e80_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_95e80_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_95e80_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_95e80_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_95e80_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_95e80_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_95e80_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_95e80_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_95e80_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_c8953_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_c8953_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_c8953_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_c8953_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_c8953_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_c8953_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_c8953_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_c8953_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_95e80_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_95e80_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_95e80_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_95e80_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_95e80_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_95e80_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_95e80_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_95e80_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_95e80_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_c8953_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_c8953_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_c8953_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_c8953_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_c8953_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_c8953_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_c8953_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_c8953_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_95e80_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_95e80_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_95e80_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_95e80_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_95e80_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_95e80_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_95e80_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_95e80_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_95e80_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_c8953_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_c8953_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_c8953_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_c8953_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_c8953_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_c8953_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_c8953_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_c8953_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_95e80_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_95e80_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_95e80_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_95e80_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_95e80_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_95e80_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_95e80_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_95e80_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_95e80_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_c8953_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_c8953_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_c8953_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_c8953_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_c8953_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_c8953_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_c8953_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_c8953_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_c8953_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_95e80_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_95e80_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_95e80_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_95e80_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_95e80_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_95e80_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_95e80_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_95e80_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_95e80_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_95e80_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_c8953_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_c8953_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_c8953_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_c8953_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_c8953_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_c8953_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_c8953_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_c8953_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_c8953_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_c8953_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_95e80_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_95e80_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_95e80_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_95e80_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_95e80_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_95e80_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_95e80_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_95e80_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_95e80_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_95e80_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbe2349b2b0>"
+       "<pandas.io.formats.style.Styler at 0x7f90475794f0>"
       ]
      },
      "execution_count": 21,
@@ -1795,7 +1797,57 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
+    "    cmap=cm\n",
+    ").format(\"{:.3f}\")\n",
+    "kinase_distance_matrix_df_styler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We export the matrix with colors as an image to use as thumbnail for the website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[1129/155624.943121:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155624.944163:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
+      "[1129/155624.947439:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155625.577404:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmp97mvbhul/temp.png.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "plt.imshow(matrix_styler_img)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1816,7 +1868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_df_styler.png b/teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_df_styler.png
new file mode 100644
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diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index 6dd72b0d..eb8512d7 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -174,10 +174,12 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
+    "import matplotlib.image as mpimg\n",
     "import seaborn as sns\n",
     "from sklearn.metrics import pairwise_distances\n",
     "from scipy.spatial import distance\n",
-    "from opencadd.databases.klifs import setup_remote"
+    "from opencadd.databases.klifs import setup_remote\n",
+    "import dataframe_image as dfi"
    ]
   },
   {
@@ -1322,227 +1324,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
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        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row3_col0 {\n",
+       "#T_d171f_row3_col0 {\n",
        "  background-color: #329832;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row3_col1, #T_7f610_row3_col5, #T_7f610_row3_col7, #T_7f610_row7_col0, #T_7f610_row7_col2, #T_7f610_row7_col3, #T_7f610_row7_col4, #T_7f610_row7_col6, #T_7f610_row7_col8 {\n",
+       "#T_d171f_row3_col1, #T_d171f_row3_col5, #T_d171f_row3_col7, #T_d171f_row7_col0, #T_d171f_row7_col2, #T_d171f_row7_col3, #T_d171f_row7_col4, #T_d171f_row7_col6, #T_d171f_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row3_col2 {\n",
+       "#T_d171f_row3_col2 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row3_col6, #T_7f610_row7_col5 {\n",
+       "#T_d171f_row3_col6, #T_d171f_row7_col5 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row3_col8 {\n",
+       "#T_d171f_row3_col8 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col1 {\n",
+       "#T_d171f_row4_col1 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col2, #T_7f610_row6_col4 {\n",
+       "#T_d171f_row4_col2, #T_d171f_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col3 {\n",
+       "#T_d171f_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col5 {\n",
+       "#T_d171f_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row4_col6 {\n",
+       "#T_d171f_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col7 {\n",
+       "#T_d171f_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row4_col8 {\n",
+       "#T_d171f_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row5_col0 {\n",
+       "#T_d171f_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row5_col1 {\n",
+       "#T_d171f_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row5_col2 {\n",
+       "#T_d171f_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row5_col4 {\n",
+       "#T_d171f_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row5_col6 {\n",
+       "#T_d171f_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row5_col7 {\n",
+       "#T_d171f_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row5_col8 {\n",
+       "#T_d171f_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row6_col0 {\n",
+       "#T_d171f_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row6_col1 {\n",
+       "#T_d171f_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row6_col2, #T_7f610_row6_col5 {\n",
+       "#T_d171f_row6_col2, #T_d171f_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row6_col3 {\n",
+       "#T_d171f_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row6_col7 {\n",
+       "#T_d171f_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row6_col8 {\n",
+       "#T_d171f_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row7_col1 {\n",
+       "#T_d171f_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row8_col1 {\n",
+       "#T_d171f_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row8_col4 {\n",
+       "#T_d171f_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_7f610_row8_col6 {\n",
+       "#T_d171f_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_7f610_row8_col7 {\n",
+       "#T_d171f_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_7f610_\">\n",
+       "<table id=\"T_d171f_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1559,121 +1561,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_7f610_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_7f610_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_7f610_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_7f610_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_7f610_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_7f610_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_7f610_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_7f610_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_d171f_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_d171f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_d171f_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_d171f_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_d171f_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_d171f_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_d171f_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_d171f_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_d171f_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_7f610_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_7f610_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_7f610_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_7f610_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_7f610_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_7f610_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_7f610_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_7f610_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_d171f_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_d171f_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_d171f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_d171f_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_d171f_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_d171f_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_d171f_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_d171f_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_d171f_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_7f610_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_7f610_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_7f610_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_7f610_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_7f610_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_7f610_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_7f610_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_7f610_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_d171f_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_d171f_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_d171f_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_d171f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_d171f_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_d171f_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_d171f_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_d171f_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_d171f_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_7f610_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_7f610_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_7f610_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_7f610_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_7f610_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_7f610_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_7f610_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_7f610_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_d171f_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_d171f_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_d171f_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_d171f_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_d171f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_d171f_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_d171f_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_d171f_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_d171f_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_7f610_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_7f610_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_7f610_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_7f610_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_7f610_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_7f610_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_7f610_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_7f610_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_d171f_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_d171f_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_d171f_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_d171f_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_d171f_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_d171f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_d171f_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_d171f_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_d171f_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_7f610_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_7f610_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_7f610_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_7f610_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_7f610_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_7f610_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_7f610_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_7f610_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_d171f_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_d171f_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_d171f_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_d171f_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_d171f_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_d171f_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_d171f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_d171f_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_d171f_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_7f610_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_7f610_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_7f610_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_7f610_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_7f610_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_7f610_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_7f610_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_7f610_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_d171f_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_d171f_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_d171f_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_d171f_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_d171f_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_d171f_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_d171f_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_d171f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_d171f_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_7f610_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_7f610_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_7f610_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_7f610_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_7f610_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_7f610_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_7f610_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_7f610_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_7f610_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_d171f_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_d171f_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_d171f_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_d171f_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_d171f_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_d171f_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_d171f_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_d171f_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_d171f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_d171f_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_7f610_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_7f610_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_7f610_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_7f610_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_7f610_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_7f610_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_7f610_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_7f610_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_7f610_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_7f610_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_d171f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_d171f_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_d171f_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_d171f_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_d171f_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_d171f_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_d171f_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_d171f_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_d171f_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_d171f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f9db0a0a820>"
+       "<pandas.io.formats.style.Styler at 0x7fa599d3b790>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1681,7 +1683,57 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
+    "    cmap=cm\n",
+    ").format(\"{:.3f}\")\n",
+    "kinase_distance_matrix_df_styler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We export the matrix with colors as an image to use as thumbnail for the website."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[1129/155348.866614:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155348.867645:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
+      "[1129/155348.868577:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155349.478593:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmpc8fcpz2a/temp.png.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "plt.imshow(matrix_styler_img)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -1702,7 +1754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_df_styler.png b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_df_styler.png
new file mode 100644
index 0000000000000000000000000000000000000000..3d010311c3cdc404830bd88322d15170bc95a530
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diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 7d52c8ae..b51ac36d 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -187,10 +187,13 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
+    "import matplotlib.image as mpimg\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "from rdkit import Chem\n",
     "from rdkit.Chem import Draw\n",
-    "from rdkit.Chem.Draw import IPythonConsole"
+    "from rdkit.Chem.Draw import IPythonConsole\n",
+    "import dataframe_image as dfi"
    ]
   },
   {
@@ -1540,46 +1543,46 @@
      "data": {
       "text/html": [
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-       "#T_56c17_row1_col2 {\n",
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-       "#T_56c17_row6_col2 {\n",
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        "  background-color: #e1e0ee;\n",
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-       "#T_56c17_row7_col2 {\n",
+       "#T_ca911_row7_col2 {\n",
        "  background-color: #4a1486;\n",
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        "}\n",
-       "#T_56c17_row8_col2 {\n",
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        "</style>\n",
-       "<table id=\"T_56c17_\">\n",
+       "<table id=\"T_ca911_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1590,64 +1593,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_56c17_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_56c17_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_56c17_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_ca911_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_ca911_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_ca911_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_ca911_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_56c17_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_56c17_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_56c17_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_ca911_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_ca911_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_ca911_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_ca911_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_56c17_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_56c17_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_56c17_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_ca911_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_ca911_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_ca911_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_ca911_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_56c17_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_56c17_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_56c17_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_ca911_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_ca911_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_ca911_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_ca911_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_56c17_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_56c17_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_56c17_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_ca911_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_ca911_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_ca911_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_ca911_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_56c17_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_56c17_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_56c17_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_ca911_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_ca911_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_ca911_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_ca911_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_56c17_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_56c17_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_56c17_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_ca911_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_ca911_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_ca911_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_ca911_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_56c17_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_56c17_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_56c17_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_ca911_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_ca911_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_ca911_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_ca911_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_56c17_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_56c17_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_56c17_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_56c17_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_ca911_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_ca911_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_ca911_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_ca911_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbf0ca752e0>"
+       "<pandas.io.formats.style.Styler at 0x7fbb6aab7d30>"
       ]
      },
      "execution_count": 24,
@@ -2249,208 +2252,208 @@
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        "  color: #000000;\n",
        "}\n",
-       "#T_bea2b_row6_col8 {\n",
+       "#T_34626_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_bea2b_row7_col3 {\n",
+       "#T_34626_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_bea2b_row7_col4 {\n",
+       "#T_34626_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_bea2b_row7_col5 {\n",
+       "#T_34626_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_bea2b_row7_col6 {\n",
+       "#T_34626_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_bea2b_row8_col0 {\n",
+       "#T_34626_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_bea2b_row8_col1 {\n",
+       "#T_34626_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_bea2b_row8_col3 {\n",
+       "#T_34626_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_bea2b_row8_col4 {\n",
+       "#T_34626_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_bea2b_row8_col6 {\n",
+       "#T_34626_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_bea2b_\">\n",
+       "<table id=\"T_34626_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2467,118 +2470,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_bea2b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_bea2b_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_bea2b_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_bea2b_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_bea2b_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_bea2b_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_bea2b_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_bea2b_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_bea2b_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_bea2b_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_bea2b_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_bea2b_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_bea2b_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_bea2b_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_bea2b_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_bea2b_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_bea2b_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_bea2b_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_bea2b_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_bea2b_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_bea2b_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_bea2b_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_bea2b_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_bea2b_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_bea2b_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_bea2b_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_bea2b_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_bea2b_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_bea2b_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_bea2b_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_bea2b_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_bea2b_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_bea2b_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_bea2b_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_bea2b_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_bea2b_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_bea2b_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_bea2b_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_bea2b_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_bea2b_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_bea2b_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_bea2b_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_bea2b_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_bea2b_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_bea2b_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_bea2b_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_bea2b_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_bea2b_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_bea2b_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_bea2b_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_bea2b_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_bea2b_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_bea2b_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_bea2b_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_bea2b_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_bea2b_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_bea2b_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_bea2b_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_bea2b_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_bea2b_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_bea2b_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_bea2b_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_bea2b_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_bea2b_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_bea2b_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_bea2b_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_bea2b_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_bea2b_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_bea2b_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_bea2b_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_bea2b_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_bea2b_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_bea2b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_bea2b_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_bea2b_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_bea2b_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_bea2b_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_bea2b_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_bea2b_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_bea2b_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_bea2b_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_bea2b_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_34626_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_34626_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_34626_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_34626_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_34626_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_34626_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_34626_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_34626_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_34626_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_34626_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_34626_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_34626_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_34626_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_34626_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_34626_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_34626_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_34626_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_34626_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_34626_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_34626_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_34626_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_34626_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_34626_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_34626_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_34626_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_34626_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_34626_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_34626_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_34626_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_34626_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_34626_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_34626_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_34626_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_34626_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_34626_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_34626_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_34626_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_34626_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_34626_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_34626_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_34626_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_34626_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_34626_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_34626_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_34626_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_34626_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_34626_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_34626_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_34626_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_34626_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_34626_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_34626_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_34626_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_34626_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_34626_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_34626_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_34626_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_34626_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_34626_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_34626_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_34626_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_34626_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_34626_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_34626_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_34626_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_34626_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_34626_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_34626_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_34626_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_34626_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_34626_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_34626_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_34626_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_34626_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_34626_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_34626_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_34626_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_34626_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_34626_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_34626_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_34626_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_34626_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_34626_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbf0cb038e0>"
+       "<pandas.io.formats.style.Styler at 0x7fbb6aab7d90>"
       ]
      },
      "execution_count": 30,
@@ -2612,212 +2615,212 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_5f495_row0_col0, #T_5f495_row1_col1, #T_5f495_row2_col2, #T_5f495_row3_col5, #T_5f495_row4_col3, #T_5f495_row4_col4, #T_5f495_row6_col6, #T_5f495_row7_col7, #T_5f495_row8_col8 {\n",
+       "#T_679b0_row0_col0, #T_679b0_row1_col1, #T_679b0_row2_col2, #T_679b0_row3_col5, #T_679b0_row4_col3, #T_679b0_row4_col4, #T_679b0_row6_col6, #T_679b0_row7_col7, #T_679b0_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row0_col1 {\n",
+       "#T_679b0_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row0_col2 {\n",
+       "#T_679b0_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row0_col3, #T_5f495_row7_col0 {\n",
+       "#T_679b0_row0_col3, #T_679b0_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row0_col4, #T_5f495_row3_col0, #T_5f495_row4_col2, #T_5f495_row5_col2 {\n",
+       "#T_679b0_row0_col4, #T_679b0_row3_col0, #T_679b0_row4_col2, #T_679b0_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row0_col5 {\n",
+       "#T_679b0_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row0_col6, #T_5f495_row4_col1 {\n",
+       "#T_679b0_row0_col6, #T_679b0_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row0_col7, #T_5f495_row3_col2 {\n",
+       "#T_679b0_row0_col7, #T_679b0_row3_col2 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row0_col8 {\n",
+       "#T_679b0_row0_col8 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col0 {\n",
+       "#T_679b0_row1_col0 {\n",
        "  background-color: #128912;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row1_col2, #T_5f495_row2_col0, #T_5f495_row2_col1, #T_5f495_row2_col3, #T_5f495_row2_col6, #T_5f495_row2_col7, #T_5f495_row2_col8, #T_5f495_row4_col5, #T_5f495_row5_col4, #T_5f495_row6_col2, #T_5f495_row7_col2, #T_5f495_row8_col2 {\n",
+       "#T_679b0_row1_col2, #T_679b0_row2_col0, #T_679b0_row2_col1, #T_679b0_row2_col3, #T_679b0_row2_col6, #T_679b0_row2_col7, #T_679b0_row2_col8, #T_679b0_row4_col5, #T_679b0_row5_col4, #T_679b0_row6_col2, #T_679b0_row7_col2, #T_679b0_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col3 {\n",
+       "#T_679b0_row1_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col4 {\n",
+       "#T_679b0_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col5 {\n",
+       "#T_679b0_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col6 {\n",
+       "#T_679b0_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col7 {\n",
+       "#T_679b0_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row1_col8 {\n",
+       "#T_679b0_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row2_col4 {\n",
+       "#T_679b0_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row2_col5 {\n",
+       "#T_679b0_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row3_col1 {\n",
+       "#T_679b0_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row3_col3 {\n",
+       "#T_679b0_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row3_col4, #T_5f495_row4_col8 {\n",
+       "#T_679b0_row3_col4, #T_679b0_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row3_col6 {\n",
+       "#T_679b0_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row3_col7 {\n",
+       "#T_679b0_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row3_col8 {\n",
+       "#T_679b0_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row4_col0 {\n",
+       "#T_679b0_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row4_col6 {\n",
+       "#T_679b0_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row4_col7, #T_5f495_row5_col8 {\n",
+       "#T_679b0_row4_col7, #T_679b0_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row5_col0 {\n",
+       "#T_679b0_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row5_col1 {\n",
+       "#T_679b0_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row5_col3 {\n",
+       "#T_679b0_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row5_col5 {\n",
+       "#T_679b0_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row5_col6 {\n",
+       "#T_679b0_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row5_col7 {\n",
+       "#T_679b0_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col0 {\n",
+       "#T_679b0_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col1 {\n",
+       "#T_679b0_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col3 {\n",
+       "#T_679b0_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col4 {\n",
+       "#T_679b0_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row6_col5, #T_5f495_row7_col8, #T_5f495_row8_col5, #T_5f495_row8_col7 {\n",
+       "#T_679b0_row6_col5, #T_679b0_row7_col8, #T_679b0_row8_col5, #T_679b0_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col7 {\n",
+       "#T_679b0_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row6_col8 {\n",
+       "#T_679b0_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row7_col1 {\n",
+       "#T_679b0_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row7_col3 {\n",
+       "#T_679b0_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row7_col4 {\n",
+       "#T_679b0_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row7_col5 {\n",
+       "#T_679b0_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row7_col6 {\n",
+       "#T_679b0_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row8_col0 {\n",
+       "#T_679b0_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row8_col1 {\n",
+       "#T_679b0_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5f495_row8_col3 {\n",
+       "#T_679b0_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row8_col4 {\n",
+       "#T_679b0_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5f495_row8_col6 {\n",
+       "#T_679b0_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_5f495_\">\n",
