week7_lecture.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "cbb43510-ca42-4625-bf6e-3164695d65f5",
   "metadata": {},
   "source": [
    "# Python Course - Networks and Knowledge Graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d211e1-a945-4cbf-878e-b1a0caf45970",
   "metadata": {},
   "source": [
    "Katarzyna Nurzynska, Sebastian Nilsson, BIKG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "969ed6da-704c-4f84-b818-847bca9ecd60",
   "metadata": {},
   "source": [
    "## <font color='green'>Overview</font>\n",
    "\n",
    "**We will go through:**\n",
    "\n",
    "* Backround on networks and graphs\n",
    "* Part 1: \n",
    "    * Building simple networks with NetworkX\n",
    "    * Introduction to network analysis using degree metric \n",
    "    * Network visualisation with Pyvis \n",
    "    * <font color='red'>*First use case: drug-drug interactions*</font>\n",
    "* Part 2: \n",
    "    * What is BIKG and how to access it?\n",
    "    * How to create graph schema?\n",
    "    * How to visualise your BIKG subgraph \n",
    "    * <font color='red'>*Second use case: Trop2*</font>\n",
    "    \n",
    "The emphasis of this course will be on data visualisation rather than deep data analysis. \n",
    "\n",
    "**What we will not cover:**\n",
    "* Other metrics than degree, e.g. betweenness centrality (as they are not very relevant for drug-drug interactions dataset)\n",
    "\n",
    "    \n",
    "#**Data:**\n",
    "\n",
    "* Part 1: For the purpose of this course we will use the data file summarising drug-drug interactions\n",
    "    * DDI_data.csv file is extracted from drug bank v5.1, it includes the following items: Drug1_id, Drug2_id, Drug1_name, Drug2_name, and the interaction types between Drug1 and Drug2.  \n",
    "    * https://data.mendeley.com/datasets/md5czfsfnd/1/files/4530a4be-8cff-4cfb-a309-9343f92f6832\n",
    "    * https://data.mendeley.com/\n",
    "* Part 2: BIKG database \n",
    "    * Genes, Compounds and