+       "<table id=\"T_679b0_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2834,118 +2837,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_5f495_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_5f495_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_5f495_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_5f495_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_5f495_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_5f495_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_5f495_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_5f495_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_5f495_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_5f495_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_5f495_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_5f495_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_5f495_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_5f495_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_5f495_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_5f495_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_5f495_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_5f495_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_5f495_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_5f495_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_5f495_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_5f495_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_5f495_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_5f495_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_5f495_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_5f495_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_5f495_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_5f495_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_5f495_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_5f495_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_5f495_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_5f495_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_5f495_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_5f495_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_5f495_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_5f495_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_5f495_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_5f495_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_5f495_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_5f495_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_5f495_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_5f495_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_5f495_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_5f495_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_5f495_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_5f495_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_5f495_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_5f495_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_5f495_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_5f495_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_5f495_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_5f495_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_5f495_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_5f495_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_5f495_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_5f495_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_5f495_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_5f495_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_5f495_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_5f495_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_5f495_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_5f495_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_5f495_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_5f495_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_5f495_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_5f495_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5f495_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_5f495_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_5f495_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_5f495_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_5f495_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_5f495_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_5f495_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_5f495_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_5f495_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_5f495_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_679b0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_679b0_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_679b0_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_679b0_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_679b0_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_679b0_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_679b0_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_679b0_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_679b0_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_679b0_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_679b0_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_679b0_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_679b0_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_679b0_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_679b0_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_679b0_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_679b0_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_679b0_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_679b0_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_679b0_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_679b0_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_679b0_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_679b0_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_679b0_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_679b0_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_679b0_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_679b0_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_679b0_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_679b0_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_679b0_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_679b0_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_679b0_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_679b0_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_679b0_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_679b0_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_679b0_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_679b0_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_679b0_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_679b0_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_679b0_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_679b0_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_679b0_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_679b0_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_679b0_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_679b0_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_679b0_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_679b0_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_679b0_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_679b0_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_679b0_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_679b0_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_679b0_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_679b0_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_679b0_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_679b0_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_679b0_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_679b0_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_679b0_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_679b0_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_679b0_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_679b0_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_679b0_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_679b0_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_679b0_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_679b0_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_679b0_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_679b0_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_679b0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_679b0_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_679b0_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_679b0_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_679b0_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_679b0_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_679b0_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_679b0_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_679b0_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_679b0_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbf0cb03c70>"
+       "<pandas.io.formats.style.Styler at 0x7fbb6452f460>"
       ]
      },
      "execution_count": 31,
@@ -3045,204 +3048,204 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_b484c_row0_col0, #T_b484c_row1_col1, #T_b484c_row2_col2, #T_b484c_row3_col3, #T_b484c_row4_col4, #T_b484c_row5_col5, #T_b484c_row6_col6, #T_b484c_row7_col7, #T_b484c_row8_col8 {\n",
+       "#T_5953e_row0_col0, #T_5953e_row1_col1, #T_5953e_row2_col2, #T_5953e_row3_col3, #T_5953e_row4_col4, #T_5953e_row5_col5, #T_5953e_row6_col6, #T_5953e_row7_col7, #T_5953e_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row0_col1 {\n",
+       "#T_5953e_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row0_col2 {\n",
+       "#T_5953e_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col3 {\n",
+       "#T_5953e_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col4 {\n",
+       "#T_5953e_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col5 {\n",
+       "#T_5953e_row0_col5 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col6 {\n",
+       "#T_5953e_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col7 {\n",
+       "#T_5953e_row0_col7 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row0_col8 {\n",
+       "#T_5953e_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col0 {\n",
+       "#T_5953e_row1_col0 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row1_col2, #T_b484c_row2_col0, #T_b484c_row2_col1, #T_b484c_row2_col3, #T_b484c_row2_col6, #T_b484c_row2_col7, #T_b484c_row2_col8, #T_b484c_row4_col5, #T_b484c_row5_col4, #T_b484c_row6_col2, #T_b484c_row7_col2, #T_b484c_row8_col2 {\n",
+       "#T_5953e_row1_col2, #T_5953e_row2_col0, #T_5953e_row2_col1, #T_5953e_row2_col3, #T_5953e_row2_col6, #T_5953e_row2_col7, #T_5953e_row2_col8, #T_5953e_row4_col5, #T_5953e_row5_col4, #T_5953e_row6_col2, #T_5953e_row7_col2, #T_5953e_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col3 {\n",
+       "#T_5953e_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col4, #T_b484c_row3_col2, #T_b484c_row6_col0 {\n",
+       "#T_5953e_row1_col4, #T_5953e_row3_col2, #T_5953e_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col5 {\n",
+       "#T_5953e_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col6, #T_b484c_row6_col1 {\n",
+       "#T_5953e_row1_col6, #T_5953e_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col7, #T_b484c_row6_col5, #T_b484c_row7_col1, #T_b484c_row8_col5 {\n",
+       "#T_5953e_row1_col7, #T_5953e_row6_col5, #T_5953e_row7_col1, #T_5953e_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row1_col8, #T_b484c_row8_col1 {\n",
+       "#T_5953e_row1_col8, #T_5953e_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row2_col4, #T_b484c_row2_col5 {\n",
+       "#T_5953e_row2_col4, #T_5953e_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row3_col0 {\n",
+       "#T_5953e_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row3_col1 {\n",
+       "#T_5953e_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row3_col4 {\n",
+       "#T_5953e_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row3_col5 {\n",
+       "#T_5953e_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row3_col6 {\n",
+       "#T_5953e_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row3_col7 {\n",
+       "#T_5953e_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row3_col8 {\n",
+       "#T_5953e_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row4_col0 {\n",
+       "#T_5953e_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row4_col1 {\n",
+       "#T_5953e_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row4_col2, #T_b484c_row5_col2 {\n",
+       "#T_5953e_row4_col2, #T_5953e_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row4_col3 {\n",
+       "#T_5953e_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row4_col6 {\n",
+       "#T_5953e_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row4_col7 {\n",
+       "#T_5953e_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row4_col8 {\n",
+       "#T_5953e_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row5_col0 {\n",
+       "#T_5953e_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row5_col1 {\n",
+       "#T_5953e_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row5_col3 {\n",
+       "#T_5953e_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row5_col6, #T_b484c_row5_col8 {\n",
+       "#T_5953e_row5_col6, #T_5953e_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row5_col7 {\n",
+       "#T_5953e_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row6_col3 {\n",
+       "#T_5953e_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row6_col4 {\n",
+       "#T_5953e_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_b484c_row6_col7, #T_b484c_row7_col6 {\n",
+       "#T_5953e_row6_col7, #T_5953e_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row6_col8, #T_b484c_row8_col6 {\n",
+       "#T_5953e_row6_col8, #T_5953e_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row7_col0 {\n",
+       "#T_5953e_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row7_col3 {\n",
+       "#T_5953e_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row7_col4 {\n",
+       "#T_5953e_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row7_col5 {\n",
+       "#T_5953e_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row7_col8, #T_b484c_row8_col7 {\n",
+       "#T_5953e_row7_col8, #T_5953e_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row8_col0 {\n",
+       "#T_5953e_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row8_col3 {\n",
+       "#T_5953e_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_b484c_row8_col4 {\n",
+       "#T_5953e_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_b484c_\">\n",
+       "<table id=\"T_5953e_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3259,118 +3262,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_b484c_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_b484c_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_b484c_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_b484c_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_b484c_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_b484c_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_b484c_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_b484c_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_b484c_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_b484c_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_b484c_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_b484c_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_b484c_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_b484c_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_b484c_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_b484c_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_b484c_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_b484c_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_b484c_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_b484c_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_b484c_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_b484c_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_b484c_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_b484c_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_b484c_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_b484c_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_b484c_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_b484c_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_b484c_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_b484c_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_b484c_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_b484c_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_b484c_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_b484c_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_b484c_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_b484c_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_b484c_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_b484c_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_b484c_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_b484c_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_b484c_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_b484c_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_b484c_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_b484c_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_b484c_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_b484c_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_b484c_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_b484c_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_b484c_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_b484c_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_b484c_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_b484c_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_b484c_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_b484c_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_b484c_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_b484c_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_b484c_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_b484c_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_b484c_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_b484c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_b484c_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_b484c_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_b484c_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_b484c_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_b484c_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_b484c_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_b484c_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_b484c_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_b484c_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_5953e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_5953e_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_5953e_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_5953e_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_5953e_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_5953e_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_5953e_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_5953e_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_5953e_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_5953e_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_5953e_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_5953e_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_5953e_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_5953e_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_5953e_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_5953e_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_5953e_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_5953e_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_5953e_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_5953e_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_5953e_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_5953e_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_5953e_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_5953e_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_5953e_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_5953e_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_5953e_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_5953e_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_5953e_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_5953e_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_5953e_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_5953e_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_5953e_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_5953e_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_5953e_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_5953e_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_5953e_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_5953e_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_5953e_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_5953e_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_5953e_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_5953e_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_5953e_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_5953e_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_5953e_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_5953e_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_5953e_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_5953e_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_5953e_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_5953e_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_5953e_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_5953e_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_5953e_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_5953e_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_5953e_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_5953e_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_5953e_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_5953e_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_5953e_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_5953e_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_5953e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_5953e_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_5953e_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_5953e_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_5953e_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_5953e_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_5953e_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_5953e_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_5953e_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_5953e_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbf0cc27af0>"
+       "<pandas.io.formats.style.Styler at 0x7fbb64532f40>"
       ]
      },
      "execution_count": 36,
@@ -3379,19 +3382,76 @@
     }
    ],
    "source": [
-    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
+    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
+    "    cmap=cm\n",
+    ").format(\"{:.3f}\")\n",
+    "kinase_distance_matrix_df_styler"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Save kinase distance matrix"
+    "We export the matrix with colors as an image to use as thumbnail for the website."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 37,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[1129/155536.382797:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155536.383888:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
+      "[1129/155536.385040:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
+      "[1129/155536.969662:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmphn_w7l90/temp.png.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
+    "plt.imshow(matrix_styler_img)\n",
+    "plt.axis(\"off\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Save kinase distance matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [

From 23264b4d18e09bec7377ccdab54895f851d12504 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 30 Nov 2021 13:01:24 +0100
Subject: [PATCH 156/158] T024-T027: Adapt thumbnail.

---
 .../talktorial.ipynb                          | 2351 ++++++++---------
 .../talktorial.ipynb                          |  338 +--
 .../talktorial.ipynb                          |  358 ++-
 .../talktorial.ipynb                          | 1109 ++++----
 4 files changed, 1990 insertions(+), 2166 deletions(-)

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
index e9595f3d..4e74466e 100644
--- a/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
+++ b/teachopencadd/talktorials/T024_kinase_similarity_sequence/talktorial.ipynb
@@ -300,10 +300,8 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "import matplotlib.image as mpimg\n",
-    "import matplotlib.pyplot as plt\n",
-    "import biotite.sequence.align as align\n",
-    "import dataframe_image as dfi"
+    "from PIL import Image\n",
+    "import biotite.sequence.align as align"
    ]
   },
   {
@@ -479,6 +477,38 @@
     "# NBVAL_CHECK_OUTPUT"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the sequence logo again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CNvdJYIWQmXVElWngauMWhQzDMAzDMHuaaTlmWV3MhxVChtkOUJUd1MVZ54oyDMMwDLNr4PDFXUFSrDKWDPzZkfPZLbBCyMwoqT7ALX2fvXVv7tm56vnq1h2CYRiGYRiGKcnkHsLmiWbzRHO3Jx9tNVxldK8RRVEQBDt9FtNk24w62CZ+q3taYOeJQb0cnqEYhmEYhmGYnYMVwr1GEASVSmWnz2LL8da86apSokOyer469Yqm3JueYRiGYXY13HmC2auwQrjXUBRF1/Xkdt/3t/9kpoKo+G2dP03U1rbCSSj2o6ffOaKdYRiGYRiG2VlYIdxraJrWarWS2+fm5rb/ZCZhm/1p0uEwsHPbDscwDMMwzOxwrO8JFGvJsIdwSzkm3Fu67dc9776dOZ39BSuEDJPC1ENSIS1qlD2EDMMwDDOD3NfsZY7cB3B+t5nUdy9026Fa5bKu2wlXGWV2GVuhRG1nV0NvzRv0nOCKMgzDMFNlro/H0iTD7FrYE7vNsELIzDTb40NLBnBySCfDMAzDMDAcyshsD6JCOK3e9EwOrBAyzDZByi1qmxwvyjAMwzCzD3urZp/tDPXak7BCyDDp/sCpTy5iodHp7plhGIZhmC2CFcIdYTzHLCfjjAcXldlr7L3G9AzDMMyWQmWom82mwbIvwzDMPoMVwr3GDDamr56vemtefDqeyt4w0nKK8ZZZnsCtLjTKIaMMwzAMs4vgFgjbyVHDwEKjnEO4DXDI6NRwXbdSqWBxs5WVlVqtFobh5LvFfdbr9ZKfx8b0SSY/k/Hw1j1UgeaenU7VZmPRaJ5o7oGQgD1wCQzDMAyzH9iNdWU8z6Oiuzt9Lsyswx7CKRBFUbVa9X2ftoRh6Lqu53m2bZumOfae6/W6uNsyzFpjejFfbqu7vY/Hdmb0JVsRMgzDMAzDMMwOwh7CKVCr1VBtMwyj2Wy2Wi3bthVFiaKoVquNndHn+77jOFM90+kwUpenIYWQdaE+HC/KMAzDMAyTBdfy2U5YIZwU3/dRL7IsC9PxdV23LKvVaimKAgDloz1F0Os45XPddqT0vN2lEG7F2c6gg5RhGIYZlblc9sDyzTDMvoIVwklxXRcAVFW1bVvcrmkabvF9fwwnYbVajaLIsqxpneeOsLs0QACIT8fsu2MYhmEYJhUucMLsSVghnBR0D6bW6aaNZUIrRTB1UNd1ScncA5Rv7rf9ymTz4SZwMCfD7H5GCmtnGGbPMDcHc3OQdNBmbS8Phy8yextWCCeCKr6klvHEgp8AMJKHEFMHFUVpNBpTOckdJKnUjarmbYN6NugDsWgAR3UyDMMwDMPMABMWd62er9JPeYfE/oSrjE4ENZZQVTX1A7i9fP8JSh1sNBpZ+2S2GqoFOptlURmGYZgZAWsH7PRZMAwjQ23PCJbocmAP4UQUKoRYV6a8Qoipg6ZpFi4w9Xo9P6ldovQ1TY3dZYzhSFGGYRiGYRhmH8IK4QyBqYNUjWZPMmtlZnaX1sowDMMwDMMw04UVwlmBug42Gg30K+Zj23Y8Clt/BTKzpvvlI3oIZ9Bb6HleapGMrO0MwzAMwzAMUwZWCGcCSh20bVvTtJ0+na1lFznlZkenHbtiLcMwDMMwE0JVRrntxPZzne/51sNFZSaCUgfDMExNI4yiCLIzDIlarRZFkaqqmqZR5VJpP7hdVVUuNrPV7KK0Y9IPuarB7oVSfLk6BcMwDMNsEbza5sAewokQFcLUD+D2QhUO9cYwDCsJ8AOu6+I/Xded2tlvMVN3r21F+WCp5wTDMMxMUa1W5+bmqokGalnbtwiKTt+REmWzDD4IkW17KAzDMNOCFcKJoPaDqW492r7no0ALEbPyxlYUvTVP/JnSqe0acqxZbOhimL1NMlaco8cZhtkGyNLBc87ehhXCSUFZPFUhLB/O12q18ovBWJaF/9yNBUibDzebJ5rbf1zRpJ1Kzuw2g3VlGIZhGIZhGGbqsEI4KegkDIJACuaMogirhmJm4M6c3M4hhnRiNOYsq1izfG4SySqjO3UmDMNsA/kFpaYeHeB5XjWN6R6FYXaKahXm5lJ+Rhrj7DRj9h5cVGZSTNP0PM/3fSwMY5qmoihBENRqtSAIAKDRaEhfoRyMregG4fs+ZR7OAqRrGUvGPozznCKGYfDawzAMbKUlKGvPHJeeBVen2Bvw6srsc9hDOAWazSb6AOv1+vLy8tzc3OrqKmmDlGe4z9mrVVtG7RAYn47j0/EkMbRJiW2mxBE2nTLMFNlmDyGzx9hF1YB2fO1gR/jY5OTmcHzBboEVwimgKEq73RZbCKqqappmp9MxTXObT0bX9VloTD+o3rl7ojFFjCUDf4o/WaJDoDgnYkk6/DAu1SWny6TkxxoXw+xP+N1nkPw8+dlXAmcKfqvGI3868jyP56tdASuEU8OyrHa7jdpXp9NpNBpZ3SZGUtJ2by0ZRHQM7iLl0Fg0mieak3g1t7NDYMlDzEj9eoZhRiLrBZ/i3JJV1WynTIoMw+wWWN/bG7BCyGw3+zOTcIoz5oS74vr1zA5CTdtKbmcYhtk2eDEcg0LL1FRMV81mL9GGjddbBCuEDDMphR0Csz6Amlj5uVL65KhfZ5gdp0yINZOKeMemfvfyww5ZAmMYJgvDMLC0EkIbEVLkmBmHq4wyW8guihGdnJIiGmUPbr8iR3VKxaNvZ2grw+TDgzCVrArDfLsYZrp4HsxQlfapkmrWmZa2RqogHYX1wF0HK4TM9rGDnSeycmC889tdvX0HyRcfWbhktgHREiEOuRl8X2aQHTEkMcyeRBQK9kOseuocy1MKQ7BCyOxrpqKgluwQmDXzjjodTy46p3oIGWbHmVnRpHq+inNF8+GJak2NR2qseHL7Vhxr6w7ETBd+RpNgGLsyezBrXhpsB3bTMWVhhXCvEQRBvV7f6bPYZUwrtDWp8k13kZY0zzFyCDn2jNlxkoNwxq0Sc88O3AfVV6o7ohMi22DOF7OApENv6XGZCeEHNwm7VCEkvDVvr7Z6HgNvne/GOLBCuNeIosj3/Z0+i6mVEt0V01xhh8CSLkSG2T/sFsNE9XyiTcsr1fj0drdhEOcQriY1C3Arjr0Ev0wMw1VG9xqaprXS2OnzAhjuSbgnScq4hXVBR/XvJQ80hly4peUKGRHP87hDtEShAWWmSLVteeuze8IMs1cJw3B5eTmrFu7q6upOn+DMYRhGfn/RvWRX2vMS5lbDCuFeQ1EUPY0dOZmsUMwy4tSurlC6DQLueIfYhg7XDFOSpCN9p84ki6yZan82U2VE5p6dwx9xo7fu4UY2GWwFnudFUZT11yAIZiE8akfImZH2czsZY9GIT8fx6XhXy5PbBiuEzPh4nldNkK+o9F7OsQw5s/xi51diwF+m0hFe2tWoMnRWLZkZlMWZvcouGmxZYtb2K4Tii7+lIaPJWX2W/bc7SPPhXrkOUfejgcHOiq1AUZQJP7DfyH95+dVmRDiHkBmfrNlkFwl8O4JhGJNb5qYyle/z9eB8bhin0mwe5ZG8lVCJlJlNihMVPzRFTV0VpFhi7Ow83Z2PwW7J7dxxSOUTc0pxeMym1XIPYBiG7/voJAzDMAxDAKAAKE3TNE3byfObPQqXeG47wRDsIWSYISgQaO7ZuWQ9iXx2xeSbdFfu+ClJ+L6fE+UyNzfHdXR3O7M25AqJT8fNE83miSbJ+tscE5j07e+6e7j3ICchQkOieYJr/W8JiqI0m00si0Djnwol2LYtfrhaHfrZ86TOSIWzBE8jDMEKIcNMn93iedst58nsYWZWwxnI94LcT7L+TqURzsI7m2qmmYUT22YGTsLzVQCovlKFnXMPVqvV5EPZkTOZBTxP/pmcG/29zGbkSOqMlD+pYu3iXTFajs3kPd9jcMgow0yB1MYSZaqJjpEEOMvOvaljWVZy404VSWKmBQ7jVBUCc9jw962r7F/mKLOWDya++1v34s9I5OpuwVgyvDXPW/PgRG8LuwcnJ2tyaDb53o4MzW+UDyzOePvQjsNkwQrhXmMWGtOTZX1GBKntpExQKH5gkjTCSUJPk7L4LMt/UhQQs/eY2QRCJOnwaT7c9Na8nXIEsQA3UzRPNOfW5gAAy41y9uBU2P5BTgc0DAB+w3Y5u6J/9QzCCuFeYxsa0xea7Wew4nYyHGK6hnBxV1tqxZdSicY4RPJUZ1YW3zZSw1FmMy5ob5BV7XZXYCwaKG2kWtyhP9sYhsEOjf0AOgnxd3YP7lJEhfDK7puTGGYKsEK418DG9MntlUplWocQo62SNJtN+MFpHWpPkerWY2VsFrhvHwjus1bNUmIGT6mQ1EDx7Tnc9t8u7HC9zQfdFZCTkN2Du5ddaJiSEeOWt+oQHMq7p2GFcK+Bjem39BD5sY6e5xk/OLV1cRvmuGkhhWJuncRGcuGEUaP4HIcdhr1qbKLgRxsNA7Z/2k/GP+u6zjmEewB6X2bWSVjYQkD0c+6rKqBic8LZtC8gLL/uLprNZo50MfVhlqyiMqsDeVbYkbn6+qwuEHsPVggZZjuQtLht9i1MwlByxbbjOE5yIyuEe4+ta7O+DY31tkchLFOkahuQwu+r1erMBsfuljl2KsysWl6eHe+inn8Lb8z8cPLWdyx3LjnTkm5vGMa2DU4xfltkp4pC7y5YIWSY7UOKGt3ZJbzk+rqzCiGzu8DSGgBArbrF7caSQUlWOYVGp0jW/qf16mVZdvaAdJ5Kam36faV3zSzbKXZvEZZlYdN5SOs7v9V2QMMou8ZxerlEahoRTQue53G0+a5gdvsQBkFAUwNRr9dXVlbm5uaWl5drtVryA8w2kG8MnvqaJJWoYUsPMUnIaMmd7Lg2GCfguqO7gpEqS+0WQXbu2Tn6wdZzeOaiRrSlhZoovmArdl6I6PzEl5HOZJKayZmHW/fEW81MTjWN2dHnsQJCVt/51BZEU2RmbsP4zGxz1NkZY0wOs6gQOo6zsrKyuroaBIG4vVqtOo6DRqMoilzXrVQqrBNuPyQNkNVH3DI10/tuS9CXREPpPmxRRdOt3tuOK4TMLqVQNNktSmA+WeVS98bVSYh5g/RLUiWezrHWPezzDgDemkeeZ2YSvAx2+rxmBb4TqSTNshJbN93NfpjuXmLmFMJ6vV6v11HrE3FdNzltBUGwFYZJZupUz1dF4/oM9qWYIvnr6yxohuW/yAohMx4j2ar3gPqEb/1Wy9bNZjOO451K2Et1fm7RsyNtcLBlhv2E8ek4Ph1LPSc8z5vrs1MnxowEax+pVKvVubm5pLBN2/MngT0QzLxPmK0cwjAMU2tIAAD21lMUxbZtVVVJP/R9PwgCTdO29URnmDAMXdfd6bOYJmKP0VnWJLdHToKp1n8veZI8mTNjUFjhYBeVVspBvIrd29jTeKVfjOHhoe2pyZ9DX9wCaS9V99tFFaeZPcksl7vEYipb+o4k52pxyzZnCaa6gshSxnPFeMyWQkiajKIozWaTcoijKMKRZ5qmaZoAoOt6pVJBLdHzvFlQCFFHxVNSVVXXdcuyVFUdaSd4pZ7nUQrlqLvKUar3M7OQ05wUf3dQcCwji+8f9+CVxOpy1DC4csCEiKacfGZZgyq8CrE6zlYohPl9X6dV4ZOa6aUa4IylvBaIU/dYZrmXq+ers9P5vVar5dhe0XK9nefDiCSbJE3LTXtshierLaJQWhDnqOnKWhQyKi3HqefjeR68O+30ZtiRMFPMlkJISYO2bYsVpVDLAgAxq9g0TdwupRpuP1EUVatVOknou+k8z7NtGzXYMmAErBQui7tyXbfRaJTZlaqqqbnXu05LTBXCZryiTPkOgaP6RsIwXF1dzcqY1TSt3W6X3xuUk+H2j0KYavqdukKYGjk23AcyJZ9n98bb7HYzbVYFc/ljW9xwYttq8SedDHT5xqLhwTbNvaL0hrVqq+erMzjz59/5IAhmRCGcYkTJjhNFEVUTJEmpUqngL5qmjVRyTLwrHNVbhv3WcHW/MVsKIelUUn1hVPk0TVMUhTbSZ0RNbEeo1Wp4Dii6KYoSBIHjODh5aZpWxoGJWiXOcaqqmqaJ3/J933Vd3BUAFOqEqqqmzom7TiEktkGsLO/KmARRBxt1kfY8L6d+UhAEvu9PvSo3Tvg8228bW90jYRZI9XeRqmxZ1lTCPQpfrvH0qCz9RHQS7t6HRQowxfriP7e5vhfdW+pc0jzRrEJ1qyPiRqXVatEowuVV13UavaZp7ripGsk3Jqqq2m63Rclqlkk1mZEE6Pu+YRgA40wghgGel5dDOOPVTZIv7zR3LiiByfltb0T+MzCDRWUQybRGCqG4cUamMN/38WWwLAtLrmGEZ6vVwjOs1+tl9kMFVE3T7HQ6lmXpuq7rum3b4q72eVXVqduJk7IOlgeYtRqnhaO9zOtAFQ7KTN+Tuwcx4zzJmLtjdg/0+iSTwfLH3rTsVqkDb7xBWH7O2e1S0SBStC9Z4j9nIUpzpAm5sCjiVE4J/VFIcsuMuAehyJgYhuGMKK5lKJR/tk5AmnGFkNg2X/r2THdj522KgQap9yRVPtnnVSpny0OYRapCOCNgFkHSL4drAzoPy5S9EavmSH+iXUVR1LeB7Qu21Oi1pUjPKF8ALZMFZBiG7/sUKiN17C3phR6J/RMvymwn+ZaLGTHzjYpoIx97fs4KGG42m6KYIvkhp7scNB9uVl+poiMu6QWlE9jSNSjVLbm75v+ZAn2VWWsHFinYyfMbBdM0XddNVqFH0Ia+RYee5YoysPUvSJYbcEunAlEJP2oYRpnQj/Mz/ZhmnNlSCBVFwWlL1KBoLpNE3qxJYZvJCRMyDAPjPMuUvUkNiyVM08RdBUGwfxRCAkM6Z7nsOLJ1sRNYZgl/r9fr6EhptVpbcSyE40WnQpnY4D3pNc2qemeaJk3dSfF06pObbdupc28ZTwIZpKRaJkl7s3jakyiE6achGIxIM9yizhNiORm8zObDBQeiGGDqSTidM0mL3Sj5XZohU5nueU5I1qufrIkyNtu/dmwdiqJ0Op2t2DOGjI7BHkv8zmebgyB2i1d2zzBbCqGmacnCoTgEFUWR1nUKHN9Bz2FW0iOiKIqu6+ghLNwVVoLZRba67cRb8zCHZKdPpCxZi0GW4WCLT4cp4MRWFl1YXV3NmgFEWW2PMahaOeze1zSNhNFU8XS6MoemaamTapmjDIVQ9tXaLLMUGoP2wLuMajC1ARSfHV5jtVoVbRwzWFsiv/GS67qzc6pZeB5Uq1PTCXcLQRBQik2yZoyu66qqplofRKsEwNQeLlWfznEPzk7id8kiWGPuPGHs3rqU6azb7q335iWxCw70W+MAACb79MLKtqUwxB5jthRCVJ8AwHEcLKzi+z5KDMlhR5P+DiqENGdlJQzg9jLOzPzqWHSx+1ZjnFltUCq4nO8O2i2GQ4pQ228SydQJgiDHHoRB4Nt5PpDRwWla3QuS7I2Fee7ZOfRZiROR4zhSijj2IsffdV3fjX4Y0uQhGbfZFwqr1SqOlqmX0hk0uphgzKiqmvPSzU52HyHdPBSD96F3xHGc5Hwo1oyh5VUyvkwjYDtl4/ZUn54uyaCMZAQKbSkvkGxbxHjqPcdWHwXtf7LTjGdWepw1ZkshNE0Ti3MCQK1WwyBJRByCvu/XajXSsnZQRypUCDH+c8Lo1iiKUOwQrd0UbbXf2FIzGMNMF03TLMsi8ZT6lOKMoSgKTno7eYpbxjb0Si4kVTEov2SIs01y2smfgUvW6tA0LceBXL5r0RShq04qhPiLqPdKf5oFGo1GTv3P1LZMO45ojUH3IP4yS/d1y8nPucAxlvOZmRqEM0Jh0xrxpmU1RkraCnc2LmCoNY7QK3X7z2SPMVsKIZZUEfVABEt30j993xe1wb09C/i+X61WoyhSFKXRaNB213X3qhxJsO63U0xinN4zPa+mhej8xxXXMAxx4169Y2LU6Hh7yHJmlp/zU4s8l7/h+VNQo9EQdSSM4yCvoKqqwc1g9cVVGI5xiu5GK8+vRHci7ajWuNQodCBPxeJ5PjdPVWk2U/0eSZN8HMdJqXFLAx8omxEpU/JULLKFq+SoHep2lj0t0eSBaoaqqlKiYDLqPjV8cXuYWQ+hWISPNhbemS9+8Ytl999vrrNFFa1KnUNGaxxITFakNHJX+vLMlkIIAKZpKopSr9dFlU9UhEQ0TdurGTgAgOGyVH201WqJwbGjFgfbkW6Nu646KCMxhok6GUcnMq04OpJKW63Wbo+jrlarOcu2ZVlTkWV3UO0c26yz466Awukr2Q5XGo3WsuV0HW/N84/7+oIOAE7Xie5EykGleaKpPqoWOpCnekFTII5jccRudY2WnpOZ/rm0j9aUMvd1L5mTdF33PA+jnyjqihpjoPxDHsJk7OLe9g2MxxYVutupFjvJFEG5GPK+mRy2gplTCKFvbsTiorQ0ilDf9hlcLKdCGIaO41DeoGmatm1L1UdN0xzp8ndjJcO9924nK5JtkXGdPFETWkzGUAinEke3r8i/J9OKDN8etVNiEDW6Q5SxF+QHSiWdhNiboeQJ2A/Ywc3Av+bXLtY6j3fCzdDpOgDQeKihzqtQwoE8XY6lvc+jOj32sB2WSPpUDyjKEV2/x7LmNQ0AgiCQXiixJsq2CSfV81WK7xXdp7S9fHXWHUcszE6RvWRqoZBR6VszWNZoR5D6iPY25t4TwzDe+c53bu1pTQOaoLJkwtR4UXRISLP3XjKgTJ1ZVAiRnFIxs6MHikas1DRCzIccKYtdLJmN3tEZTILPZ0sVua2II922NKft6eRDR5ncjDfGDgrj6CY8pb1Hu90mnRBL6pmmSfdwWv7P7VE7JcQKJbuU5CWMOr81HmysvrQa3grrl+vBzQAAzPvMnbJ23TeuLidVz0pu3LZ2Dqnhr8cMY+xLy+GIrs/3RZEbnnfd8657HkbYVioVqXmJ7/ukvYRhOPbdwDtaUnDNWhB3Y7YFFpOXCnGRe5DWDil2cTu9Van2lBmnUAUqqSPRbRe3THRmYyGmpucnEKIXcTe+CDvFgZ0+gSHq9Xq9Xi8f3BhFkehJ235EhTD1A7i9pBAcRVGlUqEk+Far1Wq19oYAbSwZ+LPTJ7Iv2KlwDkLvQ25t2rI3xvN0wf40Ylfl5JbJabfbrT64xTRN2lLS5zOXRqVSmeKQw86NOWy/ewqrmQOAsWSgv2WkqUydVxsPNQDAver613ztqGY/sGvy2VLZuhlGbIQ40heve15+nuR4zGvakm3jz+s6HdQHrtZqUCQNSxNd6osjWO5gbm7w43kjlHceu/Ti9uP7Q5eJP8vLUK8DadZ4V0UhEH8vnAm3UznB4LUZOZmh4yYed7VaxcFWcnvezhNlXbftMsUQBrpGMbs49S3w1rxklyC68FRysl32A7OlEDqOk1p3OAtUIJNFaLaNZAKJBG4v2RijUqng523b3qLMqPNzczk/3X7Dn6ljLBrNE83mieao69NOrWdbZFVqNpvbLN2OLbrRDLzT2uV2cKVaTf7ssa64W6d2YuGrzHy/EV9hrGApisv527eH5olmfDqmkDyczcp/3Vg0jCUjuhMBQOOhhnJAKfzKLDPFGIRC8M4n4x6PGQb90MYraVWIpsg9lgUAd6PoRj/2AQBarVbSbDFh0G+1OvLEO5RpOduFNHS996OqEEXgOEAF8nAuiqIIHYNUTV3UPSTH4DYrJ7NPashoVnRSGIbLy8s5OlLyiztrcU5Gxo7UcGJHImV2C7MbMlqGWXh46EZPVQhHCm2v1+s4UhuNxjbExB5JEwHnyymuYpgQbSFT01YHDm1RqNVeqn8jztfjdQ0yDDCMQTusPb/UZvUdntmCcjuLWLORWsU6jpM60kaNGs1qeDXdTCH0Q06+n/JgxiAAaEd2rHEupMVbJoMtmyeaO9gpBCnTvpJO+77+dV33vPu28qxoidycai50Vh/CkiM9J43CWDKiKKrVahjdmuz2rqpqo9HAQWFZIKmxWdsnhMqKRREsLwMA0O3E0NAwDH3f1zSNyiyJVvXUKWJ7FEJcERRFEeugovCzRWnYE1I4naqqalkWBT8n27SI5kIxanTbNPBkmK6Umi53x0l7HWjj9iRo7FJ2UiHM6qRXslkzThlbcF6jgXWxgiBwXVdU5DCcFRJzWRb4jo1aKmZslrMrPWYFERgG2DaoanoteCJfAxnV85b1bo+0k1H3z6BCiOysnyw1hGPbZu29VMt0iqBrEX/Xdd33fSntZ0K2odWY7/uV7ICIZFVnZlpk3XmSp1PbVxa6vJRmM6pWAeCG582IHWcko4Ooko8d+ppai99xnOTbJMpOhmEA7PDkJpbMMwwDI8WoAG/WW7819TMhJ1BqRoZWFqPKM3hjxc6cZdq05N/2VPlQjISiMhmmaWZ1EBiJkUz54uKVtWXfspMKYVYnPdd1R0oL3NmsJNM00UOIRjhsmxEEQa1WI4+f9BWSMmm1CIIAdeMwDPODmE3TzL9eirWYEFUF8Ti+D54HYQjt9gg7KWPlzUf6+vZEkM54vE0+oiMlWdS0JLjqzYKTcM8329wDSAWQk4whpiSH7kgj2bZtnEhTdQ9N0zCjkvYslT7C4hbbWbxk20ia21HGrdVqrusqitLtdsW/rqysYH0U0RUjRuuNXddeaj+TXNfEqI3C8bM9kjo5BktG02wP+e8X6ldS/RuipMF6utBLiR4BVQVBJeml2KDKmpV0IzafgKl6q6pViGZa6RuZ5HQ6drSF+LKPpKWTnyCKIpxnAMB1XcuyCgX45KstRp3MVJbsbmd3h4wiOx473mw2K5UKlpyW1LlGo1HG9iD2ocp3exaW5QiCIMfyTaTKOicEcyb6A4XdwuoqBAHs1a4Bk+uuM4KkEKZ+JsvH22ym27P3Q9Qog4SboRM5wY0guNF71fV7dH1BN4+bqZlvNGXliJWTR42KXe/K7yefwhaCqVz3PKhWSSek75LATVsw8o1m4ziOZ6GWTJY2q+u667poT6RHSZbKMnfmr//6Qak8ZhhCrQa+D6oKjQZI+8jaJ6k31VeqzYebxqJBHRTKsKUewmuOAwAHFKXMIVzXxQIH7XZbfDuydO+xEdVm0UOIsrKqqtM6UD6pjk1VBduWlw9JxjEMECcPwzAURUHNIQxDRVGSMp5UaHRyIZCsnznMfonRrM4TkxjXpgvaJtBxgkVDCp2E+e8aK4RTZLaKyoyBaZqiv3tHUBSl3W7btk2TPnZK7HQ6JeM/p5gMKRaKEJE+dkTXkz85uxXn63yZbOr6ufjC4++jVnQYlRkMIo2iqFqtVioVsaJjpU/Sq5yaLTAqO7X8qaqa73TiWL4twr3qrjy/4q15+oLeOtlqnWxhecz6pXrl3MDM5Pu+WGIUN/bm4TfE8IbYe7gp1RI0/lHcXotHfW3HHrqWZVF5D1ogaEt7lDiHZPGS656HBYdqtRq9gxTVQltWV1fHO/kk1T7JLZ7n1S/X556dy/nxN4pzK1AKh+F7jsqtoijiQkYqer5zxnF6NkTLgk5H1gZhuPCmaMQUDXPVV6pzz87t4IS8GQRr9Tr+XFpZwWTj4+Xi3OjOSEbebU7BCsMQb7IUbeG6Lm4ne7TjyCVAp3F0qFZBEnDiuPeDL6LjyB/Am4PrWuqNkmqljHQzcYBLUgy9WzlBvrs6NAAS0+lYxQXk0j4jgUo+AFiWhVMK6vw5X8lSwkks3FI7PpUk3fHK7dvDTnoIbduWwpRnOTe3EMuySqqmybyC5K0YG03TWmn5gVJ94ZwcwlRQ49B10DTQtOLUiCnm5om7mvrLP2RR27I6CpOXr0gN/iQ5w/d9wzBQTUpGdIxXVwZ2rtDoSGnuk3Ci8LlMQyaielEEZh0DgKIotm1jkLn4ATGPetvSG6K7Ue1CDQCaJ5r6wuCg5nEzX6NAhSF/jHke+D50OhCGg+bdyRIXhmGgoCBZtXe2kGCyeAm6ofINEyXNFoXDo16v5xT68zyvdW0wmQc3A/+aDwDW8mAxono2kFGH86hhHDUMwzBc1/U8jxaj1Nsu+WyTDyUIoFaDIBiknacirlOSAWiS5WO67sGbvn+z/yYeUJRjhkGN6QtB1xZO3SQbBEGAM1uOsRi9VfgzUl0ZaRUjDyEm+oqnAUI05nTta63WQPn3vJ6i5brpZWnoyGE4NE7wlPBGbYP5j96JHI1v9t2DSPLdSb6hU1dvSqbLonuQwtzw7ch3Eo6hhE9R+JRi46eyz1lmL4SMMluBWAkaMc3enE4l5qUpALcbhjHdVgqks+3n2ICsDJCsD6ROXoVTdnKZGC+NMD+OrkzSb/k096wA6W2uIZlFEATJNEixnpaqqp7nSVZSMY96Wi9UMrtYUjsHMaJ9bbB+ue50ByffOtmT4KVnoWmaJNBLZQnrdXAciCLwPAgCNxkVT1uCIBAVwhmsLH/MMLA7+X0laoAVlj0rMzwKT0lf0MVHhgqhFJ56vv9LTkFdVAjx6Kqq0oAZ1SRBnlEpEV0+7ezdpsYY58t5YpOYIAgkkRcTOvB3y7IKs16hjLWoBFRzDm8p9EcyZe6lHmTC133QrZuWTsPAyk90GlEUUR07+mJWldGxGft9NQyD2ollvfVkM5pwWkDFG4ZLqUns9sxhKcKWNuIvyRTTnPcFdyXd83yZELeTexADTOgzOZmEym6+57uR2VIIUQrkgj/bQFbLQfIcimt5GEIYguuCrudN8YVmp7GLtTQfblZfqY7cw3DEyvLJEm3TJQxDLPiGky86Q0qOdtM0c4IrKCo4GRMiSdU5FWJN09T1TENdeYUQM2SkjaS2TTFzZnKSmbSHVPWoYSxa1lwJkbEMaIDPqvOEAd60UqYyraJZq6ur+Wpn49MN5aAS3YnoRTCPm/qCjp5D7KEnnnb5Q9t2z7oUhgWmDfprfpzYVMgxW2BRmdRvHTUM1KlueN6x/ts0dhHaMsNjpBJrk4CW+zAM0ZVEqktqBpf0i4htg65DrQaOA74PjQaM4eOR1D9jychXCKP+szhqGLXVVemWisn5URSVqW0492xvcmg81DCPm9J20QGbdxWGUa/X0fKCqtcWmTbo5ojdupOnQU5CPI3U9LwpQstRzhymqgPpgsCRj5EvZbT3ScCBYxgDPfy+ZvO+LT3kjiL59sMwTIoW4vuCCVD0p1TrpFjjJ2u6wBGo67pk7cXGRY1GYyommCzoBSk0jNL21DI8e5xkQ1VmT4KP+5WinziOAWKAWMjBibvdWFFigNgw4ljopS61WRc3Gq8Y8A2AbwwNsOZaEzc21+T+7DlQwx9d18e//mzoVI1XDPEkaUs+4vxopIE3pNlspi5sjUZjmtfSn4tT/1koszabvacvXF1vi1F8J3rkC1tjPET8opgVNi2y3oJLmiYeGjJ6T08LOkpq5tu0Zul8XxYeunGlgSPfvGC2rrVa11qNKw31eRU3UmhizkNMzh7i9kYjbrVaORKeeAf6Y7JJr9h4F57MIYzjODWunsC0cBwMUWLo0/bC4SEeZbyTR3JO1Rg+PeuSlZx4y4P3StO0uD970BMRn4KoENLp/fzP/ydx9uh2Y9OMAWJFiW17cAi6J4UnQ1OxODM315p0/+mH3lx8WPkqRMkpiFYB+Aa0b7Sl7dalweuZPzmQxzsW1rJOpxPHcaslz7dx3Ltp+LKUR1xbcUWTFi88Da0/s+ETNE2zd1G5r23JqRc/nPzpH2TKZMsh6QsWbcdf8Ev0T4l8Manb7ebb6aRTGpusW6pp/fNPCFqiAEPbxZd3jOk0PxkKLzZ1OqKNNPKlO0OzQafTyb/n0jycKmEOjivcAXov8KXIf3Di+Uv1isrfrt3LbHkIma3mb1oxumrEQnDVKgQBaBo0GulpdLS8oiU91aZYxoIynudNzJQb9btozTWWDLGaBdWswxJ2Q6d3Aqqv5HVZzCfrJui6jl1JMDsOzfCO44RhWKvVMMejcM/k3ItLG9Ikux31+U0l1SUyRhrh9vTSrFQqOdcyUh7yEV2npKAbnnc7DDeDYMN1F4SrwNYyye8WHoUeXLPZlCr90HbaKFoup9jWD2k0GoWOEXQJOpHjb/juFRcA1MOqvqAri4oYOzoqGHykaWAYoCh6SRex5N+erisDtYJwM3Svuv6GL5ZU1Y5o1rKlHFDO5+9iRFLHj6Zpy8vLkBYYjFFVZFGa/ASwfqy/4Ye3QgBQDirGkmEsGmK+KLYowDw3POEsV22hp0tRepVFazWo1yEIoNEARend+TInbCwa8ek4+U98LsnYV4rre+ihh3Ic0YXh9yLaUS24EVTOVVonW9qRdEdn6qtKTmb08WJgCN407L2euqt6HdAf3GjASJNoYX8OjDHBqFFFUbYtBltRhi4EYzLo+WMwuaJAqwWaJlc6SNJsNsV1cML3goJFpxuZOHWXJtZuIIIAfB+qVWi1AN5Uag9iQQHp0ed3ZAUAy7LygxRc10Xbd1YtU8MwVFVNfVjibDDd+TaL/JiLHHVxX6QR7pQmymwz+Lglk2Sz2XP9iSbAVKMgbiTTKu4taU2hEZVqv6GNI3kIRe1iVEeNZCdLPTf6Z/JnVA9hKoZhoIENoyXpi2QzK+P7Eo+Sd73Z54AfaPWhu0pbut1u0kMYx7JJtRDcs6Io0nacas3RzcWFd6n8bZRA0+NrwhfvdrsXVfUVgIsZC9gY82fyZYmH35fUHY56lEIoj1/cSIPQtm3LyjRIG0bcbg/OqtBDmPzR9RjHPl1UoREdxzyKGmU+n0WqhzDuu0OVs4p1yUJ3KHlXlLNK51Znuh7CVFqtVqoFWrTil79jcbaHsH2jrZxVlLNK40rP8dS61tJe1OAbQFsQGicAgGJc8pQk50z500O6d7rmBRM9z8pZxXjFEF1wBN0ZKYyi0IGQ5QNJfROzIE8gjRM8yaSHMBXxHUENgXpQ2X2HqbQcNxqjeeQkyDGYtcjik7VtGw1D4sOdoodQfBVwSul7JQefQdAXqmlxp0N/7aFpWmqx9DLrYHkPYU7wS2Eg1XShoZ7q7ZTuf7c72J4qaCU9hLHwkpLjC7eLc1QrjU6nk1/XB9f05DwmvoZlYiWm6CGUhDoU5EpKdIV3bA+zMwqhOJjEpTR/hs1hR65id9G/24MZGSdrRYklYSZ1DlLVoQ8n3xBpS+qyNJ5CKNpsRhX6U48oKXvboBCiHJA8eeyO3W6nCEM5R8n5WM45SJ9MlZJTFcJRo0appr+4ttF6UFKlt23b6oNfxNwDRNSr46kqhHEcX2s0cPutEs+lDKkSv/i+4F9RasTBgPeQLM1TOQ26k+LdIw9nt9slhbDVGvzYdm+jqpZa1LMUQuiHwNEeyugP4hie/MLFjd07XeWsgnGw0udROeze6U6uEJYhVXtJtRdMohCi7mddGhrqrWstVHW6d1KGBAiqS0nyRbqbrVYcx907Xe1FTTmr2F0bY5JRU+3c6oi7IlMFTBBnLt6xrAjDzD30Fb9YsB20b7THUAgpahT/1OkrQOJyTNrg2AGW0kKW/AA+WU3T8EzEh7tFCmEyJpb+SdqgOJcXvlBTVAhFa1eSQt0v3742UiaIGDGRCLlMuf+4IitK3OmMoBBKAZD0FpSZ1dvtduFynJzHxElsJIWwxD0bXyEsabBORsizQrhlRxVghXC65NukaYLW9d4vnU7xPpMkZdwshVDUqcZQCEU3GowuGUhZKKlbUhXC1DSMscFLmCSvoMxCmK+aSh8urxDGgpRfElSAxQlUymDJp9Pp5Af6S8stjZCy59cnVSG81U8euy48r06nQ+rZqNJ/qsSPW0SFEN8gvDQ04dNblpSrut1Y03piQd93VyDSpQoraPrFVZAUQpF2u7dR00ZQCMXT6HR65iR8+LSHMq+DlKtWnvxQXk3TUBcS5QnaQhJ/qkJ4vdmk4UE7zBoS3W5XvMMEvn3YMTxOmx+S9oKSdyxLIYR+FmjhdlET64y4PJQR6eyuLbklW9da+jldclRSNmPyTLp3unbX1s/p9LCUs4p+Tre7ooYjD5tRc4FEhTAWdEJpexnEvpfiKdF7Pbk2GJdQCKVQbcmsNjnlFUKcuyRtMN4hhRDSgl8KR7JoSqONqaFAhZDDNrnDLOOaqvYkt/xQrKRCmLx7+RJjclXN2h4n5jFxEpsdhTDVYE3jigbehFmXu5Rd35ieGY8g6BX+wt7BInNprK6uYpEo/EwylloKTE+tCDpGDiH1SkIdY9Q0wmTfwkFLw6W8U90KtrpamhiyL7KlB03F6HcqwwGTWuI8B2pNIen/tKiUuZP1eq+3cqUy+Fldhbk5WF2FfkntFCifcFN4MTAFFM8ffy9zIYiYvyH9Iso3eMlY7Q3/m6UVRxFUKr28X8y9KQOlqtJLFIYhpj9Jg0TsTL262ssCGrFxqXjcXv3AaSdFjo+iKNpRTTmoAIB7tZdPoh3VWidbrZMt9XCeMYLaG5Tpd6coSqPRUBRFLOiKrbcURaFCU/S+0BfHTvGyH7Dj07GYeodoRzUAoH6S4WZYv1ynf4qNCqkjgpTq5nkergXSnql3s7jx/Nyc9LPWf+XwoFi3M7ob4T8txRJTGekOmKaJJyOm/VTPV+uX6uFm2HiogY/MPG4GN4L6pXrl5UFDy9T7M/Z8aB43Gw81xFq7+IIkZ5LkdvFOpqaL9zssjJY3KCEuZKmLmlhT1DRNcRZNPq/UZzctcCrIaUher9crCaZ7DiBkyGfX3s6EbqA4MmnQjrTWU692sTGJiK6DZQ1+FAXCMG8JS0Uc+VlvQTJedIxyAFvXK4imoLm5Oe9hD94A8IaBvCpWUM8R6kiYFO+zJGdmnfzerzW6I2qoOOBEc0hqBHMZduQqdhf4uMlih+YlqgInWsjwk1j+BKGXhAT0ZPiN9M8c1xx6CNs32uYFEwOZ6AetvGL8Es5HaI7qX8JojzsrY5A+IFXlGtVDKPqOkmiaRnbZqcSV5byz+clIknsh1UOYlUWmKCOnteA9wagktIAmgzRyIDOeeNNwHOIgzC/DY1kWXYuuD37QMk2W+FQPYXI7GoPF447qjcSzGvjPh58UXal0FCHOdmBoJ99gItqqZzkWr5cCAfB8qagMzrp4XZRKlBoyShsta0wPYRwPTiMe0d81apjfSIglVZtrTQxfJL9TlodQ3EjXkv9qUwBwu92m6UL8imSBlv5ZeMdKBX0N5xC2rrXGcHalnoY4tsvY+PEO985c8MqKp0EzRrfbxVEqzh40RYsrRetay7pk0U6SRv1RB1LqzaExY12yCn3yIqkB2/Re45uLM+0kfrvCZIesF4oe3GuWlfy5WW7lSnoIOx35bpBHFJ2iWSGjqcRT9RDiB6R/Jm9IzvVKYS+0ZqE3O3k3xLmUrlosPCvW+BXPWRpLVIc2HsVDGAu3d9hNPVolZPxk6goougSlF3AqHsJC3ZI+KQl1GJJG74W0Dna7XcrylS6TblTyvu1J9r4PlEFwQCdDOGiSooQC/KTknKHQHWmH1FMh54XELeIk1VxrojgiLurtG20sMyAuZuKLiucwqiwu9bpIXS8nUQjzKzKD0KMseeaNRsOyrDL5BuUWwvQ5K3V7vkIoahRYdihV9Mkhp379SHsgdQVXFEVRcLklkVHMwCRpWyyRIoIBkJrWy2croxC2221Rgh81GRKRA6qH/0k7xA7vcRxjicKkQkjaoGEko61Kiac5L1TqHaM96PoOKIRjUzJRpHOrI5qlsMAJxjSKCmGyvcH14cqfhYMBhyt2HYS0FCPxrqYOj5w7VlKkw4ulJiLwDcALx0m40MISZ8wk4rmVDxkVwzsxMU9UvfBk8DGRQY1uGqYd4iWoz6v6Od14xbAuWc21pqgiSjet8P6MykgKYSpiyGin05tpy4XVZ5zSsAQsgvqGWB1E13W6pVMplyLNHu32oFsVTVbiZ9JKzhS8UNMtKiNul/aXkwpLqQSSBij2F6Hd4nV0Or05UFXlkg3iQintMC4xneb398pSCFPT+0u+HeKEIJETZpkfmIo7LKwo0263qbyQ6HJHpITnpEIoUmiwlm7UPkkjZIVwv9CfZ1PmPimBgV4z+kCr3zpMrEsmviGpvqmkeUbcYl2ycFHXz+lo2cVaf2JpAWl+HC+NkA5tvGIk/ZbSqYpnWFIhzJ/pUIHZtiqj5clXCIcvsLdxFA9fLPYdEh9ieSS7XVKpTuYlinJklnpDUle3WyqHMKvAGiWAlSHV5yOJqhSfgyPKNM2kQkgezmS9m5LiKUktUku0wjumqvFnPjOmQiiaxmkPWeoNaeCT3PaSiSL5FNa7o7Mqs0N64wrTb6SqqoV3bFSRTqRzq4MJhBgJgtCtpi34DuYXEoyLisrgi4ZFZVADpKIyokJIIxPrM1MonRxAfq3VuNLAhYP8umJxGtpJ8jwLkRbKbje27Z4TzzTLZu0WJ7Imin6LC/EYZGXp0zuFs0qr1bJtG7fg/Dl1hZC0QelapBkGdRv6TOELtRUKYWrVtDIKYTy8Kkmef1IIbXtQyz05dVHpV/GfNEWkjjGxU2VWKl2qQpgqp01RIYyFeWwMDapQIRTJqh09OJPcfNp8g3VOOYbyl7Mb2eOXxxCjvvBJJG9MUiGULccJh1vSYNO+0cZq77iuo2RgXuitD1JI29ipvXQOWetlsq5MeYUw7hcLbaX1ckBpm3QbVVUbjUar1Wo0GmTiKqMplVkISUESJWb6olTLtLxCGI9eVEY8HxJERvtyHMdCtj0uWqI9AhGn8jHsBakKoVhlNFWTp67EI/k8aaWMEy8jjRkc8HjfbNtOKoQAgwIt44WWkaaElzbSgj2J4kHQHrLUm8LWjmWq8sZFlY3olZQeIj3xrEbJomhCp4QPKz+XgSaH1NJKySoy5e/YVJ6LCI0QaXsy4HA8wzm2ncDZHtt+oIsP/5oTc9HpdLp3ulgGVqpK2r3TlYI8C1eoHOiNa7V68reqygI9vZKpP2MohHFawM5IZKkHoj9EuMYWavvtvgks9edbpWdUupYsbTBOKITd7kDNjqekEJY8z+E0GXljSYWQVig8sWQnD7y6LCU/S/EYxMVcaxmvGNqLmvaipp/T9XO6+ryqvagZrxhox5lcIYz7Rk9p6aSVYqQSbjQ/pPYKalxpGK8YYpCCWA5qugphVtwskm+wzolNzTrc3mAXXF632xVDHTAxeqSqvkzcf1Epcw8jbfAHpxtqBkVTkiXQaDQkw7w4uRQqhNLPz3/r5/HQYsZg51aHmoBJh5AYO40wa/ZMKoRxPHLXmjh3ksqa+kuO5DILYbKkIYVQJo8yhodwVNesKKeOnT+JYn0y7QqhSyaDH43S1/ohKGIyTGQYlzTtimliVkxSIZT6EE4RvA+G0H9c+lOr1RKrEaJG0f99YBImYUvSCUUBLvkjttAQfZ7igk0SMNJs9qzpGGHb6Qw9UCuNVqsl7aTV6u1EVUdoO0FNR8hbJU5EJW94YaIIJafRmMnSErNIfaNFxCsCoV5L0j4i6lowPJcW3rFyobzpaJpmjdLHBf8khbMORaBda+ESQyIsynxURBT6eZuDfQpzcmo+TzzsOUEHIzWukBpIUkvDZC/H8lHKYlgEuuWT7h2S+HOydonU+TY1YIf2MMaUmbrA0fDI+aLoxZV+rpV+46AfvJClDZbYQ49tVghjQSdEyrtMcWSmJaENVMHUIRH3jUSiK55mPDR7kdwi1gEmA3o8okKYBY0Q0dZG5ybvOXtyiBPNLcQ/UYC62HQUIxTENj8l3dSFCmFWOJh0gakG6xyFcEszHXacHVYIO52OaZrkKkkuTjnhQ6qqljQVMzEJnf1mxNhvF19FydqKnyz0tBQutzn9/ZprTVzCRVsRhY/SyWQ9+uRkRNNoqrwoZRgnJ4hkXRk8f+MVg6KSxMAk2iKVHMifpNrtNtUfQ7tGMrwE6XQGbZpQFi+5EJLzqjDrctQcQlVNCVMshMoVjvzNPqQjZfkYSemV/HikEEqfv2KapO+RBkXSD2qDrwCgGCT6i2gPo6oNCK2USetJ791stWJBJIqH8hWHhJVUnTBHIZREOnJcSIs9yUlidNxwEfniPBAaya3WIG1GPD59uFQThaJVP5/CRJFEFqVc1AEhc7L0p/y7QaeNLy9GWNCKlpVGmLSsF94xyfKShNLwpFmdZpXUtMDCuhH0SforNr2Qyr1glwjrkiU2dgchjVCUXCU1frDn/psYC20nKJMQvgHai5q4dkinN+ooErN24zhuNmNV7dXWktLhRi0qk3oUkW6393Zjo7n+xi55mLF0beppT64Q5nymEFKhIaM6zs1WK9UDSROUOIxTD1FmHaRlWhwMmLkK34DGlUaqQihVlyl/Q+jJSguQeBSasXV9cFuSybGIaDPq3Oqg/xxtKzTUyZheaOOmY4luFVVVTdMUFzUx2yIWZrzkg8iZHOKE4Zu2l2/zMy2FMO5PLFlNznIM1jmz4t5OI9xJhZCmfgnS9EjwykIKYmRywDtG9pi4H66J5lWca/RzelxaIYwFPTD1zcxXCAt3TpqAaDzL70YAaQat3qmOohDiB1AhFD+W1eNr8IGMSSq/drNt26IYjaqgJIuXt4yixEkVxrP0qJGqjKbKPZND/j0Yq4tgPuU9hPlRUhP2VCSklTIp8eOahIfDnWcphHGaTlh+NSVBRLrn9KxRkdP1cXwU5DYh63jiAyl3IIsJFcLCykZ0hzudDi03SRlIDGIcNRWWNEBFUaRyCNLiJZqlxe2Fd6xQUacPZCmEiZGQ+Urmt+fC6VH0ZlAcChr76DPmBRP63QjLuzJGRfRXjBgdnaKqWVasKAO1cHKF8LW0QhqvKopkxGm322S5oN9N00wm044UMtrpdHBANvutNVM9hOWdhDSB4OuflCBIIaQtt9rtS5omhmlgXVmxxJGmaaS9lFkHsUaR5IVG0zNKOL0D3elalywySevndFEsmdZ0Khp50dVMptUs28cgcuFOF43OkrxEqpTdtcsohLTOUtgX/hM73/T2KcyEcW7+Rc7kIH4g+caRh5Dmh+6dLtUX3AqFEG9CzgdSDdZZlXj3Q12ZHVMIs7RBBCsHiGK0GMEoNkcaqbTDfiBfOECDE4XxUMio+ryKNVfi0RXCrMD0nIbvzbUmrRyiM0pVe6kasTAtivukYSNupMTuLNN7XBRQHqdpjFNUCPPtGoZhSGJ0UhYfKVSGPpzzEMuHjI6X1oIeYDLho/1e9BvE/RmWXud0727/WiT7a05XCU1LaTScSlIau6iqr1nW3eHaP/j4cHmQip2OhPjQxe35JTENw0gVT6llvGTSTg0ZvZY730KvPdqQSTupFpZpsy4O4NRYOzriNiiEZSob4c03TROPlWpAEVccO+01EG2XoponhjSLn09Gq8ZF/rqSdyyLnOeevCL80xh1I3CG1F7UROcM+TF6Bsf+FEo6oTSpohwsdiSiRCOcQGi71MueXA20Req9Nvbdy7gPU1MIb7Za9POaZb2qKK8AXDHNWAg2FmOzySYl2xQy1APqeIlx3WISO4bZ56RvlU8jJBUoK2o0qRDGcXy92UQ1OBbeF6xj1Gq1ms0mKYfNZrPkOkgrNY5DGmMUrIhdWJLyCemQhcpJluVUVXsLpaQQxnHc7fYmVUWJG43MzkniK0k5hGKOD/4u1unNJ7W4AL4a4qJDclSOaSweRSFMzlfJHEK0E22FQpg9nRa8tlkC7X5oT78z1ya1bktV9sSpPDn4RJNt6gq9b8lXCKd7rPzlNqe/HymE0oOltkXtdro9JtmejgR0DIqADCdhoUIodcWg7cksJlyP0d5fprkfnjZ9lyZ92tLpdMS70WgMLKxCQldTvNVJUi8qCUWRJevfdLvdVIWQbOHlBSoU9ZSzinnBRBsnrgFiho8lZP2l9pCI+3K2oihWogJKjkKoKEO6a6vVUhSFfMu0KCa1rG63FzgkdeakygEkr4yXw0zvi/S8Ugv/kDLTaDRyPLc4SEiyFJXAi6oqukPFCTMZVdhut5MSTKslD8VkxKMYDxknxCA8cxSAEDqNbVAI4xKVjUj6wZGWNC/SnIOPL9UzTPk2MFYZZCJ1eBTeMZrzpQ+IfTtp2il/GiXThKSDSjmE+AsWDJMUwrg/UUjTMs4V2osaJTXYXZvaFMXDxjtRJ0wqhOK0OZI63b7RFn2buGdRKKfIw6kohNIeSHG6PqIJIEshjDPaTti2LQ14GktZsTY5iNMpmavEd65QIZQ6NxCmaeKKWTZ14k5XLFCHI4oGRvdOl8YYrkfYkUVc/UsqhBjIk+zaWmZ9SBUtRg2ZSfUwR4ZBrSOz2iDjeBBTtHDFURQFbRBZQkXh6WXNDOWvpcyHC5eGrNV2pNd2v7EzCqFYfSupzkm1ubLGJT3vSRbg/QO9POgkFG1OZHDq3ukmpWTKBUq6rSZXCJMeQk3rHSU1yoW203wk1n3OKkjQuwO5GcZZrXumohAOnUbafJqUxSW1MKcOMk2+yYpkyllFe1EzL5hUWT5nD5qmFXaiKwMa/NTnVSmPiCJGYsHTS/n0VIU1acUUbZZ0w7OcmYoyJIJIaicK/a1eR8GhPZDyk2zxR2eCq/XYroasJSpOjOp42ENe2A6kjGRJD3rsAj8ItVlPDbNMjuR2ezCSO53tVgjLVDaiFSd13qAhJEVVSYfAoYs6oRiIJYW7w3AMfJnwljJ3THTS4hYysvTbZPcOnbMHqaxFsu0EkpUmFGfUWcHsO9PsvVbSHEuCOG2h6L5kKdHeUfpBB5JOmFQIxbuXdeFlyNjzVimEsVDvqnWthRopGdesSxb+jqU4xG/lKIQlEcOyRhXopTmK2lnR2CkMGS08bvlIGUoaxPVIOavQvcI/SStULMQ0xkUNVK430y3adBNEsUUMH0im7SUvhCYKcSOtmFJwSs5JYqBvmQxSQnz6WSEw+NcyCqG4sdvtGoaRM8O00vJLxR+p0Gh5hTB1jsp5bTFCgQRjtGqJ1VD3MDujENKAKFT2ILvIOBluk6FWTJLeS36rg5YzMc0jFkJ9RCk5RxVECu2vI3kIKZm+vLyd5Q5NtW7mK4Ti2Yobs2btVMZWCAspoxDSBYoPF6c2tM1nxagQU1EIUZ6TorlioZ6Y1OqdwLeeZFaSaFHGtYY7QKSeKhUGaLXiTqdD8Y2iMk/lCsWhTo6sLFGEZP1JYtSzkhPisXoq4vmjO3QkhTDpIcQ5luKs1OdV8fElI6WpzTqepKgtpK7lF1U16aicRCGkAWlZgx/DiBUlNgy5+lFqogiu+pLXun2jjUKhfk4nYVGsb5lqpcKbIEYUp84/+ZND40qDPFEwHGJd8o6JsamSASVp5iN7E3XVy58cEgasHmWs7xT3UTKUB2vGmBdMFMUotMy8YKJ+SII+Bf6hIyhVbZtK5k/qnkdibA8hVjUzL5hYmIcUQpRQpXU8vajMcPWO5E+yA2SyDwF2CtHP6dgghOI+MOSvP1BTLHSiTliyqEyOxapwHRQftBh1LE5o+KIln2brWsu6ZBW2QBAVwlQPIdk++jdhYIcVgxSyRAtK8KMtySAFWoPIcEAfvtlqoY79qqLc7nRGUghpAEjBFPkhTmMsiKmzynQVQnFUDG/P+7GswXrXXGuSQapzq0OhAaNe7C5iZxRCek5Zb36ZOtrxlIx/+wS8UUmFsLnWxJVGUgip+HV+8EP+I0hqg/kho1Qng4xTopyEIYhizWJBuB+QWcSvSCFEJ2GZgjdZTOAhLHgjylBYkSx/NTVNM0shpFDMMnlzkCgsIRZlLSlUSVHlIuVlO7LziYIU3pDmWjM51JPy9khSciolpTHJ+0RxPijQTzdkNAlKfrQQNq401OdVUgtTU2dpqKcu6lInj3XbJqM1HXRyhVC6/Rilpqoj1MKhkgaNKw3SLsQhSqIY6sxJvT0rGT4nzUHaTs5zlDaw3zq+v9qLWvdOt+QdIztLu98/k6Q6sWYmetGTXfVGIuuUsjyElFgVp6kN4hax7BkhdSQSX5zOrQ7ePfI0TqK2ZV5v2p7zU39N0yzsQ1gmh3AkyngIc+4SDe9kHwJ6U1ADxB8cqJIJMgeaDM0LJhlcxPqTWdo7uq/Fnj1ZiN+lPYu1ZOK0FSpJSYUw+SNlsItRS3iG5HkrGTIq9o5KZnEn59s4jm/3e6i+Zlm0lCTTMShVJP+pxcOdipLkSA7ZJuahayxzz0dSCHNLuBU79tEKQ+5B6KcxU7DVXmWHFcJt+AyD0I2iujLkEMfhjlIRiQ6mGTcavaJqOZbd/Ecg1ZXBujXllS6SRMUf5ayC72R+gfKU/jlFCmEq7RttTIMWI3ZQqSCLtXToMoN2PIUwtXh0ryRviYpkOXP6FN8j9BCKwRXSE4xzp34Ug8T0J4Kk82Z/Pca71e3GjUbPQWSacbPZU1zR4iv9oEAjDnVqNaYoebpE6oMblRxpDOVIsUcCjeHUZZWydFAnFD/Q7fZaR6AgjhdVOMZExQ+Hk3JWwVmiUMokUgWUuO8MeVVQ8lNTYY1yq36qh5AaA8RCGQlxHNK7IN5/NP3ixKKcVSQDMPml8Z+SVCdKe2SQEj3b7Xa7MGQUNZnkvESJT3THClVo1ECoomAyXRb6lauS2iC+caI3npyNyTcxq+5CqrBFhTTEzyS/1TvV/jPCxQKVw8aVBopl2otanHiJune6onNVvGOpYHza5JOeOB3RRqEycCtfIdR1vUyVUXKuNptDox1dUtJDnEQhlBIuSK0VP0PqOrXCo2Q88WNZ1WtF6xgaO+JhhVAsKtNsNrGoDMUxNhqNkRTCrItNKoTJFaqQ5FAXa8YQllCuk+pX4Z9yisrQiBILceOMQf/ElSJrvv2Wrr/SrwaEx5VeYSnipveAhAxScXt+VTxd1wsVwjIho4X3PH86FQM3cps8FSiENLalnBepS/ae5FD+28XsPdR51X4gZaEKN0PloBL0/4mSj2FAtQr1Onge2DYkZzDDMDzPE0N8RyKKolqt5nkelv0g/x6ej9N1AEA7qlnLlnJA8Td8p+tEd6LaxVrn8Y5pmqntHGzbzl+JRebm0rcbBlgWaBqo86p2VFPnVfGv+oLuXnUBwL3qpt7MsfE8z/f95HbbtqMoqlQqQRBgESYA8H3fdV3XdRuNhmma2lHNW/P8Db9+uU4nHG6G+j26vqBbilWHOm6kdohJzmfdEQAAOJHraEKMRcPpOk7X0Rd07YgGAPHpGACcyKlfqksfXkxLa7wThqnBwOiZEbf4PrgueB5oGlgWiNJCdDcKbgb4i3JgcLHRnd52eqLtNqgq1GrgulCpgG1Dbq7lVmEYhuM4vu9blhUEAQwX1kqiaaAoEEUQhrC+3tvo++B54LpgmtBsQq5hNw/lgGI/YFvLltN1cKgjNDyOpEkzy0X9D0Q8zxvz5PpoGogvehiC54HjgGGAqZnBzcC94jpdxzxu4gCoX64DgHpYtZYHD1idV1uPtOaenYvuRNayZR4fmlLwJKMomht+LzzPsyzLcZwoirAsvvRX3/fr9bphGPQ6i0KMcM6hu+QCgHRcAOg83gvfcsApeUNw5OA5U2HJoX12QFXBccBxeoPEsgA/pWma7/tBEOi6HkURnhsA4FCUXASFcrkI7j+KIAgGA7JSSf+wvqC3H2u7V93gRlB9pdrbw0FFO6rZr7OTdwkAlANK65FW7WLNveJKf7JtO+ncUBQFLxBJnW81TVtexrRekBYTHAiWBbat6bru+764AuLNx2hqDKtLv84+S6WXKkUBTYMggCiCKALfB12HMBz/HZfAwUz9iizLqtVqrutimH1wM3CvuuFmGN2J9AWhJ8QRTTuiGUuDmSqKonq9DgCKojiOYxhG8hHgxFI5V2mdHJoxMMHbdV3f96vV3tNHuyeqEwAQl1iDyhDdjbL+VD1f9dY8ADCWjOaJZnI7rmgS9LbR4IqiyHVd6DVoBcMwcL22hkvl51xRvV7HV1JcEIMgWF1drdfrmqa9KeOL85p20/dv+j4A2LYdBIHv+ysrK/hw8Z/4J3GWqNVqAKCqahiGjuOQHGWapuNkzkKGYYRh7/cs8QFr1biuGwQBVYmLogjlGXxZ5p7tfVm6vbjdWDLASXlbaQsunalHH2mt0Rf04EYQ3AxWnl/RjvaGbrgZqvOqeZ+ZOgXtGVgh3HeEYYivZRiGOCtFUYRijWEYijIkgyoKtFo9AaJSAcsCy4LlZfml9zyPBCZx8jKWDJxA6Z/it0jDaTQamqZVKpVKpUI6obfe+2LrZAvlOVyHnK4T3gr9DV85qDhdJ7gZ6Au6KPHjJ9V5VdLiRsLzwPeh04FoMYruRJJSAQCT7DwJLQmO4+BqCgCWZYmaLa4NGM9Dk3i1WvU8D0VPr+ZFUaSAooCCs6SqqviIffA1a7Aq0+Kaw/2jCPci9gN2dDdyr7irL64aSwbeKH/DD26QtaFAsQxuBvWXBwKjf80HAPXw4IEaiwaACQBRBKYJitLTBKIIDKO3MCsHFEvJk8ZoYcGludEATYNaDep1CEMoatMwfUghjKIIFzCSMm1blkqRfjQQ0CrpeaAooKrgugAApjmCvGgtW9HdKNwMq+erdKu9dU87qmlHNFGJSiVLtI2jaMN11x2n95larfBMUDaFvjYCAPRS9Foq3wpRPK1dVAYv5q+H+j+N1HkVjpsAmv2AHdwIghtB/XK98WADdQwAaJ5oSu9yFjhJpv4JFUL9F3Tv73vBraB2sdZ4sDdi3Kuu/8995aCiPaDBX+Xtv6euPAswLJ6SYAQA+j2D99TzvFTJxjTNSkLBWl1dxV9UVf3kJ/8c4AT0h7plgWn2dEJSC3Vdx+Gn63oYhpqmiQohaj7ValVUpUTQnBfHKbNiqvIozi6SEKkd0ehmJvE3emMdrU5ZH8tCURTUT0rqFZ4HQZD5V1T4Pc9DVQr6oicaCHzfx+eC0jBudByHhjfWHEK7njjY0BFkmqaqqq/AHABAvWc7+wEAUAAUAIADgXLUMATT1kSg6hJF0fLysrTdtm16KOIwQF+WZHdwHCcMQ9M0NU2r1Wr1ej1p3bMfsLWjWu1CrXKuIkkFqC3kK9LR3cjpOuKaot+j4xxV5tXW79H9a35wIzAW+47u0zEA+Bt+5dzQe+SteXCicH8DSMvzPDCMwTwmvZ6O4+T3XUN8H1y3AdDA+4cGL9cFAM22Y00DXYfzJc4K1Wx8u1GvQ7Ufa7fSx+r1ehiG6GpbXV11XRdHIJ4wfkbTtKSZSZwkpbeqXgf8ahiGq6urURRRI036ruu6YRiKO6mer4p6OIHuh6zLFO2n4gKa+MocQE+mTRIEunKgNV0r/25iR/ySZY4+rc8wCN4oikdP1iDGeKfyu0ruJP3Dw5EYFDJKsRBSv2ZKmKYANtFxTz1nscCamGdPP5j1nsz9TQ0ZpfhYMTWcapOUbC7QbrezejkkS3XhB5KRhxhTAX2NmtLHpaSywuLROUcp8+AocklMa8Gfu6OkHEl9CDFtD4MuUlvGY9f4b+n6FdO8NZztkB72M1zNkoIkMWq03R4qkWJdsii4l7anNvdrteSG7/m3dFTy05woTAsyCpOIq6kYzZVaHNg0exfSaMTdbkqFJ/EHH1BhmhANj6wLLF9UJotC93673U4WLhJnANpOaYFi++PyDys1+FyOGu2HEhmvGFSoRn1exVTnMqGJGDIqpjlhVB717iu8Y8lzzjlcDjSooO/ToB1Sb5j851LuKHkhoyMnI6VPDpn00mWtwQRCP81mbyPWGS6TaxT3o+kwzJKihfFP9PQty8LYPAzLpGdES4amaRQl2Ol0KDux008GS52Qb7Zat8r1ISy8Y/RwxXw21BbEqEKquNtut6kMsriaUOsC/IpcdFeIDo3juH2jLbaki4VIb/H0pI1iJSEMKpZybgsvFquMKmcVKeOD7p6U6jLS7ZVuBaTFk+PsUVivjmZ1TIgAoQQUkR+inzNRS1CHJ7ExPS0x9PpkphsUvbYkeWalMnY6HfGei5kvYyT74NniwBN/L3MtsZBXRQXb6fe9XWiUPYR7jSAIyJpeniiK0Kf0xBNPnDlzJoqipHvKcRycL2hj0iwtxTZAwkmIXLtyrfLfVIIgkI6C/rGen/BgzxBVOVexli10A2pHNAonCG4GwY0AXfmivy7cDHOiQVJRlKFoWE0DNCqFIQRBgGEeWKySoilwC6ptyftAW7A/WzJUTIIiZCgWFONCXdddXV3Ffgn4Scmzp6pq+fhYIjUwVdrPtxIXdX+r9Weg4+ZWqxe8hGA4EwDoOpCvWNM0TRnYAl1wAUAHHaYR49S+EXhrXvWqq76kmsdNUzG1n/XVn3L8a360ZETHTdjoX9cDtr/hr760qi/oojVa12VzJm7sDqp4QKVSkW4UGvjxd5SZ6FlnhbnmJ2CImKZZr9fRVJ8Mhw6CAI/Vbrc9z8PTQAkjeS26Dro+5OeMlcEn0AdlLVuSKdRYNMLNMNwMAWNubwT4z/BWGG6G7Ufb9MluWszfkm0XBhXHRR/AmKIcT5SiKK1HWu5V11v3/A1ffPfRNa3fo2Ngj3ZEsx+waxdqGHyoHdXoesl7A31HDQC4rlvrOzBbrZbkp0UMw6jX6+jFtSxLOaA0TzSdo47TdVaeX4nuRPo9enknJACgJ9O/5q++tGoeN9V5Nbob+Rt+ctqcBCHQMXO7rutBEKBLEANiMYgUxzD6fPC5eJ6Ho1RMOlJVFd998sJBPypM13WaLeMYvHVv9SUnuhup82q4GernVPO4CTBm3kEO9GQlTDPU9dD3fc+L8OTxukwz0jRNVVUAreeGKwKj6XA5wLki+eZaloVxHND3gOHL+9xzz9FtpFlCVdVGo0GjrkyUPtE80RzJo0Xg+UiFcKIoWllZwaGu63q1Wg2CANMN8NXA66pUKrSO4+sjuRnr9brebywkoh3R2o+2K+cq5OjD17Z2oeZ0HXVeNY+bwc0AI73N+0x8eavnq9GdyH6dLQaAmMdN/Fi4Ga6+2PONtx9ri8GHNOlZy5Z71Q1vhdXzVfsBW1/Qw83QiZysN85b98iXiIizR+oAMwyDYq1T48kdx8mqkZ4kDHvhweXjPjaDAACO6HrhqWKcAs5pALCyskJ/cl1XCiaq1+vSc9Q0rYwEoqpqu912XRf3QBKR7/voEtA0DS4NPl99pZqMy02dtD3Po+hi3EguQclnKM3kWeJruBmuvrQa3YlaJ1tidPS+YEfU0DJHn9Znto1GoyEmy5qmmdXIJZ92u00pXrgMl7S8IvmzTNz3KaFHjtKUVVXFEl7iHui4pASKFhco5yGk/n5SsRMyfEpuIvy53elQQVQxDR2bL5W/GyI5HkLJvkbmYfIQotUWZy58KPRwC7NEsKx/TtXmMuNkpOLRcZGHMGtsxEUuILJZtlpxpxM3GrFlxaYZm2aveInYaK7QmijWSEB3VrpJPturhl5i9XmVbMZJTyB9uLnWxHLt4nbso02GauWsgtUsCu/weOR7CEVrizQqqIak1LlxUEnyWsu8YOKPWEFHrAuffxrtG22sLC96CNHljvekTGGAkn6V7YTs+mKBYlGToXILYpofjV5xYsc3N71dYVot1vJIbSfwhmNpu/zXVpoTRqrLKm3H72IQYBzHmqahKap8xVS6RbRFjAQZfGy4qgcO1P6eY6p/k/ojHzHXQzhJ0eaS0DtLNWbozRUnbXIN4eDB3//Fv/gX+EtOpUfbtnGxEB1NGIBacu3YNjCzVOrvQhmJ5fdDLsHGlYbYViRO+BjjOO7e6aJHHX86twaOJqnTYKqbUfzBGlqih5Ckl9619LeX8fzTSJYq7qILGmUrDARQn1fF1sFUry4Z99Fuy3EfhVVGy5wqfgadvYRYDmd7XijJPUuiGv2TrgVlMPyrYRiko+LpSYVkpH8WXgtJnhRpgpMzzc97uNAoewinQBRF1WpV9CRgYLTneZQ5UBLRRA39pGSqGlJmD2LNaBEyhxT6lHD5cRynVqvhIoenZFnWGMVjjEUDnYSSjS2HI7p+UFVVgNbJVvV8NbzVCy7HjCCn66BnA5OIen+6GWCmmZjpVD4QHHP0CbTDaRrQ5aLVNjVoyjRN13WzHBqapmmahnZWcbvv+zRgwjD89dyk5+ONhtY/FTTY059wpAHAU0899cu//MvSUTzPoywsUXHNMpyL3EqrtfD9mmZdcrx1r4aJfB8GAFAAvHVPX9CVI5oupFwnrYkw7EYTh2EUgef16iWUBxMFozsRZhOJqUf2A3bS/WUsGuJn3Ktu7UJNOag0Hmrg+AxuBuhQMu8zezkzG7637qENG73WwY1AO6rpCz03FCayKgcU0VUV3AzQsfap5z718R/6OG60LMuC3lOgAj+ScqVpGj5i0auMvsEoiqQHV61We0UL/lcLs186397B0wg3Q/eqi0V98JwLjZ35uVuIWE0E3UGGYYzkoyYHslhEHlNh8fc4jinPWRzt+Dv6mrDgR6sFug5B0Ev0Qjc1/ojjjp4L3Q0RLJ6BL6Pv+41Gg2ZgSnJG+QP9lmjgd11X9OpkgclOyeGBUQzaEe2dX37nh97/IToTTfCeBxCYYOq63orHzOZNkp8Rh7fa8zyqUxoEAaaXj3QUcmUjqHIPdTx/uOmteU7koLeHPLoiWclIU8H3gSIdACAIIAwHY0ZRwDThRj9VPrV+0pJtz/fvEi4B6KagtCsYFqalEmi0/Stf+YrrupVKBRU/AAjDkNyJWebdQem1MHRdFe9M1h1rtYp9RJRxmhrRoGnaJwAwDVjMLb8dhjc8D2/RPZalKAZALA0w02xigjdOEk7Uy/3Dch34i5T+R37C2oUaDDv2CcrqD24EYuKfKACEt8LahVpqNpp2ROt8e6d+ue5v+OGtUDmoGEuGpVj1y3WSN0CQXrKy2iDN0YTpefRP8RmGIays9OKPmk3AMzeWDGvZotnJiZzoTuRv+KZuSs9U06DRGIr7SOYQ3vL91+p1ADigKAumCUJuXvJU0T1I/mFxeGiahtV9cInJp9AP+YlPfOLnfu7ngiCQeodg4SJJam0+3Ky+Us1J4ES3PIo0VN+L/pqaZ1i+row6r7YfbWMESv1ynerKQEa8216CFcIpQPIEVrlVFCUIAswnrtVqqBKU2Y/v+1TlSYxODMOwVqvhslq4E/RiZf2VFgnRrRVF4LqgqoMYM8uyMFgIw0R939c0rdAVlsWocSxYGuuwrmtHtM7jneBmgEuIv+FHdyIAcLqOsWSIMkT9ch0VQmnlEAOihqJH+tsRLK4gouvQbIKiFFTdPN5oqKbZFaMM08DyYll/FUX/rKqbKJJixIU4dWK0ElrxRyr9V0gyZBQA7m+19HfpFM2LjwMA9AXdW/MwfJc+TCKORGqJFEWBrLuYWtJtaIcJ3Q8A/A2f6k+k2gsoyoi0QQDQjmitk63ls8vuFdc8bnprntN19Ht0qmyEoCQR3YngV23HsWF41Y8icKoAAIYBp35yoFeXUZzIzyCiaVrjpYa35jl3Hf9yb4fR3Qh+Hcz/1bQUS51XWydbuIDBcMUjuvZChXCtXkeZTxyEd8LwdhjOa9oxwzhcYv5B8hUPAFBV1fM8rGuFEjD+AkL5AUn7xeBDcSeuC44Dvt+rMTteqWOULVCeQOGeFELHcbAIHrln8fOrq6sYSU7B+aguorZPqj4AWL8sv84U0O5f88PN8M1330x/GiP2GxGlMYIUbyz3gqVHDCM9ZBShECxKnsTbMqpCmGWURMLNUDmgNB5q9AT6m8Hqi6v+Nb/wNS9PXBQNIYVYp47rnHIdRw1jXhuq0YWFMSARYOyte96ah8o/bonuRtGdSDmo4GuLBdVw6ae6Ghhn2IvWexdoP9v7LuoP5n2m8S5D/SkV0ESVYrgbAakQa9akvTZ6HkoSf8PHQtPNh5tUCq52sRbcDLw1T1zQzeMmlRkX5159QVcPq+Gt0Ok6OOfrCzqOnNWXVsW6ZdpRLbgReGuec9RJrS6mHFCS9i+UVarnq+KWubW5HOWkMPJTfDXRboVlnACg/VjbW/MwLDa6EwU3AiyoqxxQcLqm+lLmfb15Hv9J4a8fAwCAdcdZT9hLlmz7oKqKCmHqqZJtWiwJK8ZhYhJ1/jXmg2Os1WphSflarYa2ddQGB6WJ+iGjhXo4xWlTmEyyCKpY6XBUskrxA8B4Idm7hR1WCMtku42REbedYJExGK4JiVZ/NOqnVtlKhbRBUf4wDGN1dRV1QjGibCughUBRlEajsbq6Sq+ZmMZWODuUcQKUT43Awtb4e/1yHRcJb81LtfdPCyzfLzpls1Q1URojR66UjJTlXSTqYPesvMOzECnwsZ1SPBqVdugXj47uRlhKMbgZ0MqBwoc6r+aX3CQKn4ueoVoUOpcIylIDAG/dw2LxYv3xJw4/8cfX/thb9/xrPhZ6Vg4o7lXXveoqBxVj0bCWreWDvRwVKrtK/hzcHscxnWeWvSCJeOsAAAebpQys1xWh/GlwM2hl1P+kuyj6WZNTWa/cdu66pet6439vYBYcOQABILobeWte7ULNW/O6K119QZ9WwoNUL/Rqrbbhurd8/1C7XavVirzQOhQpHiBkS2ItctqSc1aU56woCgB2Z+6l1uDbWqv1MifRSVgedBKC0DyaLg36FUFoI2k7qqrSCeeU7c0acqgWBmcHImzh8BAztFEpxd/F9SX1NLLiFyQw9w91YBD6GRamQAvEACk3XzTMKQcVL/JqF2s4koMbQaqHsOzx0tTIwgXIumThq918eCBuolsm2VegsJkKpl2QB5u2h5th4Ws7NwcApmWZkjVvbg5cF++Y/EBJW0DIYSHZBFLr4xaKIllJYkl9AwAOqSoui3PDXxHPRBx6+oLeeKiBwQu4VKF/WDmgkJExiZSO2zzRrJyrOF0n3AyNJUM5oOAORW0Qj2UsGfVL9fqlOjVAGg90WM09OycVRC2JeMtxQXddsG1ANRiw5qpgL8Cx4W/43ZWeiVS/R/c3fPeKiwmQ+fnJBxTliK4vmGZ5+x2BwWiQ6MUi1gUgk2XWFJTj1m42m6gEQt8DaZpmqmhEeri3lOKRwxQnfJ2ldj45nkBMI5xQud3j7Eig6t64CgRF89SSgDTQyyQB0muWTFAZaT9Z4B6yikFRZUUR0m/t4c70dNvFXJGkIILrCiY5UPEx/BMFx2P+GDWYxjw0DO3GlAAx7ad39NwsrMRV94L4xXQ1MYctmVrT6fQC/aW7kQVNf5jWRaXk6IaUSaxKrXpHhSLpeUmN6dFtSKHw2Ls5587Qo8nPBLC7tn5Ox47k9AvF0+P9Ny+Y1DO6da2FddtGreIYF2VeYV4cJYSIVSVpZGIF7bifS0CDtszRMVlFOatg0mDrWovSSDCpCU9Pe1ET69F173TprMSkylZrkA9J4/kznylO4SjzXLBGK2X3SZ24sb4llgOV0v9ScwhTSa3+GhnGJU2jGqGY9GK8YlCmonnBxH9iZkXhUKcrpfcFXxYx4Sorz5ny9/CTGElBUP9l0VWeNUdJSlQSmpBzHgrtARO6JGJhcsCIQZwcxOjBT33qUyWHB3rtcCrGQS4Wq8y5Flw1qNzrt3Qdf15VFHyyt4U8NNwnXjJO2lJrcvFjyXMunG+nQrfbRUdWMg1eHB44T4oLEM2cZY5C2dR0x8Sfa4IUS5WxxTbieJT81zYu8b4UUljgsUwWWeEUlJ/z35scMuo208bWtZZ1yZLqNFLNT/n8s5cGnO5wvcPvYsVRvKvi7cWJGpMJxe2UOo7Je9qLmvq8SpNYspposu5o4QyT+lyogDnWvKSllnII8XfzgomSj3jOjSsN7UVNOatI1zghgyZhhkGJvtSOUpxeSk5BhUek2U+aW6TUQUrgTOYQxv2EVRgubEHl2VORtMF88RWEbqISk9/zmYUVwulcS2rONEUSlsmozi8XXn4/+edZ5pOiTENvDm0hOQlfoVZvJWhBwjiNX8dzpt/xT7RIYC59uz2QpKk2CU736vNDmjaljDeuNMooUSXE05QPiHtIzenHlOtms4kNJ/CisLAy3iWxHOtUKm2IEy6ZDETTWqvVwjtmXjBJ+qc1Bobr2ueQqlWSXhQXSQa3hIhHsbqAKCeJIkK7Hdt2bNuxZcWNRtxqyWW149y6EViwB6uWYUFXulF0lGYztqzYMHpjTDqKVFQGL1O8jfgB7UVNXLNxC1YXoFeDxr/4BpWp+pAzsxUuP51bne6dblIhRDEUBVD1ebVzqzca0QskPhfR8194LLxFonrcudWh7e0bbaoDQc+re6eLp6Gf0+k0qAI73jR8ucrPUYU3Bz+AlmNxjhJrxtBNztoJfji1hIx4FMgWo8WLwiqdGA0YCwO45MWirIOFH1BcE4Z68bWkFp9Yt+1XAF5VlNsj1iaZpHQNNepA+w6W91DOKmT7w5rvKKOLNim7a+MriZOJtBpS4S6x3D8ML0BiZZf817bMTcBvjWeTwte2zB0jc5hUU41mxZEUwqy1o3Akl6FQIaRpCkun0MOlrjZTQVwvune6OFGTRofTI05HopmMpimcPJO6aKpCmDPDFJ6n2BhJ6qAzOEpi7Wtda5FZYaSnj2Ff+FexHz3ZpFDFajab+AuNEHGewdj4/Ckoa4yVfHBi2T9RDyeFEF9PPEnyxNC6JpWQ6e2nryXGwtoBueIrZCiEhVexq2GFcCLE9yH1AzjIygyj/E+W308WeJ5lnADJb0kLJG7EsG88JTT2SzlsrVYLxc04jlFkH0nmo2kRO6ShMwoFd+Ws0r3TLZwKu90ulRAUa8HRCXe73dT1WNcLFEIMDbUsC/9JFyX5VOk+4N5UdajgGB0ly0PYaMQAsaLEzeZgpOEtJT8kKVqtVqt7p9u40sCqZbTcYskysVwhSqWiSXuwk0QNt8FDL11fi8YMZFoTBxeOdUp1vedVQ22tTL9DGmapzplGo0FH6XTibrenFoo6Yc5ROrc60xJQ6MGhOws3WpaVqpnk6BU58qvkwJdAdTEW5FcYlpJF+VVyaIgOQFQCJQ+htGDjgagdn/GK0b7RRsmeCreKV4o3ARf1pGyBsjXdMamUXD6Fc1QZr5rVT6UTi0DiewcZrrMxoIuV3kopcq/VatGhcQaYXCEUtxdODvGIDVfzsbs2+jqUs4rYPo5K/EkvINmkyiwidG6ooaGpThe80PiCN9eaoju9uda0uzYGPmDtyiJneO8oWTYpInWOwncQckuqkkJItTHtro1GH9qOJlrcG4rgOGKTvnc9O4aludZENYMMYfjm4jPq3OpMRYUupPBdKDM5SEqUVK580Ic2McPT8peqEIpzHZ0qptKIM4zQ0bFAPhElHPwvnpKoEFLlZ7F2NA7L3ofTaplSOVNRIbQymmGSQhjHMcYcodYXDwkwZaegLPnkX//pvxbfNbpw2iLe/KEhIawvcRwbhoHrha7rZCDDe94S+txKCiFN+3Fp8RUmM47sUnYmhzDeK1G8lAWelWWB28PUcP60XU2+n5JkpQm9rnSaIs4d2DnQ931rONMGwQoNmEs51ODobiRG/A9y5fu1PdR51Vq2erVkrvmYAEbYD9jKAcX65Qh+zgluBvXLGuZhA0C4GcLPhfrPBvqCrihWs9msVCpYLAunj3q9blmW7/tiomYOuNrlfAB7NmL7R8ovwtxRlPtpTixMrJLAYHhFGSqZ2Gg0KpUK5nZGUYSpnvgn5YBiHjdTs3EwwW8O5qDfYQybPWL2o2VZmEGuL+iYi1+/XMfcJ2/dc7oOFtpSD6vrn1+fe39vJ2EY6v0qFFSpTBQ+oihyHAf/hElW+JnUBoAilJaJUWE4cur1ehiGmNuQlaWAYGmQ6G5kXXKCm4F7RNPmNeWDiv7BXvqidzPQF3RdsfD+m6Zcj6RSUQFUywLHmQMA0zQ9z2s2m7quY8dIqhJEyUjWsoW3DrtaBTeDXm4k9E612Wyurq6i1Og4TrvdpgdXCGop+Du+bpCoQSJmoPXvsy6MvX7VHE2zLAtT+V3XtYQI5DIpTymZiid6H4N+to9yQMHuXpVzFW/NM5YMqiACw5O/mBSt90syUvoKvrw49WHbLkrWxZoK6mFVO6Kp86p2VMOpg2ot4MfKzFGQPZysfm2tlZUVUoGwpDBKVFRn1fd9tH9hgU293wvLsuJ+pcehPXter4oV3Qzbtuv1erVabbVa1Wo1DEPbtjGz0bZtLPwQhiFJLVjFWrRbp15CPtccB6gaoePQaaRODlIFbEQsZtNut33V9zd8fCg4hHCqD24GUiVJS7H0BX31xVX1sCpVbJLw1j1KOY7uRno/Ya9er4uvAPZ2g+H3AhONqtVqFEVi4Uc6PeWLw/ly0EsCDDdDqWwvbgSAledX3Ctu+7G2eM+lumL4p/zcYPE8s/pDAkDrpRYAwEvgQ29WNE1TN3QddABQ1hTsDlKr1bBDZrPZdF0XbwWKuZTolawLQE3nsAJzcDNwr7orz6/oC3pwM8DVBB9NmSLnWEFUOzJYjqO7UbgZ+hu+vqBjEjt9hqYRf8MPbgb0ATwxHITtdhufXaPRoBondPew0h4AOI4jFgeWwIJb1JwQAKxly+k6TuTgeWLaHvQnsaxEQcxqk3euqtIMkxThpNTgZLWt1iMtbIRYOVfR79HFJHasst54qEHLOhYlWnl+xVvz2o+1AcgiLB9Uyvq0MpphilBinhR4mTUFoVglfjhLPnn97df/1AM/RR/DRRP7xMq3YxhM4BTPZHV1Fe92p9OhoY4lUlM7EIr/pA+UXBr2G3N7RjfbEbBdO2SruIUfIHDxsIYbtefsBwWC8qeKYpb7+OM/9MILAPDZxx+nPz14/frrrl9/4d57v/Tgg9948MGvf/3r9KcXXngBAO6///6lpSXc8pa3vOUP//APAeDMmTP33nvvl770pY2Njfn5+e/5nu957bXXnnnmGfrumTNnlpeX/+Iv/mJjY2NhYeFd73pXt9vFD/zAD/zA7du3B+f2z30AePyPH3/if3sCt5w8efLcuXO3j91++d0vXzhz4bVHXwOA+Y355a8vP/Lnjyx/YxkAbh+7/cL7X1h7dG3ppaV7X7r30Mah7pPd6w9cB4ALZy489MxDb/t/v+327dsvv/zy2bNnAeB7vud7AOBLX/rS5ubmE0888cgjjxw6dOiP/uj/CwCPP/7pJ574LTqfs2f/7gsvPIXbH39L44X3v3DxzMXlry8f2jgEALcXbm8ubOJ/733p3if/7ZNnz55dW1tbWlq69957Dx3qGVnW1tYOHTp07NixpaWlp5/+T8mjAIDvtwDg4Yd/5ZVX/kcAePzxx/G2PPjggxcvXgSAQ4cO4SP4wAc+8B//438EAF3XX3zxxeeeew4ATp069dhjj+GTffvb337lyhWUS+hh0ZksLS3Nz8/jt86cOXPs2LEvfelL8/Pzm5ubZ86c2dzcxOfykY985Ny5cy9WXsQ7eezyMQB47dHXjl0+9tqjrz3+x48/8UdP+J9rAUT33vvvFhbO0SHu3Dl26dIPzM+fXln5777+9d+h4fH5z39+c3NTGh6r7129/OHLWbf02OVjb/mtt+BF6br+9a9//bXXXgOAe++998knn8TtTz75JI1SHGaQ4MqdK5c/fDlneFQ+V/nd3/3XZZ7LmTNnbt++/bWvfe2hhx66cOHCE088cezYMbyW7/yh7zzw2IHbC7dxzwCAo+L6A9eXXloCAGjDM3/5DD04fJqPPPLIE088gddCTzbnWl5++eXr16/T0/zWt76FowW3zM/Pr6ysjPTapj6Xd7zjHde/9/qFMxduL9zGk197dO1bT34L9/D4Hz/+ut953V/91V8BwBvf+MZut4unevHixQcffPBrX/saDsLjx48PbmPivQaAF1988fr169evX88apffddx8eJeuOvf3tb/+r3/0r2vP1+693n+yuPboGAOfefQ63v/CzL+RfrHjbUxNIDh06hC/jyy+/fPHiRbznAHDvvfcuLy8//vjj169f/8u//Es8yvXr18+ePZscHvhC4fVK+z927NixY8cOHTr0R3/0R7iT+fn5Z555JvlWnjp1qtvtLi0t4Vfw67dv337ttdeWl5fxjuG1nDp1Cjfic1leXsZXXtf13/F9ALi4sHCxv4dvf+21i8eOvXDvvZ997LHw9m26FnFy+K7v+q7r16/jabz5zW/+xje+sbm5mbxXALCwsPD+97//b777b7pPdpdeWjp2+dixy8fWHl3Df77w/hcWLi+c/uTpB+4+IH7L/+f+/V+//23/7G3iMDj0+KGX3/3y2qNrNAV968lvLVxe2Hhg443/5o1aRzt37tzLL7/c7XavX7+ON+T69euHDh269957H3zwwbe+9a2//du/DbkL0Bvf+Ea6k0mWl5cPHTp0ceXiy+9+GQDmN+YPbRy6/sD1C2cu4AdO/vnJ7/3C93b+unPhwoXr16/fe++9169fx0n70KFD169fn5+ff+SRR/CWQvZ7nbUAQX8Kestb3Pn5QdwdnvzJkycffPBB3DI/P4/v0de//vULFy4sLy+/5S1vuX79+uc///n5+fkzZ84sLS1dvXpVfKFS147C13bt29Y2F3qP/uKZi/iinflfztAHHuw82Hlvp/tkd/nry8cuHzu0cUh8+vd//f63/8bbb9++jYvL0ktLy19fvv7A9e6T3fmN+XPvPofDAM8n510Q18GzZ8++/PLLkDs50Om9/D0vf+0nvwYAD//Ww9/xzHd039C9eOZi98nuoY1D8xvz0hh7x4vvOHdusLplPLjea5s1wzz++L9FKULXh6r9kHTxlt9+619/918DgP4PevPP9fuvv/zul19+98t4q3/ql37qufnnXn73y/gi4CDsPtnFvz70zEPf9hvfdvml/z7/KG9843fg5Kzr+ssvv4y/v/GNb3zkkUfobuMJ67p+8eLFr371qwDw1re+9cEHH6TV9s6dO8kpCAC63e6999577Nix1157jY6SOsakOTZL5Mu559BfI86ePfvCCy+cPHnyySefvH379uc//3kA+K7v+i6cTkXBMsnJkydpcsgSX1dXVy9fvozzJ4lzAHD79u3Nzc1jx4597GMf+4f/8B/mn+puZadck3sDKzfxr8wHCPxYVuhFcj/5bquxefTRR/M/8OY3vzn/A1M5yqlTpyY/yuOC0jveB4q5d1uOAnDmzJn8DwhHUbEUqPBTlr/1t/5W5t+OAjwMJ0+e7P3zXoDvFn7eVP4gxbzzne+c5u6S3LMtRwGAEs+l8MmWYfBcMsh7smU4CnCk+LUtPI0yFB7l0Ucf7Y26vwfwAwD/PcDfA6j2/1luNI7yQo1P4W2f9uQgDbCeq2F7hvrb3/72vD/fM/EBFgDuL76WqbxQeYNwCeDk1h8FAHD8SEO9OvJQf/zxx/sxqI3ECIkB4uPHf2SEk/7u4Z+ho2RwT9EHlgDuLXXwwheq8JYW8KZSD3d71vS8ob4IMIW5tugo02NwlJPD46d/Fdv3Qk3MtAKkZxDuQ7hbkbqIFoLRJvmxdgCwsbHxoz/6IwDw7nf//77jO/4rbX/kkUd+8Re/BwDe+tZv/Nf/+gYA+OAHz73nPRfF737842cA4KmnXvihH/r8Zz/72Wefffb06dOnTp1CE+ba2tozzzxz/vz506dP/8iP/Ahag7oHu90Dg2Cb5bvLy3eWT5w4cfLkyfe+97248cSJEz//8z9/6tSp5557zrKstbU13I7X8sUvfvGb3/wm7eHo0aNPPvkk6pOXLl36gz/4g6effhoNpcmL/Y7v+I7v/u7vBoD19fXFxUXxT7Tly1/+cs7tetvb3obmZwD48peHbMAnTlx//euv92/pjwLA6dOnP/rRj9IHnnnmmU9/+tOnT59+5zvf+c53vnN9fb3b7f714l/TB5aWls7cPAMAi4uLf/7nH3zmmUcB4FOfekY8ytNPP/i5z50EgF//9T/A00jDwiv6d//uOz/96cdzdvJP/sln0c6XxcmTJ3/yJ38SAJYPPfjo9YE4cHTp4Bfh98+cOfP93//93/zmz+Qf5R/+6v/11/79r8FxeOQdj9y8dfPE60/cOnZr/S/Xj6wcCf8wBIAf/vEf/uIXvwDZY+zHfuyrf//vXwSAV1999fz5QcOwEydOvP71r8ff/+RP/uRXfuVXAODNb37zxsbGI488gp9fXl5Gy/0P//APf/jDH8652Icffvi1117D5yKGhD3yyCNoJV1cXDxx4gSOk09+8s83Nwe2yfe+99Hv/34VAGh4pGGB4InKgUbjc4efWz+wTttP3Tq1eHdRPMof/uEf3rhxgz7wxBNPvO1tbxOP8uqrx86fH/KNvO1tXfEozz333Pr6uviBxcVFfKHoKF/4woVz5+bFG/Kudw0N9fe9732XL1++//77T5069dWvfvX69esnT5783Oc+BwBPPfV8/vD4Z/+sN5KzTpWO8o677+h2uydvnjx16tRzl57rfqN73333Pf2Fp6HERHfjxo3f+73fS04O58+fX1xcXFpa+pEf+REc6h/84AfxT+95z3uefvppvEslr+XQoV6g2vvf/37Rt/anf/qnJ06c+OAHP/jDP/zD+Rdb5rX95CcLXttbt2598YtfxEuAvuz19NNPLy0tPf300wDwyU9+8pvf/Ob58+dxfk7dyY//+I9Lk6QE+nMgexBmDTAEX94LFy589rOfTX0uJ06cAIAf/dEf/aVf+qWc0yjzQv0vv33fH4ZXAODd7/4Dem2Pnzt59aT6l//qA/CaQoMw5yi/93u/hy4F6Y7RwnfmzJn+IpK+NHzhC1/4hV/4BQB46qmnzp8/j8/lmWeeOXHixKc//WkA+Omf/un/8nP/BQBOvPBDi4uXTt5846lT73juub889+WvPffco9hy4qMf/Wjqg6OR/BM/8RMLCxiGreKcI+ADwMbG3/3GN94NAIuLi6mPZnFx8Wd+5jX87slvfuSBBx545JFHXn755atXr0b36Veu/DgA/Pqv/8EnP/nJZ5555oMf/GCn03nTm94EAF/96le//du//Y//+I/PnDnzi7/4i/m3tAz0cHNW23a7/cwzz9DgOXHiBD4RvLHvfe97/+qv/ip5x9bW1v7sz/7s1JFTp999+m//7b+dOjlAfxy+733vM82/ByVWqBz5hK4la1anF2r9wPpzh5+jDyzeXTx16xQArK+vf+5zn0t9+njJi4uL3e7/8Pu/fxqyZw/bfvr7vu89+dcyP39v/hT0mc/8h/zJ4ebhm7+78LtPLzx95saZpbu9W/rs4WdP3D7xZwt/durWqZ+Lfu5PfvdPcl6oEydOaN+nffbhzz57+NnTt06funUK93P+0Pnzh84/e/jZMzfO/PCrP1z42pZfbbPG2ChteHYZHDI6ETsYMjoquP/OrQ52RsZ0CAyg145omOMBAE8cfuLsrbMAgFv8DR8bBEV3Igz6p+yLHDB5DPsyiRnhQRBg8j2psmEI//7fD00xKAapqorpDZgCgf1q8L+apmE6Qb7Mh4fAq8ZOSvQn/DrWKMcPNBqNKIrCMKQ2XIqiYE4CJYl95OZHDl0/BACnb51eO7D2zNFnPrf4OYDitulZp4Hg/aG8KVH/JEG8pOaPfdixk56/4VNzJ3Vexd8LezFR8t4nPvGJP//zP8ejP/fccz/5kz+Jd+NTn/rUxz/zcfzwU089hb+8/vLrX33g1ddffv0bT75RH24KlMpv/MZv/MzP/Iy0E+LUqVN/5+/8nd/8zd/EFCkxsj+KoiiKsL485HZXE9PZfR++/OXBGKNpfX19/UMf+hAAfOQjH3n++edJSv7e7/3eX/u1X4N+awS6M6k773a+8m//i/Nn158+c+TM4oElAFi/u7Z2d309Xjt/+/yp+dP/pPqf8OnTaSOO42Aag23b2amMAMP5RVJ7KBDGRvJP0sccx8HPSH3G6T2l0wtuBtGdwb0VM4Iw9ZcmB3VexZyW8pNDGQrvWM7F4tOpVCo56abiE9xxUi82iiLM88QG5fl7oIv9yEc+Impin/70pzHHCW8IbkyGluCEXPjaUlZSVj5ktxsFyQQp4Sh4CFzLdF3vdrtPPfXUpz/96UcfffQ//If/gGsfTUFilrLv+6qq4gr4mf/jM4vvXITcQbhyWIXcF+p/MsM7YQgA39h8bv3uQBZ//QNvWO++vH53/dqRtxXeMVyhUmd1AMCVznGc5DwWhqGiKJjHVZi5VDjU87+O5CxAAKD3e13+xE/8xDe+8Y3Tp0+fOXPm6aefvnbt2unTpzFZut1ul5xvc8AJBB8WPj7loBLdiXDS0Bd0GsniPRffBf3HdKfreOse9bUHYclT59XGgw3vf/ZyJjpVVSnbUFXVZrNJqb90gVORiimDFGu0SH+lVNicPTzxxBOY7ZLKiA1Ct5b8+RZKyzD5RHcjp+vgyMFVyd/wUZT11jz9Ht36moWvLRZBxWIztVrN8zx8uJQanfPaYkNjSExBiqLguyAKBnsMVggngvLsO51O6suJXThVVS3sKb+yshKGYb5C2C+MOQ5SgnvWJFVmoOevLtsvbP3GV35DtGu+7UYvI4XWueSpTncyLVxuz8/NAcARXZ8X7sxN36cesnOCzpy67mrv0woXwpXDKwBgmiYWvcAEcfwdJzKsiQLTkJLF5vIAQFYD7ahW/UA1f1EnBfjFF1989tln0Sj41FNPPfbYY/hh0zTLi6eizXJxcfErXzmN4mm7nX4niTJDHVUjlGDwMoMbgXZUIyHme/+i98nNYU/yoVOn5hYXAeBaiQdXXomShENVBbxPJEbDsI6Nt7SkxF+oUpY5w8LJgYriTDIIsTQFAJzsnrxx8wYMG5t/8fYv/siZ4ii4YrWzb2FByaNXhuSgQhYW/9f8MAzRkCTuGSV+VVWjvxvl1xwqY05yIgdfNzLo4KuHw1KdVxsPNbLGmLfmaUe1MmOsUIVOfXBhGGIrc13X7d+wyV5w4G8OPHP0GXR0rB9YP3PjDAB818Pf9R71PSBYP3EnODlkrX1JyhtQsiDTxrd96dXnDw1CDA6dPgWLizBsGCX7Gn6mvO0jVb0BAMdxkop66m1XVZUqyaV6Vh9++OHf/u3fzn8uqY2/R4Xu2JefGUx0bz/29ttvvE3lYchsnVShDcPQNC3nA2iQKjQ6TEVcxeGXb2vG59JsNtEYAQCoG0gfyJno4M0grh30tgIAKcD44d/4jWfpyS4uLn7gA6dpqqah/sQTQ3Zz9N39/b9/5Td/UwEA27bRWopKJpobsH5M905XmhzwTAazx9FGqtkC+io0FivKnxyybB+0E9M00YCSaxidy7+WVAEGh/px5fj79fcbhoHPpdvt1mo1fHewtBIm8e5hXW4qsEI4ESSHZY0zXBLE0s9Z5H+y/H6y6K/BBVXvWq1BKbMoisazjqDE42/4/oZvLBrGkhFuht6aF26GWKVQO6otH1yGImksuBl4a55yUMGduFddlG/QPlT/oXq+4lFoWccj5rtNgsDAOyYuYVEErguKApYFZXI5USFctCyxuOudMLy4sgIAxxuNf/zhkARHAEBvnig46v8gcyqc4qov6hWpI03TtHvSjJpzihJHEQAcVNVf9bxUKZkoI/aRxE9CsCTgHju8AgkdGwBueN5BVT2i61/QtHy18ynzqcuvv4x7hr5/FWV9kvlEpXdwE45qJJ1HUc8rcvvLX351ofvqPV0A+M5LvUR57e1vf933fV/hxRYqUYWarei7U+dVsYSv4DZJWY/F94WG+r/4F8+KoifFBcVxr4yneZ+JlSTRWBvcDOqX6gBgLVuFVeMKQTNwvgrt/rxLn0+WVFVVFR3+WaqaoigAdqHoWah4BEGBQ+MHgmDdcQBgcXiauBtFG667aFlHDeN/Vv38oT4Vpyspt/Q7Hg4N7WXCB3I0RrSvhWGIawSSfC5lDI5lpqBCr1qhcvuW/gsV3Byy42hHesbEwyWkRiqYKSqHqLoHNwNj0Ugt+Dwq+LDW19efe7YXNHjq1qnzy+efeutT+HYUPpcyLxRODjlTkKJoqJk+t/ks+VQX79tcWn/0/O3zAPDRd50pLNPo+713RzzTIOjV4SwpipYIuOh9gDrmoc8H5wSYmSr34lBP1ZCTytUWUXhLJz8EXewkgmWZoR7c6Eme6ryqL+jKQQX/2ZM8j2j5DmRN05JeaFKhcd1vnpAbVOwZWCGclPxQz/y/iuT7AJeXl7G9wdgvJ55J4ePOn6R6oTK3V2C4SwS+KqlCYe1iDVWaofrdo4gO4Wa48nzviNg+oSQUYKAcUEShCoNmpTLoWaB5Hk370s6hX2GfpGS6IXgIuiEfe50D2R7CeyxLDNLz1r1ex4KTg1nP+VEnfyqEEkoUFnDPcTWUmenwEI+/On/2lS/RxrnFxa889ppyQEF1PX8P9ct10n7RtShpv/HpuHCJEneCmtsYvpdC6CgfXPzg0oGlxbnFE4dOAMDf3Pqb31/7fQDofHuHlMZXv/Tq62+/HgC+fHQgZX7gkQ/8m8a/yTGdlpGSc2ICMRXtX/7Lf1l4LYWOpuj/EuU7NMoUshLdjCQB0ENUFMV7zMNbSsM73AyVg4p71cXuMmUeHBZAJ/uxpmm+76MzHO/zLEWEAiS0yjCEWg0MAxoNcN10mxTqrpqmeZ6Xr96c9rxCtXO+6IbkDw/UG3vF4o9qKGzpC7o6r9I7UvKN8zd8b92DfpMSnJ/DzdBatmj+zPt6iRUK58PoblQ4vd/y/ecP9ZyE7+uHlswpyrymFU5BkkIY3Y38DR+ncey2os6reGeaDw/m1ehO5G/43poHAN0numTHgf7UiquVFP76Pd+/8W3fOcjvunX1nt//f75uSdn8nX934z3ff1uKo4PR9dLyr+1Tr1mLdxUAOH1LWzsQXXjCp/k26QAk44jYvTCHwjmqzApFDkA8GWrwQw1U2jfaqDaEm6GxZJjHTW/N89a96E5kLBlk6iKLtnncDDdDb91DMQAt2u7/wy352k5isC6MJ1d/RKVr0Rd0Y8kIbgTuVRcA8FpyJAekzHOhd5wmbZR/aNLuPlHghyzz4IKbQf5zkTse5ZIlN6J1fsE0jwm3dDMINoPgbhQd0fVf6od1ZF3sVESL2YQVwkmpVqvYhw37w4pgByQAaLfbhQIKfbjZbEozBS2ByT+VB2cW8rlPIvNNThmbJRpyMOifot0wohLjIV/nuqliEPmIbv0PpqQQop4GAP41Xz2sYv+ffD9koXpTBuzvFG6G6IDCjbjUmcdNfUFfq9fzr4UmKeWggpOjsWQoB5SRpLHCaR3VGzSz0b3CTA8pxfS5zefW765//NLHAeCDix/86PGPLs4tAoA6rxaqnfZ1GzsKouNL1/UwDClKBDs3Tm4ZRQdRzlEohxCy47WOrRT4IS8YBh4lCAIcrmiERuuGlEObRfnsvizKNPac3NFU6L0RI9wgQ7Iso6pJFhZ8cUbyQyaHuqR2vvavrHwl6i8ee+wH+pmuWRfrKcUWllSFEO+iaYJlwTbkAZUxwL2pUoFsOenu93/Pr370Bk01qPxgPlhwI8CxF5+OSVGH/rOmGVtU1PGe4E1F3VhRoNkEXe9pMjlTEOk8OVA7SpIgJQvdD3Z/8EvPfemZP33m2S8/2/ly5+3a26MoejZ89sx7z5x626nTbzttfLfx+PLjE9/47aAwsar1SCtpspRsuPpXBqL22K8tmjVRucUhIYbXqvNqasgo5jWU9ERheYJU+xpCoYnoG6xUKughbLValBsmCr2pBmuRwg+MTVIKkjxR9nV7JJf72KcqxX2MMd+WP4p+j07Xi+ZgbDNYxpGQVZwCV09Myi08jUKF8J5tkYFnE1YIJ4XSCBuNhti8FWcfrFBSmECIYBphMi4U/QPl95NKSQ9hlmYixTjli0HqrzcnzwRAKVlSCFGmx7mAbJaNhxqYm47/LeMjEgP66RJS7UmFqhqqnfnKbeXlSlZ7WQBoP9Yu71WLT8doSMN7HtwMyGolrfpoIB9NjO6ncEgPiLZ3OlOICitDoUqgHFRSPbdE4cVStCeSFIPKJJqKaidKvbiFvq5p2vKlZch9LvhkAUA7qqGVF8eD2Ga90KuGH6adUAd52gnuIec01N8fqHOo38Kw6FZGYssppYD3R32bOrktOTUpl+aoBdM8WPTgCm1S/+1//W9/+gd+GrdnueXprbRfZ6MglfR1F15L4WsLRU9ftONA/37qCzrGQQCAdlTz/73vum4QBCR5h2FYq9Vwi2EYD6+uwnbJSaJCCACVCqgqtFqgqulRYVJsfMmc7UmuZXIV+oiu342i/LXjHssaL/9c2knWVZTxkZYEH5ltg6r2WqtHEdD7rapQonF9MYV2nKk4Z2iOsm07VdEts4QVTkG/5fuTT6eFFM63U4nIKPTckjNcPayiKy8ZPjCFSbtIIfzxH//x3/zN30wtZ4ieW9M0PcXLj/TGUCMqgIRXiotpeZvULoUVwilAa5ht2/haBkGAay2kxUbTOyzdfHISGoZh2zaa2+v1OjagL+kelFYyApe0RqMx+SSVqqqhkwSNNJ6n5esVWI8O31Loh/ujsocy9FSKvqTqcnejaDMIKFZzwkOIR7lfkGDwKJtBcFBVF0zzq28G0UMYboYUXGQsGY2HGvjXpHqDc7c6r1pKwamW8d4U+rt+KbIm1+QLF/XWS63J04QKKbwhUg23VIXQddX8DFL1H3gYnyMRbobKAcVYMgofHAg6m3ncJG2fltiSBlraibVsYeANioPldyL5VDVNi/rgK/nEmSd+8+5vpsZg4+i1li3ndT3lNisnJPV9uR2GNzwPAHLEaNEm9fQbB0mVSXeoclDxflGbfCSnCuu+7zuOg27bQoN0TtQ63bHC51Ic49R/bZOjFKAXmIrvk+/31EIMFcFLKPmuFU50t8yf+C9vHJR0z3JTk8UEACqVCt1enPNLZsRJEZI9R+XNABMBbhxczr9j//s/UFNfW0Rf0E0wPc+jRZNSnur1OuZu6LqO0+kkamcZmxR6obMsX+q8+hO/EibflztheDsM5zXtmGHc9P3JXyicjpIjmTCPm5jXfa9t0/py0/dv+v68pt1r23OK4vs+xabivRXVmDKyRxmNMSvMEiO5TNNEKQsyFEJ8NWjtSLlS07Qsa3L5JLWCThQBnh2+tkEw5HKnayGXO0bDwgTz7T2WNbmqJip+KN6gHoVxBDBKMfZUVFW1LCvsl1pQD6uWYpF5nY7efV03q759lkKYjPTewymChXAfwinQbDbRGViv17EaEkFrSRmwTi4WyUUlUNxPyWDRMAxxqk1F13XXdV3XxWUYZ0PXdev1Os6VURRJDg1IxNsokCJA4K5wkgqCCjY4qteHViDXdTGSTlHsQlcSTg66DtKMTaKPqrr5yu0n+r8sDa80WM3lpu/PKco9NTP/KPiLbYOm9WQs8ZOqCj+Rcw19tCNaavYjGtjI2oQRFAhZ4MLNMLoTFUpj7zHNYuNOxgfo/tiQXqBP14Fq1ohRYSiiSVFhtmanyriDBKHTxZ7qwiXqVwuj9fRm4VHE/sKpH/6YM/cxAABYFPt3KfCrFgDAvKodXTQKE58Kr+WVNwAALJjRMSM8CcqPgg4Am0FQD4y7UXREV9aiAjf1PZYlrrjkHgTBU1roagiFmodZt86ZswHANEFPy4izGmDHk5q9b/+y9Te/oGNNKTSFKAcVp+t4677+S6a+oGlHFH1+cAmpooZmF4xkUbLEuEQYzu/KkmBw4kJpozB8QLHK6ns5wyP/6wBgZ1xsss5UzqxLNyQrRfA1uYWdzDNf+toPrPaCbOM4Hs+ZM0bVNEz30o5o/jU/uhOZx02va/sbvnZEUefDgRPg7Uq4qQY3A2NRMUu8tqZpYv1JdE6iyRI34gfmvoH/d42lyFjq7fCwrvdNM5ophL+muhqeiJ8ovLpCi9Ia9EQOaZm7WqttuO4t3z/0XBtfKH/D145q5nETtbusFwqh2lpE8UguuhbM9cWSnt1ul0zn0F+4C18o2ypaXEowkhck68PjFQ4A4VpM09D1ngDjeR7VVhHeAK1QepyKR+d3una/+kBvS6/6wMcV7WjQeBCOFa0dn4jgYw4AwK2WfxLgI6ACqLfD8OOeAqAf0XWw0iU6kqmaghaWpdzWL/eGur6g1y7UvHXPUixxlCqKkuVpp98tsKR3ajB4TgBsVwuN2YQ9hFMDQ9tJUMZE6lQzUpaHEMEZUwxCG6kHfb6HEI8oBuKjr48OQfZXlAYk+6s6r9oP2IXu8qxy26iIYiGNQnfWyooKaTXE8ebhjvMNbP/8kfrtX0vxEGIHqn7EDuQfZRphHUDhfMaSoS/oZNkir07hWlvGD3l4dRVyK8WhLpcsvYiBzaqqkgU368FhozBUCLFEmK7rnufhL+hHwtiznMUjmdvZO42bQXAjiO5ErZOtyj06ZJTi0DRoNGQFPkmWcya4GYSbYXgrbJ1sVe5JNwNPsboa1T1LqDce1T1Dy3qOq+HC/82g0jWpNYeeuPHEH/9//jjfoVGm7USh53Z5WYHcjLgg8Cg0Eb1P6FKj0MSRyr1gIDfmzSYLA0BCcETmNW0zo7xnmUC7KSJ2Z0l9cFOJPipUbql4CZLqu3OvuqKNv/cyCimCnW/vUKUNHLp4CKodvf759Q+9vyApt2QMNv4uSsn0gTKO/e1xApBKoBxQsEyOedw0j5vLZ5eh76tPXUzpppUJH5DSMnGjaIBLLSkkegjFiqljV2grE2Yp9kzC50IfFrMQSfzAxkiWZZGOTe/1/WnjZ05RbpSon4TrYE5J9lSJfyTPP6TVmJXc1IXvdeFQ1zTtxnKKr5v8rgum+X+up9daJ3fo2FUnRMRyL/iyY+UF6Jeuecc/9vP9kF8o0WGluNfUsB/SiRz3iqvfoysHFLLiFUp0zt+ZAwD1cK+ceDK3YioJk7sU9hBODcuySlZkyVfCJ8zCUlU1VZAV3YZoR8mXd6M7UXQgws71lLHmXnGVAwqavnLkV9ooHUL8ZwlLxBwAOA5E0VBwPN5jLIGTegXCjm34p4XvdsFRXBfy/ZDWL1uFlY57n1y2tKOav+Evd5ZRdKCduG6Bt9P8JTP4R5q/4fsbtSFp7B3UyUMtvKWFsnh+JzokOTilp4x24pu+L63ZODDmFOWeAwrZywf54nejcDPEEjtZaYGioaNQJVAM42P98MVh8aK388OndbqiLNGBMmfQP+z7IIbX6ToUvqm06sCwetN5fJAP7BQ6NIRsBzTlgtDGCgBgodihUXjHdMPIH0Jr9for4ADA/caQVeI1K9gMgoOgLkSmahiGYaBVa2VlBTtAGIbRbDbx/ZJki2RZPH1Bp1M92WrdhN6xbkHvwg/r+onh8wzDUBImjhrGUtrsIG7MvyGLlvUpcPNlPud/VJI2KcfpjRPLAssqenBCQwgqyIHKAzWEAIB8acx+IN1tYh430eadPCgkXUC/aoNjA0DdAgAVQAUwFAAlwGgPUGNQ51XUIWsXa1gKYqh8RaWUvwL1Add1m80mahG1Wg2XrWn1hrWUAidAIaJxJP+isNC0/YCND2X1pdXkZ2gxRQMB/tQv1d9w/Q0XP3uR6qzgq+q6Lm2RLKfpbpPHPOfvAACohz0siYlPpC89uzHEKS/US0MvVKF6M0XPgSh+ZAkhN9NWoiO6vmTbqe81ABzvO8THO1XR81+YS588QtJN7XSdwrpWx/pK1Pcaxj8zDJCMvIpyQz7OOORMdPOadtQw8vXweU3TFE07oqHEUrtYQ4vS0Ltv68nnchiAAhz0Es+lUJeDnxtsxOoMlmK5V91k+Df1MsnCWDTwnfU3fPeqK5qMC93Ue7jqDCuE+wsysRhLBvTrT2hHNNH0kpQtpHiM85AZkoqUCftJllIAALHuWc53URsstCbOa1qha67wKGQZbbVaWauXJPHjdYkSP2EsGjgTuVfd6vkqbTdLBIYVSGMlYs8+0c+IE28YVfkTA8xyNPl8U7GiKA/3f89a1KO7kbFooOOO/CdoT8XMN31BfwXmAADcId3jL/pnMa9qc6Os+skzmde0OIpKWiXq9d7FSsZWRVEARvB3qfNqmTg60aGBooNoO8iS/vvVLE3bHnw4DGFurpeO8jDEgM49QaocOF0NgKLeJ+8ofaU5Vi3tyJBssfriKmSXxct6cP9HEJCbGsU47AdAbuoypWtOFD794e6PosyHxouPOcsYUXw/DO7S91qAwZVzigKg0TT1j1utfww6ANx6aXBRh/Xe1JFfUb1AGiuh3M4Jloz8QShNcvU6JN/jxoONVP8SzquYcpZlfUd9QNf1er2OefKWZUmtHXzfN02z0Whge9UgCKrVahiG+MXCSO/8LHfDMEZym6SayVRVtf5V7DjgAPRjaTUATY9sDIFTDFCs4jBLMAHtOKQHYh5+ckUI0hrAKooCvb6q0DzRdK+6qy+top0l9WhZL5R8rMRQj16I8hOMMYSE9pD6XMg1lzXxFr6SYsPV6E7krXvURXPQqOP5lZw96PfomtbC2yBNUfX6oMUobRERn4BkdCB/Lz1x5aCi36NjlSnqR085DsaiAZDS5Fait5i6bsswaEY9rOtLiU9KS3ZqX+IjaXOy+PSTw+Ogqh5U1YPC7JH67qfmQ+LiAn0dL3+OOmoYuJjm6HLJtQ9j1pJvmabJ8xg5RMTZDxcj2nn/MHL0NZJlidhLsEK4T8GYGYwpwrAW3F4mZR8/ueG6B4arERxU1XlFOWoYAOCte96aR2XugPLlDiqWYuVIxjTJOn210xCmwlSyFI/BPpctfUH31j10zaUuloVHSV2PYdhjlnVd0kblgCItJ956em2S3iGGg4uypLGexK8mJH5oaD40MkQgTFZVFNQJGxi4Uq/X092hVglTcdEHDg8nuRGpwlPWEvVbBzzMKNCOpmS/WMuWdkTLFy+iKHLqdQwTwhrluLFaraI8mu9CL9/srtD+muPQiB7qeelzArn7b2VKhBKux4oCWZ3tRaerWOo23Z2VZgaG3lreo7DDiigW/Coov9qTpt21/q7mtYIHBwDwLvDe6Pnf55u/ZCoHlBBC48NGcDMIIIAjoC/r5LfJ2Udhh8D6sgVggQ6goeyma2BFEYBrP6pvhtb8m/rfKpyCUj+Dsviry8t0Q5I7kVoIZr37hXcs6QIizyRO9bZtpI53SlBMXRqoOp84R5nHTfsBG6MkUGSX9nl+bu5NAH/aD8OAILgWhgumOacoqqq2Wi3sTxCGYbPZ9H0fXYjUvSknGQkfXCpYuqxkwSpd11vXWhgI6sHgEnCh1Bd07Xjeu69pYBjFUrL5S2Fv2v8w6B/W0Sar36OHR0Kc3PQFvXC+9S73yg1gpjr2m6VoYaRweNSfrQOAf83H/m80IzldJ7wVulddW7UxESZnJ/6Gj9YKa9lCu56+oAc3AtwoLoKpOrau6zl3DE2WhqH0swl0wwAdbPpMpf/aigdKjQMkdSNH30NyjCOYBUCOen1B99Y8zNLEF0TalWTmUw4opt0snE6Fc0s3B1AQPrX0EGvhYgRs4dPPsRUaBjQMuJGtyx3oiXwp5jx6z3D1KZ7Ve+ecqcuV8dxmHYS258+EykElclJK60nFfiaueTejsEK4v0B1LrgZOJGDEhsKfJjgpBxQrt69Kn0lGQtxrP+nVIPT3Sh6cTNMurCiu5G35tUu1Lw178V/aRZmAhQuhDiLtVqgKL1pGucdTQNFAVUdmqJwcbIfsPEcaHvhUcrEnxQKKMvLvbpniqKg7oFdSaIowkjROI4L0+LHE4NEiT+1+MTwMxzEG9f7yQmjptKJMYGofmNUxkjtZQsXD+2mRolASXcWebpyQE+Fpmm1Wm1lZQXd2rVaDcVQ9CG0rg3WBCnzKoIIl5DCAxFZyu2fHglItsCQYyzUht57LPJB15WMPfOv+fqCHsdp1l/hLtpdx9/wlSNaKASgGq1A2fCVA0p03Dw/1wtOW7SsTwB8AiwQSlYcVNWbHzUKU9Gq56tZHVa8Na/9WJscyDnm6kJVTVlQzOODrll0dLyHwY3gTfcMriV5lJxG7YUqNPIObV7TYBliAGg2B1MQIk5Bk0hj6Ln16oMaVwSVum00QP+xzHRZSsodfLGfZIihp9oRzek6+j36u5d6AQupd+yIrsP75Ds2qCV4zQcASymubCTvU+BuFB0UCk4ahlGtVldXV9GFmJyFJMER1Wp8cOXTN7IIN0PJExtuhu5VVzui4YKozqsARv9sB19EHcN1M9swiFIy+jfoT72iPke0kbKYkoYPjEMRDQeFL1Tvi0uGOq/WLtagnw9Z/jSkUwIAb92rX67T6wkA1v9maRsaAPjgUxccUrZ1SF8XRJNlFuJrW15Yz9L3qCpepSKkX4ZhFEW6rpmmCWDg8LYUSwwLaj861IyaVgdphaIbWxjMOVIaCCqEUhGBevbsAblVEsRbWijy2TZ8zJkDAKgMXctrVu9afN9A7ytqWb4vPynTBF2v+L7vOBAEg6MEQaCqiqZphmF5nkYXJULKshjpnToD6LoOGpBFtbexPxOilBLbVlEu0p6FFcJ9SlKydK+64a3wM498RlzISeYTNxaKOCpA8+Gmt+bVLtZwTqR2eQBgHjdv/7L16z8Hwc1AX1BET4K/4cP/ZKnzqrGkLqcVj8bywZgtjclgvt9TaTAFD8U4XYcwBFVNcc1J61xhiWrcjnkvGJ+GsWrJqJ4cAQWxbdtxnEql0m63KeceS2AH/RA4rBYgNvhGkzaZZnOOkvpY2sIKVRiz8U1FoYpEZJIUC+eWKVIvxQSi+j1Sq1zf74XudDoQBIPpXlF6yrCul3BnlQM9w9VqFZ8I5hPSNRZnXpWg0P6qL6hJ2UIKOS4pI1INYQDI6itACyG1pkBn/u/k7nnBNBdK3BCMW/PWPdRPpA4r6ryqjLuuigIK5rQkbSgkBFPBwyy1k/Q3w4AwhP7bBo0GqGpPnUOkNw7txI4zkPjDEDQNVBXCsPeyB0FvCgrD3khutyEIBkdR1d5RsnzMaXXBUhQPlJLRHZq+oz5YP8kHX0xFwweH3z0PmTXfD6rqYV0/DNBPyu2lB78DAD0D/xz+4VE4AnZ6hwyxBgbq4WV8BZqmpfbdjaLIshzHcXxfi6KBY1/Tqr7vA5iKYp+fW87a7SFVPd5oHC7K/VXn1dbJFqo0IKQ6I/41XzuS7lMdMrq9OQh+tu5f8z1B7YnuRvq5EMNkUv0qo0Ix/BTpQD2QcXuWUpG0fVAAHtqncPUvj76giwuukSjlKkoRvdSVRWOoSqTl6D/ra8NGK/MzkYYNNo9qAEZqNsHN/q+H1eLJWdf9fhV0OWPfsrD6Wu+TUjNMDB4JgkCUFrLCgkZaoVKnqaj7QnBUqJ+UZoDz/1+DbocIlrvvnYOmpZotogg8D6Ko9+hTB8jQxlEm7dRr+X4tsi45wc2gthkaiwZ8Zy+gxVv3MLxcFWJkxNseBAFahQBAVZvJa4GMsPYkei+TIeX0hmbCIjEpy5i4B2CFcH9RRnRI/ZY4keWXD8YKkMnFAMG86sG3EqVrMDBpYPUfrpGDFqB+DYkKADgO6HrLslJecjIXob6H+8G+jji/lMl1xKqtrutKVmqM0EC1MHW27HblLYqiNBqNSqWysrISRVGr1aKabJiAkZ/ZUniUnInsoKqKIlqmc0ZRcIE0TZNujpjAUybaSgz7MaBhQAMA/P8khv0U7qNHGPZEXlHb17Setk8UBgemUr5uRO8z0+iJTKSatLNki+BmUL9c96/5okEdt6OjTFlXsG2arus0SrGI38rKChb8TAamDl7qEwC5KgEArNXrS7Zdu1hD4cZYNCh6HCOxtaOasWgUdlgptFhbv9wTHdQFPRKsRXY3wJhz5bg515f4syptFKscggChqhBFvcGGIw23pO5DLI9efJD+UaIo8ygIVi0i24eu94QeVU137IsbyYCSan1XlEmbht8Jww3XzWmAcRSOlGmPgVQq4PvQag1uDqrH+Bhzhgd+ptEYOPZXV1ebzSZ2LxQd+2QOSBXpbofh4RLnidoLqgFe4AGAqqrNZtPW+mGKaEXdDJWDihgdHd2NMN8b8yPwLaOYBbSS1C/XtaPymzLJDIPWxmq12mq1aLomxaBQ4o9h6BNiblV5AiEJGbUX+3U27gfT/MrsRPLe4GyD4UVRNxJvUVYM9lzRIiVmOKcGwlBEq9SNEAUJHGPJyEMAwPBpsZAYkrVC5U9ThwF0eFzaKAljolU6NZZH+29C7R8F/oa/8oJQHPiGr36lV6HqP7Te8aH3LwFAswlBMJg6RPNrzltJa3rhlKtvDBquDDYu6MoBJboTiZ5k6babponSUXY/tRSyIptITEqC1iL6Z2HK5d6DFcK9htTrVmLUMug5S1Sq4Rwhm6XYdlbEtlPmR0mBxBzCMAyTrR1RfaK6qTnXizSbzdXVVYzYdByn3W5TO1ckWZpVzMlOrcjaFlxvIwV+dIc1xfIl0XImsgXTLJzI7kbR7/SqWWpiNUtUxfuVLQYJPEEQtFotz/Ow9gMl8IxHmbAfQteHhJUcaz523QzDEJUf6JfmG/s8JfwN34kcSRPDPKKStcWju+nqjXXJBwBUbwodGtG7QDuiYUCgGA0bboZO10HJUiwoSui6TiMZhw/mP4hvDN4tTSte0cPN0Fg0ekUIhdPAsrpYK0gswiQ+COWAIt2rHIt1b+OwtQjr0GLgOn3G87xkPpKqqpERJcU1AMBAXLwzhWNMnHmS+TmGYfiqnyoUUsc5XdfNCzV/w6/h/Na3i7tXXfO4qRzVFEoWjUBVQdN6MhmWtEU348OrBRZrXdesS3XJ+o43TZ1XjSVDEabWrDsWWnFP5hO2k5nPMAq6EKLG+OuXLG/dA/CNxcGzDt8Uqj+HBWYGE4iigK73dFe070hWnlToMeU79ktmKxXieR52ycOdVyqV1dVV27bRm1R5uSJFR9cv18WisrjG5SdLY5ol/j7eDIOgezDV2piagQz9zF4cuiuHVcgw8uo6WFZxOWVIyPq4JVlT11v3ojsRmpNwglUOKNjIMdmHg75OyvNIDzdVgKHuSpAWCCNKLGVMxlLABQD04re/pqApGdPR0XPrOI7jOP3IIx0VH0kTywofSL0W13VxiIoLdBAEmJDSaDRM08wvR+f3xQo0uPj+YAqCvvmVyApSyBpjlIukHdVwvCXzLDAAm25jzm3/BNQ/Bg4ArAkNrm/6/scV5ROWtmhZc4reudVxr7rBzaB6vkrB3ljMggrwIlnWomnNHrsR7kO41xD9HknExy1WFhU/U79cxyVK1NCwSkROSwCRfIWwZE4a7iSnJUAhdCviOMaJGHplGG3ceZZ7YVQKq4YcWFagP9ETGGCGy20ZVSs/kmHBNA8WyVNO5KCMSPbaIfvrnYH9Fb2CWEQuNYFnq0m1v5Luqs6r0Vcj13U9z5O0IAyYLKMWlvEQYtNCvGlkScHbpR3VpK64qYilOKgrGjnD/Ws+tTCGjCeLTSYnvxaphQat+igHiPKH7/sYyRxFka7r1DGiDFkK4TZTZqiL3k76Ijpd1XlVklrwuqR3ofAo2ACQomfpi1hqJekjSiV/egGAw3qv7bi0HcOxMCdNrLdMXdRBrLf8q3aWEwA7TBZqa9HdCJNwMA5Z3F7+fUmtGxFFsLICUYRWyOKhjrOHKJjiSqce7k0mrUcK1pQoiur1uuu6ogkA+j2BDMNoNBrRYoSip5S6CQB4H155Q++fmSH6b1UwRFzKVKTPlFlwU703YQieB5oGug7WL6dPQWRhsZYt53U2ZCiEloWBlPlnUZblznJ0J6LrxYhcVIBbj7RojgIYsg1RUi7FYKOHOalERVGe/AMA2B26VqthPn+WElV4IYXxOOioRJMlJpvgf6k9NTn2221Q1Z4mhuDlaBq4kL4OYrVSnHZEs4WmaXghAGDbdpkLKUP+cgwA2lFNGmNoF+t13bwVtk62xkjokKCGEJLOdqVave5585r22heaqy+tRnei9mNtcQIPbgarL64qB5X2o21RdxWTlfBmWstW8CcKPpdGoxf2j1NfYZz/HoAVwr2GGEEukl/umSizqAOAWAMDe+sFNwKqr7V6tOd/m0QoFFvzpZbP0jSNMqaoBQL2YcP0P13Xac2m0s/QV26xZVPhUabS1zW1wU4YwsoKqCo0GqXsr+WPgjsnAY5cQNtTGks8DTF0bVBpo0TiDK1AoqiNRkTsRVso0hVSJgF9B1UaEYpbJl2XmrnhUJ+fn//Qhz6EHx677S9SFyqvon8YALBlHEkwtt1TKfFFEYOL8o1BqqoaHzWybMkoWK9/vhfCJMl84lEmh8pjwrC3U1TVCj2Ek/jMickNcFmTNkk5pBDmdFEnhVBcIkjZaLfB8wqDOcteMgrTpN9ikiF2mUttAia2Wf8zIaIsRyEMbgRiJCfRqxsxjfDvfAvdEV3/Vv888zV59JjBcKYiZt2XaZOdqkIHAayugqLk1RsTSV2hqtVeZEenM8KkTe81qQQg9E3BKsS92G/h3fev+dRV3LbBNCEIeq8/9P3JCCmEqERJebnr6/6HPlScCJCvRGGTevwktY8X14Jms3mjXxw49eFitbDiW1ZEyXWQzr/VamFdNNJ1Sf+hyZYKOuj36ADwieVP3I5vo0VJOaigmoQ1VKmRsrfulTQlTwKeOf6O8qq4bOm6/jNRNKpCSDURK+cqqBBiK0ip7ByuBZhDG/1njUoYYLWwpKK+V6uMskK4XyCf2Eh5U/mINTDKmLrHJtU8j6Bk7HkeWuBIFcTTw9lBsk5RLyMpPjbnKDkkJ9zoboSxuACg36Obx83qkgEJDyF2rzbNzDJfIiSLx3EvnAznKbTp4iSFq2mzCYYxWE1RnkYXEMbJ5qhq/v0uxS+Fm2F4K4T+soHYD9iFqSAUmigu6nQaUE6gr1+uoxovSsmigFu8i9mAHlxOhkaWQwMA1MOqdqRXT5WGOjad13WdhnqpMxmuk64d0VAIIDuO8rJSrVaDIJB0Ocy5tSxL1228lm63t1JSdV8cgWW0/aTLVLIlf+q54OM/dAaGDecoF5Y/CknJSconstZqNVS5xXBEjNFC9wK8GbBtj3ZUw9Ro1GmpJVphISVRlxPjgUUDHMk0Up10zF5T51UKtMtx7Ur193EkeOsevuPWsmUeN/Mz4vx/rOcohLY9uKXkXoN+LV/JU1FGIZRkvqu12obrHlLV/9JoFNpxlP+T4q157lVXO6rhlBXdjbDANbZVKBfngnuTnWae18vwPLaS5yMqn1Q5ITn5XZoGpgmqOmTHIVvh0BTUj6ARnWZkG8JEzcIzIe1F6kqCD2JyE14Z8iOkxMU9R4kSoXYOo8pLhbESoyax50MeTsnzSfOt5MfudS26EYjS0dgSHSpUvQXliKYd1aI7EU5TPa1yWNYqJFUYS7UW3fT9A4oyr2HIqILjja6XVFYAsJYta9miEujJu6HOq9ayFf3nSIwolppvYZvQ8heyu2CFcL+QVAhTPQmappHpK8kRXT/eaBSavmiJEqdBdL5jEMuoEYips0NO2DoaID9wzwfe9833RXci+3U2OTZ7MSo3AkxjUH9fndBDmFQIabsUkCPZX2u1XiGNdrs4CIH0CtPsmUVNE0xztOiFkVS1rIji7SE1BI4cIFIywCRHSSbw0IGmcpTUuCBJvfGUAodG66ViO06qowltyVIpv+2x45AgJQkoOAhbrV6CnOeB5/VkU13HqLACz61pmoXiaarbBDeiTx7eNRhgZDKnMjnQH2P14U6VqA1icRFN01D4kPxy2OwBk/eSOVFjkAxMhf4sJ/q7cly7kKHehGEvU9GyoHr/SjIjDvrzGABo/6yVH8yJWdVBENRqNazDbBgG/k5pVIUXW+ghLCwQOji3u5F71XWvutgjDh9H+Tc6RyHEKMp8D+FBVf0L1Uz1qIuxZ9iSBzCduG9PxPVLem2zyGrOtrzcey7/6B8BRg+goy/LjoMPDqvRGIaBKv1I5lEslhPcDAZtVO9GGDpkP2CL7wi+R7quY6b6IG7zZiBZWNAIgsMef3ciB4c99vpDuR/1DeWgEv3nqLyWlaVEDd/eSRVCCl9Ksg3C2FRITto4hGjSFs+idrGGBvGRSosnjjiOdT4HfK3UeRV7PmNdAByW/oaP/VEwaEI857Gf/i6FFcL9Ar5g9Lhp2QYA0zTFGiqFkTD/VJMt1giFi7g/ryaXKIyQBADTLBXEUhjMqf+YnupqQGMVhq3j+580CKnzqnvFRYuRe9X1N3yyn9HEUVIfEH13AOB5vU4JtNZOJcJNOgpJ0lEEhtGTWpw7QoRbv8cxSZB4ReWPOLZCiEFB7hWXFnUx4qWkpRCdFVICumjGG6MIngTpRckEHhQ4ysRrbQ+FtuSppG+VB0sv4uupKApqR+IHyLdmGIbkecDZBZs94GtiWWAYA3/1VAznhWqnKMHkD3UUXjVNwxLB2Dm9fFJlIah3Iamvbe0+k64F7TjStQCE+a5d27ZTYwIxsBDLg6nvCfMz4srMA47jYA0qqYsPDgasByYWNsNalFjVrIxDFYZnQlRosZ9H1nw79jyWesdIy8L+tyVPVfKoQ18nfOBt4du+NTQFQb/WC05NUipUKoV66Ui+SpSAsQAbPq/y34X+vC1uwZVXXH1wcsBUPWyPQT7kpIUF7RFkYSE/NgzX4EGDmrFo6F/RJ589xLybrJBRF1xvzQs3Q+pBL7pGpzXZFgpjRwSVMsMPKcsnydii1BRBECQ6rDmEduSMSbv4KIXU63Wq8ZMaMhpFVtLTQKYW9DSgoKsoysBHGkVBEGBADc1LKKV4ax42IURVEJ+dVHOIPYTMHkRUCMkwZtu253koSTRKp4AUxrWTLTnVQ4iurRlh9aXV4EZALkRETEEulFFoKiQZ1zDkOjE0rafW/zxqGGXW7Jx6dPqCTtMZIkWI0Ycpkgo1BLTppkZSjS1I4YItJW3iom4sGVPR5aZFagIPWgS8NW9aCmFh+Gt+0JdhgKZNwWmWqqiTBFA+pEdMvwEAqnFPJ4YCH51qGIYY5oo6oaSqodyAYXiahm9N3sWWuVIQ1E6Mr05KMEQURbVaDUvUStGhBJXyk8NHBfE31c046vhJfW3JVQVQcC15e07zd6FFqWTUOgjvi5QABqO3U++d1bNzMDzDFJoDxPkWH25yvi18oaYi0dFI1rTeMA6CnqKOP2UekOQhDDdDDOoGAGPJaDzUKHwraXHJqY6YOgWJeapZLTTwxLC3TeG1oO9FOoo4PKIowlZ+4nuN84M4geRQmPj95je/+ad/+qfx9yzhFu/YoeE0v80gmNe0I7q+YJqFjSsgbZlDp324GeJiikEuOYkA+Bn6blaQQv5plAhMHcQW4RBNxhYVSnT+yRb0s1EyJu3BUWhWGTWCqZDU0DPq1oOehmQxQmrLqSiKVOM9db6d+qy+i+C2E/sO0gY1TcNUEyxZAcO9FnKcAPVlC8Cy+hZrz4Mg0HEhbGkNTQNK3pHiIJaXIQjAdUtJMyi/4oQrLVElk0CgSBNbTsRphJuht+6Np7FggzI04dfrPc0QZ8xj2d/Cfs2FOw83Q7SmS+4s7YiGsftUWjoH7C6FTtHaxRpmkRlLRmpox3jN/QBgJD/kzpJXtOPENI8CiZrsYppNTn9qlF+PrVReAYAcJ0AJHQnbAIreJ+wa31PUDxYr6lLpRVSiDMNAg3oQBI1Ggz6QbC5cqVRarVb7Bjhdp3Yz0C/qyr0KfBiUD4P+fw/DzRDmVX3Ziv7z4IgTxguZJuh6L8wvCKBe7xmS8b8gyAroW3McB08SVb6B7+7DAN8HAKDcozh3HLgMkHC5Y6NI2DJxAUv/51xLGaReQeg7dRwwjF6CcUuY1THJeeCH/DZ5CqKUod7ehqMwyjBJODrOt3hDHKenGUoabxJ8ocqQqkRB35M5aLDuAMCQ24QQ496zJP6UKegEgNC3s9S5AgDAWloxuSV70PpCesFR4sclNb+FRnBDbhWQSlbzYeKQ4/yO7wPAorhVVX/Lsg4qCpSQxcWu4lmTw0/91E8VnioAHDWMJWEPcRRdrdVeq9eve97BVgunBcMwmkLjUTGp2FSKKj9v+NLjowtB31TzRJMe/VbPHgC9sCx8tWu1odgiNIl6656xaFC1GHS6oq08uBE4Xad+M/CFSVv9hB8dVMJ51XzADv5EoaM0GulHKRT5CltoAPTuuSi4UZiD+OJTgiiiqip6CKUjps4/2zmrzxrsIdwvoOGk3W7j2yXZk6rVKlbwR51QdAJg21+M60CbHBmbyVkvLYT5ati8pi2VkPNWXpBzWnCNLB9Lk38mS7Y9r2kYayda8tBeixGD5dtAlTyNSVrcFNajc/rdC8oUd011ANJG9bBqKZYoHJT3Fu4WA5tU4T0ZrVeyxmMhheGv3i9q+QUe8zvRlXmbRLKy+6ZCYQCksWQki7xBWmjZJAQ3g6za4liFRZ1XRcsxygq+7wdBkOonTLUlSxc+Yc4tyUOKorTb7ZJVYacFTlG9pFYv3Q+JU5BkpMPtUnWKQvDmK4oiZZEVuoAmzCCFcjnbUCKNGePoUIWGfpic7w8U9SGnZcbwwHGFLiN9QTeWDNx/4Xgj8qMKAeDZTxo0J4tTkBikIJXiEK+3fFXnSqWSX8w8q2JQKql3LH940NjAgYpFqgAAi2dStxW8Y4uWlVQIsVjlA+02BQ6QUITaINVqKpzotscwSh5CTdNS49hbrVaqaQN70ugLuqVYqUOdajE0HmykVuZEg8K0ku17Z5Vb/bUwXbbVguVl2UMokZUbnNpyc2crKWw/rBDuNbIa01NHvsKvF/ZfWl5WoL8QooEWQwhoIST5dRL9R/IQojcDpyTKBi7cydiaGGUSlvlwTkCOdlQzFo2paMgYkBPdicSaYJjDgCE9hdEjqVIyxb1YyxYm9ANA46GGEzkAgGrh2NPiLppPy0tgY0OxlJqmtbEERzkKZb7yOiGeA3axpyJ709UJidRbSmlCMFzIRxRPC4d64aEpqVLajvHS2hHNOmihNii3FnQc7Fsj6YRbPTzQiaooiq7rnueJJ5D62o4qjaXOUajeKAcVS7HEUF6qY4wRqqP6IQtBbRAA2u02xhCONAizakFBP75xKlY8vGPiIIQJ0pjzFUKq8uqte/qCbiwa6Iyd+ngbewoqQ3mFMDmPJfvoFiqEWUdxr7q1CzXloNJ4qIGjPbgZ1C7UghsBltEqHzJKsyUA4C+pmTWpkwNN2qkcbzRe1fX8ZkK6rk8lVzlL38OUAUrLzCu8eSfKr8ypHdGSMwwWjtaOaGLj00JEJRAzOae7QuUohNNtubkb4ZDRvYbkKx8VTCXPzycU52FsnCC54s/3f+mmnUkyUDOV1aMaQENVB/F0GkAYQje6u/Zj6/Cpe2GU9zb1TJZs+7/rz8ii/Od5HlZvF0MXcsgJyEGRdBLFmMDSWOrhwYyMyWDRnahkSA8qqJL4iP9U51VsxYFgjnX9cr1+ue5EzrTsf/sZWukVRfF9v1ar0VuWKvGjMIrZnuqUzHYkDoZhiMU/cLrYOp0wCfZ6yv/M5EM9uhMlS+yA4FpHL1zyi5ZlbX/NgDAMRUP46uoqRuHiCEl9bdFGhiXyyvgiUucoBBOf2jeiQSjvAQXeC8p7Qf2FILoTBfOq8YDt+yrVjaC2N1E0Wt0IELRBXddxEGqaRoOwMMzyB+/5wdTgeegXv4nuRMaSkeMfLmlMLBylWYo66tiiXhoEgV/zIQAAqJm11OUVT8x+wHavuqL3qRBJkydHJYXqkdaDUxCqGeiKoTNB7eWIrs8LT3EzCO5GESY1zI8yPyQ7Iauqmt8kfcN1JatWes7Cu8C61BsMlZcrqVG4ePdIGwQA7YjWOtlaPrvsXnFHmk7x/uCcOVKdBZFUK96dMFRVFaOuHMfBTjbYTIgqA5eBQpywmituxLb1WFkguBlQF8Ehde5OhOmdUCLLo/JyT3yiV1I9rGLd417dsiNacoZRDig4CQc3gvIKIQpjlUqFuvsI4QNDpWswMBUrbOFo//+39zY9ciNpnqe5VEpplKlVWkxWJrIqUd1Nb1RjTnNgfAQ6sMCe6R+BDuxlD7sA/bZXcrD3BYk5zaUBJzBz2L3MkB/BidlrJ8qZlQW1crKURUqllw5JLXEPj8cTJr45/Y2kh/9/CFQpGR6kuZE0s789b5qWzWazLMuuP1/MTGPbthCGKDnPT6dCCOG6FGl5yHIgpwUE4W2D9pjLxzlvUjNtduCa3bHUQbyl9mvANMvF3O/843/8n/7937dKDLBRiTmOE8dxHMeu65KhgHxFhBAtc6xR2GHyPinuJSsDLo3a8VVM1SnUz7Q3eoS/DbmCoqCw7w/r0m2UJmvjGXiVLD7dWefywQWcrxz7wnZTV3WtbE8cx/7EF5kQQgSLYGMBj1sMlQGgWFwp5Xg85m1g0SDU7xUNIPvALk910X2dacKN7P+ot5GdW3E8Ezdl2qDc90EQUGSmEML3fU3TbNum1R671PIf1r22lVAVysIYRcepWlf7h3A6FVKKIBCmuUveiDopTqh+2nUBPOF3YfAqoHW/mguKykuImheKqxRsG5tXR+VVhLIVuK4Dfr1Fu1wuySIkrpVGFEViIoQQrnCdfP0daTNu28ZEkfB9EQTrCA4l8G0ND0Ge59GelDoEEWVfFSr/+C6Kfr1abWxDnZMe88hxWvoycOSYEGKxWPDc0ebxqDibMu1mHzOyZdF/VpbbVf1j6/bHVR2e/jonZXIRr+OWdV08vVpeL5OySn8cem+oUOfOwdKTJ5P4KrYv7IJP7/yX+eznmf/CX/5u2VxbqE3ohHpyMoeaX5hqt2cf16udwh7cblvJuq4Xsr8UmM9FEIgkEbYtwlBNGS3VmE/ymTJNs9y9nKOLNrakFI5zmyvOtwSC8LZB+yvl46NGB4at2Djdcg6VSruc2EYoUgYFhtKLW1YrNchVBwl1Z4uOmF+YlrTCMLy8vKQSXqZp0uLMtu3mHU1Gu6dtLObGozZVGN8BrnZYWNJRnhjz0Sdaiyb+whl0XXecDXliXs7n/5srhBCpcXPj/vck+T+kRkXAxO9bN/h6mWtZFldvG47kIGhuE0LYF7a64j+sfyCVeBFCSCn5H7TvoOu6ruuVd1YIQdu67Rf9zVSubHRdP94maKWI2hj0svivi/aPeh0HCWQt2158f10CtGWahDZQPp5KoU7Oq9PJdH1UF5m8eRikkEKIWMSixWi68ftSpsE2DyGtuDxP+L5w3U+yZ7XKq3m957jzg7cxFxR9kXKSm8O+UGSZae4xFjaappHdjKpZik9TuO0P5XUkoe6662QefDs2DkH0sfdxrGameR/HQoiHltWycEWDR+h9w9iigOT13MHWs63mDucrZ/bzbPbzTAhBWVKmP00przLpHHX7lR1TVc3TvvIN1YAhHW5Zn2RS0D5u8MehpG7ltL2kqSjYpOVXZmjrufCf7F9dqX4LlljqjW2TBgcvAzbsq1ehbo/fxoUQ+ko2xrwwmiYcR/i+8H2RZcK2xbYB1/P5Oli6nALjnIEgBFtDVd0bpttDebiJkoVwMhFRJFy3lSDU7+sbd7bEdQ5VclGgUEkqo9yyherkUTmKOY4jfi0ExSVe75+RDwYNnW1C9uUduXGA3rjA2rgz+p/FesguKPZ3UfSXyeSN78vF4kG7sXMymSRJQm42lL9xNpsdtoDbAXFTN3oTed94By+JQasuWtyUY9Umk8lqtXJ/7Yiq/PWmLnRdaLoQ9w/bqCOyscJK85+HYUirByGEvCsLHkri2i1wownlgJni2PZCKQcOvnRoL9Qdx2nvS1ZHXUohW9pq6Z0KDOGkbvQmulhdO6f9LyL7nzMpRHJPyx6Zcvv1a0XzPs1rxXsKakRZ8zP2yHEM/TDpoJrZ2GOsBssp3EgTHtBpgjQ5lWIjoT6fr0247EfXMAQdJKihgfZqUFzPHdzabecOiqolG/L06XozhUYPeUeW3cj3IUludosIvqUb/XHKmcPJX3ptWL5ax/g1NyD8LqSNiYvVBb2V5FdP/vaUAqCcHK5NiqCt6OYqN5ezhBDCNNdZ68djYRi0T/eJyyhBldXo3+wy6jifBEvPZuvXp7DtUBl+5TjO0La2DwUEIailHAkghDAMw/1362yWdQ7lwf8Z0JqjkoYEUBuhGYGzmx4KKpo8nU5d1zUM44B7tzR2hB9D2kK7/NPlTRTTHWk8NIIPQfMZiDayc+MgpS6IK0ftl2KL2JUGSFoLJThKXO9SL8rOTH1jX9jyrnRT9/LHy4NncWgfq9aQv/52sK6wMpvRf9bJm5b7OJ3Bxhb/eue9r1tTafnfaizlvPmmaZIYYE3YxqYaX8UUqsQhQ+I6exaZArb9Oup/GoahPg/aPW328yx4FVDF1JbnfGCa95Ral/tUCNzc/o15JnWr0vONx0A1jqNutt24BVCQclKuc2OoxzYOQdxjUoooWhfUpbjQg/TYG99vaWbkuUPNhlA5dzQURqq0IZO75gHVoLgubGNZIkmE74vZTNXhm73Wyfl59udZIW0vvYBUFL75DPKOrNuYKMSntGTI6d8Mo/i06/q60MU1cuMCo5ACwzSFaYpPV3zGuYUO3pCD8+BIt1v8kxD/JOxndt0HeMo5+KW3ZWNTd0NdkNVeWuRC5GHIf5KbZi5Ebhj5YrH1VYwSy+Vy22bbz2zqEPXgX237qRBPhfiLYfDPnzXtma4/t6y3/AUaITlNRWD5YJqmFNht2wfu/31QH4n0Q0p9ov+oH+lRqW3Gp4/HYpFbVi5lbpq55+Xb39s+oefnadO7sCZs9zh1fC8+ufSn92W1ym07lzLXtNy289Wqs2Zs12N1sA88KQFSGrRhUfhk5eCwercKX4fh6zD9kPK/Fy8XxhND/JMwnhj7tK3y0surpfWzJf5JmE9N+gcdb/GMrUdXw8ilzHW97TC7Mzs/pdSBxhPDfmbTj/nU1H/Uzadm+LrV7baf2XQG44mh/aBRS/hIy/tCPabruW3nmtZFj1Wy1dzBzzOZ0xl+PNSfvxjGK8ehD/Azpv+oL69uHn4+3mZOL7SZmr3qbFAosVqteLeobqCofK8Pzs5X2TjQqXez/PNX286vn2RNW7/+9KNp60Gbmsc//LLwEe+5t1wuqSed6wcmz/M0TWnjzLKsHfvlFICFEAwUtQjszvk/VSq3vjZ6uLXc12wJ+btS1M1qtbXjO7GzfXUj7YP+G6CSBoWDNFnueeajQk651mNr9udZx5cubEdW7VmeHpXxw/d03X5mk0eoK9zZH2fNGR32py47RRsK94UCV/Z+P/ph/5RC8q6MXkSUWf4mRceHLPuYmY/MlrGdW6Hf172vPVva/gu/Mq9V5TP2wDSpgDWXFKPEm6pX2HAcvjihLtuIKHVQ/DaO3kRtTKMb/T7aQ350jrP2xNu2x8iOlyQJ58lkK1/LzJnt5w7KmErbzeRTWvDreWhZ/+b6ZX8fx69c920UvY/jLxULUvYxI6+QhoLDzU6DHJxpmiYtTvoKiyBXSY7YZ1VzWJIkuby8pOG0LtKvwXLbkkpXCCHEf2r8K3XpUpWMULiuME3h6JsCJnURhuFsNqNmWJbFFWsLTte3kL4VKeiII93u41kIy1syvAfmeV7LkzipYzwx5B+kumOq/aAZTwwndfIWm80HgXet6B+mubV5Yas9y53p5irDYeNuYjeoDye9UPSIsumgu6YcgvB1aP1s6T/q+o+68cTQf9S1HzTzqbl4WTQ6bNxL5tHDMHYxQLFtgVaZldawBvhd2NM0d3J0Y0nYnzobUceoi+/FTpY1MreGr0MndexnNptDy69MM8czzrQkTVPac/G8taVF1/WD283ILZByUeZ5TusEXhXQhP7XT0fwt2FIx/9lseBeYq8Q7QctfB3ycdteTw3lH8NQXQZuFiT9WpBIpWialuc5ibQjeeLwyelyqiGX4H7YoQ113U7OMstlq9Va3Zyu63n50W5YvvIX4Wzz7Zedp8vQR3xwKLoUhM2FvNqv7Xg9R7PsDiOd+dQkNWg/s3nSVb1EGpwQ/truQt5zr9ljZ3m1pEGKzud5a2VoWVvIQp6r6n5a+hcRde4l5ykIC/d5tVof78VXs0cPyYNAD6r51Ew/3KwVVu9W5lPTeGIUFrjN69eCJVx14GmJukSjQYkXkW0YiCA8iE/g/rQZ6A5yIR6T/2rb5R/+GC2CSXIUFuJd7rDwJEVCaNtNB9qy1H/UrZ8t9luzfraonepL1IbeBeG6GbZNXXEMdUQ6k8prMTSdUc9XCkL1eKGXVu9W9JzwU83KRCUMb/RJ/qnyoTbwf+4wUu0D70fQveNhs3wrd5ZqBL1xtH6rW4zRW2DbNr0UW4mo5m43zQ0uo38xKPavOKenaS7l+gxbod7ic1CDOQTh7aPZn7CbNvA0XPfT/lQ8+tNm8FYTTPoh3Rj0clgLYd18XAhGyneVhc1XaQ+N5usieMo+33kKwpa7iR016cQFYXl7Ql2dF3eO6p9kNXCI15dbmRrYfMSrRvpPs/W6YCCCUKUD210be9dRm9FmTCYbEakvbnDlou3YL5QqR+mR22rTYSAM8FGvhNfoBdGlBh+2txB+cobnnvyD3CgIpczpynSvC7vbnLyny26sc/YuWPBoFLUsSxXP7WkjO3nTjT+w1f5IQ7drWh6GrbbFKzd5SRDq+idrrf0N+7ePs1j2dYPnebzm0DTNsqwdPCXSNKXz8JO67am6FITdxDFz2qht3cbSD2nZZbTgwNYmTLk9Aw/aJmifj0btwj5fc+qa3bLXDJkD7iYerEknLgg5Mc/q3c2QlX5IzadmOfVIw5PMa2v6T1r0tB8BDpLZiN8FXdfL70Iv6ZGOPcJQF1ElHtqJq3zluxGEZfPgv65WubKSYzORdh2NXW7tUV8o1Wd1t00Hgj0td9OTq3crMiOTkzbPdBwZsRF+1C3LskuclnmEHh7KgkY/rxznf0j5VIis3X1p6TI6BHizrHmgI6VKH1NlW3s2yk6O4eR3gV7SSs/Smu9S3eeVERyVQ1CapnTRwuBc7iW1i7YVrrcYCMIDkKZpZXCtlHKrkXS5XGr1aUb2HJSPIQjpW9NIUffaDzwcpXnDaVt3rLove1gXpn26tL17yTnQfjcRtKccQ6j/qNvPbNqXaXOGhtThXbpj8ThQeEN7FO3HHk7VaJlCpFZnzWh2Gf3lv/93amRhzccepIXWHu9mHSqdshqtROfcShOu3q3ItMVmedW594CuvKdCXZbR19crKB5Manexq0xVy+WNJhwODXF3lpXTQ8QPGD2Tdaqp8SobZGdhXcHQNkfLjbzKbq88mNcPQbykYYHHGYlUM6DaITtEE9xWBrpMPy14b880zcViEYah4zhsOmu58cBvF4krx3HCMAzD0LZtPtU+mvDgglCNY66coYmdlw7NtrvX7bpiq6wh+y8d2nzZbq5Sx8Z9vsrNZrKpes9PaZ94K1iBlBeUXXJuTiyVMS116xVe8XeW253eMlptqxnqb6sgZKssW0HpBpXtXcdrBocMEcvlTSEEx9k6rLeND0u/8GK9YO1svzxlQVgYn8PXYUEoNjXjQ0qbOOZTk0MZt7IxnhC7CcL8eq9Q0/LhCIeNcXd5Y06HQZnFKr8LFegSolgHpWEIou/LKpTDGvkD+0cT3FYgCPeFNyQKyxde27XeHVm/tOUwOdVDZufV6mEFITeJlq0Nhqb9BeE+7dwqa8gBpFr9TTzkVXbt0o37fOXN5vVnrqMxd27zkGEFQv/b16zA3nocNzuo2frg0NPIG14D/LL8qi5eLijhBL22p+7WWwnZpsqOHjw4dONPzmtZx8l1fT1677wD0MaHpUcKphuGvfrba0LaxePgCPJAIT/tNuP24uWCRn5VEFLAhf3MVt2/TwKOFmEjP6XtoS+yURCeEM2C0LZr12Ykk/pNz7sPzfHnbHKnr6++SidUJ7l7IAj3hd4rMpQVYKfqNjMlbxNWzgF8qp3tBocVhC3jmPPjWAjbh/YdKgdxG1hXEHV2jCGvJsubzex9VE4WcmtgtxZOCdCLda6lt97AoUzuhcBdzvTLWRP3j2npAPVV5Qz1+o96X6+wOsJ0r5+7qnxzIwg9b11ZfofMW3lrH5Z+aXaEedftXV5eLWnjz3vuqdUvTss3ZHm1pFnMSR0acPjllX+Qy6tlS0FIkpKDO+gfQ+uKhri7bnOdHgYKN9B+0NSSRdbPVnnh0byw5PmFZN4AdxuHCQThvtDIUjnTpGna8NvK8zSYE9ufquHPd/vbAhsNTX+17bIT/581LTPNt6335I5kIWzIGrLnOo8dFYzrHJ7VTerEDrkz5c1m+mm/2Xxa8H4hCfjKZ7sDyuWYiZNzYuF5evFywfJPzfebHyKmpRvKrypnqO9FEHJa4OaZ4kg0WwgPteQquIzSEctax26136hp78PSL/tPc7wFo8all7dgmqHk24eqftEv9IaWX09SuYuXN/HJDQ8D7ftUZkXWfxzQ1lVD3F2VkWLQUPcaTwz1eUs/pOoNpR1qzgdL6Rgq41m4gnzHVUBOGgjCvdg4zZA2aDN5UzqvhhFqOIJwIywI+cjHNH3teZTpq2X430Hyf26VNWSfdR4vainys2AkDF+H5lOT02zQ5E0BG9tKrIJ/Y2fxVLcSNcSoRyV2m5xY0g+p99yjpSTP3PTY88ryVGJaBmW9oVGFXDDU6qwdN6DLeWQf2vuw9EvDM9ZyolS3YNi3k1QiVd89ZvOHSNmZpZAorm7wuRGKr8M669PQPGWa4+5Oi7q+5dvhpE7hM+QbzL/Na1IK8U97g8R5cmqPzMBgT866dTnv6R7qQjtvcPYrCNXj/4MKu27iZnQ+XKlcURLnh7pKIY65YCSkAasg/xYvFzRn73AhSg4ujmAkJO3KPhusXSvdNk6dyv2afh1HTx3ahi8kolBn9BOKaTlskdI9oWdVtXftP61sxQkJwpa5+IfAQZ4xeul4uCbfSOOJcSsH7Y3UWQjZAJW3MDQVLISLlwuaBOmTwzGZVgpCsqgLkZ9UuZCihZCKFZlPTbqh5lNzebWk+0I3iyNdw9chb4UMatA+OUa5siYG2zKfz13XFULUdePGD7Qhy7LxeJxlma7rvFrlM29FN7f75Xz+ynWFEN9+ejk6fkfKb66daRsYjUb0jzAMK6t6tCeKoslkQok6kiRxHIf14UGuwjeo/KvVaqVpWvI+8V/4yfsk+5gJIeKrOPuQ6Q90eVcaDw3rsSXvVNup6i5EDlHj8ZgvsVvLy4y+HwkhzEemfWHr99e77PHbOPuQybuSjwBQCT3q8ds4vor1B9fPz1WsP9CNh4Yta22DA+Sn68HhfmlkeGCaD2u8fI+B7/uz2ax83LZt9owCp0jDMybaPWbBqyC+ioUQ9oXN80j2MZv9PAteBvKuTMebZ9vbRPw2njyZZB8y+8K2HlvavfX866auEML43DAeGvNncyFE+F1oPDSEENnHzH/h00Hn1w4NU27mRm+i5H2i3dOEEPFVrN3T4qvYvrDVrgYHJHoTBa+C6E0k70h5VwohoteR9pmWvEu8bzzrscUfE0Ik7xNaUwUvg+xDZn1peV97/EKV+cK2H2G0bORXfTcAbCCKoul0mmUZVTXsuzk7kmfZvwTBG98XQnT8TmZZRmspqgUynU5d1zVN84Aiaj6fZ1lGtar5oOu6URS5ruv8347/whdCmI9M8wuTPxC9iSZPJtHrSAjRcqHsui5diNPBB0Hguu4BH4zl3yyDl0H8Np7/Ms8+ZPFVLO9K/YEu70gejm8NyfvEzdz4KqYVFWF8buj3dUz5u6Hd05yvbtuke6EYx7ony7L5fC6E8DyPR60kSWazme/7al2izphMJlEUCSHUzTWwA9/uvUVrPDSS90n0Jhr/MOYtGJIx1pfW7Ru0N6Lf11d/t3JTN34bj38Y00Hjc8P7xpv/MhdCGA8N/YEeX8WzP8/0+zrpPXlXht+F2j1Nu6fNf5mTeiSSdwn9I/4QCyHc1DUeGqQke2fyz+slRB3570/G5EOb0WWo///x5T9mHzOSgtZji5dSyfsk+5AFL4PgZeB9fbMQ2v/NOkf6NVCeOjwX7vyBBlSb1f456Lu83QdJKsOP6J7OnFxci/6z4CW4/1Uqy57miofVarXiGEI17p/+3b7KUyGLqcqhIgmXV0tyw1BrGZPnBrkhHeQqw4Ezrd9kv1ScUvptG+idg5RC3Z+6LKzNyauOBxeoEH3X7QSgTLnmBDnQlvOOFFKq0nRg/fzJC3XUyp/7w4lhKT8nh9jxT98N3JHKhA58Z1VfX/Iy5eIocBndB/TaZhqE9JEE4Wq1UrMOWpa1/6R7cvr/IIKQC46XodxTh5KdHUCPhGEYocLBF4Xec68uhlBVibcD+5lNUwvNKByTcJ7JGADYiJo9q1zxGXRPs4W240DTIUBSUPtB8557JIpY1JHYI1FhPDEWL2/2cFfvVpX7ngMXhCon1NSN7JzhrzmpTPuKZefJbXh0jk2DIDxGUhl1fDcM41DGny4FIRvHCkaz9jlX803m/jYnqUtnT1E3VGlg/6tsBffMtvN0GzvkQVt6RqzerXjdYD+zyUJIFav6bhrok9W7lfWzxYUH1QrXZ/ts0PRUqBeCwadH0jTl/UHeR+YjZ3hrOIWbmvqFRWDeztDEnJDKOqGmbqRBEHKtQvXGUc3JE0p8NUzQa3txwLITeZ6naco+omQIOmBTO35JaN2g5vguFHzbSOUGzzNdf25Z/3qyk5yat8YbdgqwurystwYqcFReBCxeLtQ01uBsKXsUc5VO8+mJ1Yc8CHU7aLd1iDg59glRuTV4zz1WehSXQRkp1cqo7TkhlXVCTd1InSAkt1j9R52tu2za1X/UIQj3BL22L/Tw1bnNNP+2AJdOOkYlze5fksra0+1T+ZdN/P+6WnHhijZFwLjKXKWFkDa2K2Un1Tn8eOjYGB6tDlWM5HhQUykv65EeyCFAe40FE1BdoVtwblR6FC9eLs7WnZj2s6heLsMepH23DkAQ3sDBgbSDw8GE6t6fWoe27gE+IZV1Qk3dSKUgXF4t6XhZ2JOPz3/5b/8FgnAf0Gv70lA+iwPY2uSD4aH8SIaj7l8SKo1AX3+Honl1Pt+ZaZKpsM1JKgtSUToEakz5Ku+WS77EYTUhx/uxPB6mkZALdjmOs61dF4BbBjyKCd7PKgS0szNIXw0DDARh+iGlt7Xg+Zl+SMsag2fkgjeTygmprBNq6kaaBSHfXP7K9PMf/t//AEG4D+i1feEwwsLiPk1TMpG1nClVlXIMenlJyBbH/bBVapw6QVhX9b6SsvRihUONqbzKxzSl468OZxljmxtdd8irqOa8rACcA5Rcl2yDqvaDRzEYLBCE+XVNefkHSSXLw9fh4uWCE8bwu8yLk1wxfffacLCZSpdR77kHQXgQUIdwXyzLCoIgiqLZbJZlmWVZUso4jmezWRzHQohyjTguhp5f67Q4jpMkEUIkSUIlpxou11xAL47j5jN0iW3bcRwHQSCEWCwWB6mX9THLhBD39FYV0qWUlmW5ruu6Lukc3/eFEHSb9m9Me1zXFUJkWXZxccEHkyTxfd/qsML1RoIgoC6K43ik1HilmoqoOQbOBHlHOl85bubGb2O1KJn2mWY+Mo2HxhlWeAMDJMsyWmnQv+kfVCVSCKFp2gEr7p4E4Xeh/8KP3kRu6mYf1h2iP9C5Tr0QIo5jmpFJFtq2TROcYRhqnL8QYj6f0yctyxpgIeiGOuziZEuxZ1k2Ho/pYV6tVuoD7H3tmV+Y/gt//st8+nQqhJB3pfHQsL60/Od+by2+NfStSG8DbAwsU+kTWO789gPNxlCNsLGG8oG/eW0bciFy9WqLRa5puRC5ZeUtfQ/3jyEkVCNhwUxXeZVjuIyyWVKNvRmgkbBNXta+2gYAAKAAyk7sQN2CreDHVKj9O8DpjxMfvA3D8s+Jpt9TH+mtnOaaV7/lhQ0oAEF4MBzH4VFG0zTLsuqGj7I8a2942SgI1STUKoUrHhVVEIZhbhi5ELlhtJWCxAGzjJIe03WdNv/UFCndJJWhLa7CYDTwSEIAAAADh1MVFNYGlN0AgrBMZWYBjpxXp2n6JJsNjxfRszMNgrD9pvmgUHelG3Q4G3KxfDogEITnQi+CkKSgEHnr3KLHgr0OMEECAAC4NZTDvLlAyBkul2lfvhD0vlwut+0QVibqlvrQjIQNRdj/cpph/yzCcyXrT+EzqiVQXdGFr0M1x0z5Z4dK92cFBOG50JcgtO1ciFzKrTVh84tNFWbbU5f7h09YKDNw2OGD5KhpflK7jOfs21rUAQAAwLEpyxWKSz/PTGCV1aHVUk8NIkrVUaoyyevFSb9ws/tuyGFQRXhe9WATnASIVlZ1Ih8icFtuyWMENtKXy2ie58tlruu7eI0yu73Y7EtTCUXEqSJTTRt42KGEnR9UHxUuh7hV8lUAAABARZUraZpWOpGeD9QbvAPLe6/UIW1EVCF6UGVQRsLmGMKT8xqtC55SdbiaCYIWUXVuXxCE2wJBeC70KAgJNhXuYA/b7cVO09S+hj3O+QjZ5fjMlKva+tna54p1VDrwkGfL0HYcAQAAnBaqLaVg2jpDCpalgr202UJIn+E/URMxDNBI2NLaeRKoyyTuc/btYh2u1ghpTsQAQbgtEITnQpeC8ODs/2LXFWjiM6cfUuOJQZqwsojtnjTvWQIAAAA7QxrGNE1aIi9PzTp0WFi8FbwQ8xYWQp6dF58Gugw2kvB2UCiAzKg6vC6PaKWREIJwW+5Udi4A+zD/ZT76fjT6fnT5p8vsY1Y+3mPb6pB3ZPjbkKrZTJ5MDn5+yvkWBAFV16HajJqmFaoeAQAAANtCm540xViWVVdZ4Uwgy5Lv+/P5PMsy0zTbT7VJopGLE83aQogsE5eXYjIxNC1fLvPhlHYcjcRoJK6rTgohxHy+Pjifiyyr/8vh4fueELnjLPmI69K3C217nQZiNpsJIWzbVi23Ukoq6dxb028LKEx/2xhUYfr4Kp48mYTfhfJOp1Xgd8b72hNCHKPCqWVZNDMFQWBZFglCnm8AAACAnaFiV7Qsbl/I6rbCvUEd4lTVZ6+t6h7m6n8liZhORRwLwxCLhagJLeyfJBGzmYgioWnC88SpbzXPZsL3hZTC8wQtlHzfT5JECGHbthrhaVmW67qu65KNEewMLIS3jSzLoip6aYz+QCdNqNoJB473tWd9eZRhheRfFEVJksRxLITA+AUAAOCw0Pxy5rAqtixLtel9m+fNP+pJ4lhcXoo4FrYtwnC4atB1xeWliCJh22K5PG01SPZY3xe6LpZLwdvmHL1ZyPdD+SBWq1UUra2jRP77PP11nvyvzmgkLi5EEHT7NU4QCMLbhq7rDYXpOyb8LrS+tOKrePzDOH673RSl+kLQiy3+r/WL7boHa2H++zz/fe589cn2ofe1V3l8T0j+BUFAe5amaQ7H8wQAAMDpQu4n9O++toAHBceVcWKSbfF9MZkIIYTniSoT41CYzdYOopYlHGe4qrUNcSwmExHHwrJEGAp1ieS67mg0Go1GZCdkZrPZaDS6uLgonCqKxHgsgkCYplitBPyxNgKX0duGlHI4YWnyjvS+9uQd6abu5MnEeLhjw4JAzOciSbYb77Isc6+1I++Ysj+tlNK2bdpMCsP1jlqWCdddC07bPvAcoOu6rutxHFOr4C8KAADgIHCAOv17ZxV06tT5gWqasCyxlS/tbLb+x6cCZHDQ0sj31z6WQ9auG+GAp7KR2zRNWr8FQaA6RdP2R2FBNZ8L1xVSisUCUrA1vaWzAd3S5e22n9mU34mPeM89+QfJFf/anISiuheL3DRzIXJdz7fNx7lYLOpKCQkhpJTL5ZKuQmcOw1zT1tc6UoY2jmSoq5wDAAAAbAstiHk7uJAh83ygOd2y8jC8+aGqV0LkNTXMP4HrZhlG7jg3JxwgvEzK83y5zKVcN/sUaxvTd9G03PPWX0TXi1+EUiVxtvY8z5fLJT/w6o1Tewa0BC6joAusx1b4XSjvbu3KMJ2uPb+lFNtmTTNNs6Hme5qmnIcty8R8LiYTkSRrF/wjZWjjoEGYBwEAABwE9hc1TZPmtTP3GpVSGMYnP8RWiTfDUNi2IFOr798YDIcG7XtTxJ2uiyhaxz2eIp63dhaV8iaAkylkaxfXz7mUUl1TUQYgIdZZdkBLIAjB4TH+P0P8gxD/IEaK94Z+X0/HKcXmtT+Vba+dv9kdvD2FCGNiNlsfVHMUz2bHchMtIKUkOVqZ9AwAAADYFo4eNE2TF829tqhnqGIB/1AooGWJHfK4WdYJaEJC09bxL0kiJhNxuoUYdF2sVkLX11+ENSGrPt7vqPQXXa3WYj7LxGRyyKwTtxsIQjBoDENomlgshOOILBPT6V7VdTiR8XJZnBg8T0h5E0B4WEbXnPmuLQAAgIPDy2JOIqDmmDlDyMpEP1QrQteFYeyYcGWwmjDPRZ5/klNUShGGIs9Fmu6ifocDfRFdX4s60oSaphUM4PSPQuIMusuOs75r8/mw7tpggSAEpwF7crruJztGLSEx6fvrLbSCR+hiceOlQGPHaVV0BQAAcJ6w9qO1sq7rlL/6nPcfVZdR0xSGIeK4rSowjLXQUrGs9cEBJusJXgXTn6YXq4vR96PR96PxH8ezP8+2zeveO5Xidrlci1tes3H5LnFtBi/4i6rwus73xeUl1nUbQJbR20aSJP7pOgoo5CXHUnKR3wHeYdL1plJC9FuyIsbxoIsOAQAAAELxDp3P55xGW5x3rtECpAS20gPZx8x/4Udvouh1JISQd6Xx0DAfmeYXw4r/n/155j/35V1pPjLlHSmEiN5E/nPff+5733jW4xOzEgavguBlEL2Jsg+ZEEJ/oBsPDfvCpq8mrnONUjFnyh7PapCUfAHDEGnaXftPGgjC20aSJO6t8Jgefb8O/lv8ZqEOwXTcvrDbFwlki+LGkhWkCenz43GFLREAAAAYDnWWQLIcIoGZEDfl7Kgq3Uayj9nkySS+irXPNPvCFkIk75PgZRC8DKwvLe/rocjs+S9z/7mvfaYtf7dkySSECF4F8dWJWQhFlbgNXgVu6vov/PC7UL+vi2uv0TiOoyiirRD2F305n78qrX7vSHnfMB6Y5gO8CJuAILxtaJpmV5XaOV2VOPt5pt3TaCzYjTgWUq69Rgsar7yfRF4KAAAAwMBhf1HP8ywlaGw8HidJEkXRuQlCntOjKJpMJkIITdNWq9VWttLpT9P4KjYfmYtvF3wwehxNnkz857712NpnQXJAgleBEMKWtqoGhRDmF4OzZG6kUtzaFzYpczd1+V6YphnHse/7SZJU+ov+2zDkf7+Noleu+y9B8IVtP0Iyv0YgCG8bmqZVZrA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+      "text/plain": [
+       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1200x480 at 0x7FFB82A3A8B0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image.open(HERE / \"images/sequence_logo.png\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -495,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,7 +564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -637,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -687,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -696,7 +726,7 @@
        "True"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -715,7 +745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -778,7 +808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -786,7 +816,7 @@
       "text/html": [
        "<style type=\"text/css\">\n",
        "</style>\n",
-       "<table id=\"T_f07d8_\">\n",
+       "<table id=\"T_464db_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -818,661 +848,661 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
-       "      <td id=\"T_f07d8_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
-       "      <td id=\"T_f07d8_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
-       "      <td id=\"T_f07d8_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
-       "      <td id=\"T_f07d8_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
-       "      <td id=\"T_f07d8_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
-       "      <td id=\"T_f07d8_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
-       "      <td id=\"T_f07d8_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
-       "      <td id=\"T_f07d8_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
-       "      <td id=\"T_f07d8_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
-       "      <td id=\"T_f07d8_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
-       "      <td id=\"T_f07d8_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
-       "      <td id=\"T_f07d8_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
-       "      <td id=\"T_f07d8_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
-       "      <td id=\"T_f07d8_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
-       "      <td id=\"T_f07d8_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
-       "      <td id=\"T_f07d8_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
-       "      <td id=\"T_f07d8_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
-       "      <td id=\"T_f07d8_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
-       "      <td id=\"T_f07d8_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
-       "      <td id=\"T_f07d8_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
-       "      <td id=\"T_f07d8_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
-       "      <td id=\"T_f07d8_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
-       "      <td id=\"T_f07d8_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
-       "      <td id=\"T_f07d8_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
-       "      <td id=\"T_f07d8_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
-       "      <td id=\"T_f07d8_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
-       "      <td id=\"T_f07d8_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
-       "      <td id=\"T_f07d8_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
-       "      <td id=\"T_f07d8_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
-       "      <td id=\"T_f07d8_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
-       "      <td id=\"T_f07d8_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
-       "      <td id=\"T_f07d8_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
-       "      <td id=\"T_f07d8_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
-       "      <td id=\"T_f07d8_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
-       "      <td id=\"T_f07d8_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
-       "      <td id=\"T_f07d8_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
-       "      <td id=\"T_f07d8_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
-       "      <td id=\"T_f07d8_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
-       "      <td id=\"T_f07d8_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
-       "      <td id=\"T_f07d8_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
-       "      <td id=\"T_f07d8_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
-       "      <td id=\"T_f07d8_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
-       "      <td id=\"T_f07d8_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
-       "      <td id=\"T_f07d8_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
-       "      <td id=\"T_f07d8_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
-       "      <td id=\"T_f07d8_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
-       "      <td id=\"T_f07d8_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
-       "      <td id=\"T_f07d8_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
-       "      <td id=\"T_f07d8_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
-       "      <td id=\"T_f07d8_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
-       "      <td id=\"T_f07d8_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
-       "      <td id=\"T_f07d8_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
-       "      <td id=\"T_f07d8_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
-       "      <td id=\"T_f07d8_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
-       "      <td id=\"T_f07d8_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
-       "      <td id=\"T_f07d8_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
-       "      <td id=\"T_f07d8_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
-       "      <td id=\"T_f07d8_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
-       "      <td id=\"T_f07d8_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
-       "      <td id=\"T_f07d8_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
-       "      <td id=\"T_f07d8_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
-       "      <td id=\"T_f07d8_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
-       "      <td id=\"T_f07d8_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
-       "      <td id=\"T_f07d8_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
-       "      <td id=\"T_f07d8_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
-       "      <td id=\"T_f07d8_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
-       "      <td id=\"T_f07d8_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
-       "      <td id=\"T_f07d8_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
-       "      <td id=\"T_f07d8_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
-       "      <td id=\"T_f07d8_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
-       "      <td id=\"T_f07d8_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
-       "      <td id=\"T_f07d8_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
-       "      <td id=\"T_f07d8_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
-       "      <td id=\"T_f07d8_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
-       "      <td id=\"T_f07d8_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
-       "      <td id=\"T_f07d8_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
-       "      <td id=\"T_f07d8_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
-       "      <td id=\"T_f07d8_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
-       "      <td id=\"T_f07d8_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
-       "      <td id=\"T_f07d8_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
-       "      <td id=\"T_f07d8_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
-       "      <td id=\"T_f07d8_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
-       "      <td id=\"T_f07d8_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
-       "      <td id=\"T_f07d8_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
-       "      <td id=\"T_f07d8_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
-       "      <td id=\"T_f07d8_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
-       "      <td id=\"T_f07d8_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
-       "      <td id=\"T_f07d8_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
-       "      <td id=\"T_f07d8_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
-       "      <td id=\"T_f07d8_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
-       "      <td id=\"T_f07d8_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
-       "      <td id=\"T_f07d8_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
-       "      <td id=\"T_f07d8_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
-       "      <td id=\"T_f07d8_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
-       "      <td id=\"T_f07d8_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
-       "      <td id=\"T_f07d8_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
-       "      <td id=\"T_f07d8_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
-       "      <td id=\"T_f07d8_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
-       "      <td id=\"T_f07d8_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
-       "      <td id=\"T_f07d8_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
-       "      <td id=\"T_f07d8_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
-       "      <td id=\"T_f07d8_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
-       "      <td id=\"T_f07d8_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
-       "      <td id=\"T_f07d8_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
-       "      <td id=\"T_f07d8_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
-       "      <td id=\"T_f07d8_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
-       "      <td id=\"T_f07d8_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
-       "      <td id=\"T_f07d8_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
-       "      <td id=\"T_f07d8_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
-       "      <td id=\"T_f07d8_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
-       "      <td id=\"T_f07d8_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_f07d8_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
-       "      <td id=\"T_f07d8_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
-       "      <td id=\"T_f07d8_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
+       "      <th id=\"T_464db_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
+       "      <td id=\"T_464db_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+       "      <td id=\"T_464db_row0_col1\" class=\"data row0 col1\" >0.39</td>\n",
+       "      <td id=\"T_464db_row0_col2\" class=\"data row0 col2\" >0.22</td>\n",
+       "      <td id=\"T_464db_row0_col3\" class=\"data row0 col3\" >0.35</td>\n",
+       "      <td id=\"T_464db_row0_col4\" class=\"data row0 col4\" >0.22</td>\n",
+       "      <td id=\"T_464db_row0_col5\" class=\"data row0 col5\" >0.45</td>\n",
+       "      <td id=\"T_464db_row0_col6\" class=\"data row0 col6\" >0.20</td>\n",
+       "      <td id=\"T_464db_row0_col7\" class=\"data row0 col7\" >0.38</td>\n",
+       "      <td id=\"T_464db_row0_col8\" class=\"data row0 col8\" >0.35</td>\n",
+       "      <td id=\"T_464db_row0_col9\" class=\"data row0 col9\" >0.38</td>\n",
+       "      <td id=\"T_464db_row0_col10\" class=\"data row0 col10\" >0.35</td>\n",
+       "      <td id=\"T_464db_row0_col11\" class=\"data row0 col11\" >0.22</td>\n",
+       "      <td id=\"T_464db_row0_col12\" class=\"data row0 col12\" >0.32</td>\n",
+       "      <td id=\"T_464db_row0_col13\" class=\"data row0 col13\" >0.35</td>\n",
+       "      <td id=\"T_464db_row0_col14\" class=\"data row0 col14\" >0.35</td>\n",
+       "      <td id=\"T_464db_row0_col15\" class=\"data row0 col15\" >0.62</td>\n",
+       "      <td id=\"T_464db_row0_col16\" class=\"data row0 col16\" >0.47</td>\n",
+       "      <td id=\"T_464db_row0_col17\" class=\"data row0 col17\" >0.50</td>\n",
+       "      <td id=\"T_464db_row0_col18\" class=\"data row0 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row0_col19\" class=\"data row0 col19\" >0.21</td>\n",
+       "      <td id=\"T_464db_row0_col20\" class=\"data row0 col20\" >0.25</td>\n",
+       "      <td id=\"T_464db_row0_col21\" class=\"data row0 col21\" >0.38</td>\n",
+       "      <td id=\"T_464db_row0_col22\" class=\"data row0 col22\" >0.82</td>\n",
+       "      <td id=\"T_464db_row0_col23\" class=\"data row0 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row1\" class=\"row_heading level0 row1\" >C</th>\n",
+       "      <td id=\"T_464db_row1_col0\" class=\"data row1 col0\" >0.39</td>\n",
+       "      <td id=\"T_464db_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+       "      <td id=\"T_464db_row1_col2\" class=\"data row1 col2\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col3\" class=\"data row1 col3\" >0.00</td>\n",
+       "      <td id=\"T_464db_row1_col4\" class=\"data row1 col4\" >0.18</td>\n",
+       "      <td id=\"T_464db_row1_col5\" class=\"data row1 col5\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col6\" class=\"data row1 col6\" >0.08</td>\n",
+       "      <td id=\"T_464db_row1_col7\" class=\"data row1 col7\" >0.29</td>\n",
+       "      <td id=\"T_464db_row1_col8\" class=\"data row1 col8\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col9\" class=\"data row1 col9\" >0.29</td>\n",
+       "      <td id=\"T_464db_row1_col10\" class=\"data row1 col10\" >0.28</td>\n",
+       "      <td id=\"T_464db_row1_col11\" class=\"data row1 col11\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col12\" class=\"data row1 col12\" >0.08</td>\n",
+       "      <td id=\"T_464db_row1_col13\" class=\"data row1 col13\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col14\" class=\"data row1 col14\" >0.09</td>\n",
+       "      <td id=\"T_464db_row1_col15\" class=\"data row1 col15\" >0.29</td>\n",
+       "      <td id=\"T_464db_row1_col16\" class=\"data row1 col16\" >0.28</td>\n",
+       "      <td id=\"T_464db_row1_col17\" class=\"data row1 col17\" >0.29</td>\n",
+       "      <td id=\"T_464db_row1_col18\" class=\"data row1 col18\" >0.14</td>\n",
+       "      <td id=\"T_464db_row1_col19\" class=\"data row1 col19\" >0.17</td>\n",
+       "      <td id=\"T_464db_row1_col20\" class=\"data row1 col20\" >0.10</td>\n",
+       "      <td id=\"T_464db_row1_col21\" class=\"data row1 col21\" >0.10</td>\n",
+       "      <td id=\"T_464db_row1_col22\" class=\"data row1 col22\" >0.32</td>\n",
+       "      <td id=\"T_464db_row1_col23\" class=\"data row1 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row2\" class=\"row_heading level0 row2\" >D</th>\n",
+       "      <td id=\"T_464db_row2_col0\" class=\"data row2 col0\" >0.22</td>\n",
+       "      <td id=\"T_464db_row2_col1\" class=\"data row2 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+       "      <td id=\"T_464db_row2_col3\" class=\"data row2 col3\" >0.63</td>\n",
+       "      <td id=\"T_464db_row2_col4\" class=\"data row2 col4\" >0.10</td>\n",
+       "      <td id=\"T_464db_row2_col5\" class=\"data row2 col5\" >0.30</td>\n",
+       "      <td id=\"T_464db_row2_col6\" class=\"data row2 col6\" >0.27</td>\n",
+       "      <td id=\"T_464db_row2_col7\" class=\"data row2 col7\" >0.11</td>\n",
+       "      <td id=\"T_464db_row2_col8\" class=\"data row2 col8\" >0.32</td>\n",
+       "      <td id=\"T_464db_row2_col9\" class=\"data row2 col9\" >0.00</td>\n",
+       "      <td id=\"T_464db_row2_col10\" class=\"data row2 col10\" >0.11</td>\n",
+       "      <td id=\"T_464db_row2_col11\" class=\"data row2 col11\" >0.50</td>\n",
+       "      <td id=\"T_464db_row2_col12\" class=\"data row2 col12\" >0.29</td>\n",
+       "      <td id=\"T_464db_row2_col13\" class=\"data row2 col13\" >0.42</td>\n",
+       "      <td id=\"T_464db_row2_col14\" class=\"data row2 col14\" >0.21</td>\n",
+       "      <td id=\"T_464db_row2_col15\" class=\"data row2 col15\" >0.45</td>\n",
+       "      <td id=\"T_464db_row2_col16\" class=\"data row2 col16\" >0.32</td>\n",
+       "      <td id=\"T_464db_row2_col17\" class=\"data row2 col17\" >0.11</td>\n",
+       "      <td id=\"T_464db_row2_col18\" class=\"data row2 col18\" >0.00</td>\n",
+       "      <td id=\"T_464db_row2_col19\" class=\"data row2 col19\" >0.10</td>\n",
+       "      <td id=\"T_464db_row2_col20\" class=\"data row2 col20\" >0.89</td>\n",
+       "      <td id=\"T_464db_row2_col21\" class=\"data row2 col21\" >0.56</td>\n",
+       "      <td id=\"T_464db_row2_col22\" class=\"data row2 col22\" >0.55</td>\n",
+       "      <td id=\"T_464db_row2_col23\" class=\"data row2 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row3\" class=\"row_heading level0 row3\" >E</th>\n",
+       "      <td id=\"T_464db_row3_col0\" class=\"data row3 col0\" >0.35</td>\n",
+       "      <td id=\"T_464db_row3_col1\" class=\"data row3 col1\" >0.00</td>\n",
+       "      <td id=\"T_464db_row3_col2\" class=\"data row3 col2\" >0.63</td>\n",
+       "      <td id=\"T_464db_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+       "      <td id=\"T_464db_row3_col4\" class=\"data row3 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row3_col5\" class=\"data row3 col5\" >0.21</td>\n",
+       "      <td id=\"T_464db_row3_col6\" class=\"data row3 col6\" >0.38</td>\n",
+       "      <td id=\"T_464db_row3_col7\" class=\"data row3 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row3_col8\" class=\"data row3 col8\" >0.56</td>\n",
+       "      <td id=\"T_464db_row3_col9\" class=\"data row3 col9\" >0.12</td>\n",
+       "      <td id=\"T_464db_row3_col10\" class=\"data row3 col10\" >0.22</td>\n",
+       "      <td id=\"T_464db_row3_col11\" class=\"data row3 col11\" >0.42</td>\n",
+       "      <td id=\"T_464db_row3_col12\" class=\"data row3 col12\" >0.30</td>\n",
+       "      <td id=\"T_464db_row3_col13\" class=\"data row3 col13\" >0.67</td>\n",
+       "      <td id=\"T_464db_row3_col14\" class=\"data row3 col14\" >0.44</td>\n",
+       "      <td id=\"T_464db_row3_col15\" class=\"data row3 col15\" >0.47</td>\n",
+       "      <td id=\"T_464db_row3_col16\" class=\"data row3 col16\" >0.33</td>\n",
+       "      <td id=\"T_464db_row3_col17\" class=\"data row3 col17\" >0.24</td>\n",
+       "      <td id=\"T_464db_row3_col18\" class=\"data row3 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row3_col19\" class=\"data row3 col19\" >0.20</td>\n",
+       "      <td id=\"T_464db_row3_col20\" class=\"data row3 col20\" >0.59</td>\n",
+       "      <td id=\"T_464db_row3_col21\" class=\"data row3 col21\" >0.94</td>\n",
+       "      <td id=\"T_464db_row3_col22\" class=\"data row3 col22\" >0.58</td>\n",
+       "      <td id=\"T_464db_row3_col23\" class=\"data row3 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row4\" class=\"row_heading level0 row4\" >F</th>\n",
+       "      <td id=\"T_464db_row4_col0\" class=\"data row4 col0\" >0.22</td>\n",
+       "      <td id=\"T_464db_row4_col1\" class=\"data row4 col1\" >0.18</td>\n",
+       "      <td id=\"T_464db_row4_col2\" class=\"data row4 col2\" >0.10</td>\n",
+       "      <td id=\"T_464db_row4_col3\" class=\"data row4 col3\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+       "      <td id=\"T_464db_row4_col5\" class=\"data row4 col5\" >0.10</td>\n",
+       "      <td id=\"T_464db_row4_col6\" class=\"data row4 col6\" >0.27</td>\n",
+       "      <td id=\"T_464db_row4_col7\" class=\"data row4 col7\" >0.45</td>\n",
+       "      <td id=\"T_464db_row4_col8\" class=\"data row4 col8\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col9\" class=\"data row4 col9\" >0.45</td>\n",
+       "      <td id=\"T_464db_row4_col10\" class=\"data row4 col10\" >0.42</td>\n",
+       "      <td id=\"T_464db_row4_col11\" class=\"data row4 col11\" >0.10</td>\n",
+       "      <td id=\"T_464db_row4_col12\" class=\"data row4 col12\" >0.00</td>\n",
+       "      <td id=\"T_464db_row4_col13\" class=\"data row4 col13\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col14\" class=\"data row4 col14\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col15\" class=\"data row4 col15\" >0.22</td>\n",
+       "      <td id=\"T_464db_row4_col16\" class=\"data row4 col16\" >0.21</td>\n",
+       "      <td id=\"T_464db_row4_col17\" class=\"data row4 col17\" >0.34</td>\n",
+       "      <td id=\"T_464db_row4_col18\" class=\"data row4 col18\" >0.41</td>\n",
+       "      <td id=\"T_464db_row4_col19\" class=\"data row4 col19\" >0.67</td>\n",
+       "      <td id=\"T_464db_row4_col20\" class=\"data row4 col20\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col21\" class=\"data row4 col21\" >0.11</td>\n",
+       "      <td id=\"T_464db_row4_col22\" class=\"data row4 col22\" >0.55</td>\n",
+       "      <td id=\"T_464db_row4_col23\" class=\"data row4 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row5\" class=\"row_heading level0 row5\" >G</th>\n",
+       "      <td id=\"T_464db_row5_col0\" class=\"data row5 col0\" >0.45</td>\n",
+       "      <td id=\"T_464db_row5_col1\" class=\"data row5 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row5_col2\" class=\"data row5 col2\" >0.30</td>\n",
+       "      <td id=\"T_464db_row5_col3\" class=\"data row5 col3\" >0.21</td>\n",
+       "      <td id=\"T_464db_row5_col4\" class=\"data row5 col4\" >0.10</td>\n",
+       "      <td id=\"T_464db_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+       "      <td id=\"T_464db_row5_col6\" class=\"data row5 col6\" >0.18</td>\n",
+       "      <td id=\"T_464db_row5_col7\" class=\"data row5 col7\" >0.00</td>\n",
+       "      <td id=\"T_464db_row5_col8\" class=\"data row5 col8\" >0.21</td>\n",
+       "      <td id=\"T_464db_row5_col9\" class=\"data row5 col9\" >0.00</td>\n",
+       "      <td id=\"T_464db_row5_col10\" class=\"data row5 col10\" >0.11</td>\n",
+       "      <td id=\"T_464db_row5_col11\" class=\"data row5 col11\" >0.40</td>\n",
+       "      <td id=\"T_464db_row5_col12\" class=\"data row5 col12\" >0.19</td>\n",
+       "      <td id=\"T_464db_row5_col13\" class=\"data row5 col13\" >0.21</td>\n",
+       "      <td id=\"T_464db_row5_col14\" class=\"data row5 col14\" >0.21</td>\n",
+       "      <td id=\"T_464db_row5_col15\" class=\"data row5 col15\" >0.45</td>\n",
+       "      <td id=\"T_464db_row5_col16\" class=\"data row5 col16\" >0.21</td>\n",
+       "      <td id=\"T_464db_row5_col17\" class=\"data row5 col17\" >0.11</td>\n",
+       "      <td id=\"T_464db_row5_col18\" class=\"data row5 col18\" >0.16</td>\n",
+       "      <td id=\"T_464db_row5_col19\" class=\"data row5 col19\" >0.10</td>\n",
+       "      <td id=\"T_464db_row5_col20\" class=\"data row5 col20\" >0.34</td>\n",
+       "      <td id=\"T_464db_row5_col21\" class=\"data row5 col21\" >0.22</td>\n",
+       "      <td id=\"T_464db_row5_col22\" class=\"data row5 col22\" >0.55</td>\n",
+       "      <td id=\"T_464db_row5_col23\" class=\"data row5 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row6\" class=\"row_heading level0 row6\" >H</th>\n",
+       "      <td id=\"T_464db_row6_col0\" class=\"data row6 col0\" >0.20</td>\n",
+       "      <td id=\"T_464db_row6_col1\" class=\"data row6 col1\" >0.08</td>\n",
+       "      <td id=\"T_464db_row6_col2\" class=\"data row6 col2\" >0.27</td>\n",
+       "      <td id=\"T_464db_row6_col3\" class=\"data row6 col3\" >0.38</td>\n",
+       "      <td id=\"T_464db_row6_col4\" class=\"data row6 col4\" >0.27</td>\n",
+       "      <td id=\"T_464db_row6_col5\" class=\"data row6 col5\" >0.18</td>\n",
+       "      <td id=\"T_464db_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+       "      <td id=\"T_464db_row6_col7\" class=\"data row6 col7\" >0.10</td>\n",
+       "      <td id=\"T_464db_row6_col8\" class=\"data row6 col8\" >0.29</td>\n",
+       "      <td id=\"T_464db_row6_col9\" class=\"data row6 col9\" >0.10</td>\n",
+       "      <td id=\"T_464db_row6_col10\" class=\"data row6 col10\" >0.19</td>\n",
+       "      <td id=\"T_464db_row6_col11\" class=\"data row6 col11\" >0.46</td>\n",
+       "      <td id=\"T_464db_row6_col12\" class=\"data row6 col12\" >0.17</td>\n",
+       "      <td id=\"T_464db_row6_col13\" class=\"data row6 col13\" >0.38</td>\n",
+       "      <td id=\"T_464db_row6_col14\" class=\"data row6 col14\" >0.38</td>\n",
+       "      <td id=\"T_464db_row6_col15\" class=\"data row6 col15\" >0.31</td>\n",
+       "      <td id=\"T_464db_row6_col16\" class=\"data row6 col16\" >0.19</td>\n",
+       "      <td id=\"T_464db_row6_col17\" class=\"data row6 col17\" >0.10</td>\n",
+       "      <td id=\"T_464db_row6_col18\" class=\"data row6 col18\" >0.15</td>\n",
+       "      <td id=\"T_464db_row6_col19\" class=\"data row6 col19\" >0.52</td>\n",
+       "      <td id=\"T_464db_row6_col20\" class=\"data row6 col20\" >0.41</td>\n",
+       "      <td id=\"T_464db_row6_col21\" class=\"data row6 col21\" >0.41</td>\n",
+       "      <td id=\"T_464db_row6_col22\" class=\"data row6 col22\" >0.50</td>\n",
+       "      <td id=\"T_464db_row6_col23\" class=\"data row6 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row7\" class=\"row_heading level0 row7\" >I</th>\n",
+       "      <td id=\"T_464db_row7_col0\" class=\"data row7 col0\" >0.38</td>\n",
+       "      <td id=\"T_464db_row7_col1\" class=\"data row7 col1\" >0.29</td>\n",
+       "      <td id=\"T_464db_row7_col2\" class=\"data row7 col2\" >0.11</td>\n",
+       "      <td id=\"T_464db_row7_col3\" class=\"data row7 col3\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col4\" class=\"data row7 col4\" >0.45</td>\n",
+       "      <td id=\"T_464db_row7_col5\" class=\"data row7 col5\" >0.00</td>\n",
+       "      <td id=\"T_464db_row7_col6\" class=\"data row7 col6\" >0.10</td>\n",
+       "      <td id=\"T_464db_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+       "      <td id=\"T_464db_row7_col8\" class=\"data row7 col8\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col9\" class=\"data row7 col9\" >0.75</td>\n",
+       "      <td id=\"T_464db_row7_col10\" class=\"data row7 col10\" >0.59</td>\n",
+       "      <td id=\"T_464db_row7_col11\" class=\"data row7 col11\" >0.11</td>\n",
+       "      <td id=\"T_464db_row7_col12\" class=\"data row7 col12\" >0.11</td>\n",
+       "      <td id=\"T_464db_row7_col13\" class=\"data row7 col13\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col14\" class=\"data row7 col14\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col15\" class=\"data row7 col15\" >0.25</td>\n",
+       "      <td id=\"T_464db_row7_col16\" class=\"data row7 col16\" >0.35</td>\n",
+       "      <td id=\"T_464db_row7_col17\" class=\"data row7 col17\" >0.88</td>\n",
+       "      <td id=\"T_464db_row7_col18\" class=\"data row7 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row7_col19\" class=\"data row7 col19\" >0.32</td>\n",
+       "      <td id=\"T_464db_row7_col20\" class=\"data row7 col20\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col21\" class=\"data row7 col21\" >0.12</td>\n",
+       "      <td id=\"T_464db_row7_col22\" class=\"data row7 col22\" >0.61</td>\n",
+       "      <td id=\"T_464db_row7_col23\" class=\"data row7 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row8\" class=\"row_heading level0 row8\" >K</th>\n",
+       "      <td id=\"T_464db_row8_col0\" class=\"data row8 col0\" >0.35</td>\n",
+       "      <td id=\"T_464db_row8_col1\" class=\"data row8 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row8_col2\" class=\"data row8 col2\" >0.32</td>\n",
+       "      <td id=\"T_464db_row8_col3\" class=\"data row8 col3\" >0.56</td>\n",
+       "      <td id=\"T_464db_row8_col4\" class=\"data row8 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row8_col5\" class=\"data row8 col5\" >0.21</td>\n",
+       "      <td id=\"T_464db_row8_col6\" class=\"data row8 col6\" >0.29</td>\n",
+       "      <td id=\"T_464db_row8_col7\" class=\"data row8 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+       "      <td id=\"T_464db_row8_col9\" class=\"data row8 col9\" >0.24</td>\n",
+       "      <td id=\"T_464db_row8_col10\" class=\"data row8 col10\" >0.33</td>\n",
+       "      <td id=\"T_464db_row8_col11\" class=\"data row8 col11\" >0.42</td>\n",
+       "      <td id=\"T_464db_row8_col12\" class=\"data row8 col12\" >0.30</td>\n",
+       "      <td id=\"T_464db_row8_col13\" class=\"data row8 col13\" >0.56</td>\n",
+       "      <td id=\"T_464db_row8_col14\" class=\"data row8 col14\" >0.67</td>\n",
+       "      <td id=\"T_464db_row8_col15\" class=\"data row8 col15\" >0.47</td>\n",
+       "      <td id=\"T_464db_row8_col16\" class=\"data row8 col16\" >0.33</td>\n",
+       "      <td id=\"T_464db_row8_col17\" class=\"data row8 col17\" >0.24</td>\n",
+       "      <td id=\"T_464db_row8_col18\" class=\"data row8 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row8_col19\" class=\"data row8 col19\" >0.20</td>\n",
+       "      <td id=\"T_464db_row8_col20\" class=\"data row8 col20\" >0.47</td>\n",
+       "      <td id=\"T_464db_row8_col21\" class=\"data row8 col21\" >0.59</td>\n",
+       "      <td id=\"T_464db_row8_col22\" class=\"data row8 col22\" >0.58</td>\n",
+       "      <td id=\"T_464db_row8_col23\" class=\"data row8 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row9\" class=\"row_heading level0 row9\" >L</th>\n",
+       "      <td id=\"T_464db_row9_col0\" class=\"data row9 col0\" >0.38</td>\n",
+       "      <td id=\"T_464db_row9_col1\" class=\"data row9 col1\" >0.29</td>\n",
+       "      <td id=\"T_464db_row9_col2\" class=\"data row9 col2\" >0.00</td>\n",
+       "      <td id=\"T_464db_row9_col3\" class=\"data row9 col3\" >0.12</td>\n",
+       "      <td id=\"T_464db_row9_col4\" class=\"data row9 col4\" >0.45</td>\n",
+       "      <td id=\"T_464db_row9_col5\" class=\"data row9 col5\" >0.00</td>\n",
+       "      <td id=\"T_464db_row9_col6\" class=\"data row9 col6\" >0.10</td>\n",
+       "      <td id=\"T_464db_row9_col7\" class=\"data row9 col7\" >0.75</td>\n",
+       "      <td id=\"T_464db_row9_col8\" class=\"data row9 col8\" >0.24</td>\n",
+       "      <td id=\"T_464db_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+       "      <td id=\"T_464db_row9_col10\" class=\"data row9 col10\" >0.71</td>\n",
+       "      <td id=\"T_464db_row9_col11\" class=\"data row9 col11\" >0.11</td>\n",
+       "      <td id=\"T_464db_row9_col12\" class=\"data row9 col12\" >0.11</td>\n",
+       "      <td id=\"T_464db_row9_col13\" class=\"data row9 col13\" >0.24</td>\n",
+       "      <td id=\"T_464db_row9_col14\" class=\"data row9 col14\" >0.24</td>\n",
+       "      <td id=\"T_464db_row9_col15\" class=\"data row9 col15\" >0.25</td>\n",
+       "      <td id=\"T_464db_row9_col16\" class=\"data row9 col16\" >0.35</td>\n",
+       "      <td id=\"T_464db_row9_col17\" class=\"data row9 col17\" >0.62</td>\n",
+       "      <td id=\"T_464db_row9_col18\" class=\"data row9 col18\" >0.18</td>\n",
+       "      <td id=\"T_464db_row9_col19\" class=\"data row9 col19\" >0.32</td>\n",
+       "      <td id=\"T_464db_row9_col20\" class=\"data row9 col20\" >0.00</td>\n",
+       "      <td id=\"T_464db_row9_col21\" class=\"data row9 col21\" >0.12</td>\n",
+       "      <td id=\"T_464db_row9_col22\" class=\"data row9 col22\" >0.61</td>\n",
+       "      <td id=\"T_464db_row9_col23\" class=\"data row9 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row10\" class=\"row_heading level0 row10\" >M</th>\n",
+       "      <td id=\"T_464db_row10_col0\" class=\"data row10 col0\" >0.35</td>\n",
+       "      <td id=\"T_464db_row10_col1\" class=\"data row10 col1\" >0.28</td>\n",
+       "      <td id=\"T_464db_row10_col2\" class=\"data row10 col2\" >0.11</td>\n",
+       "      <td id=\"T_464db_row10_col3\" class=\"data row10 col3\" >0.22</td>\n",
+       "      <td id=\"T_464db_row10_col4\" class=\"data row10 col4\" >0.42</td>\n",
+       "      <td id=\"T_464db_row10_col5\" class=\"data row10 col5\" >0.11</td>\n",
+       "      <td id=\"T_464db_row10_col6\" class=\"data row10 col6\" >0.19</td>\n",
+       "      <td id=\"T_464db_row10_col7\" class=\"data row10 col7\" >0.59</td>\n",
+       "      <td id=\"T_464db_row10_col8\" class=\"data row10 col8\" >0.33</td>\n",
+       "      <td id=\"T_464db_row10_col9\" class=\"data row10 col9\" >0.71</td>\n",
+       "      <td id=\"T_464db_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+       "      <td id=\"T_464db_row10_col11\" class=\"data row10 col11\" >0.21</td>\n",
+       "      <td id=\"T_464db_row10_col12\" class=\"data row10 col12\" >0.20</td>\n",
+       "      <td id=\"T_464db_row10_col13\" class=\"data row10 col13\" >0.44</td>\n",
+       "      <td id=\"T_464db_row10_col14\" class=\"data row10 col14\" >0.33</td>\n",
+       "      <td id=\"T_464db_row10_col15\" class=\"data row10 col15\" >0.35</td>\n",
+       "      <td id=\"T_464db_row10_col16\" class=\"data row10 col16\" >0.33</td>\n",
+       "      <td id=\"T_464db_row10_col17\" class=\"data row10 col17\" >0.59</td>\n",
+       "      <td id=\"T_464db_row10_col18\" class=\"data row10 col18\" >0.26</td>\n",
+       "      <td id=\"T_464db_row10_col19\" class=\"data row10 col19\" >0.30</td>\n",
+       "      <td id=\"T_464db_row10_col20\" class=\"data row10 col20\" >0.12</td>\n",
+       "      <td id=\"T_464db_row10_col21\" class=\"data row10 col21\" >0.35</td>\n",
+       "      <td id=\"T_464db_row10_col22\" class=\"data row10 col22\" >0.58</td>\n",
+       "      <td id=\"T_464db_row10_col23\" class=\"data row10 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row11\" class=\"row_heading level0 row11\" >N</th>\n",
+       "      <td id=\"T_464db_row11_col0\" class=\"data row11 col0\" >0.22</td>\n",
+       "      <td id=\"T_464db_row11_col1\" class=\"data row11 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row11_col2\" class=\"data row11 col2\" >0.50</td>\n",
+       "      <td id=\"T_464db_row11_col3\" class=\"data row11 col3\" >0.42</td>\n",
+       "      <td id=\"T_464db_row11_col4\" class=\"data row11 col4\" >0.10</td>\n",
+       "      <td id=\"T_464db_row11_col5\" class=\"data row11 col5\" >0.40</td>\n",
+       "      <td id=\"T_464db_row11_col6\" class=\"data row11 col6\" >0.46</td>\n",
+       "      <td id=\"T_464db_row11_col7\" class=\"data row11 col7\" >0.11</td>\n",
+       "      <td id=\"T_464db_row11_col8\" class=\"data row11 col8\" >0.42</td>\n",
+       "      <td id=\"T_464db_row11_col9\" class=\"data row11 col9\" >0.11</td>\n",
+       "      <td id=\"T_464db_row11_col10\" class=\"data row11 col10\" >0.21</td>\n",
+       "      <td id=\"T_464db_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+       "      <td id=\"T_464db_row11_col12\" class=\"data row11 col12\" >0.19</td>\n",
+       "      <td id=\"T_464db_row11_col13\" class=\"data row11 col13\" >0.42</td>\n",
+       "      <td id=\"T_464db_row11_col14\" class=\"data row11 col14\" >0.42</td>\n",
+       "      <td id=\"T_464db_row11_col15\" class=\"data row11 col15\" >0.56</td>\n",
+       "      <td id=\"T_464db_row11_col16\" class=\"data row11 col16\" >0.42</td>\n",
+       "      <td id=\"T_464db_row11_col17\" class=\"data row11 col17\" >0.11</td>\n",
+       "      <td id=\"T_464db_row11_col18\" class=\"data row11 col18\" >0.00</td>\n",
+       "      <td id=\"T_464db_row11_col19\" class=\"data row11 col19\" >0.19</td>\n",
+       "      <td id=\"T_464db_row11_col20\" class=\"data row11 col20\" >0.78</td>\n",
+       "      <td id=\"T_464db_row11_col21\" class=\"data row11 col21\" >0.45</td>\n",
+       "      <td id=\"T_464db_row11_col22\" class=\"data row11 col22\" >0.55</td>\n",
+       "      <td id=\"T_464db_row11_col23\" class=\"data row11 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row12\" class=\"row_heading level0 row12\" >P</th>\n",
+       "      <td id=\"T_464db_row12_col0\" class=\"data row12 col0\" >0.32</td>\n",
+       "      <td id=\"T_464db_row12_col1\" class=\"data row12 col1\" >0.08</td>\n",
+       "      <td id=\"T_464db_row12_col2\" class=\"data row12 col2\" >0.29</td>\n",
+       "      <td id=\"T_464db_row12_col3\" class=\"data row12 col3\" >0.30</td>\n",
+       "      <td id=\"T_464db_row12_col4\" class=\"data row12 col4\" >0.00</td>\n",
+       "      <td id=\"T_464db_row12_col5\" class=\"data row12 col5\" >0.19</td>\n",
+       "      <td id=\"T_464db_row12_col6\" class=\"data row12 col6\" >0.17</td>\n",
+       "      <td id=\"T_464db_row12_col7\" class=\"data row12 col7\" >0.11</td>\n",
+       "      <td id=\"T_464db_row12_col8\" class=\"data row12 col8\" >0.30</td>\n",
+       "      <td id=\"T_464db_row12_col9\" class=\"data row12 col9\" >0.11</td>\n",
+       "      <td id=\"T_464db_row12_col10\" class=\"data row12 col10\" >0.20</td>\n",
+       "      <td id=\"T_464db_row12_col11\" class=\"data row12 col11\" >0.19</td>\n",
+       "      <td id=\"T_464db_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+       "      <td id=\"T_464db_row12_col13\" class=\"data row12 col13\" >0.30</td>\n",
+       "      <td id=\"T_464db_row12_col14\" class=\"data row12 col14\" >0.20</td>\n",
+       "      <td id=\"T_464db_row12_col15\" class=\"data row12 col15\" >0.32</td>\n",
+       "      <td id=\"T_464db_row12_col16\" class=\"data row12 col16\" >0.30</td>\n",
+       "      <td id=\"T_464db_row12_col17\" class=\"data row12 col17\" >0.21</td>\n",
+       "      <td id=\"T_464db_row12_col18\" class=\"data row12 col18\" >0.00</td>\n",
+       "      <td id=\"T_464db_row12_col19\" class=\"data row12 col19\" >0.09</td>\n",
+       "      <td id=\"T_464db_row12_col20\" class=\"data row12 col20\" >0.21</td>\n",
+       "      <td id=\"T_464db_row12_col21\" class=\"data row12 col21\" >0.32</td>\n",
+       "      <td id=\"T_464db_row12_col22\" class=\"data row12 col22\" >0.35</td>\n",
+       "      <td id=\"T_464db_row12_col23\" class=\"data row12 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row13\" class=\"row_heading level0 row13\" >Q</th>\n",
+       "      <td id=\"T_464db_row13_col0\" class=\"data row13 col0\" >0.35</td>\n",
+       "      <td id=\"T_464db_row13_col1\" class=\"data row13 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row13_col2\" class=\"data row13 col2\" >0.42</td>\n",
+       "      <td id=\"T_464db_row13_col3\" class=\"data row13 col3\" >0.67</td>\n",
+       "      <td id=\"T_464db_row13_col4\" class=\"data row13 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row13_col5\" class=\"data row13 col5\" >0.21</td>\n",
+       "      <td id=\"T_464db_row13_col6\" class=\"data row13 col6\" >0.38</td>\n",
+       "      <td id=\"T_464db_row13_col7\" class=\"data row13 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row13_col8\" class=\"data row13 col8\" >0.56</td>\n",
+       "      <td id=\"T_464db_row13_col9\" class=\"data row13 col9\" >0.24</td>\n",
+       "      <td id=\"T_464db_row13_col10\" class=\"data row13 col10\" >0.44</td>\n",
+       "      <td id=\"T_464db_row13_col11\" class=\"data row13 col11\" >0.42</td>\n",
+       "      <td id=\"T_464db_row13_col12\" class=\"data row13 col12\" >0.30</td>\n",
+       "      <td id=\"T_464db_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+       "      <td id=\"T_464db_row13_col14\" class=\"data row13 col14\" >0.56</td>\n",
+       "      <td id=\"T_464db_row13_col15\" class=\"data row13 col15\" >0.47</td>\n",
+       "      <td id=\"T_464db_row13_col16\" class=\"data row13 col16\" >0.33</td>\n",
+       "      <td id=\"T_464db_row13_col17\" class=\"data row13 col17\" >0.24</td>\n",
+       "      <td id=\"T_464db_row13_col18\" class=\"data row13 col18\" >0.17</td>\n",
+       "      <td id=\"T_464db_row13_col19\" class=\"data row13 col19\" >0.30</td>\n",
+       "      <td id=\"T_464db_row13_col20\" class=\"data row13 col20\" >0.47</td>\n",
+       "      <td id=\"T_464db_row13_col21\" class=\"data row13 col21\" >0.82</td>\n",
+       "      <td id=\"T_464db_row13_col22\" class=\"data row13 col22\" >0.58</td>\n",
+       "      <td id=\"T_464db_row13_col23\" class=\"data row13 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row14\" class=\"row_heading level0 row14\" >R</th>\n",
+       "      <td id=\"T_464db_row14_col0\" class=\"data row14 col0\" >0.35</td>\n",
+       "      <td id=\"T_464db_row14_col1\" class=\"data row14 col1\" >0.09</td>\n",
+       "      <td id=\"T_464db_row14_col2\" class=\"data row14 col2\" >0.21</td>\n",
+       "      <td id=\"T_464db_row14_col3\" class=\"data row14 col3\" >0.44</td>\n",
+       "      <td id=\"T_464db_row14_col4\" class=\"data row14 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row14_col5\" class=\"data row14 col5\" >0.21</td>\n",
+       "      <td id=\"T_464db_row14_col6\" class=\"data row14 col6\" >0.38</td>\n",
+       "      <td id=\"T_464db_row14_col7\" class=\"data row14 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row14_col8\" class=\"data row14 col8\" >0.67</td>\n",
+       "      <td id=\"T_464db_row14_col9\" class=\"data row14 col9\" >0.24</td>\n",
+       "      <td id=\"T_464db_row14_col10\" class=\"data row14 col10\" >0.33</td>\n",
+       "      <td id=\"T_464db_row14_col11\" class=\"data row14 col11\" >0.42</td>\n",
+       "      <td id=\"T_464db_row14_col12\" class=\"data row14 col12\" >0.20</td>\n",
+       "      <td id=\"T_464db_row14_col13\" class=\"data row14 col13\" >0.56</td>\n",
+       "      <td id=\"T_464db_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+       "      <td id=\"T_464db_row14_col15\" class=\"data row14 col15\" >0.35</td>\n",
+       "      <td id=\"T_464db_row14_col16\" class=\"data row14 col16\" >0.33</td>\n",
+       "      <td id=\"T_464db_row14_col17\" class=\"data row14 col17\" >0.12</td>\n",
+       "      <td id=\"T_464db_row14_col18\" class=\"data row14 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row14_col19\" class=\"data row14 col19\" >0.20</td>\n",
+       "      <td id=\"T_464db_row14_col20\" class=\"data row14 col20\" >0.35</td>\n",
+       "      <td id=\"T_464db_row14_col21\" class=\"data row14 col21\" >0.47</td>\n",
+       "      <td id=\"T_464db_row14_col22\" class=\"data row14 col22\" >0.58</td>\n",
+       "      <td id=\"T_464db_row14_col23\" class=\"data row14 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row15\" class=\"row_heading level0 row15\" >S</th>\n",
+       "      <td id=\"T_464db_row15_col0\" class=\"data row15 col0\" >0.62</td>\n",
+       "      <td id=\"T_464db_row15_col1\" class=\"data row15 col1\" >0.29</td>\n",
+       "      <td id=\"T_464db_row15_col2\" class=\"data row15 col2\" >0.45</td>\n",
+       "      <td id=\"T_464db_row15_col3\" class=\"data row15 col3\" >0.47</td>\n",
+       "      <td id=\"T_464db_row15_col4\" class=\"data row15 col4\" >0.22</td>\n",
+       "      <td id=\"T_464db_row15_col5\" class=\"data row15 col5\" >0.45</td>\n",
+       "      <td id=\"T_464db_row15_col6\" class=\"data row15 col6\" >0.31</td>\n",
+       "      <td id=\"T_464db_row15_col7\" class=\"data row15 col7\" >0.25</td>\n",
+       "      <td id=\"T_464db_row15_col8\" class=\"data row15 col8\" >0.47</td>\n",
+       "      <td id=\"T_464db_row15_col9\" class=\"data row15 col9\" >0.25</td>\n",
+       "      <td id=\"T_464db_row15_col10\" class=\"data row15 col10\" >0.35</td>\n",
+       "      <td id=\"T_464db_row15_col11\" class=\"data row15 col11\" >0.56</td>\n",
+       "      <td id=\"T_464db_row15_col12\" class=\"data row15 col12\" >0.32</td>\n",
+       "      <td id=\"T_464db_row15_col13\" class=\"data row15 col13\" >0.47</td>\n",
+       "      <td id=\"T_464db_row15_col14\" class=\"data row15 col14\" >0.35</td>\n",
+       "      <td id=\"T_464db_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+       "      <td id=\"T_464db_row15_col16\" class=\"data row15 col16\" >0.59</td>\n",
+       "      <td id=\"T_464db_row15_col17\" class=\"data row15 col17\" >0.25</td>\n",
+       "      <td id=\"T_464db_row15_col18\" class=\"data row15 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row15_col19\" class=\"data row15 col19\" >0.21</td>\n",
+       "      <td id=\"T_464db_row15_col20\" class=\"data row15 col20\" >0.50</td>\n",
+       "      <td id=\"T_464db_row15_col21\" class=\"data row15 col21\" >0.50</td>\n",
+       "      <td id=\"T_464db_row15_col22\" class=\"data row15 col22\" >0.82</td>\n",
+       "      <td id=\"T_464db_row15_col23\" class=\"data row15 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row16\" class=\"row_heading level0 row16\" >T</th>\n",
+       "      <td id=\"T_464db_row16_col0\" class=\"data row16 col0\" >0.47</td>\n",
+       "      <td id=\"T_464db_row16_col1\" class=\"data row16 col1\" >0.28</td>\n",
+       "      <td id=\"T_464db_row16_col2\" class=\"data row16 col2\" >0.32</td>\n",
+       "      <td id=\"T_464db_row16_col3\" class=\"data row16 col3\" >0.33</td>\n",
+       "      <td id=\"T_464db_row16_col4\" class=\"data row16 col4\" >0.21</td>\n",
+       "      <td id=\"T_464db_row16_col5\" class=\"data row16 col5\" >0.21</td>\n",
+       "      <td id=\"T_464db_row16_col6\" class=\"data row16 col6\" >0.19</td>\n",
+       "      <td id=\"T_464db_row16_col7\" class=\"data row16 col7\" >0.35</td>\n",
+       "      <td id=\"T_464db_row16_col8\" class=\"data row16 col8\" >0.33</td>\n",
+       "      <td id=\"T_464db_row16_col9\" class=\"data row16 col9\" >0.35</td>\n",
+       "      <td id=\"T_464db_row16_col10\" class=\"data row16 col10\" >0.33</td>\n",
+       "      <td id=\"T_464db_row16_col11\" class=\"data row16 col11\" >0.42</td>\n",
+       "      <td id=\"T_464db_row16_col12\" class=\"data row16 col12\" >0.30</td>\n",
+       "      <td id=\"T_464db_row16_col13\" class=\"data row16 col13\" >0.33</td>\n",
+       "      <td id=\"T_464db_row16_col14\" class=\"data row16 col14\" >0.33</td>\n",
+       "      <td id=\"T_464db_row16_col15\" class=\"data row16 col15\" >0.59</td>\n",
+       "      <td id=\"T_464db_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+       "      <td id=\"T_464db_row16_col17\" class=\"data row16 col17\" >0.47</td>\n",
+       "      <td id=\"T_464db_row16_col18\" class=\"data row16 col18\" >0.17</td>\n",
+       "      <td id=\"T_464db_row16_col19\" class=\"data row16 col19\" >0.20</td>\n",
+       "      <td id=\"T_464db_row16_col20\" class=\"data row16 col20\" >0.35</td>\n",
+       "      <td id=\"T_464db_row16_col21\" class=\"data row16 col21\" >0.35</td>\n",
+       "      <td id=\"T_464db_row16_col22\" class=\"data row16 col22\" >0.77</td>\n",
+       "      <td id=\"T_464db_row16_col23\" class=\"data row16 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row17\" class=\"row_heading level0 row17\" >V</th>\n",
+       "      <td id=\"T_464db_row17_col0\" class=\"data row17 col0\" >0.50</td>\n",
+       "      <td id=\"T_464db_row17_col1\" class=\"data row17 col1\" >0.29</td>\n",
+       "      <td id=\"T_464db_row17_col2\" class=\"data row17 col2\" >0.11</td>\n",
+       "      <td id=\"T_464db_row17_col3\" class=\"data row17 col3\" >0.24</td>\n",
+       "      <td id=\"T_464db_row17_col4\" class=\"data row17 col4\" >0.34</td>\n",
+       "      <td id=\"T_464db_row17_col5\" class=\"data row17 col5\" >0.11</td>\n",
+       "      <td id=\"T_464db_row17_col6\" class=\"data row17 col6\" >0.10</td>\n",
+       "      <td id=\"T_464db_row17_col7\" class=\"data row17 col7\" >0.88</td>\n",
+       "      <td id=\"T_464db_row17_col8\" class=\"data row17 col8\" >0.24</td>\n",
+       "      <td id=\"T_464db_row17_col9\" class=\"data row17 col9\" >0.62</td>\n",
+       "      <td id=\"T_464db_row17_col10\" class=\"data row17 col10\" >0.59</td>\n",
+       "      <td id=\"T_464db_row17_col11\" class=\"data row17 col11\" >0.11</td>\n",
+       "      <td id=\"T_464db_row17_col12\" class=\"data row17 col12\" >0.21</td>\n",
+       "      <td id=\"T_464db_row17_col13\" class=\"data row17 col13\" >0.24</td>\n",
+       "      <td id=\"T_464db_row17_col14\" class=\"data row17 col14\" >0.12</td>\n",
+       "      <td id=\"T_464db_row17_col15\" class=\"data row17 col15\" >0.25</td>\n",
+       "      <td id=\"T_464db_row17_col16\" class=\"data row17 col16\" >0.47</td>\n",
+       "      <td id=\"T_464db_row17_col17\" class=\"data row17 col17\" >1.00</td>\n",
+       "      <td id=\"T_464db_row17_col18\" class=\"data row17 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row17_col19\" class=\"data row17 col19\" >0.32</td>\n",
+       "      <td id=\"T_464db_row17_col20\" class=\"data row17 col20\" >0.12</td>\n",
+       "      <td id=\"T_464db_row17_col21\" class=\"data row17 col21\" >0.25</td>\n",
+       "      <td id=\"T_464db_row17_col22\" class=\"data row17 col22\" >0.61</td>\n",
+       "      <td id=\"T_464db_row17_col23\" class=\"data row17 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row18\" class=\"row_heading level0 row18\" >W</th>\n",
+       "      <td id=\"T_464db_row18_col0\" class=\"data row18 col0\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col1\" class=\"data row18 col1\" >0.14</td>\n",
+       "      <td id=\"T_464db_row18_col2\" class=\"data row18 col2\" >0.00</td>\n",
+       "      <td id=\"T_464db_row18_col3\" class=\"data row18 col3\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col4\" class=\"data row18 col4\" >0.41</td>\n",
+       "      <td id=\"T_464db_row18_col5\" class=\"data row18 col5\" >0.16</td>\n",
+       "      <td id=\"T_464db_row18_col6\" class=\"data row18 col6\" >0.15</td>\n",
+       "      <td id=\"T_464db_row18_col7\" class=\"data row18 col7\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col8\" class=\"data row18 col8\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col9\" class=\"data row18 col9\" >0.18</td>\n",
+       "      <td id=\"T_464db_row18_col10\" class=\"data row18 col10\" >0.26</td>\n",
+       "      <td id=\"T_464db_row18_col11\" class=\"data row18 col11\" >0.00</td>\n",
+       "      <td id=\"T_464db_row18_col12\" class=\"data row18 col12\" >0.00</td>\n",
+       "      <td id=\"T_464db_row18_col13\" class=\"data row18 col13\" >0.17</td>\n",
+       "      <td id=\"T_464db_row18_col14\" class=\"data row18 col14\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col15\" class=\"data row18 col15\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col16\" class=\"data row18 col16\" >0.17</td>\n",
+       "      <td id=\"T_464db_row18_col17\" class=\"data row18 col17\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col18\" class=\"data row18 col18\" >1.00</td>\n",
+       "      <td id=\"T_464db_row18_col19\" class=\"data row18 col19\" >0.47</td>\n",
+       "      <td id=\"T_464db_row18_col20\" class=\"data row18 col20\" >0.00</td>\n",
+       "      <td id=\"T_464db_row18_col21\" class=\"data row18 col21\" >0.09</td>\n",
+       "      <td id=\"T_464db_row18_col22\" class=\"data row18 col22\" >0.30</td>\n",
+       "      <td id=\"T_464db_row18_col23\" class=\"data row18 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row19\" class=\"row_heading level0 row19\" >Y</th>\n",
+       "      <td id=\"T_464db_row19_col0\" class=\"data row19 col0\" >0.21</td>\n",
+       "      <td id=\"T_464db_row19_col1\" class=\"data row19 col1\" >0.17</td>\n",
+       "      <td id=\"T_464db_row19_col2\" class=\"data row19 col2\" >0.10</td>\n",
+       "      <td id=\"T_464db_row19_col3\" class=\"data row19 col3\" >0.20</td>\n",
+       "      <td id=\"T_464db_row19_col4\" class=\"data row19 col4\" >0.67</td>\n",
+       "      <td id=\"T_464db_row19_col5\" class=\"data row19 col5\" >0.10</td>\n",
+       "      <td id=\"T_464db_row19_col6\" class=\"data row19 col6\" >0.52</td>\n",
+       "      <td id=\"T_464db_row19_col7\" class=\"data row19 col7\" >0.32</td>\n",
+       "      <td id=\"T_464db_row19_col8\" class=\"data row19 col8\" >0.20</td>\n",
+       "      <td id=\"T_464db_row19_col9\" class=\"data row19 col9\" >0.32</td>\n",
+       "      <td id=\"T_464db_row19_col10\" class=\"data row19 col10\" >0.30</td>\n",
+       "      <td id=\"T_464db_row19_col11\" class=\"data row19 col11\" >0.19</td>\n",
+       "      <td id=\"T_464db_row19_col12\" class=\"data row19 col12\" >0.09</td>\n",
+       "      <td id=\"T_464db_row19_col13\" class=\"data row19 col13\" >0.30</td>\n",
+       "      <td id=\"T_464db_row19_col14\" class=\"data row19 col14\" >0.20</td>\n",
+       "      <td id=\"T_464db_row19_col15\" class=\"data row19 col15\" >0.21</td>\n",
+       "      <td id=\"T_464db_row19_col16\" class=\"data row19 col16\" >0.20</td>\n",
+       "      <td id=\"T_464db_row19_col17\" class=\"data row19 col17\" >0.32</td>\n",
+       "      <td id=\"T_464db_row19_col18\" class=\"data row19 col18\" >0.47</td>\n",
+       "      <td id=\"T_464db_row19_col19\" class=\"data row19 col19\" >1.00</td>\n",
+       "      <td id=\"T_464db_row19_col20\" class=\"data row19 col20\" >0.11</td>\n",
+       "      <td id=\"T_464db_row19_col21\" class=\"data row19 col21\" >0.21</td>\n",
+       "      <td id=\"T_464db_row19_col22\" class=\"data row19 col22\" >0.52</td>\n",
+       "      <td id=\"T_464db_row19_col23\" class=\"data row19 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row20\" class=\"row_heading level0 row20\" >B</th>\n",
+       "      <td id=\"T_464db_row20_col0\" class=\"data row20 col0\" >0.25</td>\n",
+       "      <td id=\"T_464db_row20_col1\" class=\"data row20 col1\" >0.10</td>\n",
+       "      <td id=\"T_464db_row20_col2\" class=\"data row20 col2\" >0.89</td>\n",
+       "      <td id=\"T_464db_row20_col3\" class=\"data row20 col3\" >0.59</td>\n",
+       "      <td id=\"T_464db_row20_col4\" class=\"data row20 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row20_col5\" class=\"data row20 col5\" >0.34</td>\n",
+       "      <td id=\"T_464db_row20_col6\" class=\"data row20 col6\" >0.41</td>\n",
+       "      <td id=\"T_464db_row20_col7\" class=\"data row20 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row20_col8\" class=\"data row20 col8\" >0.47</td>\n",
+       "      <td id=\"T_464db_row20_col9\" class=\"data row20 col9\" >0.00</td>\n",
+       "      <td id=\"T_464db_row20_col10\" class=\"data row20 col10\" >0.12</td>\n",
+       "      <td id=\"T_464db_row20_col11\" class=\"data row20 col11\" >0.78</td>\n",
+       "      <td id=\"T_464db_row20_col12\" class=\"data row20 col12\" >0.21</td>\n",
+       "      <td id=\"T_464db_row20_col13\" class=\"data row20 col13\" >0.47</td>\n",
+       "      <td id=\"T_464db_row20_col14\" class=\"data row20 col14\" >0.35</td>\n",
+       "      <td id=\"T_464db_row20_col15\" class=\"data row20 col15\" >0.50</td>\n",
+       "      <td id=\"T_464db_row20_col16\" class=\"data row20 col16\" >0.35</td>\n",
+       "      <td id=\"T_464db_row20_col17\" class=\"data row20 col17\" >0.12</td>\n",
+       "      <td id=\"T_464db_row20_col18\" class=\"data row20 col18\" >0.00</td>\n",
+       "      <td id=\"T_464db_row20_col19\" class=\"data row20 col19\" >0.11</td>\n",
+       "      <td id=\"T_464db_row20_col20\" class=\"data row20 col20\" >1.00</td>\n",
+       "      <td id=\"T_464db_row20_col21\" class=\"data row20 col21\" >0.62</td>\n",
+       "      <td id=\"T_464db_row20_col22\" class=\"data row20 col22\" >0.61</td>\n",
+       "      <td id=\"T_464db_row20_col23\" class=\"data row20 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row21\" class=\"row_heading level0 row21\" >Z</th>\n",
+       "      <td id=\"T_464db_row21_col0\" class=\"data row21 col0\" >0.38</td>\n",
+       "      <td id=\"T_464db_row21_col1\" class=\"data row21 col1\" >0.10</td>\n",
+       "      <td id=\"T_464db_row21_col2\" class=\"data row21 col2\" >0.56</td>\n",
+       "      <td id=\"T_464db_row21_col3\" class=\"data row21 col3\" >0.94</td>\n",
+       "      <td id=\"T_464db_row21_col4\" class=\"data row21 col4\" >0.11</td>\n",
+       "      <td id=\"T_464db_row21_col5\" class=\"data row21 col5\" >0.22</td>\n",
+       "      <td id=\"T_464db_row21_col6\" class=\"data row21 col6\" >0.41</td>\n",
+       "      <td id=\"T_464db_row21_col7\" class=\"data row21 col7\" >0.12</td>\n",
+       "      <td id=\"T_464db_row21_col8\" class=\"data row21 col8\" >0.59</td>\n",
+       "      <td id=\"T_464db_row21_col9\" class=\"data row21 col9\" >0.12</td>\n",
+       "      <td id=\"T_464db_row21_col10\" class=\"data row21 col10\" >0.35</td>\n",
+       "      <td id=\"T_464db_row21_col11\" class=\"data row21 col11\" >0.45</td>\n",
+       "      <td id=\"T_464db_row21_col12\" class=\"data row21 col12\" >0.32</td>\n",
+       "      <td id=\"T_464db_row21_col13\" class=\"data row21 col13\" >0.82</td>\n",
+       "      <td id=\"T_464db_row21_col14\" class=\"data row21 col14\" >0.47</td>\n",
+       "      <td id=\"T_464db_row21_col15\" class=\"data row21 col15\" >0.50</td>\n",
+       "      <td id=\"T_464db_row21_col16\" class=\"data row21 col16\" >0.35</td>\n",
+       "      <td id=\"T_464db_row21_col17\" class=\"data row21 col17\" >0.25</td>\n",
+       "      <td id=\"T_464db_row21_col18\" class=\"data row21 col18\" >0.09</td>\n",
+       "      <td id=\"T_464db_row21_col19\" class=\"data row21 col19\" >0.21</td>\n",
+       "      <td id=\"T_464db_row21_col20\" class=\"data row21 col20\" >0.62</td>\n",
+       "      <td id=\"T_464db_row21_col21\" class=\"data row21 col21\" >1.00</td>\n",
+       "      <td id=\"T_464db_row21_col22\" class=\"data row21 col22\" >0.61</td>\n",
+       "      <td id=\"T_464db_row21_col23\" class=\"data row21 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row22\" class=\"row_heading level0 row22\" >X</th>\n",
+       "      <td id=\"T_464db_row22_col0\" class=\"data row22 col0\" >0.82</td>\n",
+       "      <td id=\"T_464db_row22_col1\" class=\"data row22 col1\" >0.32</td>\n",
+       "      <td id=\"T_464db_row22_col2\" class=\"data row22 col2\" >0.55</td>\n",
+       "      <td id=\"T_464db_row22_col3\" class=\"data row22 col3\" >0.58</td>\n",
+       "      <td id=\"T_464db_row22_col4\" class=\"data row22 col4\" >0.55</td>\n",
+       "      <td id=\"T_464db_row22_col5\" class=\"data row22 col5\" >0.55</td>\n",
+       "      <td id=\"T_464db_row22_col6\" class=\"data row22 col6\" >0.50</td>\n",
+       "      <td id=\"T_464db_row22_col7\" class=\"data row22 col7\" >0.61</td>\n",
+       "      <td id=\"T_464db_row22_col8\" class=\"data row22 col8\" >0.58</td>\n",
+       "      <td id=\"T_464db_row22_col9\" class=\"data row22 col9\" >0.61</td>\n",
+       "      <td id=\"T_464db_row22_col10\" class=\"data row22 col10\" >0.58</td>\n",
+       "      <td id=\"T_464db_row22_col11\" class=\"data row22 col11\" >0.55</td>\n",
+       "      <td id=\"T_464db_row22_col12\" class=\"data row22 col12\" >0.35</td>\n",
+       "      <td id=\"T_464db_row22_col13\" class=\"data row22 col13\" >0.58</td>\n",
+       "      <td id=\"T_464db_row22_col14\" class=\"data row22 col14\" >0.58</td>\n",
+       "      <td id=\"T_464db_row22_col15\" class=\"data row22 col15\" >0.82</td>\n",
+       "      <td id=\"T_464db_row22_col16\" class=\"data row22 col16\" >0.77</td>\n",
+       "      <td id=\"T_464db_row22_col17\" class=\"data row22 col17\" >0.61</td>\n",
+       "      <td id=\"T_464db_row22_col18\" class=\"data row22 col18\" >0.30</td>\n",
+       "      <td id=\"T_464db_row22_col19\" class=\"data row22 col19\" >0.52</td>\n",
+       "      <td id=\"T_464db_row22_col20\" class=\"data row22 col20\" >0.61</td>\n",
+       "      <td id=\"T_464db_row22_col21\" class=\"data row22 col21\" >0.61</td>\n",
+       "      <td id=\"T_464db_row22_col22\" class=\"data row22 col22\" >1.00</td>\n",
+       "      <td id=\"T_464db_row22_col23\" class=\"data row22 col23\" >0.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_464db_level0_row23\" class=\"row_heading level0 row23\" >*</th>\n",
+       "      <td id=\"T_464db_row23_col0\" class=\"data row23 col0\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col1\" class=\"data row23 col1\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col2\" class=\"data row23 col2\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col3\" class=\"data row23 col3\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col4\" class=\"data row23 col4\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col5\" class=\"data row23 col5\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col6\" class=\"data row23 col6\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col7\" class=\"data row23 col7\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col8\" class=\"data row23 col8\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col9\" class=\"data row23 col9\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col10\" class=\"data row23 col10\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col11\" class=\"data row23 col11\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col12\" class=\"data row23 col12\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col13\" class=\"data row23 col13\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col14\" class=\"data row23 col14\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col15\" class=\"data row23 col15\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col16\" class=\"data row23 col16\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col17\" class=\"data row23 col17\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col18\" class=\"data row23 col18\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col19\" class=\"data row23 col19\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col20\" class=\"data row23 col20\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col21\" class=\"data row23 col21\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col22\" class=\"data row23 col22\" >0.00</td>\n",
+       "      <td id=\"T_464db_row23_col23\" class=\"data row23 col23\" >1.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f8414d8de20>"
+       "<pandas.io.formats.style.Styler at 0x7ffb83449a00>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1490,7 +1520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1599,7 +1629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1622,7 +1652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1647,7 +1677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1678,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1702,7 +1732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1740,7 +1770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1807,7 +1837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1979,7 +2009,7 @@
        "p38a   0.364706  1.000000  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1994,151 +2024,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_3271c_row0_col0, #T_3271c_row1_col1, #T_3271c_row2_col2, #T_3271c_row3_col3, #T_3271c_row4_col4, #T_3271c_row5_col5, #T_3271c_row6_col6, #T_3271c_row7_col7, #T_3271c_row8_col8 {\n",
+       "#T_1a72b_row0_col0, #T_1a72b_row1_col1, #T_1a72b_row2_col2, #T_1a72b_row3_col3, #T_1a72b_row4_col4, #T_1a72b_row5_col5, #T_1a72b_row6_col6, #T_1a72b_row7_col7, #T_1a72b_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3271c_row0_col1, #T_3271c_row1_col0 {\n",
+       "#T_1a72b_row0_col1, #T_1a72b_row1_col0 {\n",
        "  background-color: #1c8e1c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_3271c_row0_col2, #T_3271c_row1_col2 {\n",
+       "#T_1a72b_row0_col2, #T_1a72b_row1_col2 {\n",
        "  background-color: #e8f2e8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col3, #T_3271c_row7_col3 {\n",
+       "#T_1a72b_row0_col3, #T_1a72b_row7_col3 {\n",
        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col4, #T_3271c_row3_col5, #T_3271c_row7_col4, #T_3271c_row8_col4 {\n",
+       "#T_1a72b_row0_col4, #T_1a72b_row3_col5, #T_1a72b_row7_col4, #T_1a72b_row8_col4 {\n",
        "  background-color: #add5ad;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col5 {\n",
+       "#T_1a72b_row0_col5 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col6 {\n",
+       "#T_1a72b_row0_col6 {\n",
        "  background-color: #97ca97;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col7 {\n",
+       "#T_1a72b_row0_col7 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row0_col8, #T_3271c_row3_col8, #T_3271c_row4_col6, #T_3271c_row6_col8, #T_3271c_row8_col6 {\n",
+       "#T_1a72b_row0_col8, #T_1a72b_row3_col8, #T_1a72b_row4_col6, #T_1a72b_row6_col8, #T_1a72b_row8_col6 {\n",
        "  background-color: #a7d2a7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col3, #T_3271c_row6_col3 {\n",
+       "#T_1a72b_row1_col3, #T_1a72b_row6_col3 {\n",
        "  background-color: #9ccd9c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col4, #T_3271c_row3_col4, #T_3271c_row4_col3, #T_3271c_row5_col7, #T_3271c_row7_col5, #T_3271c_row8_col7 {\n",
+       "#T_1a72b_row1_col4, #T_1a72b_row3_col4, #T_1a72b_row4_col3, #T_1a72b_row5_col7, #T_1a72b_row7_col5, #T_1a72b_row8_col7 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col5, #T_3271c_row4_col5 {\n",
+       "#T_1a72b_row1_col5, #T_1a72b_row4_col5 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col6, #T_3271c_row6_col7 {\n",
+       "#T_1a72b_row1_col6, #T_1a72b_row6_col7 {\n",
        "  background-color: #9dcd9d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col7, #T_3271c_row3_col7, #T_3271c_row8_col5 {\n",
+       "#T_1a72b_row1_col7, #T_1a72b_row3_col7, #T_1a72b_row8_col5 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row1_col8, #T_3271c_row4_col7, #T_3271c_row6_col5, #T_3271c_row7_col6 {\n",
+       "#T_1a72b_row1_col8, #T_1a72b_row4_col7, #T_1a72b_row6_col5, #T_1a72b_row7_col6 {\n",
        "  background-color: #a4d0a4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row2_col0, #T_3271c_row2_col1, #T_3271c_row2_col3, #T_3271c_row2_col4, #T_3271c_row2_col5, #T_3271c_row2_col6, #T_3271c_row2_col7, #T_3271c_row2_col8, #T_3271c_row5_col2, #T_3271c_row7_col2 {\n",
+       "#T_1a72b_row2_col0, #T_1a72b_row2_col1, #T_1a72b_row2_col3, #T_1a72b_row2_col4, #T_1a72b_row2_col5, #T_1a72b_row2_col6, #T_1a72b_row2_col7, #T_1a72b_row2_col8, #T_1a72b_row5_col2, #T_1a72b_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row3_col0, #T_3271c_row7_col1 {\n",
+       "#T_1a72b_row3_col0, #T_1a72b_row7_col1 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row3_col1 {\n",
+       "#T_1a72b_row3_col1 {\n",
        "  background-color: #96c996;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row3_col2, #T_3271c_row4_col2 {\n",
+       "#T_1a72b_row3_col2, #T_1a72b_row4_col2 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row3_col6 {\n",
+       "#T_1a72b_row3_col6 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row4_col0 {\n",
+       "#T_1a72b_row4_col0 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row4_col1, #T_3271c_row8_col1 {\n",
+       "#T_1a72b_row4_col1, #T_1a72b_row8_col1 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row4_col8, #T_3271c_row5_col6 {\n",
+       "#T_1a72b_row4_col8, #T_1a72b_row5_col6 {\n",
        "  background-color: #aad3aa;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row5_col0 {\n",
+       "#T_1a72b_row5_col0 {\n",
        "  background-color: #b5d9b5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row5_col1 {\n",
+       "#T_1a72b_row5_col1 {\n",
        "  background-color: #b2d7b2;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row5_col3 {\n",
+       "#T_1a72b_row5_col3 {\n",
        "  background-color: #b7dab7;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row5_col4 {\n",
+       "#T_1a72b_row5_col4 {\n",
        "  background-color: #badbba;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row5_col8, #T_3271c_row7_col0 {\n",
+       "#T_1a72b_row5_col8, #T_1a72b_row7_col0 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row6_col0 {\n",
+       "#T_1a72b_row6_col0 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row6_col1 {\n",
+       "#T_1a72b_row6_col1 {\n",
        "  background-color: #9acb9a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row6_col2, #T_3271c_row8_col2 {\n",
+       "#T_1a72b_row6_col2, #T_1a72b_row8_col2 {\n",
        "  background-color: #e1eee1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row6_col4, #T_3271c_row8_col3 {\n",
+       "#T_1a72b_row6_col4, #T_1a72b_row8_col3 {\n",
        "  background-color: #a9d3a9;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row7_col8 {\n",
+       "#T_1a72b_row7_col8 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_3271c_row8_col0 {\n",
+       "#T_1a72b_row8_col0 {\n",
        "  background-color: #a3d0a3;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_3271c_\">\n",
+       "<table id=\"T_1a72b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2155,121 +2185,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_3271c_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
-       "      <td id=\"T_3271c_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
-       "      <td id=\"T_3271c_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
-       "      <td id=\"T_3271c_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
-       "      <td id=\"T_3271c_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
-       "      <td id=\"T_3271c_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_3271c_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
-       "      <td id=\"T_3271c_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
-       "      <td id=\"T_3271c_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
-       "      <td id=\"T_3271c_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
-       "      <td id=\"T_3271c_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
-       "      <td id=\"T_3271c_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_3271c_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
-       "      <td id=\"T_3271c_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
-       "      <td id=\"T_3271c_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
-       "      <td id=\"T_3271c_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
-       "      <td id=\"T_3271c_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
-       "      <td id=\"T_3271c_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
-       "      <td id=\"T_3271c_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
-       "      <td id=\"T_3271c_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_3271c_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
-       "      <td id=\"T_3271c_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
-       "      <td id=\"T_3271c_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
-       "      <td id=\"T_3271c_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
-       "      <td id=\"T_3271c_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_3271c_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
-       "      <td id=\"T_3271c_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
-       "      <td id=\"T_3271c_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
-       "      <td id=\"T_3271c_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_3271c_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
-       "      <td id=\"T_3271c_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
-       "      <td id=\"T_3271c_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
-       "      <td id=\"T_3271c_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
-       "      <td id=\"T_3271c_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
-       "      <td id=\"T_3271c_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
-       "      <td id=\"T_3271c_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_3271c_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
-       "      <td id=\"T_3271c_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
-       "      <td id=\"T_3271c_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
-       "      <td id=\"T_3271c_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
-       "      <td id=\"T_3271c_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
-       "      <td id=\"T_3271c_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_3271c_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
-       "      <td id=\"T_3271c_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
-       "      <td id=\"T_3271c_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
-       "      <td id=\"T_3271c_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_3271c_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_3271c_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_3271c_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
-       "      <td id=\"T_3271c_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
-       "      <td id=\"T_3271c_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
-       "      <td id=\"T_3271c_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
-       "      <td id=\"T_3271c_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
-       "      <td id=\"T_3271c_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
-       "      <td id=\"T_3271c_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
-       "      <td id=\"T_3271c_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
-       "      <td id=\"T_3271c_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_1a72b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1a72b_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row0_col1\" class=\"data row0 col1\" >0.894</td>\n",
+       "      <td id=\"T_1a72b_row0_col2\" class=\"data row0 col2\" >0.118</td>\n",
+       "      <td id=\"T_1a72b_row0_col3\" class=\"data row0 col3\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row0_col4\" class=\"data row0 col4\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row0_col5\" class=\"data row0 col5\" >0.318</td>\n",
+       "      <td id=\"T_1a72b_row0_col6\" class=\"data row0 col6\" >0.447</td>\n",
+       "      <td id=\"T_1a72b_row0_col7\" class=\"data row0 col7\" >0.459</td>\n",
+       "      <td id=\"T_1a72b_row0_col8\" class=\"data row0 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1a72b_row1_col0\" class=\"data row1 col0\" >0.894</td>\n",
+       "      <td id=\"T_1a72b_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row1_col2\" class=\"data row1 col2\" >0.118</td>\n",
+       "      <td id=\"T_1a72b_row1_col3\" class=\"data row1 col3\" >0.435</td>\n",
+       "      <td id=\"T_1a72b_row1_col4\" class=\"data row1 col4\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row1_col5\" class=\"data row1 col5\" >0.329</td>\n",
+       "      <td id=\"T_1a72b_row1_col6\" class=\"data row1 col6\" >0.424</td>\n",
+       "      <td id=\"T_1a72b_row1_col7\" class=\"data row1 col7\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row1_col8\" class=\"data row1 col8\" >0.400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_1a72b_row2_col0\" class=\"data row2 col0\" >0.118</td>\n",
+       "      <td id=\"T_1a72b_row2_col1\" class=\"data row2 col1\" >0.118</td>\n",
+       "      <td id=\"T_1a72b_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row2_col3\" class=\"data row2 col3\" >0.153</td>\n",
+       "      <td id=\"T_1a72b_row2_col4\" class=\"data row2 col4\" >0.153</td>\n",
+       "      <td id=\"T_1a72b_row2_col5\" class=\"data row2 col5\" >0.106</td>\n",
+       "      <td id=\"T_1a72b_row2_col6\" class=\"data row2 col6\" >0.141</td>\n",
+       "      <td id=\"T_1a72b_row2_col7\" class=\"data row2 col7\" >0.106</td>\n",
+       "      <td id=\"T_1a72b_row2_col8\" class=\"data row2 col8\" >0.141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_1a72b_row3_col0\" class=\"data row3 col0\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row3_col1\" class=\"data row3 col1\" >0.435</td>\n",
+       "      <td id=\"T_1a72b_row3_col2\" class=\"data row3 col2\" >0.153</td>\n",
+       "      <td id=\"T_1a72b_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row3_col4\" class=\"data row3 col4\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row3_col5\" class=\"data row3 col5\" >0.341</td>\n",
+       "      <td id=\"T_1a72b_row3_col6\" class=\"data row3 col6\" >0.435</td>\n",
+       "      <td id=\"T_1a72b_row3_col7\" class=\"data row3 col7\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row3_col8\" class=\"data row3 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_1a72b_row4_col0\" class=\"data row4 col0\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row4_col1\" class=\"data row4 col1\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row4_col2\" class=\"data row4 col2\" >0.153</td>\n",
+       "      <td id=\"T_1a72b_row4_col3\" class=\"data row4 col3\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row4_col5\" class=\"data row4 col5\" >0.329</td>\n",
+       "      <td id=\"T_1a72b_row4_col6\" class=\"data row4 col6\" >0.388</td>\n",
+       "      <td id=\"T_1a72b_row4_col7\" class=\"data row4 col7\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row4_col8\" class=\"data row4 col8\" >0.376</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_1a72b_row5_col0\" class=\"data row5 col0\" >0.318</td>\n",
+       "      <td id=\"T_1a72b_row5_col1\" class=\"data row5 col1\" >0.329</td>\n",
+       "      <td id=\"T_1a72b_row5_col2\" class=\"data row5 col2\" >0.106</td>\n",
+       "      <td id=\"T_1a72b_row5_col3\" class=\"data row5 col3\" >0.341</td>\n",
+       "      <td id=\"T_1a72b_row5_col4\" class=\"data row5 col4\" >0.329</td>\n",
+       "      <td id=\"T_1a72b_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row5_col6\" class=\"data row5 col6\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row5_col7\" class=\"data row5 col7\" >0.365</td>\n",
+       "      <td id=\"T_1a72b_row5_col8\" class=\"data row5 col8\" >0.471</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_1a72b_row6_col0\" class=\"data row6 col0\" >0.447</td>\n",
+       "      <td id=\"T_1a72b_row6_col1\" class=\"data row6 col1\" >0.424</td>\n",
+       "      <td id=\"T_1a72b_row6_col2\" class=\"data row6 col2\" >0.141</td>\n",
+       "      <td id=\"T_1a72b_row6_col3\" class=\"data row6 col3\" >0.435</td>\n",
+       "      <td id=\"T_1a72b_row6_col4\" class=\"data row6 col4\" >0.388</td>\n",
+       "      <td id=\"T_1a72b_row6_col5\" class=\"data row6 col5\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row6_col7\" class=\"data row6 col7\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row6_col8\" class=\"data row6 col8\" >0.388</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_1a72b_row7_col0\" class=\"data row7 col0\" >0.459</td>\n",
+       "      <td id=\"T_1a72b_row7_col1\" class=\"data row7 col1\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row7_col2\" class=\"data row7 col2\" >0.106</td>\n",
+       "      <td id=\"T_1a72b_row7_col3\" class=\"data row7 col3\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row7_col4\" class=\"data row7 col4\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row7_col5\" class=\"data row7 col5\" >0.365</td>\n",
+       "      <td id=\"T_1a72b_row7_col6\" class=\"data row7 col6\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_1a72b_row7_col8\" class=\"data row7 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1a72b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_1a72b_row8_col0\" class=\"data row8 col0\" >0.388</td>\n",
+       "      <td id=\"T_1a72b_row8_col1\" class=\"data row8 col1\" >0.400</td>\n",
+       "      <td id=\"T_1a72b_row8_col2\" class=\"data row8 col2\" >0.141</td>\n",
+       "      <td id=\"T_1a72b_row8_col3\" class=\"data row8 col3\" >0.388</td>\n",
+       "      <td id=\"T_1a72b_row8_col4\" class=\"data row8 col4\" >0.376</td>\n",
+       "      <td id=\"T_1a72b_row8_col5\" class=\"data row8 col5\" >0.471</td>\n",
+       "      <td id=\"T_1a72b_row8_col6\" class=\"data row8 col6\" >0.388</td>\n",
+       "      <td id=\"T_1a72b_row8_col7\" class=\"data row8 col7\" >0.365</td>\n",
+       "      <td id=\"T_1a72b_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f8414e43880>"
+       "<pandas.io.formats.style.Styler at 0x7ffb838591c0>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2289,7 +2319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2461,7 +2491,7 @@
        "p38a   0.629355  1.000000  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2476,195 +2506,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
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-       "#T_2433d_row0_col1 {\n",
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        "  background-color: #178b17;\n",
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-       "#T_2433d_row0_col2 {\n",
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        "  background-color: #deedde;\n",
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-       "#T_2433d_row0_col3 {\n",
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        "  background-color: #8fc68f;\n",
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-       "#T_2433d_row0_col6 {\n",
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        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
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-       "#T_2433d_row0_col7 {\n",
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-       "#T_2433d_row0_col8, #T_2433d_row8_col1 {\n",
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-       "#T_2433d_row1_col0 {\n",
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        "  background-color: #e3efe3;\n",
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-       "#T_2433d_row1_col3 {\n",
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-       "#T_2433d_row1_col6 {\n",
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        "  background-color: #86c286;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row1_col7 {\n",
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        "  background-color: #75b975;\n",
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-       "#T_2433d_row2_col0, #T_2433d_row2_col1, #T_2433d_row2_col3, #T_2433d_row2_col4, #T_2433d_row2_col5, #T_2433d_row2_col6, #T_2433d_row2_col7, #T_2433d_row2_col8, #T_2433d_row7_col2 {\n",
+       "#T_32cd0_row2_col0, #T_32cd0_row2_col1, #T_32cd0_row2_col3, #T_32cd0_row2_col4, #T_32cd0_row2_col5, #T_32cd0_row2_col6, #T_32cd0_row2_col7, #T_32cd0_row2_col8, #T_32cd0_row7_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row3_col0, #T_2433d_row7_col3 {\n",
+       "#T_32cd0_row3_col0, #T_32cd0_row7_col3 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row3_col1, #T_2433d_row6_col7 {\n",
+       "#T_32cd0_row3_col1, #T_32cd0_row6_col7 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row3_col2, #T_2433d_row5_col2 {\n",
+       "#T_32cd0_row3_col2, #T_32cd0_row5_col2 {\n",
        "  background-color: #e0eedf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row3_col4 {\n",
+       "#T_32cd0_row3_col4 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row3_col5, #T_2433d_row4_col0 {\n",
+       "#T_32cd0_row3_col5, #T_32cd0_row4_col0 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row3_col6, #T_2433d_row4_col3, #T_2433d_row5_col7 {\n",
+       "#T_32cd0_row3_col6, #T_32cd0_row4_col3, #T_32cd0_row5_col7 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row3_col7 {\n",
+       "#T_32cd0_row3_col7 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row4_col1 {\n",
+       "#T_32cd0_row4_col1 {\n",
        "  background-color: #8ac48a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row4_col2 {\n",
+       "#T_32cd0_row4_col2 {\n",
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        "}\n",
-       "#T_2433d_row4_col6, #T_2433d_row4_col7, #T_2433d_row7_col5 {\n",
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        "  background-color: #8cc58c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row4_col8 {\n",
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        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col0, #T_2433d_row8_col6 {\n",
+       "#T_32cd0_row5_col0, #T_32cd0_row8_col6 {\n",
        "  background-color: #90c790;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col1 {\n",
+       "#T_32cd0_row5_col1 {\n",
        "  background-color: #94c994;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col3, #T_2433d_row8_col3 {\n",
+       "#T_32cd0_row5_col3, #T_32cd0_row8_col3 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col4 {\n",
+       "#T_32cd0_row5_col4 {\n",
        "  background-color: #93c893;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col6, #T_2433d_row6_col4 {\n",
+       "#T_32cd0_row5_col6, #T_32cd0_row6_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row5_col8 {\n",
+       "#T_32cd0_row5_col8 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row6_col0 {\n",
+       "#T_32cd0_row6_col0 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row6_col1 {\n",
+       "#T_32cd0_row6_col1 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row6_col2 {\n",
+       "#T_32cd0_row6_col2 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row6_col3 {\n",
+       "#T_32cd0_row6_col3 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row6_col5 {\n",
+       "#T_32cd0_row6_col5 {\n",
        "  background-color: #82c082;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row6_col8 {\n",
+       "#T_32cd0_row6_col8 {\n",
        "  background-color: #8bc48b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row7_col0 {\n",
+       "#T_32cd0_row7_col0 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_2433d_row7_col1 {\n",
+       "#T_32cd0_row7_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row7_col6 {\n",
+       "#T_32cd0_row7_col6 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row7_col8 {\n",
+       "#T_32cd0_row7_col8 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row8_col0 {\n",
+       "#T_32cd0_row8_col0 {\n",
        "  background-color: #91c791;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row8_col2 {\n",
+       "#T_32cd0_row8_col2 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_2433d_row8_col5 {\n",
+       "#T_32cd0_row8_col5 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_2433d_\">\n",
+       "<table id=\"T_32cd0_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2681,121 +2711,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_2433d_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
-       "      <td id=\"T_2433d_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
-       "      <td id=\"T_2433d_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
-       "      <td id=\"T_2433d_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
-       "      <td id=\"T_2433d_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
-       "      <td id=\"T_2433d_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
-       "      <td id=\"T_2433d_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
-       "      <td id=\"T_2433d_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_2433d_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
-       "      <td id=\"T_2433d_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
-       "      <td id=\"T_2433d_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
-       "      <td id=\"T_2433d_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
-       "      <td id=\"T_2433d_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
-       "      <td id=\"T_2433d_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
-       "      <td id=\"T_2433d_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
-       "      <td id=\"T_2433d_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_2433d_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
-       "      <td id=\"T_2433d_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
-       "      <td id=\"T_2433d_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
-       "      <td id=\"T_2433d_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
-       "      <td id=\"T_2433d_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
-       "      <td id=\"T_2433d_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
-       "      <td id=\"T_2433d_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
-       "      <td id=\"T_2433d_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_2433d_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
-       "      <td id=\"T_2433d_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
-       "      <td id=\"T_2433d_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
-       "      <td id=\"T_2433d_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
-       "      <td id=\"T_2433d_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
-       "      <td id=\"T_2433d_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
-       "      <td id=\"T_2433d_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
-       "      <td id=\"T_2433d_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_2433d_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
-       "      <td id=\"T_2433d_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
-       "      <td id=\"T_2433d_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
-       "      <td id=\"T_2433d_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
-       "      <td id=\"T_2433d_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
-       "      <td id=\"T_2433d_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
-       "      <td id=\"T_2433d_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
-       "      <td id=\"T_2433d_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_2433d_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
-       "      <td id=\"T_2433d_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
-       "      <td id=\"T_2433d_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
-       "      <td id=\"T_2433d_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
-       "      <td id=\"T_2433d_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
-       "      <td id=\"T_2433d_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
-       "      <td id=\"T_2433d_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
-       "      <td id=\"T_2433d_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_2433d_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
-       "      <td id=\"T_2433d_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
-       "      <td id=\"T_2433d_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
-       "      <td id=\"T_2433d_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
-       "      <td id=\"T_2433d_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
-       "      <td id=\"T_2433d_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
-       "      <td id=\"T_2433d_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
-       "      <td id=\"T_2433d_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_2433d_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
-       "      <td id=\"T_2433d_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
-       "      <td id=\"T_2433d_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
-       "      <td id=\"T_2433d_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
-       "      <td id=\"T_2433d_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
-       "      <td id=\"T_2433d_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
-       "      <td id=\"T_2433d_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
-       "      <td id=\"T_2433d_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
-       "      <td id=\"T_2433d_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_2433d_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_2433d_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
-       "      <td id=\"T_2433d_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
-       "      <td id=\"T_2433d_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
-       "      <td id=\"T_2433d_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
-       "      <td id=\"T_2433d_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
-       "      <td id=\"T_2433d_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
-       "      <td id=\"T_2433d_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
-       "      <td id=\"T_2433d_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
-       "      <td id=\"T_2433d_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
+       "      <th id=\"T_32cd0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_32cd0_row0_col0\" class=\"data row0 col0\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row0_col1\" class=\"data row0 col1\" >0.941</td>\n",
+       "      <td id=\"T_32cd0_row0_col2\" class=\"data row0 col2\" >0.427</td>\n",
+       "      <td id=\"T_32cd0_row0_col3\" class=\"data row0 col3\" >0.716</td>\n",
+       "      <td id=\"T_32cd0_row0_col4\" class=\"data row0 col4\" >0.655</td>\n",
+       "      <td id=\"T_32cd0_row0_col5\" class=\"data row0 col5\" >0.648</td>\n",
+       "      <td id=\"T_32cd0_row0_col6\" class=\"data row0 col6\" >0.711</td>\n",
+       "      <td id=\"T_32cd0_row0_col7\" class=\"data row0 col7\" >0.711</td>\n",
+       "      <td id=\"T_32cd0_row0_col8\" class=\"data row0 col8\" >0.645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_32cd0_row1_col0\" class=\"data row1 col0\" >0.941</td>\n",
+       "      <td id=\"T_32cd0_row1_col1\" class=\"data row1 col1\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row1_col2\" class=\"data row1 col2\" >0.414</td>\n",
+       "      <td id=\"T_32cd0_row1_col3\" class=\"data row1 col3\" >0.702</td>\n",
+       "      <td id=\"T_32cd0_row1_col4\" class=\"data row1 col4\" >0.655</td>\n",
+       "      <td id=\"T_32cd0_row1_col5\" class=\"data row1 col5\" >0.630</td>\n",
+       "      <td id=\"T_32cd0_row1_col6\" class=\"data row1 col6\" >0.685</td>\n",
+       "      <td id=\"T_32cd0_row1_col7\" class=\"data row1 col7\" >0.698</td>\n",
+       "      <td id=\"T_32cd0_row1_col8\" class=\"data row1 col8\" >0.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_32cd0_row2_col0\" class=\"data row2 col0\" >0.427</td>\n",
+       "      <td id=\"T_32cd0_row2_col1\" class=\"data row2 col1\" >0.414</td>\n",
+       "      <td id=\"T_32cd0_row2_col2\" class=\"data row2 col2\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row2_col3\" class=\"data row2 col3\" >0.423</td>\n",
+       "      <td id=\"T_32cd0_row2_col4\" class=\"data row2 col4\" >0.436</td>\n",
+       "      <td id=\"T_32cd0_row2_col5\" class=\"data row2 col5\" >0.424</td>\n",
+       "      <td id=\"T_32cd0_row2_col6\" class=\"data row2 col6\" >0.451</td>\n",
+       "      <td id=\"T_32cd0_row2_col7\" class=\"data row2 col7\" >0.394</td>\n",
+       "      <td id=\"T_32cd0_row2_col8\" class=\"data row2 col8\" >0.431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_32cd0_row3_col0\" class=\"data row3 col0\" >0.716</td>\n",
+       "      <td id=\"T_32cd0_row3_col1\" class=\"data row3 col1\" >0.702</td>\n",
+       "      <td id=\"T_32cd0_row3_col2\" class=\"data row3 col2\" >0.423</td>\n",
+       "      <td id=\"T_32cd0_row3_col3\" class=\"data row3 col3\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row3_col4\" class=\"data row3 col4\" >0.671</td>\n",
+       "      <td id=\"T_32cd0_row3_col5\" class=\"data row3 col5\" >0.653</td>\n",
+       "      <td id=\"T_32cd0_row3_col6\" class=\"data row3 col6\" >0.688</td>\n",
+       "      <td id=\"T_32cd0_row3_col7\" class=\"data row3 col7\" >0.714</td>\n",
+       "      <td id=\"T_32cd0_row3_col8\" class=\"data row3 col8\" >0.653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_32cd0_row4_col0\" class=\"data row4 col0\" >0.655</td>\n",
+       "      <td id=\"T_32cd0_row4_col1\" class=\"data row4 col1\" >0.655</td>\n",
+       "      <td id=\"T_32cd0_row4_col2\" class=\"data row4 col2\" >0.436</td>\n",
+       "      <td id=\"T_32cd0_row4_col3\" class=\"data row4 col3\" >0.671</td>\n",
+       "      <td id=\"T_32cd0_row4_col4\" class=\"data row4 col4\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row4_col5\" class=\"data row4 col5\" >0.647</td>\n",
+       "      <td id=\"T_32cd0_row4_col6\" class=\"data row4 col6\" >0.673</td>\n",
+       "      <td id=\"T_32cd0_row4_col7\" class=\"data row4 col7\" >0.638</td>\n",
+       "      <td id=\"T_32cd0_row4_col8\" class=\"data row4 col8\" >0.638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_32cd0_row5_col0\" class=\"data row5 col0\" >0.648</td>\n",
+       "      <td id=\"T_32cd0_row5_col1\" class=\"data row5 col1\" >0.630</td>\n",
+       "      <td id=\"T_32cd0_row5_col2\" class=\"data row5 col2\" >0.424</td>\n",
+       "      <td id=\"T_32cd0_row5_col3\" class=\"data row5 col3\" >0.653</td>\n",
+       "      <td id=\"T_32cd0_row5_col4\" class=\"data row5 col4\" >0.647</td>\n",
+       "      <td id=\"T_32cd0_row5_col5\" class=\"data row5 col5\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row5_col6\" class=\"data row5 col6\" >0.681</td>\n",
+       "      <td id=\"T_32cd0_row5_col7\" class=\"data row5 col7\" >0.656</td>\n",
+       "      <td id=\"T_32cd0_row5_col8\" class=\"data row5 col8\" >0.723</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_32cd0_row6_col0\" class=\"data row6 col0\" >0.711</td>\n",
+       "      <td id=\"T_32cd0_row6_col1\" class=\"data row6 col1\" >0.685</td>\n",
+       "      <td id=\"T_32cd0_row6_col2\" class=\"data row6 col2\" >0.451</td>\n",
+       "      <td id=\"T_32cd0_row6_col3\" class=\"data row6 col3\" >0.688</td>\n",
+       "      <td id=\"T_32cd0_row6_col4\" class=\"data row6 col4\" >0.673</td>\n",
+       "      <td id=\"T_32cd0_row6_col5\" class=\"data row6 col5\" >0.681</td>\n",
+       "      <td id=\"T_32cd0_row6_col6\" class=\"data row6 col6\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row6_col7\" class=\"data row6 col7\" >0.691</td>\n",
+       "      <td id=\"T_32cd0_row6_col8\" class=\"data row6 col8\" >0.663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_32cd0_row7_col0\" class=\"data row7 col0\" >0.711</td>\n",
+       "      <td id=\"T_32cd0_row7_col1\" class=\"data row7 col1\" >0.698</td>\n",
+       "      <td id=\"T_32cd0_row7_col2\" class=\"data row7 col2\" >0.394</td>\n",
+       "      <td id=\"T_32cd0_row7_col3\" class=\"data row7 col3\" >0.714</td>\n",
+       "      <td id=\"T_32cd0_row7_col4\" class=\"data row7 col4\" >0.638</td>\n",
+       "      <td id=\"T_32cd0_row7_col5\" class=\"data row7 col5\" >0.656</td>\n",
+       "      <td id=\"T_32cd0_row7_col6\" class=\"data row7 col6\" >0.691</td>\n",
+       "      <td id=\"T_32cd0_row7_col7\" class=\"data row7 col7\" >1.000</td>\n",
+       "      <td id=\"T_32cd0_row7_col8\" class=\"data row7 col8\" >0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_32cd0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_32cd0_row8_col0\" class=\"data row8 col0\" >0.645</td>\n",
+       "      <td id=\"T_32cd0_row8_col1\" class=\"data row8 col1\" >0.635</td>\n",
+       "      <td id=\"T_32cd0_row8_col2\" class=\"data row8 col2\" >0.431</td>\n",
+       "      <td id=\"T_32cd0_row8_col3\" class=\"data row8 col3\" >0.653</td>\n",
+       "      <td id=\"T_32cd0_row8_col4\" class=\"data row8 col4\" >0.638</td>\n",
+       "      <td id=\"T_32cd0_row8_col5\" class=\"data row8 col5\" >0.723</td>\n",
+       "      <td id=\"T_32cd0_row8_col6\" class=\"data row8 col6\" >0.663</td>\n",
+       "      <td id=\"T_32cd0_row8_col7\" class=\"data row8 col7\" >0.629</td>\n",
+       "      <td id=\"T_32cd0_row8_col8\" class=\"data row8 col8\" >1.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f840ffb5bb0>"
+       "<pandas.io.formats.style.Styler at 0x7ffb83847040>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2817,7 +2847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2833,7 +2863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2857,7 +2887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2879,7 +2909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2888,195 +2918,195 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_29a76_row0_col0, #T_29a76_row1_col1, #T_29a76_row2_col2, #T_29a76_row3_col3, #T_29a76_row4_col4, #T_29a76_row5_col5, #T_29a76_row6_col6, #T_29a76_row7_col7, #T_29a76_row8_col8 {\n",
+       "#T_1cfc3_row0_col0, #T_1cfc3_row1_col1, #T_1cfc3_row2_col2, #T_1cfc3_row3_col3, #T_1cfc3_row4_col4, #T_1cfc3_row5_col5, #T_1cfc3_row6_col6, #T_1cfc3_row7_col7, #T_1cfc3_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row0_col1 {\n",
+       "#T_1cfc3_row0_col1 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row0_col2 {\n",
+       "#T_1cfc3_row0_col2 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row0_col3 {\n",
+       "#T_1cfc3_row0_col3 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row0_col4, #T_29a76_row0_col5, #T_29a76_row1_col4, #T_29a76_row4_col5, #T_29a76_row8_col7 {\n",
+       "#T_1cfc3_row0_col4, #T_1cfc3_row0_col5, #T_1cfc3_row1_col4, #T_1cfc3_row4_col5, #T_1cfc3_row8_col7 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row0_col6 {\n",
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        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row0_col7 {\n",
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        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row0_col8, #T_29a76_row8_col1 {\n",
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        "  background-color: #58ab58;\n",
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        "}\n",
-       "#T_29a76_row1_col0 {\n",
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        "  background-color: #d3e7d3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row1_col2 {\n",
+       "#T_1cfc3_row1_col2 {\n",
        "  background-color: #078407;\n",
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        "}\n",
-       "#T_29a76_row1_col3 {\n",
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-       "#T_29a76_row1_col5, #T_29a76_row1_col8, #T_29a76_row7_col4, #T_29a76_row8_col4 {\n",
+       "#T_1cfc3_row1_col5, #T_1cfc3_row1_col8, #T_1cfc3_row7_col4, #T_1cfc3_row8_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
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-       "#T_29a76_row1_col6 {\n",
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        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row1_col7 {\n",
+       "#T_1cfc3_row1_col7 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
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-       "#T_29a76_row2_col0, #T_29a76_row2_col1, #T_29a76_row2_col3, #T_29a76_row2_col4, #T_29a76_row2_col5, #T_29a76_row2_col6, #T_29a76_row2_col7, #T_29a76_row2_col8, #T_29a76_row7_col2 {\n",
+       "#T_1cfc3_row2_col0, #T_1cfc3_row2_col1, #T_1cfc3_row2_col3, #T_1cfc3_row2_col4, #T_1cfc3_row2_col5, #T_1cfc3_row2_col6, #T_1cfc3_row2_col7, #T_1cfc3_row2_col8, #T_1cfc3_row7_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col0, #T_29a76_row7_col3 {\n",
+       "#T_1cfc3_row3_col0, #T_1cfc3_row7_col3 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col1, #T_29a76_row6_col7 {\n",
+       "#T_1cfc3_row3_col1, #T_1cfc3_row6_col7 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col2, #T_29a76_row5_col2 {\n",
+       "#T_1cfc3_row3_col2, #T_1cfc3_row5_col2 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col4 {\n",
+       "#T_1cfc3_row3_col4 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col5, #T_29a76_row4_col0 {\n",
+       "#T_1cfc3_row3_col5, #T_1cfc3_row4_col0 {\n",
        "  background-color: #5dae5d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col6, #T_29a76_row4_col3, #T_29a76_row5_col7 {\n",
+       "#T_1cfc3_row3_col6, #T_1cfc3_row4_col3, #T_1cfc3_row5_col7 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row3_col7 {\n",
+       "#T_1cfc3_row3_col7 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row3_col8 {\n",
+       "#T_1cfc3_row3_col8 {\n",
        "  background-color: #5cad5c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row4_col1 {\n",
+       "#T_1cfc3_row4_col1 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row4_col2 {\n",
+       "#T_1cfc3_row4_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row4_col6, #T_29a76_row4_col7, #T_29a76_row7_col5 {\n",
+       "#T_1cfc3_row4_col6, #T_1cfc3_row4_col7, #T_1cfc3_row7_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row4_col8, #T_29a76_row5_col1 {\n",
+       "#T_1cfc3_row4_col8, #T_1cfc3_row5_col1 {\n",
        "  background-color: #56aa56;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row5_col0, #T_29a76_row8_col6 {\n",
+       "#T_1cfc3_row5_col0, #T_1cfc3_row8_col6 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row5_col3, #T_29a76_row8_col3 {\n",
+       "#T_1cfc3_row5_col3, #T_1cfc3_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row5_col4 {\n",
+       "#T_1cfc3_row5_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row5_col6, #T_29a76_row6_col4 {\n",
+       "#T_1cfc3_row5_col6, #T_1cfc3_row6_col4 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row5_col8 {\n",
+       "#T_1cfc3_row5_col8 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_29a76_row6_col0 {\n",
+       "#T_1cfc3_row6_col0 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row6_col1 {\n",
+       "#T_1cfc3_row6_col1 {\n",
        "  background-color: #6db56d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row6_col2 {\n",
+       "#T_1cfc3_row6_col2 {\n",
        "  background-color: #168b16;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row6_col3 {\n",
+       "#T_1cfc3_row6_col3 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row6_col5 {\n",
+       "#T_1cfc3_row6_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row6_col8 {\n",
+       "#T_1cfc3_row6_col8 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row7_col0 {\n",
+       "#T_1cfc3_row7_col0 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row7_col1 {\n",
+       "#T_1cfc3_row7_col1 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row7_col6 {\n",
+       "#T_1cfc3_row7_col6 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row7_col8 {\n",
+       "#T_1cfc3_row7_col8 {\n",
        "  background-color: #52a852;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row8_col0 {\n",
+       "#T_1cfc3_row8_col0 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row8_col2 {\n",
+       "#T_1cfc3_row8_col2 {\n",
        "  background-color: #0e870e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_29a76_row8_col5 {\n",
+       "#T_1cfc3_row8_col5 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_29a76_\">\n",
+       "<table id=\"T_1cfc3_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3093,184 +3123,127 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_29a76_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
-       "      <td id=\"T_29a76_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
-       "      <td id=\"T_29a76_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
-       "      <td id=\"T_29a76_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
-       "      <td id=\"T_29a76_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
-       "      <td id=\"T_29a76_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
-       "      <td id=\"T_29a76_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
-       "      <td id=\"T_29a76_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_29a76_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
-       "      <td id=\"T_29a76_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
-       "      <td id=\"T_29a76_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
-       "      <td id=\"T_29a76_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
-       "      <td id=\"T_29a76_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
-       "      <td id=\"T_29a76_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
-       "      <td id=\"T_29a76_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
-       "      <td id=\"T_29a76_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_29a76_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
-       "      <td id=\"T_29a76_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
-       "      <td id=\"T_29a76_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
-       "      <td id=\"T_29a76_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
-       "      <td id=\"T_29a76_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
-       "      <td id=\"T_29a76_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
-       "      <td id=\"T_29a76_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
-       "      <td id=\"T_29a76_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_29a76_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
-       "      <td id=\"T_29a76_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
-       "      <td id=\"T_29a76_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
-       "      <td id=\"T_29a76_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
-       "      <td id=\"T_29a76_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
-       "      <td id=\"T_29a76_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
-       "      <td id=\"T_29a76_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
-       "      <td id=\"T_29a76_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_29a76_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
-       "      <td id=\"T_29a76_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
-       "      <td id=\"T_29a76_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
-       "      <td id=\"T_29a76_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
-       "      <td id=\"T_29a76_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
-       "      <td id=\"T_29a76_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
-       "      <td id=\"T_29a76_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
-       "      <td id=\"T_29a76_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_29a76_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
-       "      <td id=\"T_29a76_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
-       "      <td id=\"T_29a76_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
-       "      <td id=\"T_29a76_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
-       "      <td id=\"T_29a76_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
-       "      <td id=\"T_29a76_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
-       "      <td id=\"T_29a76_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
-       "      <td id=\"T_29a76_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_29a76_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
-       "      <td id=\"T_29a76_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
-       "      <td id=\"T_29a76_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
-       "      <td id=\"T_29a76_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
-       "      <td id=\"T_29a76_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
-       "      <td id=\"T_29a76_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
-       "      <td id=\"T_29a76_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
-       "      <td id=\"T_29a76_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_29a76_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
-       "      <td id=\"T_29a76_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
-       "      <td id=\"T_29a76_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
-       "      <td id=\"T_29a76_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
-       "      <td id=\"T_29a76_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
-       "      <td id=\"T_29a76_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
-       "      <td id=\"T_29a76_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
-       "      <td id=\"T_29a76_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_29a76_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_29a76_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_29a76_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
-       "      <td id=\"T_29a76_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
-       "      <td id=\"T_29a76_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
-       "      <td id=\"T_29a76_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
-       "      <td id=\"T_29a76_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
-       "      <td id=\"T_29a76_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
-       "      <td id=\"T_29a76_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
-       "      <td id=\"T_29a76_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
-       "      <td id=\"T_29a76_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_1cfc3_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1cfc3_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row0_col1\" class=\"data row0 col1\" >0.059</td>\n",
+       "      <td id=\"T_1cfc3_row0_col2\" class=\"data row0 col2\" >0.573</td>\n",
+       "      <td id=\"T_1cfc3_row0_col3\" class=\"data row0 col3\" >0.284</td>\n",
+       "      <td id=\"T_1cfc3_row0_col4\" class=\"data row0 col4\" >0.345</td>\n",
+       "      <td id=\"T_1cfc3_row0_col5\" class=\"data row0 col5\" >0.352</td>\n",
+       "      <td id=\"T_1cfc3_row0_col6\" class=\"data row0 col6\" >0.289</td>\n",
+       "      <td id=\"T_1cfc3_row0_col7\" class=\"data row0 col7\" >0.289</td>\n",
+       "      <td id=\"T_1cfc3_row0_col8\" class=\"data row0 col8\" >0.355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1cfc3_row1_col0\" class=\"data row1 col0\" >0.059</td>\n",
+       "      <td id=\"T_1cfc3_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row1_col2\" class=\"data row1 col2\" >0.586</td>\n",
+       "      <td id=\"T_1cfc3_row1_col3\" class=\"data row1 col3\" >0.298</td>\n",
+       "      <td id=\"T_1cfc3_row1_col4\" class=\"data row1 col4\" >0.345</td>\n",
+       "      <td id=\"T_1cfc3_row1_col5\" class=\"data row1 col5\" >0.370</td>\n",
+       "      <td id=\"T_1cfc3_row1_col6\" class=\"data row1 col6\" >0.315</td>\n",
+       "      <td id=\"T_1cfc3_row1_col7\" class=\"data row1 col7\" >0.302</td>\n",
+       "      <td id=\"T_1cfc3_row1_col8\" class=\"data row1 col8\" >0.365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_1cfc3_row2_col0\" class=\"data row2 col0\" >0.573</td>\n",
+       "      <td id=\"T_1cfc3_row2_col1\" class=\"data row2 col1\" >0.586</td>\n",
+       "      <td id=\"T_1cfc3_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row2_col3\" class=\"data row2 col3\" >0.577</td>\n",
+       "      <td id=\"T_1cfc3_row2_col4\" class=\"data row2 col4\" >0.564</td>\n",
+       "      <td id=\"T_1cfc3_row2_col5\" class=\"data row2 col5\" >0.576</td>\n",
+       "      <td id=\"T_1cfc3_row2_col6\" class=\"data row2 col6\" >0.549</td>\n",
+       "      <td id=\"T_1cfc3_row2_col7\" class=\"data row2 col7\" >0.606</td>\n",
+       "      <td id=\"T_1cfc3_row2_col8\" class=\"data row2 col8\" >0.569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_1cfc3_row3_col0\" class=\"data row3 col0\" >0.284</td>\n",
+       "      <td id=\"T_1cfc3_row3_col1\" class=\"data row3 col1\" >0.298</td>\n",
+       "      <td id=\"T_1cfc3_row3_col2\" class=\"data row3 col2\" >0.577</td>\n",
+       "      <td id=\"T_1cfc3_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row3_col4\" class=\"data row3 col4\" >0.329</td>\n",
+       "      <td id=\"T_1cfc3_row3_col5\" class=\"data row3 col5\" >0.347</td>\n",
+       "      <td id=\"T_1cfc3_row3_col6\" class=\"data row3 col6\" >0.312</td>\n",
+       "      <td id=\"T_1cfc3_row3_col7\" class=\"data row3 col7\" >0.286</td>\n",
+       "      <td id=\"T_1cfc3_row3_col8\" class=\"data row3 col8\" >0.347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_1cfc3_row4_col0\" class=\"data row4 col0\" >0.345</td>\n",
+       "      <td id=\"T_1cfc3_row4_col1\" class=\"data row4 col1\" >0.345</td>\n",
+       "      <td id=\"T_1cfc3_row4_col2\" class=\"data row4 col2\" >0.564</td>\n",
+       "      <td id=\"T_1cfc3_row4_col3\" class=\"data row4 col3\" >0.329</td>\n",
+       "      <td id=\"T_1cfc3_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row4_col5\" class=\"data row4 col5\" >0.353</td>\n",
+       "      <td id=\"T_1cfc3_row4_col6\" class=\"data row4 col6\" >0.327</td>\n",
+       "      <td id=\"T_1cfc3_row4_col7\" class=\"data row4 col7\" >0.362</td>\n",
+       "      <td id=\"T_1cfc3_row4_col8\" class=\"data row4 col8\" >0.362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_1cfc3_row5_col0\" class=\"data row5 col0\" >0.352</td>\n",
+       "      <td id=\"T_1cfc3_row5_col1\" class=\"data row5 col1\" >0.370</td>\n",
+       "      <td id=\"T_1cfc3_row5_col2\" class=\"data row5 col2\" >0.576</td>\n",
+       "      <td id=\"T_1cfc3_row5_col3\" class=\"data row5 col3\" >0.347</td>\n",
+       "      <td id=\"T_1cfc3_row5_col4\" class=\"data row5 col4\" >0.353</td>\n",
+       "      <td id=\"T_1cfc3_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row5_col6\" class=\"data row5 col6\" >0.319</td>\n",
+       "      <td id=\"T_1cfc3_row5_col7\" class=\"data row5 col7\" >0.344</td>\n",
+       "      <td id=\"T_1cfc3_row5_col8\" class=\"data row5 col8\" >0.277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_1cfc3_row6_col0\" class=\"data row6 col0\" >0.289</td>\n",
+       "      <td id=\"T_1cfc3_row6_col1\" class=\"data row6 col1\" >0.315</td>\n",
+       "      <td id=\"T_1cfc3_row6_col2\" class=\"data row6 col2\" >0.549</td>\n",
+       "      <td id=\"T_1cfc3_row6_col3\" class=\"data row6 col3\" >0.312</td>\n",
+       "      <td id=\"T_1cfc3_row6_col4\" class=\"data row6 col4\" >0.327</td>\n",
+       "      <td id=\"T_1cfc3_row6_col5\" class=\"data row6 col5\" >0.319</td>\n",
+       "      <td id=\"T_1cfc3_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row6_col7\" class=\"data row6 col7\" >0.309</td>\n",
+       "      <td id=\"T_1cfc3_row6_col8\" class=\"data row6 col8\" >0.337</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_1cfc3_row7_col0\" class=\"data row7 col0\" >0.289</td>\n",
+       "      <td id=\"T_1cfc3_row7_col1\" class=\"data row7 col1\" >0.302</td>\n",
+       "      <td id=\"T_1cfc3_row7_col2\" class=\"data row7 col2\" >0.606</td>\n",
+       "      <td id=\"T_1cfc3_row7_col3\" class=\"data row7 col3\" >0.286</td>\n",
+       "      <td id=\"T_1cfc3_row7_col4\" class=\"data row7 col4\" >0.362</td>\n",
+       "      <td id=\"T_1cfc3_row7_col5\" class=\"data row7 col5\" >0.344</td>\n",
+       "      <td id=\"T_1cfc3_row7_col6\" class=\"data row7 col6\" >0.309</td>\n",
+       "      <td id=\"T_1cfc3_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_1cfc3_row7_col8\" class=\"data row7 col8\" >0.371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1cfc3_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_1cfc3_row8_col0\" class=\"data row8 col0\" >0.355</td>\n",
+       "      <td id=\"T_1cfc3_row8_col1\" class=\"data row8 col1\" >0.365</td>\n",
+       "      <td id=\"T_1cfc3_row8_col2\" class=\"data row8 col2\" >0.569</td>\n",
+       "      <td id=\"T_1cfc3_row8_col3\" class=\"data row8 col3\" >0.347</td>\n",
+       "      <td id=\"T_1cfc3_row8_col4\" class=\"data row8 col4\" >0.362</td>\n",
+       "      <td id=\"T_1cfc3_row8_col5\" class=\"data row8 col5\" >0.277</td>\n",
+       "      <td id=\"T_1cfc3_row8_col6\" class=\"data row8 col6\" >0.337</td>\n",
+       "      <td id=\"T_1cfc3_row8_col7\" class=\"data row8 col7\" >0.371</td>\n",
+       "      <td id=\"T_1cfc3_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f8414ebe400>"
+       "<pandas.io.formats.style.Styler at 0x7ffb83897940>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
-    "    cmap=cm\n",
-    ").format(\"{:.3f}\")\n",
-    "kinase_distance_matrix_df_styler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We export the matrix with colors as an image to use as thumbnail for the website."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[1129/155748.371060:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155748.372200:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
-      "[1129/155748.373300:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155748.966480:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmpkm8yq0yn/temp.png.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "plt.imshow(matrix_styler_img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
diff --git a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
index 4ea372fa..31a66fc5 100644
--- a/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
+++ b/teachopencadd/talktorials/T025_kinase_similarity_kissim/talktorial.ipynb
@@ -159,10 +159,8 @@
     "import pandas as pd\n",
     "from sklearn.metrics import pairwise\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
     "import seaborn as sns\n",
-    "from opencadd.databases.klifs import setup_remote\n",
-    "import dataframe_image as dfi"
+    "from opencadd.databases.klifs import setup_remote"
    ]
   },
   {
@@ -831,7 +829,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -1477,188 +1479,188 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_95e80_row0_col0, #T_95e80_row1_col1, #T_95e80_row2_col2, #T_95e80_row3_col3, #T_95e80_row4_col4, #T_95e80_row5_col5, #T_95e80_row6_col6, #T_95e80_row7_col7, #T_95e80_row8_col8 {\n",
+       "#T_01361_row0_col0, #T_01361_row1_col1, #T_01361_row2_col2, #T_01361_row3_col3, #T_01361_row4_col4, #T_01361_row5_col5, #T_01361_row6_col6, #T_01361_row7_col7, #T_01361_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row0_col1, #T_95e80_row5_col4 {\n",
+       "#T_01361_row0_col1, #T_01361_row5_col4 {\n",
        "  background-color: #7dbd7d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row0_col2, #T_95e80_row4_col1, #T_95e80_row6_col0 {\n",
+       "#T_01361_row0_col2, #T_01361_row4_col1, #T_01361_row6_col0 {\n",
        "  background-color: #6cb56c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row0_col3, #T_95e80_row2_col0, #T_95e80_row4_col6 {\n",
+       "#T_01361_row0_col3, #T_01361_row2_col0, #T_01361_row4_col6 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row0_col4, #T_95e80_row0_col6 {\n",
+       "#T_01361_row0_col4, #T_01361_row0_col6 {\n",
        "  background-color: #72b872;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row0_col5 {\n",
+       "#T_01361_row0_col5 {\n",
        "  background-color: #69b369;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row0_col7 {\n",
+       "#T_01361_row0_col7 {\n",
        "  background-color: #148a14;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row0_col8 {\n",
+       "#T_01361_row0_col8 {\n",
        "  background-color: #66b266;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row1_col0, #T_95e80_row3_col2, #T_95e80_row5_col2 {\n",
+       "#T_01361_row1_col0, #T_01361_row3_col2, #T_01361_row5_col2 {\n",
        "  background-color: #80bf80;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row1_col2 {\n",
+       "#T_01361_row1_col2 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row1_col3 {\n",
+       "#T_01361_row1_col3 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row1_col4 {\n",
+       "#T_01361_row1_col4 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row1_col5 {\n",
+       "#T_01361_row1_col5 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row1_col6 {\n",
+       "#T_01361_row1_col6 {\n",
        "  background-color: #7ebe7e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row1_col7 {\n",
+       "#T_01361_row1_col7 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row1_col8 {\n",
+       "#T_01361_row1_col8 {\n",
        "  background-color: #74b974;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row2_col1, #T_95e80_row4_col5, #T_95e80_row6_col5, #T_95e80_row8_col4 {\n",
+       "#T_01361_row2_col1, #T_01361_row4_col5, #T_01361_row6_col5, #T_01361_row8_col4 {\n",
        "  background-color: #77ba77;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row2_col3 {\n",
+       "#T_01361_row2_col3 {\n",
        "  background-color: #8ec58e;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row2_col4 {\n",
+       "#T_01361_row2_col4 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row2_col5 {\n",
+       "#T_01361_row2_col5 {\n",
        "  background-color: #87c287;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row2_col6 {\n",
+       "#T_01361_row2_col6 {\n",
        "  background-color: #83c083;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row2_col7 {\n",
+       "#T_01361_row2_col7 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row2_col8, #T_95e80_row4_col3, #T_95e80_row8_col0, #T_95e80_row8_col5 {\n",
+       "#T_01361_row2_col8, #T_01361_row4_col3, #T_01361_row8_col0, #T_01361_row8_col5 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col0 {\n",
+       "#T_01361_row3_col0 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col1, #T_95e80_row3_col6, #T_95e80_row5_col0, #T_95e80_row6_col8 {\n",
+       "#T_01361_row3_col1, #T_01361_row3_col6, #T_01361_row5_col0, #T_01361_row6_col8 {\n",
        "  background-color: #64b164;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col4, #T_95e80_row5_col3 {\n",
+       "#T_01361_row3_col4, #T_01361_row5_col3 {\n",
        "  background-color: #62b062;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col5 {\n",
+       "#T_01361_row3_col5 {\n",
        "  background-color: #5bad5b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col7, #T_95e80_row7_col0, #T_95e80_row7_col1, #T_95e80_row7_col2, #T_95e80_row7_col3, #T_95e80_row7_col4, #T_95e80_row7_col5, #T_95e80_row7_col6, #T_95e80_row7_col8 {\n",
+       "#T_01361_row3_col7, #T_01361_row7_col0, #T_01361_row7_col1, #T_01361_row7_col2, #T_01361_row7_col3, #T_01361_row7_col4, #T_01361_row7_col5, #T_01361_row7_col6, #T_01361_row7_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row3_col8 {\n",
+       "#T_01361_row3_col8 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row4_col0 {\n",
+       "#T_01361_row4_col0 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row4_col2 {\n",
+       "#T_01361_row4_col2 {\n",
        "  background-color: #7abc7a;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row4_col7 {\n",
+       "#T_01361_row4_col7 {\n",
        "  background-color: #018001;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row4_col8 {\n",
+       "#T_01361_row4_col8 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row5_col1 {\n",
+       "#T_01361_row5_col1 {\n",
        "  background-color: #78bb78;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row5_col6, #T_95e80_row6_col2 {\n",
+       "#T_01361_row5_col6, #T_01361_row6_col2 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_95e80_row5_col7 {\n",
+       "#T_01361_row5_col7 {\n",
        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row5_col8 {\n",
+       "#T_01361_row5_col8 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row6_col1, #T_95e80_row6_col4 {\n",
+       "#T_01361_row6_col1, #T_01361_row6_col4 {\n",
        "  background-color: #76ba76;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row6_col3 {\n",
+       "#T_01361_row6_col3 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row6_col7 {\n",
+       "#T_01361_row6_col7 {\n",
        "  background-color: #0a850a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row8_col1 {\n",
+       "#T_01361_row8_col1 {\n",
        "  background-color: #6eb66e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row8_col2 {\n",
+       "#T_01361_row8_col2 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row8_col3 {\n",
+       "#T_01361_row8_col3 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row8_col6 {\n",
+       "#T_01361_row8_col6 {\n",
        "  background-color: #68b368;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_95e80_row8_col7 {\n",
+       "#T_01361_row8_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_95e80_\">\n",
+       "<table id=\"T_01361_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1675,118 +1677,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_95e80_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
-       "      <td id=\"T_95e80_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
-       "      <td id=\"T_95e80_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
-       "      <td id=\"T_95e80_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
-       "      <td id=\"T_95e80_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
-       "      <td id=\"T_95e80_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
-       "      <td id=\"T_95e80_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
-       "      <td id=\"T_95e80_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
+       "      <th id=\"T_01361_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_01361_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_01361_row0_col1\" class=\"data row0 col1\" >17.156</td>\n",
+       "      <td id=\"T_01361_row0_col2\" class=\"data row0 col2\" >19.515</td>\n",
+       "      <td id=\"T_01361_row0_col3\" class=\"data row0 col3\" >21.383</td>\n",
+       "      <td id=\"T_01361_row0_col4\" class=\"data row0 col4\" >21.089</td>\n",
+       "      <td id=\"T_01361_row0_col5\" class=\"data row0 col5\" >21.583</td>\n",
+       "      <td id=\"T_01361_row0_col6\" class=\"data row0 col6\" >20.297</td>\n",
+       "      <td id=\"T_01361_row0_col7\" class=\"data row0 col7\" >37.611</td>\n",
+       "      <td id=\"T_01361_row0_col8\" class=\"data row0 col8\" >21.731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_95e80_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
-       "      <td id=\"T_95e80_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
-       "      <td id=\"T_95e80_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
-       "      <td id=\"T_95e80_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
-       "      <td id=\"T_95e80_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
-       "      <td id=\"T_95e80_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
-       "      <td id=\"T_95e80_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
-       "      <td id=\"T_95e80_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
+       "      <th id=\"T_01361_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_01361_row1_col0\" class=\"data row1 col0\" >17.156</td>\n",
+       "      <td id=\"T_01361_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_01361_row1_col2\" class=\"data row1 col2\" >18.147</td>\n",
+       "      <td id=\"T_01361_row1_col3\" class=\"data row1 col3\" >21.106</td>\n",
+       "      <td id=\"T_01361_row1_col4\" class=\"data row1 col4\" >19.880</td>\n",
+       "      <td id=\"T_01361_row1_col5\" class=\"data row1 col5\" >17.973</td>\n",
+       "      <td id=\"T_01361_row1_col6\" class=\"data row1 col6\" >18.304</td>\n",
+       "      <td id=\"T_01361_row1_col7\" class=\"data row1 col7\" >36.780</td>\n",
+       "      <td id=\"T_01361_row1_col8\" class=\"data row1 col8\" >19.481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_95e80_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
-       "      <td id=\"T_95e80_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
-       "      <td id=\"T_95e80_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
-       "      <td id=\"T_95e80_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
-       "      <td id=\"T_95e80_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
-       "      <td id=\"T_95e80_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
-       "      <td id=\"T_95e80_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
-       "      <td id=\"T_95e80_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
+       "      <th id=\"T_01361_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_01361_row2_col0\" class=\"data row2 col0\" >19.515</td>\n",
+       "      <td id=\"T_01361_row2_col1\" class=\"data row2 col1\" >18.147</td>\n",
+       "      <td id=\"T_01361_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_01361_row2_col3\" class=\"data row2 col3\" >16.392</td>\n",
+       "      <td id=\"T_01361_row2_col4\" class=\"data row2 col4\" >17.282</td>\n",
+       "      <td id=\"T_01361_row2_col5\" class=\"data row2 col5\" >16.467</td>\n",
+       "      <td id=\"T_01361_row2_col6\" class=\"data row2 col6\" >17.498</td>\n",
+       "      <td id=\"T_01361_row2_col7\" class=\"data row2 col7\" >36.046</td>\n",
+       "      <td id=\"T_01361_row2_col8\" class=\"data row2 col8\" >22.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_95e80_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
-       "      <td id=\"T_95e80_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
-       "      <td id=\"T_95e80_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
-       "      <td id=\"T_95e80_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
-       "      <td id=\"T_95e80_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
-       "      <td id=\"T_95e80_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
-       "      <td id=\"T_95e80_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
-       "      <td id=\"T_95e80_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
+       "      <th id=\"T_01361_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_01361_row3_col0\" class=\"data row3 col0\" >21.383</td>\n",
+       "      <td id=\"T_01361_row3_col1\" class=\"data row3 col1\" >21.106</td>\n",
+       "      <td id=\"T_01361_row3_col2\" class=\"data row3 col2\" >16.392</td>\n",
+       "      <td id=\"T_01361_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_01361_row3_col4\" class=\"data row3 col4\" >23.851</td>\n",
+       "      <td id=\"T_01361_row3_col5\" class=\"data row3 col5\" >23.881</td>\n",
+       "      <td id=\"T_01361_row3_col6\" class=\"data row3 col6\" >22.563</td>\n",
+       "      <td id=\"T_01361_row3_col7\" class=\"data row3 col7\" >41.277</td>\n",
+       "      <td id=\"T_01361_row3_col8\" class=\"data row3 col8\" >24.682</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_95e80_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
-       "      <td id=\"T_95e80_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
-       "      <td id=\"T_95e80_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
-       "      <td id=\"T_95e80_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
-       "      <td id=\"T_95e80_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
-       "      <td id=\"T_95e80_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
-       "      <td id=\"T_95e80_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
-       "      <td id=\"T_95e80_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
+       "      <th id=\"T_01361_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_01361_row4_col0\" class=\"data row4 col0\" >21.089</td>\n",
+       "      <td id=\"T_01361_row4_col1\" class=\"data row4 col1\" >19.880</td>\n",
+       "      <td id=\"T_01361_row4_col2\" class=\"data row4 col2\" >17.282</td>\n",
+       "      <td id=\"T_01361_row4_col3\" class=\"data row4 col3\" >23.851</td>\n",
+       "      <td id=\"T_01361_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_01361_row4_col5\" class=\"data row4 col5\" >19.255</td>\n",
+       "      <td id=\"T_01361_row4_col6\" class=\"data row4 col6\" >20.431</td>\n",
+       "      <td id=\"T_01361_row4_col7\" class=\"data row4 col7\" >41.104</td>\n",
+       "      <td id=\"T_01361_row4_col8\" class=\"data row4 col8\" >20.263</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_95e80_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
-       "      <td id=\"T_95e80_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
-       "      <td id=\"T_95e80_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
-       "      <td id=\"T_95e80_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
-       "      <td id=\"T_95e80_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
-       "      <td id=\"T_95e80_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
-       "      <td id=\"T_95e80_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
-       "      <td id=\"T_95e80_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
+       "      <th id=\"T_01361_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_01361_row5_col0\" class=\"data row5 col0\" >21.583</td>\n",
+       "      <td id=\"T_01361_row5_col1\" class=\"data row5 col1\" >17.973</td>\n",
+       "      <td id=\"T_01361_row5_col2\" class=\"data row5 col2\" >16.467</td>\n",
+       "      <td id=\"T_01361_row5_col3\" class=\"data row5 col3\" >23.881</td>\n",
+       "      <td id=\"T_01361_row5_col4\" class=\"data row5 col4\" >19.255</td>\n",
+       "      <td id=\"T_01361_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_01361_row5_col6\" class=\"data row5 col6\" >19.221</td>\n",
+       "      <td id=\"T_01361_row5_col7\" class=\"data row5 col7\" >39.022</td>\n",
+       "      <td id=\"T_01361_row5_col8\" class=\"data row5 col8\" >22.457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_95e80_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
-       "      <td id=\"T_95e80_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
-       "      <td id=\"T_95e80_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
-       "      <td id=\"T_95e80_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
-       "      <td id=\"T_95e80_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
-       "      <td id=\"T_95e80_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
-       "      <td id=\"T_95e80_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
-       "      <td id=\"T_95e80_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
+       "      <th id=\"T_01361_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_01361_row6_col0\" class=\"data row6 col0\" >20.297</td>\n",
+       "      <td id=\"T_01361_row6_col1\" class=\"data row6 col1\" >18.304</td>\n",
+       "      <td id=\"T_01361_row6_col2\" class=\"data row6 col2\" >17.498</td>\n",
+       "      <td id=\"T_01361_row6_col3\" class=\"data row6 col3\" >22.563</td>\n",
+       "      <td id=\"T_01361_row6_col4\" class=\"data row6 col4\" >20.431</td>\n",
+       "      <td id=\"T_01361_row6_col5\" class=\"data row6 col5\" >19.221</td>\n",
+       "      <td id=\"T_01361_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_01361_row6_col7\" class=\"data row6 col7\" >39.414</td>\n",
+       "      <td id=\"T_01361_row6_col8\" class=\"data row6 col8\" >21.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_95e80_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
-       "      <td id=\"T_95e80_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
-       "      <td id=\"T_95e80_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
-       "      <td id=\"T_95e80_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
-       "      <td id=\"T_95e80_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
-       "      <td id=\"T_95e80_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
-       "      <td id=\"T_95e80_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
-       "      <td id=\"T_95e80_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_95e80_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
+       "      <th id=\"T_01361_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_01361_row7_col0\" class=\"data row7 col0\" >37.611</td>\n",
+       "      <td id=\"T_01361_row7_col1\" class=\"data row7 col1\" >36.780</td>\n",
+       "      <td id=\"T_01361_row7_col2\" class=\"data row7 col2\" >36.046</td>\n",
+       "      <td id=\"T_01361_row7_col3\" class=\"data row7 col3\" >41.277</td>\n",
+       "      <td id=\"T_01361_row7_col4\" class=\"data row7 col4\" >41.104</td>\n",
+       "      <td id=\"T_01361_row7_col5\" class=\"data row7 col5\" >39.022</td>\n",
+       "      <td id=\"T_01361_row7_col6\" class=\"data row7 col6\" >39.414</td>\n",
+       "      <td id=\"T_01361_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_01361_row7_col8\" class=\"data row7 col8\" >38.530</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_95e80_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_95e80_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
-       "      <td id=\"T_95e80_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
-       "      <td id=\"T_95e80_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
-       "      <td id=\"T_95e80_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
-       "      <td id=\"T_95e80_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
-       "      <td id=\"T_95e80_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
-       "      <td id=\"T_95e80_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
-       "      <td id=\"T_95e80_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
-       "      <td id=\"T_95e80_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_01361_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_01361_row8_col0\" class=\"data row8 col0\" >21.731</td>\n",
+       "      <td id=\"T_01361_row8_col1\" class=\"data row8 col1\" >19.481</td>\n",
+       "      <td id=\"T_01361_row8_col2\" class=\"data row8 col2\" >22.128</td>\n",
+       "      <td id=\"T_01361_row8_col3\" class=\"data row8 col3\" >24.682</td>\n",
+       "      <td id=\"T_01361_row8_col4\" class=\"data row8 col4\" >20.263</td>\n",
+       "      <td id=\"T_01361_row8_col5\" class=\"data row8 col5\" >22.457</td>\n",
+       "      <td id=\"T_01361_row8_col6\" class=\"data row8 col6\" >21.983</td>\n",
+       "      <td id=\"T_01361_row8_col7\" class=\"data row8 col7\" >38.530</td>\n",
+       "      <td id=\"T_01361_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f90475794f0>"
+       "<pandas.io.formats.style.Styler at 0x7fdaf0f77dc0>"
       ]
      },
      "execution_count": 21,
@@ -1797,57 +1799,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
-    "    cmap=cm\n",
-    ").format(\"{:.3f}\")\n",
-    "kinase_distance_matrix_df_styler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We export the matrix with colors as an image to use as thumbnail for the website."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[1129/155624.943121:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155624.944163:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
-      "[1129/155624.947439:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155625.577404:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmp97mvbhul/temp.png.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "plt.imshow(matrix_styler_img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -1868,7 +1820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
index eb8512d7..8dbc688a 100644
--- a/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
+++ b/teachopencadd/talktorials/T026_kinase_similarity_ifp/talktorial.ipynb
@@ -174,12 +174,10 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.image as mpimg\n",
     "import seaborn as sns\n",
     "from sklearn.metrics import pairwise_distances\n",
     "from scipy.spatial import distance\n",
-    "from opencadd.databases.klifs import setup_remote\n",
-    "import dataframe_image as dfi"
+    "from opencadd.databases.klifs import setup_remote"
    ]
   },
   {
@@ -1247,7 +1245,11 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -1324,227 +1326,227 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
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-       "#T_d171f_row3_col2 {\n",
+       "#T_079c7_row3_col2 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row3_col6, #T_d171f_row7_col5 {\n",
+       "#T_079c7_row3_col6, #T_079c7_row7_col5 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row3_col8 {\n",
+       "#T_079c7_row3_col8 {\n",
        "  background-color: #6bb46b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col1 {\n",
+       "#T_079c7_row4_col1 {\n",
        "  background-color: #6fb76f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col2, #T_d171f_row6_col4 {\n",
+       "#T_079c7_row4_col2, #T_079c7_row6_col4 {\n",
        "  background-color: #57ab57;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col3 {\n",
+       "#T_079c7_row4_col3 {\n",
        "  background-color: #43a143;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col5 {\n",
+       "#T_079c7_row4_col5 {\n",
        "  background-color: #cae3ca;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row4_col6 {\n",
+       "#T_079c7_row4_col6 {\n",
        "  background-color: #349934;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col7 {\n",
+       "#T_079c7_row4_col7 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row4_col8 {\n",
+       "#T_079c7_row4_col8 {\n",
        "  background-color: #d5e9d5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row5_col0 {\n",
+       "#T_079c7_row5_col0 {\n",
        "  background-color: #55a955;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row5_col1 {\n",
+       "#T_079c7_row5_col1 {\n",
        "  background-color: #88c388;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row5_col2 {\n",
+       "#T_079c7_row5_col2 {\n",
        "  background-color: #84c084;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row5_col4 {\n",
+       "#T_079c7_row5_col4 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row5_col6 {\n",
+       "#T_079c7_row5_col6 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row5_col7 {\n",
+       "#T_079c7_row5_col7 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row5_col8 {\n",
+       "#T_079c7_row5_col8 {\n",
        "  background-color: #8dc58d;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row6_col0 {\n",
+       "#T_079c7_row6_col0 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row6_col1 {\n",
+       "#T_079c7_row6_col1 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row6_col2, #T_d171f_row6_col5 {\n",
+       "#T_079c7_row6_col2, #T_079c7_row6_col5 {\n",
        "  background-color: #5fae5f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row6_col3 {\n",
+       "#T_079c7_row6_col3 {\n",
        "  background-color: #4da64d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row6_col7 {\n",
+       "#T_079c7_row6_col7 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row6_col8 {\n",
+       "#T_079c7_row6_col8 {\n",
        "  background-color: #3c9d3c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row7_col1 {\n",
+       "#T_079c7_row7_col1 {\n",
        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row8_col1 {\n",
+       "#T_079c7_row8_col1 {\n",
        "  background-color: #5aac5a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row8_col4 {\n",
+       "#T_079c7_row8_col4 {\n",
        "  background-color: #d8ead8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_d171f_row8_col6 {\n",
+       "#T_079c7_row8_col6 {\n",
        "  background-color: #299429;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_d171f_row8_col7 {\n",
+       "#T_079c7_row8_col7 {\n",
        "  background-color: #289328;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_d171f_\">\n",
+       "<table id=\"T_079c7_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1561,121 +1563,121 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
-       "      <td id=\"T_d171f_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
-       "      <td id=\"T_d171f_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
-       "      <td id=\"T_d171f_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
-       "      <td id=\"T_d171f_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
-       "      <td id=\"T_d171f_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
-       "      <td id=\"T_d171f_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
-       "      <td id=\"T_d171f_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
-       "      <td id=\"T_d171f_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
+       "      <th id=\"T_079c7_level0_row0\" class=\"row_heading level0 row0\" >BRAF</th>\n",
+       "      <td id=\"T_079c7_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row0_col1\" class=\"data row0 col1\" >0.300</td>\n",
+       "      <td id=\"T_079c7_row0_col2\" class=\"data row0 col2\" >0.278</td>\n",
+       "      <td id=\"T_079c7_row0_col3\" class=\"data row0 col3\" >0.394</td>\n",
+       "      <td id=\"T_079c7_row0_col4\" class=\"data row0 col4\" >0.364</td>\n",
+       "      <td id=\"T_079c7_row0_col5\" class=\"data row0 col5\" >0.320</td>\n",
+       "      <td id=\"T_079c7_row0_col6\" class=\"data row0 col6\" >0.368</td>\n",
+       "      <td id=\"T_079c7_row0_col7\" class=\"data row0 col7\" >0.500</td>\n",
+       "      <td id=\"T_079c7_row0_col8\" class=\"data row0 col8\" >0.333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
-       "      <td id=\"T_d171f_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
-       "      <td id=\"T_d171f_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
-       "      <td id=\"T_d171f_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
-       "      <td id=\"T_d171f_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
-       "      <td id=\"T_d171f_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
-       "      <td id=\"T_d171f_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
-       "      <td id=\"T_d171f_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
-       "      <td id=\"T_d171f_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
+       "      <th id=\"T_079c7_level0_row1\" class=\"row_heading level0 row1\" >CDK2</th>\n",
+       "      <td id=\"T_079c7_row1_col0\" class=\"data row1 col0\" >0.300</td>\n",
+       "      <td id=\"T_079c7_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row1_col2\" class=\"data row1 col2\" >0.111</td>\n",
+       "      <td id=\"T_079c7_row1_col3\" class=\"data row1 col3\" >0.452</td>\n",
+       "      <td id=\"T_079c7_row1_col4\" class=\"data row1 col4\" >0.238</td>\n",
+       "      <td id=\"T_079c7_row1_col5\" class=\"data row1 col5\" >0.190</td>\n",
+       "      <td id=\"T_079c7_row1_col6\" class=\"data row1 col6\" >0.133</td>\n",
+       "      <td id=\"T_079c7_row1_col7\" class=\"data row1 col7\" >0.348</td>\n",
+       "      <td id=\"T_079c7_row1_col8\" class=\"data row1 col8\" >0.278</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
-       "      <td id=\"T_d171f_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
-       "      <td id=\"T_d171f_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
-       "      <td id=\"T_d171f_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
-       "      <td id=\"T_d171f_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
-       "      <td id=\"T_d171f_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
-       "      <td id=\"T_d171f_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
-       "      <td id=\"T_d171f_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
-       "      <td id=\"T_d171f_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
+       "      <th id=\"T_079c7_level0_row2\" class=\"row_heading level0 row2\" >EGFR</th>\n",
+       "      <td id=\"T_079c7_row2_col0\" class=\"data row2 col0\" >0.278</td>\n",
+       "      <td id=\"T_079c7_row2_col1\" class=\"data row2 col1\" >0.111</td>\n",
+       "      <td id=\"T_079c7_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row2_col3\" class=\"data row2 col3\" >0.258</td>\n",
+       "      <td id=\"T_079c7_row2_col4\" class=\"data row2 col4\" >0.238</td>\n",
+       "      <td id=\"T_079c7_row2_col5\" class=\"data row2 col5\" >0.167</td>\n",
+       "      <td id=\"T_079c7_row2_col6\" class=\"data row2 col6\" >0.227</td>\n",
+       "      <td id=\"T_079c7_row2_col7\" class=\"data row2 col7\" >0.381</td>\n",
+       "      <td id=\"T_079c7_row2_col8\" class=\"data row2 col8\" >0.174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
-       "      <td id=\"T_d171f_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
-       "      <td id=\"T_d171f_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
-       "      <td id=\"T_d171f_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
-       "      <td id=\"T_d171f_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
-       "      <td id=\"T_d171f_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
-       "      <td id=\"T_d171f_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
-       "      <td id=\"T_d171f_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
-       "      <td id=\"T_d171f_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
+       "      <th id=\"T_079c7_level0_row3\" class=\"row_heading level0 row3\" >ErbB2</th>\n",
+       "      <td id=\"T_079c7_row3_col0\" class=\"data row3 col0\" >0.394</td>\n",
+       "      <td id=\"T_079c7_row3_col1\" class=\"data row3 col1\" >0.452</td>\n",
+       "      <td id=\"T_079c7_row3_col2\" class=\"data row3 col2\" >0.258</td>\n",
+       "      <td id=\"T_079c7_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row3_col4\" class=\"data row3 col4\" >0.406</td>\n",
+       "      <td id=\"T_079c7_row3_col5\" class=\"data row3 col5\" >0.419</td>\n",
+       "      <td id=\"T_079c7_row3_col6\" class=\"data row3 col6\" >0.382</td>\n",
+       "      <td id=\"T_079c7_row3_col7\" class=\"data row3 col7\" >0.571</td>\n",
+       "      <td id=\"T_079c7_row3_col8\" class=\"data row3 col8\" >0.258</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
-       "      <td id=\"T_d171f_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
-       "      <td id=\"T_d171f_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
-       "      <td id=\"T_d171f_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
-       "      <td id=\"T_d171f_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
-       "      <td id=\"T_d171f_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
-       "      <td id=\"T_d171f_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
-       "      <td id=\"T_d171f_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
-       "      <td id=\"T_d171f_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
+       "      <th id=\"T_079c7_level0_row4\" class=\"row_heading level0 row4\" >KDR</th>\n",
+       "      <td id=\"T_079c7_row4_col0\" class=\"data row4 col0\" >0.364</td>\n",
+       "      <td id=\"T_079c7_row4_col1\" class=\"data row4 col1\" >0.238</td>\n",
+       "      <td id=\"T_079c7_row4_col2\" class=\"data row4 col2\" >0.238</td>\n",
+       "      <td id=\"T_079c7_row4_col3\" class=\"data row4 col3\" >0.406</td>\n",
+       "      <td id=\"T_079c7_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row4_col5\" class=\"data row4 col5\" >0.059</td>\n",
+       "      <td id=\"T_079c7_row4_col6\" class=\"data row4 col6\" >0.333</td>\n",
+       "      <td id=\"T_079c7_row4_col7\" class=\"data row4 col7\" >0.533</td>\n",
+       "      <td id=\"T_079c7_row4_col8\" class=\"data row4 col8\" >0.043</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
-       "      <td id=\"T_d171f_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
-       "      <td id=\"T_d171f_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
-       "      <td id=\"T_d171f_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
-       "      <td id=\"T_d171f_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
-       "      <td id=\"T_d171f_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
-       "      <td id=\"T_d171f_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
-       "      <td id=\"T_d171f_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
-       "      <td id=\"T_d171f_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
+       "      <th id=\"T_079c7_level0_row5\" class=\"row_heading level0 row5\" >LCK</th>\n",
+       "      <td id=\"T_079c7_row5_col0\" class=\"data row5 col0\" >0.320</td>\n",
+       "      <td id=\"T_079c7_row5_col1\" class=\"data row5 col1\" >0.190</td>\n",
+       "      <td id=\"T_079c7_row5_col2\" class=\"data row5 col2\" >0.167</td>\n",
+       "      <td id=\"T_079c7_row5_col3\" class=\"data row5 col3\" >0.419</td>\n",
+       "      <td id=\"T_079c7_row5_col4\" class=\"data row5 col4\" >0.059</td>\n",
+       "      <td id=\"T_079c7_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row5_col6\" class=\"data row5 col6\" >0.250</td>\n",
+       "      <td id=\"T_079c7_row5_col7\" class=\"data row5 col7\" >0.375</td>\n",
+       "      <td id=\"T_079c7_row5_col8\" class=\"data row5 col8\" >0.190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
-       "      <td id=\"T_d171f_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
-       "      <td id=\"T_d171f_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
-       "      <td id=\"T_d171f_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
-       "      <td id=\"T_d171f_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
-       "      <td id=\"T_d171f_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
-       "      <td id=\"T_d171f_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
-       "      <td id=\"T_d171f_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
-       "      <td id=\"T_d171f_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
+       "      <th id=\"T_079c7_level0_row6\" class=\"row_heading level0 row6\" >MET</th>\n",
+       "      <td id=\"T_079c7_row6_col0\" class=\"data row6 col0\" >0.368</td>\n",
+       "      <td id=\"T_079c7_row6_col1\" class=\"data row6 col1\" >0.133</td>\n",
+       "      <td id=\"T_079c7_row6_col2\" class=\"data row6 col2\" >0.227</td>\n",
+       "      <td id=\"T_079c7_row6_col3\" class=\"data row6 col3\" >0.382</td>\n",
+       "      <td id=\"T_079c7_row6_col4\" class=\"data row6 col4\" >0.333</td>\n",
+       "      <td id=\"T_079c7_row6_col5\" class=\"data row6 col5\" >0.250</td>\n",
+       "      <td id=\"T_079c7_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row6_col7\" class=\"data row6 col7\" >0.429</td>\n",
+       "      <td id=\"T_079c7_row6_col8\" class=\"data row6 col8\" >0.353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
-       "      <td id=\"T_d171f_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
-       "      <td id=\"T_d171f_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
-       "      <td id=\"T_d171f_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
-       "      <td id=\"T_d171f_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
-       "      <td id=\"T_d171f_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
-       "      <td id=\"T_d171f_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
-       "      <td id=\"T_d171f_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
-       "      <td id=\"T_d171f_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_d171f_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
+       "      <th id=\"T_079c7_level0_row7\" class=\"row_heading level0 row7\" >p110a</th>\n",
+       "      <td id=\"T_079c7_row7_col0\" class=\"data row7 col0\" >0.500</td>\n",
+       "      <td id=\"T_079c7_row7_col1\" class=\"data row7 col1\" >0.348</td>\n",
+       "      <td id=\"T_079c7_row7_col2\" class=\"data row7 col2\" >0.381</td>\n",
+       "      <td id=\"T_079c7_row7_col3\" class=\"data row7 col3\" >0.571</td>\n",
+       "      <td id=\"T_079c7_row7_col4\" class=\"data row7 col4\" >0.533</td>\n",
+       "      <td id=\"T_079c7_row7_col5\" class=\"data row7 col5\" >0.375</td>\n",
+       "      <td id=\"T_079c7_row7_col6\" class=\"data row7 col6\" >0.429</td>\n",
+       "      <td id=\"T_079c7_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_079c7_row7_col8\" class=\"data row7 col8\" >0.474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_d171f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_d171f_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
-       "      <td id=\"T_d171f_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
-       "      <td id=\"T_d171f_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
-       "      <td id=\"T_d171f_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
-       "      <td id=\"T_d171f_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
-       "      <td id=\"T_d171f_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
-       "      <td id=\"T_d171f_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
-       "      <td id=\"T_d171f_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
-       "      <td id=\"T_d171f_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_079c7_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_079c7_row8_col0\" class=\"data row8 col0\" >0.333</td>\n",
+       "      <td id=\"T_079c7_row8_col1\" class=\"data row8 col1\" >0.278</td>\n",
+       "      <td id=\"T_079c7_row8_col2\" class=\"data row8 col2\" >0.174</td>\n",
+       "      <td id=\"T_079c7_row8_col3\" class=\"data row8 col3\" >0.258</td>\n",
+       "      <td id=\"T_079c7_row8_col4\" class=\"data row8 col4\" >0.043</td>\n",
+       "      <td id=\"T_079c7_row8_col5\" class=\"data row8 col5\" >0.190</td>\n",
+       "      <td id=\"T_079c7_row8_col6\" class=\"data row8 col6\" >0.353</td>\n",
+       "      <td id=\"T_079c7_row8_col7\" class=\"data row8 col7\" >0.474</td>\n",
+       "      <td id=\"T_079c7_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fa599d3b790>"
+       "<pandas.io.formats.style.Styler at 0x7fce93ad8820>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,57 +1685,7 @@
    "source": [
     "# Show matrix with background gradient\n",
     "cm = sns.light_palette(\"green\", as_cmap=True)\n",
-    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
-    "    cmap=cm\n",
-    ").format(\"{:.3f}\")\n",
-    "kinase_distance_matrix_df_styler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We export the matrix with colors as an image to use as thumbnail for the website."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[1129/155348.866614:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155348.867645:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
-      "[1129/155348.868577:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155349.478593:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmpc8fcpz2a/temp.png.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "plt.imshow(matrix_styler_img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -1754,7 +1706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index b51ac36d..3714ef43 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -989,7 +989,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "nbsphinx-thumbnail"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -1543,46 +1547,46 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_ca911_row0_col2 {\n",
+       "#T_e1a92_row0_col2 {\n",
        "  background-color: #dadaeb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ca911_row1_col2 {\n",
+       "#T_e1a92_row1_col2 {\n",
        "  background-color: #dcdcec;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ca911_row2_col2 {\n",
+       "#T_e1a92_row2_col2 {\n",
        "  background-color: #bdbedc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ca911_row3_col2 {\n",
+       "#T_e1a92_row3_col2 {\n",
        "  background-color: #8c88bf;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ca911_row4_col2 {\n",
+       "#T_e1a92_row4_col2 {\n",
        "  background-color: #3f007d;\n",
        "  color: #f1f1f1;\n",
        "  background-color: red;\n",
        "}\n",
-       "#T_ca911_row5_col2 {\n",
+       "#T_e1a92_row5_col2 {\n",
        "  background-color: #fcfbfd;\n",
        "  color: #000000;\n",
        "  background-color: yellow;\n",
        "}\n",
-       "#T_ca911_row6_col2 {\n",
+       "#T_e1a92_row6_col2 {\n",
        "  background-color: #e1e0ee;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_ca911_row7_col2 {\n",
+       "#T_e1a92_row7_col2 {\n",
        "  background-color: #4a1486;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_ca911_row8_col2 {\n",
+       "#T_e1a92_row8_col2 {\n",
        "  background-color: #5d3897;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_ca911_\">\n",
+       "<table id=\"T_e1a92_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -1593,64 +1597,64 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_ca911_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
-       "      <td id=\"T_ca911_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
-       "      <td id=\"T_ca911_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
+       "      <th id=\"T_e1a92_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_e1a92_row0_col0\" class=\"data row0 col0\" >5965</td>\n",
+       "      <td id=\"T_e1a92_row0_col1\" class=\"data row0 col1\" >3635</td>\n",
+       "      <td id=\"T_e1a92_row0_col2\" class=\"data row0 col2\" >0.609</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_ca911_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
-       "      <td id=\"T_ca911_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
-       "      <td id=\"T_ca911_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
+       "      <th id=\"T_e1a92_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_e1a92_row1_col0\" class=\"data row1 col0\" >1700</td>\n",
+       "      <td id=\"T_e1a92_row1_col1\" class=\"data row1 col1\" >1031</td>\n",
+       "      <td id=\"T_e1a92_row1_col2\" class=\"data row1 col2\" >0.606</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_ca911_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
-       "      <td id=\"T_ca911_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
-       "      <td id=\"T_ca911_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <th id=\"T_e1a92_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_e1a92_row2_col0\" class=\"data row2 col0\" >4393</td>\n",
+       "      <td id=\"T_e1a92_row2_col1\" class=\"data row2 col1\" >2827</td>\n",
+       "      <td id=\"T_e1a92_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_ca911_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
-       "      <td id=\"T_ca911_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
-       "      <td id=\"T_ca911_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
+       "      <th id=\"T_e1a92_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_e1a92_row3_col0\" class=\"data row3 col0\" >7641</td>\n",
+       "      <td id=\"T_e1a92_row3_col1\" class=\"data row3 col1\" >5328</td>\n",
+       "      <td id=\"T_e1a92_row3_col2\" class=\"data row3 col2\" >0.697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_ca911_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
-       "      <td id=\"T_ca911_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
-       "      <td id=\"T_ca911_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
+       "      <th id=\"T_e1a92_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_e1a92_row4_col0\" class=\"data row4 col0\" >3688</td>\n",
+       "      <td id=\"T_e1a92_row4_col1\" class=\"data row4 col1\" >2992</td>\n",
+       "      <td id=\"T_e1a92_row4_col2\" class=\"data row4 col2\" >0.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_ca911_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
-       "      <td id=\"T_ca911_row5_col1\" class=\"data row5 col1\" >815</td>\n",
-       "      <td id=\"T_ca911_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
+       "      <th id=\"T_e1a92_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_e1a92_row5_col0\" class=\"data row5 col0\" >1500</td>\n",
+       "      <td id=\"T_e1a92_row5_col1\" class=\"data row5 col1\" >815</td>\n",
+       "      <td id=\"T_e1a92_row5_col2\" class=\"data row5 col2\" >0.543</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_ca911_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
-       "      <td id=\"T_ca911_row6_col1\" class=\"data row6 col1\" >935</td>\n",
-       "      <td id=\"T_ca911_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
+       "      <th id=\"T_e1a92_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_e1a92_row6_col0\" class=\"data row6 col0\" >1560</td>\n",
+       "      <td id=\"T_e1a92_row6_col1\" class=\"data row6 col1\" >935</td>\n",
+       "      <td id=\"T_e1a92_row6_col2\" class=\"data row6 col2\" >0.599</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_ca911_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
-       "      <td id=\"T_ca911_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
-       "      <td id=\"T_ca911_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
+       "      <th id=\"T_e1a92_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_e1a92_row7_col0\" class=\"data row7 col0\" >2832</td>\n",
+       "      <td id=\"T_e1a92_row7_col1\" class=\"data row7 col1\" >2248</td>\n",
+       "      <td id=\"T_e1a92_row7_col2\" class=\"data row7 col2\" >0.794</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th id=\"T_ca911_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_ca911_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
-       "      <td id=\"T_ca911_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
-       "      <td id=\"T_ca911_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
+       "      <th id=\"T_e1a92_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_e1a92_row8_col0\" class=\"data row8 col0\" >3637</td>\n",
+       "      <td id=\"T_e1a92_row8_col1\" class=\"data row8 col1\" >2778</td>\n",
+       "      <td id=\"T_e1a92_row8_col2\" class=\"data row8 col2\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbb6aab7d30>"
+       "<pandas.io.formats.style.Styler at 0x7ff0852a95e0>"
       ]
      },
      "execution_count": 24,
@@ -2252,208 +2256,208 @@
      "data": {
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-       "#T_34626_row1_col5 {\n",
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-       "#T_34626_row1_col8 {\n",
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        "  background-color: #ebf3eb;\n",
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-       "#T_34626_row2_col4 {\n",
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-       "#T_34626_row2_col5 {\n",
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-       "#T_34626_row3_col1 {\n",
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-       "#T_34626_row3_col3 {\n",
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-       "#T_34626_row6_col3 {\n",
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-       "#T_34626_row6_col4 {\n",
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        "}\n",
-       "#T_34626_row7_col6 {\n",
+       "#T_9049f_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_34626_row8_col0 {\n",
+       "#T_9049f_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_34626_row8_col1 {\n",
+       "#T_9049f_row8_col1 {\n",
        "  background-color: #c6e1c6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_34626_row8_col3 {\n",
+       "#T_9049f_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_34626_row8_col4 {\n",
+       "#T_9049f_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_34626_row8_col6 {\n",
+       "#T_9049f_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_34626_\">\n",
+       "<table id=\"T_9049f_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2470,118 +2474,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_34626_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_34626_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_34626_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_34626_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_34626_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_34626_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_34626_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_34626_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_34626_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_34626_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_34626_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_34626_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_34626_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
-       "      <td id=\"T_34626_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_34626_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_34626_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_34626_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_34626_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_34626_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_34626_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_34626_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
-       "      <td id=\"T_34626_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_34626_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_34626_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_34626_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_34626_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_34626_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_34626_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_34626_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_34626_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_34626_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_34626_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_34626_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_34626_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_34626_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_34626_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_34626_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_34626_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_34626_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_34626_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_34626_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_34626_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_34626_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_34626_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_34626_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_34626_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_34626_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_34626_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_34626_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_34626_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_34626_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_34626_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_34626_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_34626_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_34626_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_34626_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_34626_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_34626_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_34626_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_34626_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_34626_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_34626_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_34626_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_34626_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_34626_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_34626_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_34626_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_34626_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_34626_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_34626_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_34626_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_34626_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_34626_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_34626_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_34626_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_34626_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_34626_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_34626_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_34626_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_34626_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_34626_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_34626_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_34626_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_9049f_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_9049f_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_9049f_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_9049f_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_9049f_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_9049f_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_9049f_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_9049f_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_9049f_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_9049f_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_9049f_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_9049f_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_9049f_row1_col2\" class=\"data row1 col2\" >nan</td>\n",
+       "      <td id=\"T_9049f_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_9049f_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_9049f_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_9049f_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_9049f_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_9049f_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_9049f_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_9049f_row2_col1\" class=\"data row2 col1\" >nan</td>\n",
+       "      <td id=\"T_9049f_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_9049f_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_9049f_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_9049f_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_9049f_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_9049f_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_9049f_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_9049f_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_9049f_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_9049f_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_9049f_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_9049f_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_9049f_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_9049f_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_9049f_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_9049f_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_9049f_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_9049f_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_9049f_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_9049f_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_9049f_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_9049f_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_9049f_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_9049f_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_9049f_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_9049f_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_9049f_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_9049f_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_9049f_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_9049f_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_9049f_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_9049f_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_9049f_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_9049f_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_9049f_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_9049f_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_9049f_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_9049f_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_9049f_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_9049f_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_9049f_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_9049f_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_9049f_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_9049f_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_9049f_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_9049f_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_9049f_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_9049f_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_9049f_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_9049f_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_9049f_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_9049f_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_9049f_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_9049f_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_9049f_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_9049f_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_9049f_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_9049f_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_9049f_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_9049f_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_9049f_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_9049f_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbb6aab7d90>"
+       "<pandas.io.formats.style.Styler at 0x7ff07ec3aac0>"
       ]
      },
      "execution_count": 30,
@@ -2615,212 +2619,212 @@
      "data": {
       "text/html": [
        "<style type=\"text/css\">\n",
-       "#T_679b0_row0_col0, #T_679b0_row1_col1, #T_679b0_row2_col2, #T_679b0_row3_col5, #T_679b0_row4_col3, #T_679b0_row4_col4, #T_679b0_row6_col6, #T_679b0_row7_col7, #T_679b0_row8_col8 {\n",
+       "#T_6f48b_row0_col0, #T_6f48b_row1_col1, #T_6f48b_row2_col2, #T_6f48b_row3_col5, #T_6f48b_row4_col3, #T_6f48b_row4_col4, #T_6f48b_row6_col6, #T_6f48b_row7_col7, #T_6f48b_row8_col8 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row0_col1 {\n",
+       "#T_6f48b_row0_col1 {\n",
        "  background-color: #108810;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row0_col2 {\n",
+       "#T_6f48b_row0_col2 {\n",
        "  background-color: #d1e6d1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row0_col3, #T_679b0_row7_col0 {\n",
+       "#T_6f48b_row0_col3, #T_6f48b_row7_col0 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
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-       "#T_679b0_row0_col4, #T_679b0_row3_col0, #T_679b0_row4_col2, #T_679b0_row5_col2 {\n",
+       "#T_6f48b_row0_col4, #T_6f48b_row3_col0, #T_6f48b_row4_col2, #T_6f48b_row5_col2 {\n",
        "  background-color: #71b771;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row0_col5 {\n",
+       "#T_6f48b_row0_col5 {\n",
        "  background-color: #d6e9d6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row0_col6, #T_679b0_row4_col1 {\n",
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-       "#T_679b0_row0_col7, #T_679b0_row3_col2 {\n",
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        "  background-color: #a7d2a7;\n",
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-       "#T_679b0_row0_col8 {\n",
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        "  background-color: #80bf80;\n",
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        "}\n",
-       "#T_679b0_row1_col0 {\n",
+       "#T_6f48b_row1_col0 {\n",
        "  background-color: #128912;\n",
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-       "#T_679b0_row1_col2, #T_679b0_row2_col0, #T_679b0_row2_col1, #T_679b0_row2_col3, #T_679b0_row2_col6, #T_679b0_row2_col7, #T_679b0_row2_col8, #T_679b0_row4_col5, #T_679b0_row5_col4, #T_679b0_row6_col2, #T_679b0_row7_col2, #T_679b0_row8_col2 {\n",
+       "#T_6f48b_row1_col2, #T_6f48b_row2_col0, #T_6f48b_row2_col1, #T_6f48b_row2_col3, #T_6f48b_row2_col6, #T_6f48b_row2_col7, #T_6f48b_row2_col8, #T_6f48b_row4_col5, #T_6f48b_row5_col4, #T_6f48b_row6_col2, #T_6f48b_row7_col2, #T_6f48b_row8_col2 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col3 {\n",
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        "  background-color: #92c892;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col4 {\n",
+       "#T_6f48b_row1_col4 {\n",
        "  background-color: #b3d8b3;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col5 {\n",
+       "#T_6f48b_row1_col5 {\n",
        "  background-color: #cce4cc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col6 {\n",
+       "#T_6f48b_row1_col6 {\n",
        "  background-color: #b0d6b0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col7 {\n",
+       "#T_6f48b_row1_col7 {\n",
        "  background-color: #e0eee0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row1_col8 {\n",
+       "#T_6f48b_row1_col8 {\n",
        "  background-color: #c5e0c5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row2_col4 {\n",
+       "#T_6f48b_row2_col4 {\n",
        "  background-color: #99cb99;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row2_col5 {\n",
+       "#T_6f48b_row2_col5 {\n",
        "  background-color: #7bbc7b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row3_col1 {\n",
+       "#T_6f48b_row3_col1 {\n",
        "  background-color: #50a750;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row3_col3 {\n",
+       "#T_6f48b_row3_col3 {\n",
        "  background-color: #198c19;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row3_col4, #T_679b0_row4_col8 {\n",
+       "#T_6f48b_row3_col4, #T_6f48b_row4_col8 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row3_col6 {\n",
+       "#T_6f48b_row3_col6 {\n",
        "  background-color: #40a040;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row3_col7 {\n",
+       "#T_6f48b_row3_col7 {\n",
        "  background-color: #4ba54a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row3_col8 {\n",
+       "#T_6f48b_row3_col8 {\n",
        "  background-color: #4ba54b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row4_col0 {\n",
+       "#T_6f48b_row4_col0 {\n",
        "  background-color: #42a042;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row4_col6 {\n",
+       "#T_6f48b_row4_col6 {\n",
        "  background-color: #138913;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row4_col7, #T_679b0_row5_col8 {\n",
+       "#T_6f48b_row4_col7, #T_6f48b_row5_col8 {\n",
        "  background-color: #c9e2c8;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row5_col0 {\n",
+       "#T_6f48b_row5_col0 {\n",
        "  background-color: #d4e8d4;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row5_col1 {\n",
+       "#T_6f48b_row5_col1 {\n",
        "  background-color: #b2d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row5_col3 {\n",
+       "#T_6f48b_row5_col3 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row5_col5 {\n",
+       "#T_6f48b_row5_col5 {\n",
        "  background-color: #209020;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row5_col6 {\n",
+       "#T_6f48b_row5_col6 {\n",
        "  background-color: #bfdebf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row5_col7 {\n",
+       "#T_6f48b_row5_col7 {\n",
        "  background-color: #d0e6d0;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col0 {\n",
+       "#T_6f48b_row6_col0 {\n",
        "  background-color: #9fce9f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col1 {\n",
+       "#T_6f48b_row6_col1 {\n",
        "  background-color: #b1d7b1;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col3 {\n",
+       "#T_6f48b_row6_col3 {\n",
        "  background-color: #85c185;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col4 {\n",
+       "#T_6f48b_row6_col4 {\n",
        "  background-color: #54a954;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row6_col5, #T_679b0_row7_col8, #T_679b0_row8_col5, #T_679b0_row8_col7 {\n",
+       "#T_6f48b_row6_col5, #T_6f48b_row7_col8, #T_6f48b_row8_col5, #T_6f48b_row8_col7 {\n",
        "  background-color: #dcecdc;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col7 {\n",
+       "#T_6f48b_row6_col7 {\n",
        "  background-color: #9bcc9b;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row6_col8 {\n",
+       "#T_6f48b_row6_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row7_col1 {\n",
+       "#T_6f48b_row7_col1 {\n",
        "  background-color: #ddecdd;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row7_col3 {\n",
+       "#T_6f48b_row7_col3 {\n",
        "  background-color: #59ac59;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row7_col4 {\n",
+       "#T_6f48b_row7_col4 {\n",
        "  background-color: #dfeddf;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row7_col5 {\n",
+       "#T_6f48b_row7_col5 {\n",
        "  background-color: #e5f0e5;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row7_col6 {\n",
+       "#T_6f48b_row7_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row8_col0 {\n",
+       "#T_6f48b_row8_col0 {\n",
        "  background-color: #73b873;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row8_col1 {\n",
+       "#T_6f48b_row8_col1 {\n",
        "  background-color: #bbdcbb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_679b0_row8_col3 {\n",
+       "#T_6f48b_row8_col3 {\n",
        "  background-color: #63b163;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row8_col4 {\n",
+       "#T_6f48b_row8_col4 {\n",
        "  background-color: #219021;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_679b0_row8_col6 {\n",
+       "#T_6f48b_row8_col6 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_679b0_\">\n",
+       "<table id=\"T_6f48b_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -2837,118 +2841,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_679b0_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
-       "      <td id=\"T_679b0_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
-       "      <td id=\"T_679b0_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
-       "      <td id=\"T_679b0_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
-       "      <td id=\"T_679b0_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
-       "      <td id=\"T_679b0_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
-       "      <td id=\"T_679b0_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
-       "      <td id=\"T_679b0_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
-       "      <td id=\"T_679b0_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_679b0_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
-       "      <td id=\"T_679b0_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
-       "      <td id=\"T_679b0_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
-       "      <td id=\"T_679b0_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
-       "      <td id=\"T_679b0_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
-       "      <td id=\"T_679b0_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
-       "      <td id=\"T_679b0_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
-       "      <td id=\"T_679b0_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_679b0_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
-       "      <td id=\"T_679b0_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
-       "      <td id=\"T_679b0_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
-       "      <td id=\"T_679b0_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
-       "      <td id=\"T_679b0_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
-       "      <td id=\"T_679b0_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_679b0_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
-       "      <td id=\"T_679b0_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
-       "      <td id=\"T_679b0_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
-       "      <td id=\"T_679b0_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
-       "      <td id=\"T_679b0_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
-       "      <td id=\"T_679b0_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
-       "      <td id=\"T_679b0_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
-       "      <td id=\"T_679b0_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
-       "      <td id=\"T_679b0_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_679b0_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
-       "      <td id=\"T_679b0_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
-       "      <td id=\"T_679b0_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
-       "      <td id=\"T_679b0_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
-       "      <td id=\"T_679b0_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
-       "      <td id=\"T_679b0_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
-       "      <td id=\"T_679b0_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
-       "      <td id=\"T_679b0_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
-       "      <td id=\"T_679b0_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_679b0_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
-       "      <td id=\"T_679b0_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
-       "      <td id=\"T_679b0_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
-       "      <td id=\"T_679b0_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
-       "      <td id=\"T_679b0_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
-       "      <td id=\"T_679b0_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
-       "      <td id=\"T_679b0_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
-       "      <td id=\"T_679b0_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
-       "      <td id=\"T_679b0_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_679b0_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
-       "      <td id=\"T_679b0_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
-       "      <td id=\"T_679b0_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
-       "      <td id=\"T_679b0_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
-       "      <td id=\"T_679b0_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
-       "      <td id=\"T_679b0_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
-       "      <td id=\"T_679b0_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
-       "      <td id=\"T_679b0_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_679b0_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
-       "      <td id=\"T_679b0_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
-       "      <td id=\"T_679b0_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
-       "      <td id=\"T_679b0_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
-       "      <td id=\"T_679b0_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
-       "      <td id=\"T_679b0_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
-       "      <td id=\"T_679b0_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
-       "      <td id=\"T_679b0_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_679b0_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_679b0_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
-       "      <td id=\"T_679b0_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
-       "      <td id=\"T_679b0_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
-       "      <td id=\"T_679b0_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
-       "      <td id=\"T_679b0_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
-       "      <td id=\"T_679b0_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
-       "      <td id=\"T_679b0_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_679b0_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
-       "      <td id=\"T_679b0_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
+       "      <th id=\"T_6f48b_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_6f48b_row0_col0\" class=\"data row0 col0\" >0.609</td>\n",
+       "      <td id=\"T_6f48b_row0_col1\" class=\"data row0 col1\" >0.566</td>\n",
+       "      <td id=\"T_6f48b_row0_col2\" class=\"data row0 col2\" >0.073</td>\n",
+       "      <td id=\"T_6f48b_row0_col3\" class=\"data row0 col3\" >0.351</td>\n",
+       "      <td id=\"T_6f48b_row0_col4\" class=\"data row0 col4\" >0.458</td>\n",
+       "      <td id=\"T_6f48b_row0_col5\" class=\"data row0 col5\" >0.125</td>\n",
+       "      <td id=\"T_6f48b_row0_col6\" class=\"data row0 col6\" >0.246</td>\n",
+       "      <td id=\"T_6f48b_row0_col7\" class=\"data row0 col7\" >0.228</td>\n",
+       "      <td id=\"T_6f48b_row0_col8\" class=\"data row0 col8\" >0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_6f48b_row1_col0\" class=\"data row1 col0\" >0.566</td>\n",
+       "      <td id=\"T_6f48b_row1_col1\" class=\"data row1 col1\" >0.606</td>\n",
+       "      <td id=\"T_6f48b_row1_col2\" class=\"data row1 col2\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row1_col3\" class=\"data row1 col3\" >0.400</td>\n",
+       "      <td id=\"T_6f48b_row1_col4\" class=\"data row1 col4\" >0.250</td>\n",
+       "      <td id=\"T_6f48b_row1_col5\" class=\"data row1 col5\" >0.148</td>\n",
+       "      <td id=\"T_6f48b_row1_col6\" class=\"data row1 col6\" >0.152</td>\n",
+       "      <td id=\"T_6f48b_row1_col7\" class=\"data row1 col7\" >0.036</td>\n",
+       "      <td id=\"T_6f48b_row1_col8\" class=\"data row1 col8\" >0.125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_6f48b_row2_col0\" class=\"data row2 col0\" >0.073</td>\n",
+       "      <td id=\"T_6f48b_row2_col1\" class=\"data row2 col1\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row2_col2\" class=\"data row2 col2\" >0.644</td>\n",
+       "      <td id=\"T_6f48b_row2_col3\" class=\"data row2 col3\" >0.184</td>\n",
+       "      <td id=\"T_6f48b_row2_col4\" class=\"data row2 col4\" >0.333</td>\n",
+       "      <td id=\"T_6f48b_row2_col5\" class=\"data row2 col5\" >0.333</td>\n",
+       "      <td id=\"T_6f48b_row2_col6\" class=\"data row2 col6\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row2_col7\" class=\"data row2 col7\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row2_col8\" class=\"data row2 col8\" >0.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_6f48b_row3_col0\" class=\"data row3 col0\" >0.351</td>\n",
+       "      <td id=\"T_6f48b_row3_col1\" class=\"data row3 col1\" >0.400</td>\n",
+       "      <td id=\"T_6f48b_row3_col2\" class=\"data row3 col2\" >0.184</td>\n",
+       "      <td id=\"T_6f48b_row3_col3\" class=\"data row3 col3\" >0.697</td>\n",
+       "      <td id=\"T_6f48b_row3_col4\" class=\"data row3 col4\" >0.760</td>\n",
+       "      <td id=\"T_6f48b_row3_col5\" class=\"data row3 col5\" >0.617</td>\n",
+       "      <td id=\"T_6f48b_row3_col6\" class=\"data row3 col6\" >0.433</td>\n",
+       "      <td id=\"T_6f48b_row3_col7\" class=\"data row3 col7\" >0.541</td>\n",
+       "      <td id=\"T_6f48b_row3_col8\" class=\"data row3 col8\" >0.516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_6f48b_row4_col0\" class=\"data row4 col0\" >0.458</td>\n",
+       "      <td id=\"T_6f48b_row4_col1\" class=\"data row4 col1\" >0.250</td>\n",
+       "      <td id=\"T_6f48b_row4_col2\" class=\"data row4 col2\" >0.333</td>\n",
+       "      <td id=\"T_6f48b_row4_col3\" class=\"data row4 col3\" >0.760</td>\n",
+       "      <td id=\"T_6f48b_row4_col4\" class=\"data row4 col4\" >0.811</td>\n",
+       "      <td id=\"T_6f48b_row4_col5\" class=\"data row4 col5\" >0.077</td>\n",
+       "      <td id=\"T_6f48b_row4_col6\" class=\"data row4 col6\" >0.550</td>\n",
+       "      <td id=\"T_6f48b_row4_col7\" class=\"data row4 col7\" >0.115</td>\n",
+       "      <td id=\"T_6f48b_row4_col8\" class=\"data row4 col8\" >0.707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_6f48b_row5_col0\" class=\"data row5 col0\" >0.125</td>\n",
+       "      <td id=\"T_6f48b_row5_col1\" class=\"data row5 col1\" >0.148</td>\n",
+       "      <td id=\"T_6f48b_row5_col2\" class=\"data row5 col2\" >0.333</td>\n",
+       "      <td id=\"T_6f48b_row5_col3\" class=\"data row5 col3\" >0.617</td>\n",
+       "      <td id=\"T_6f48b_row5_col4\" class=\"data row5 col4\" >0.077</td>\n",
+       "      <td id=\"T_6f48b_row5_col5\" class=\"data row5 col5\" >0.543</td>\n",
+       "      <td id=\"T_6f48b_row5_col6\" class=\"data row5 col6\" >0.111</td>\n",
+       "      <td id=\"T_6f48b_row5_col7\" class=\"data row5 col7\" >0.091</td>\n",
+       "      <td id=\"T_6f48b_row5_col8\" class=\"data row5 col8\" >0.111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_6f48b_row6_col0\" class=\"data row6 col0\" >0.246</td>\n",
+       "      <td id=\"T_6f48b_row6_col1\" class=\"data row6 col1\" >0.152</td>\n",
+       "      <td id=\"T_6f48b_row6_col2\" class=\"data row6 col2\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row6_col3\" class=\"data row6 col3\" >0.433</td>\n",
+       "      <td id=\"T_6f48b_row6_col4\" class=\"data row6 col4\" >0.550</td>\n",
+       "      <td id=\"T_6f48b_row6_col5\" class=\"data row6 col5\" >0.111</td>\n",
+       "      <td id=\"T_6f48b_row6_col6\" class=\"data row6 col6\" >0.599</td>\n",
+       "      <td id=\"T_6f48b_row6_col7\" class=\"data row6 col7\" >0.270</td>\n",
+       "      <td id=\"T_6f48b_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_6f48b_row7_col0\" class=\"data row7 col0\" >0.228</td>\n",
+       "      <td id=\"T_6f48b_row7_col1\" class=\"data row7 col1\" >0.036</td>\n",
+       "      <td id=\"T_6f48b_row7_col2\" class=\"data row7 col2\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row7_col3\" class=\"data row7 col3\" >0.541</td>\n",
+       "      <td id=\"T_6f48b_row7_col4\" class=\"data row7 col4\" >0.115</td>\n",
+       "      <td id=\"T_6f48b_row7_col5\" class=\"data row7 col5\" >0.091</td>\n",
+       "      <td id=\"T_6f48b_row7_col6\" class=\"data row7 col6\" >0.270</td>\n",
+       "      <td id=\"T_6f48b_row7_col7\" class=\"data row7 col7\" >0.794</td>\n",
+       "      <td id=\"T_6f48b_row7_col8\" class=\"data row7 col8\" >0.050</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_6f48b_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_6f48b_row8_col0\" class=\"data row8 col0\" >0.346</td>\n",
+       "      <td id=\"T_6f48b_row8_col1\" class=\"data row8 col1\" >0.125</td>\n",
+       "      <td id=\"T_6f48b_row8_col2\" class=\"data row8 col2\" >0.000</td>\n",
+       "      <td id=\"T_6f48b_row8_col3\" class=\"data row8 col3\" >0.516</td>\n",
+       "      <td id=\"T_6f48b_row8_col4\" class=\"data row8 col4\" >0.707</td>\n",
+       "      <td id=\"T_6f48b_row8_col5\" class=\"data row8 col5\" >0.111</td>\n",
+       "      <td id=\"T_6f48b_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_6f48b_row8_col7\" class=\"data row8 col7\" >0.050</td>\n",
+       "      <td id=\"T_6f48b_row8_col8\" class=\"data row8 col8\" >0.764</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbb6452f460>"
+       "<pandas.io.formats.style.Styler at 0x7ff07ed32760>"
       ]
      },
      "execution_count": 31,
@@ -3048,204 +3052,204 @@
      "data": {
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-       "#T_5953e_row0_col0, #T_5953e_row1_col1, #T_5953e_row2_col2, #T_5953e_row3_col3, #T_5953e_row4_col4, #T_5953e_row5_col5, #T_5953e_row6_col6, #T_5953e_row7_col7, #T_5953e_row8_col8 {\n",
+       "#T_1fe83_row0_col0, #T_1fe83_row1_col1, #T_1fe83_row2_col2, #T_1fe83_row3_col3, #T_1fe83_row4_col4, #T_1fe83_row5_col5, #T_1fe83_row6_col6, #T_1fe83_row7_col7, #T_1fe83_row8_col8 {\n",
        "  background-color: #ebf3eb;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row0_col1 {\n",
+       "#T_1fe83_row0_col1 {\n",
        "  background-color: #84c184;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row0_col2 {\n",
+       "#T_1fe83_row0_col2 {\n",
        "  background-color: #118811;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col3 {\n",
+       "#T_1fe83_row0_col3 {\n",
        "  background-color: #309730;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col4 {\n",
+       "#T_1fe83_row0_col4 {\n",
        "  background-color: #61af61;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col5 {\n",
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        "  background-color: #0c860c;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col6 {\n",
+       "#T_1fe83_row0_col6 {\n",
        "  background-color: #399c39;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col7 {\n",
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        "  background-color: #359a35;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row0_col8 {\n",
+       "#T_1fe83_row0_col8 {\n",
        "  background-color: #51a851;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col0 {\n",
+       "#T_1fe83_row1_col0 {\n",
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-       "#T_5953e_row1_col2, #T_5953e_row2_col0, #T_5953e_row2_col1, #T_5953e_row2_col3, #T_5953e_row2_col6, #T_5953e_row2_col7, #T_5953e_row2_col8, #T_5953e_row4_col5, #T_5953e_row5_col4, #T_5953e_row6_col2, #T_5953e_row7_col2, #T_5953e_row8_col2 {\n",
+       "#T_1fe83_row1_col2, #T_1fe83_row2_col0, #T_1fe83_row2_col1, #T_1fe83_row2_col3, #T_1fe83_row2_col6, #T_1fe83_row2_col7, #T_1fe83_row2_col8, #T_1fe83_row4_col5, #T_1fe83_row5_col4, #T_1fe83_row6_col2, #T_1fe83_row7_col2, #T_1fe83_row8_col2 {\n",
        "  background-color: #008000;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col3 {\n",
+       "#T_1fe83_row1_col3 {\n",
        "  background-color: #3e9e3e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col4, #T_5953e_row3_col2, #T_5953e_row6_col0 {\n",
+       "#T_1fe83_row1_col4, #T_1fe83_row3_col2, #T_1fe83_row6_col0 {\n",
        "  background-color: #2b952b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col5 {\n",
+       "#T_1fe83_row1_col5 {\n",
        "  background-color: #118911;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col6, #T_5953e_row6_col1 {\n",
+       "#T_1fe83_row1_col6, #T_1fe83_row6_col1 {\n",
        "  background-color: #239123;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col7, #T_5953e_row6_col5, #T_5953e_row7_col1, #T_5953e_row8_col5 {\n",
+       "#T_1fe83_row1_col7, #T_1fe83_row6_col5, #T_1fe83_row7_col1, #T_1fe83_row8_col5 {\n",
        "  background-color: #088408;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row1_col8, #T_5953e_row8_col1 {\n",
+       "#T_1fe83_row1_col8, #T_1fe83_row8_col1 {\n",
        "  background-color: #1d8e1d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row2_col4, #T_5953e_row2_col5 {\n",
+       "#T_1fe83_row2_col4, #T_1fe83_row2_col5 {\n",
        "  background-color: #41a041;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row3_col0 {\n",
+       "#T_1fe83_row3_col0 {\n",
        "  background-color: #46a246;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row3_col1 {\n",
+       "#T_1fe83_row3_col1 {\n",
        "  background-color: #5eae5e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row3_col4 {\n",
+       "#T_1fe83_row3_col4 {\n",
        "  background-color: #aed5ae;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row3_col5 {\n",
+       "#T_1fe83_row3_col5 {\n",
        "  background-color: #89c389;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row3_col6 {\n",
+       "#T_1fe83_row3_col6 {\n",
        "  background-color: #65b265;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row3_col7 {\n",
+       "#T_1fe83_row3_col7 {\n",
        "  background-color: #7fbe7f;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row3_col8 {\n",
+       "#T_1fe83_row3_col8 {\n",
        "  background-color: #79bc79;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row4_col0 {\n",
+       "#T_1fe83_row4_col0 {\n",
        "  background-color: #61b061;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row4_col1 {\n",
+       "#T_1fe83_row4_col1 {\n",
        "  background-color: #3a9c3a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row4_col2, #T_5953e_row5_col2 {\n",
+       "#T_1fe83_row4_col2, #T_1fe83_row5_col2 {\n",
        "  background-color: #4ea64e;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row4_col3 {\n",
+       "#T_1fe83_row4_col3 {\n",
        "  background-color: #a6d1a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row4_col6 {\n",
+       "#T_1fe83_row4_col6 {\n",
        "  background-color: #81bf81;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row4_col7 {\n",
+       "#T_1fe83_row4_col7 {\n",
        "  background-color: #1b8d1b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row4_col8 {\n",
+       "#T_1fe83_row4_col8 {\n",
        "  background-color: #a6d2a6;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row5_col0 {\n",
+       "#T_1fe83_row5_col0 {\n",
        "  background-color: #0d860d;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row5_col1 {\n",
+       "#T_1fe83_row5_col1 {\n",
        "  background-color: #229122;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row5_col3 {\n",
+       "#T_1fe83_row5_col3 {\n",
        "  background-color: #7cbd7c;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row5_col6, #T_5953e_row5_col8 {\n",
+       "#T_1fe83_row5_col6, #T_1fe83_row5_col8 {\n",
        "  background-color: #1a8d1a;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row5_col7 {\n",
+       "#T_1fe83_row5_col7 {\n",
        "  background-color: #158a15;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row6_col3 {\n",
+       "#T_1fe83_row6_col3 {\n",
        "  background-color: #48a348;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row6_col4 {\n",
+       "#T_1fe83_row6_col4 {\n",
        "  background-color: #78bb78;\n",
        "  color: #000000;\n",
        "}\n",
-       "#T_5953e_row6_col7, #T_5953e_row7_col6 {\n",
+       "#T_1fe83_row6_col7, #T_1fe83_row7_col6 {\n",
        "  background-color: #3f9f3f;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row6_col8, #T_5953e_row8_col6 {\n",
+       "#T_1fe83_row6_col8, #T_1fe83_row8_col6 {\n",
        "  background-color: #75b975;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row7_col0 {\n",
+       "#T_1fe83_row7_col0 {\n",
        "  background-color: #279327;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row7_col3 {\n",
+       "#T_1fe83_row7_col3 {\n",
        "  background-color: #67b267;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row7_col4 {\n",
+       "#T_1fe83_row7_col4 {\n",
        "  background-color: #098509;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row7_col5 {\n",
+       "#T_1fe83_row7_col5 {\n",
        "  background-color: #038103;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row7_col8, #T_5953e_row8_col7 {\n",
+       "#T_1fe83_row7_col8, #T_1fe83_row8_col7 {\n",
        "  background-color: #0b850b;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row8_col0 {\n",
+       "#T_1fe83_row8_col0 {\n",
        "  background-color: #45a245;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row8_col3 {\n",
+       "#T_1fe83_row8_col3 {\n",
        "  background-color: #60af60;\n",
        "  color: #f1f1f1;\n",
        "}\n",
-       "#T_5953e_row8_col4 {\n",
+       "#T_1fe83_row8_col4 {\n",
        "  background-color: #a0cea0;\n",
        "  color: #000000;\n",
        "}\n",
        "</style>\n",
-       "<table id=\"T_5953e_\">\n",
+       "<table id=\"T_1fe83_\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th class=\"blank level0\" >&nbsp;</th>\n",
@@ -3262,118 +3266,118 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
-       "      <td id=\"T_5953e_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
-       "      <td id=\"T_5953e_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
-       "      <td id=\"T_5953e_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
-       "      <td id=\"T_5953e_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
-       "      <td id=\"T_5953e_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
-       "      <td id=\"T_5953e_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
-       "      <td id=\"T_5953e_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
-       "      <td id=\"T_5953e_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
-       "      <td id=\"T_5953e_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
-       "      <td id=\"T_5953e_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
-       "      <td id=\"T_5953e_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
-       "      <td id=\"T_5953e_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
-       "      <td id=\"T_5953e_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
-       "      <td id=\"T_5953e_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
-       "      <td id=\"T_5953e_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
-       "      <td id=\"T_5953e_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
-       "      <td id=\"T_5953e_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
-       "      <td id=\"T_5953e_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
-       "      <td id=\"T_5953e_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
-       "      <td id=\"T_5953e_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
-       "      <td id=\"T_5953e_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
-       "      <td id=\"T_5953e_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
-       "      <td id=\"T_5953e_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
-       "      <td id=\"T_5953e_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
-       "      <td id=\"T_5953e_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
-       "      <td id=\"T_5953e_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
-       "      <td id=\"T_5953e_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
-       "      <td id=\"T_5953e_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
-       "      <td id=\"T_5953e_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
-       "      <td id=\"T_5953e_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
-       "      <td id=\"T_5953e_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
-       "      <td id=\"T_5953e_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
-       "      <td id=\"T_5953e_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
-       "      <td id=\"T_5953e_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
-       "      <td id=\"T_5953e_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
-       "      <td id=\"T_5953e_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
-       "      <td id=\"T_5953e_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
-       "      <td id=\"T_5953e_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
-       "      <td id=\"T_5953e_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
-       "      <td id=\"T_5953e_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
-       "      <td id=\"T_5953e_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
-       "      <td id=\"T_5953e_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
-       "      <td id=\"T_5953e_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
-       "      <td id=\"T_5953e_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
-       "      <td id=\"T_5953e_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
-       "      <td id=\"T_5953e_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
-       "      <td id=\"T_5953e_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
-       "      <td id=\"T_5953e_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
-       "      <td id=\"T_5953e_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
-       "      <td id=\"T_5953e_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
-       "      <td id=\"T_5953e_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
-       "      <td id=\"T_5953e_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
-       "      <td id=\"T_5953e_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
-       "      <td id=\"T_5953e_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
-       "      <td id=\"T_5953e_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
-       "      <td id=\"T_5953e_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
-       "      <td id=\"T_5953e_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
-       "      <td id=\"T_5953e_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
-       "      <td id=\"T_5953e_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_5953e_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
-       "      <td id=\"T_5953e_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
-       "      <td id=\"T_5953e_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
-       "      <td id=\"T_5953e_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
-       "      <td id=\"T_5953e_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
-       "      <td id=\"T_5953e_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
-       "      <td id=\"T_5953e_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
-       "      <td id=\"T_5953e_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
-       "      <td id=\"T_5953e_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
-       "      <td id=\"T_5953e_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
+       "      <th id=\"T_1fe83_level0_row0\" class=\"row_heading level0 row0\" >EGFR</th>\n",
+       "      <td id=\"T_1fe83_row0_col0\" class=\"data row0 col0\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row0_col1\" class=\"data row0 col1\" >0.434</td>\n",
+       "      <td id=\"T_1fe83_row0_col2\" class=\"data row0 col2\" >0.927</td>\n",
+       "      <td id=\"T_1fe83_row0_col3\" class=\"data row0 col3\" >0.649</td>\n",
+       "      <td id=\"T_1fe83_row0_col4\" class=\"data row0 col4\" >0.542</td>\n",
+       "      <td id=\"T_1fe83_row0_col5\" class=\"data row0 col5\" >0.875</td>\n",
+       "      <td id=\"T_1fe83_row0_col6\" class=\"data row0 col6\" >0.754</td>\n",
+       "      <td id=\"T_1fe83_row0_col7\" class=\"data row0 col7\" >0.772</td>\n",
+       "      <td id=\"T_1fe83_row0_col8\" class=\"data row0 col8\" >0.654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row1\" class=\"row_heading level0 row1\" >ErbB2</th>\n",
+       "      <td id=\"T_1fe83_row1_col0\" class=\"data row1 col0\" >0.434</td>\n",
+       "      <td id=\"T_1fe83_row1_col1\" class=\"data row1 col1\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row1_col2\" class=\"data row1 col2\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row1_col3\" class=\"data row1 col3\" >0.600</td>\n",
+       "      <td id=\"T_1fe83_row1_col4\" class=\"data row1 col4\" >0.750</td>\n",
+       "      <td id=\"T_1fe83_row1_col5\" class=\"data row1 col5\" >0.852</td>\n",
+       "      <td id=\"T_1fe83_row1_col6\" class=\"data row1 col6\" >0.848</td>\n",
+       "      <td id=\"T_1fe83_row1_col7\" class=\"data row1 col7\" >0.964</td>\n",
+       "      <td id=\"T_1fe83_row1_col8\" class=\"data row1 col8\" >0.875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row2\" class=\"row_heading level0 row2\" >p110a</th>\n",
+       "      <td id=\"T_1fe83_row2_col0\" class=\"data row2 col0\" >0.927</td>\n",
+       "      <td id=\"T_1fe83_row2_col1\" class=\"data row2 col1\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row2_col2\" class=\"data row2 col2\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row2_col3\" class=\"data row2 col3\" >0.816</td>\n",
+       "      <td id=\"T_1fe83_row2_col4\" class=\"data row2 col4\" >0.667</td>\n",
+       "      <td id=\"T_1fe83_row2_col5\" class=\"data row2 col5\" >0.667</td>\n",
+       "      <td id=\"T_1fe83_row2_col6\" class=\"data row2 col6\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row2_col7\" class=\"data row2 col7\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row2_col8\" class=\"data row2 col8\" >1.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row3\" class=\"row_heading level0 row3\" >KDR</th>\n",
+       "      <td id=\"T_1fe83_row3_col0\" class=\"data row3 col0\" >0.649</td>\n",
+       "      <td id=\"T_1fe83_row3_col1\" class=\"data row3 col1\" >0.600</td>\n",
+       "      <td id=\"T_1fe83_row3_col2\" class=\"data row3 col2\" >0.816</td>\n",
+       "      <td id=\"T_1fe83_row3_col3\" class=\"data row3 col3\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row3_col4\" class=\"data row3 col4\" >0.240</td>\n",
+       "      <td id=\"T_1fe83_row3_col5\" class=\"data row3 col5\" >0.383</td>\n",
+       "      <td id=\"T_1fe83_row3_col6\" class=\"data row3 col6\" >0.567</td>\n",
+       "      <td id=\"T_1fe83_row3_col7\" class=\"data row3 col7\" >0.459</td>\n",
+       "      <td id=\"T_1fe83_row3_col8\" class=\"data row3 col8\" >0.484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row4\" class=\"row_heading level0 row4\" >BRAF</th>\n",
+       "      <td id=\"T_1fe83_row4_col0\" class=\"data row4 col0\" >0.542</td>\n",
+       "      <td id=\"T_1fe83_row4_col1\" class=\"data row4 col1\" >0.750</td>\n",
+       "      <td id=\"T_1fe83_row4_col2\" class=\"data row4 col2\" >0.667</td>\n",
+       "      <td id=\"T_1fe83_row4_col3\" class=\"data row4 col3\" >0.240</td>\n",
+       "      <td id=\"T_1fe83_row4_col4\" class=\"data row4 col4\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row4_col5\" class=\"data row4 col5\" >0.923</td>\n",
+       "      <td id=\"T_1fe83_row4_col6\" class=\"data row4 col6\" >0.450</td>\n",
+       "      <td id=\"T_1fe83_row4_col7\" class=\"data row4 col7\" >0.885</td>\n",
+       "      <td id=\"T_1fe83_row4_col8\" class=\"data row4 col8\" >0.293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row5\" class=\"row_heading level0 row5\" >CDK2</th>\n",
+       "      <td id=\"T_1fe83_row5_col0\" class=\"data row5 col0\" >0.875</td>\n",
+       "      <td id=\"T_1fe83_row5_col1\" class=\"data row5 col1\" >0.852</td>\n",
+       "      <td id=\"T_1fe83_row5_col2\" class=\"data row5 col2\" >0.667</td>\n",
+       "      <td id=\"T_1fe83_row5_col3\" class=\"data row5 col3\" >0.383</td>\n",
+       "      <td id=\"T_1fe83_row5_col4\" class=\"data row5 col4\" >0.923</td>\n",
+       "      <td id=\"T_1fe83_row5_col5\" class=\"data row5 col5\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row5_col6\" class=\"data row5 col6\" >0.889</td>\n",
+       "      <td id=\"T_1fe83_row5_col7\" class=\"data row5 col7\" >0.909</td>\n",
+       "      <td id=\"T_1fe83_row5_col8\" class=\"data row5 col8\" >0.889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row6\" class=\"row_heading level0 row6\" >LCK</th>\n",
+       "      <td id=\"T_1fe83_row6_col0\" class=\"data row6 col0\" >0.754</td>\n",
+       "      <td id=\"T_1fe83_row6_col1\" class=\"data row6 col1\" >0.848</td>\n",
+       "      <td id=\"T_1fe83_row6_col2\" class=\"data row6 col2\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row6_col3\" class=\"data row6 col3\" >0.567</td>\n",
+       "      <td id=\"T_1fe83_row6_col4\" class=\"data row6 col4\" >0.450</td>\n",
+       "      <td id=\"T_1fe83_row6_col5\" class=\"data row6 col5\" >0.889</td>\n",
+       "      <td id=\"T_1fe83_row6_col6\" class=\"data row6 col6\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row6_col7\" class=\"data row6 col7\" >0.730</td>\n",
+       "      <td id=\"T_1fe83_row6_col8\" class=\"data row6 col8\" >0.500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row7\" class=\"row_heading level0 row7\" >MET</th>\n",
+       "      <td id=\"T_1fe83_row7_col0\" class=\"data row7 col0\" >0.772</td>\n",
+       "      <td id=\"T_1fe83_row7_col1\" class=\"data row7 col1\" >0.964</td>\n",
+       "      <td id=\"T_1fe83_row7_col2\" class=\"data row7 col2\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row7_col3\" class=\"data row7 col3\" >0.459</td>\n",
+       "      <td id=\"T_1fe83_row7_col4\" class=\"data row7 col4\" >0.885</td>\n",
+       "      <td id=\"T_1fe83_row7_col5\" class=\"data row7 col5\" >0.909</td>\n",
+       "      <td id=\"T_1fe83_row7_col6\" class=\"data row7 col6\" >0.730</td>\n",
+       "      <td id=\"T_1fe83_row7_col7\" class=\"data row7 col7\" >0.000</td>\n",
+       "      <td id=\"T_1fe83_row7_col8\" class=\"data row7 col8\" >0.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_1fe83_level0_row8\" class=\"row_heading level0 row8\" >p38a</th>\n",
+       "      <td id=\"T_1fe83_row8_col0\" class=\"data row8 col0\" >0.654</td>\n",
+       "      <td id=\"T_1fe83_row8_col1\" class=\"data row8 col1\" >0.875</td>\n",
+       "      <td id=\"T_1fe83_row8_col2\" class=\"data row8 col2\" >1.000</td>\n",
+       "      <td id=\"T_1fe83_row8_col3\" class=\"data row8 col3\" >0.484</td>\n",
+       "      <td id=\"T_1fe83_row8_col4\" class=\"data row8 col4\" >0.293</td>\n",
+       "      <td id=\"T_1fe83_row8_col5\" class=\"data row8 col5\" >0.889</td>\n",
+       "      <td id=\"T_1fe83_row8_col6\" class=\"data row8 col6\" >0.500</td>\n",
+       "      <td id=\"T_1fe83_row8_col7\" class=\"data row8 col7\" >0.950</td>\n",
+       "      <td id=\"T_1fe83_row8_col8\" class=\"data row8 col8\" >0.000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fbb64532f40>"
+       "<pandas.io.formats.style.Styler at 0x7ff07ec3abe0>"
       ]
      },
      "execution_count": 36,
@@ -3382,64 +3386,7 @@
     }
    ],
    "source": [
-    "kinase_distance_matrix_df_styler = kinase_distance_matrix_df.style.background_gradient(\n",
-    "    cmap=cm\n",
-    ").format(\"{:.3f}\")\n",
-    "kinase_distance_matrix_df_styler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We export the matrix with colors as an image to use as thumbnail for the website."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "tags": [
-     "nbsphinx-thumbnail"
-    ]
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[1129/155536.382797:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155536.383888:ERROR:file_io.cc(94)] ReadExactly: expected 8, observed 0\n",
-      "[1129/155536.385040:ERROR:xattr.cc(63)] setxattr org.chromium.crashpad.database.initialized on file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/: Operation not permitted (1)\n",
-      "[1129/155536.969662:INFO:headless_shell.cc(653)] Written to file /var/folders/h4/b6vj1lm52pv9p2mw4q7kltr80000gn/T/tmphn_w7l90/temp.png.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfi.export(kinase_distance_matrix_df_styler, f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "matrix_styler_img = mpimg.imread(f\"{DATA}/kinase_distance_matrix_df_styler.png\")\n",
-    "plt.imshow(matrix_styler_img)\n",
-    "plt.axis(\"off\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: Since this is a distance matrix, lighter colors indicate similarity, darker colors dissimilarity."
+    "kinase_distance_matrix_df.style.background_gradient(cmap=cm).format(\"{:.3f}\")"
    ]
   },
   {
@@ -3451,7 +3398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [

From ba38d4eb76a0e27494b46f6f9c6da0e591c8b316 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 30 Nov 2021 14:36:25 +0100
Subject: [PATCH 157/158] Remove unecessary imports.

---
 .../T027_kinase_similarity_ligand_profile/talktorial.ipynb    | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
index 3714ef43..b7ebc5d8 100644
--- a/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
+++ b/teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/talktorial.ipynb
@@ -187,13 +187,11 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
-    "import matplotlib.image as mpimg\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from rdkit import Chem\n",
     "from rdkit.Chem import Draw\n",
-    "from rdkit.Chem.Draw import IPythonConsole\n",
-    "import dataframe_image as dfi"
+    "from rdkit.Chem.Draw import IPythonConsole"
    ]
   },
   {

From 9da0a818a4e237bf48d7dcd618beb6fafb46d6d0 Mon Sep 17 00:00:00 2001
From: t-kimber <talia.kimber@gmail.com>
Date: Tue, 30 Nov 2021 14:37:13 +0100
Subject: [PATCH 158/158] Remove png of dist. matrix.

---
 .../data/kinase_distance_matrix_df_styler.png   | Bin 84604 -> 0 bytes
 .../data/kinase_distance_matrix_df_styler.png   | Bin 91861 -> 0 bytes
 .../data/kinase_distance_matrix_df_styler.png   | Bin 85081 -> 0 bytes
 .../data/kinase_distance_matrix_df_styler.png   | Bin 84551 -> 0 bytes
 4 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png
 delete mode 100644 teachopencadd/talktorials/T025_kinase_similarity_kissim/data/kinase_distance_matrix_df_styler.png
 delete mode 100644 teachopencadd/talktorials/T026_kinase_similarity_ifp/data/kinase_distance_matrix_df_styler.png
 delete mode 100644 teachopencadd/talktorials/T027_kinase_similarity_ligand_profile/data/kinase_distance_matrix_df_styler.png

diff --git a/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png b/teachopencadd/talktorials/T024_kinase_similarity_sequence/data/kinase_distance_matrix_df_styler.png
deleted file mode 100644
index 0ace8e58fe308e0b4540eddae1e7624012dc586a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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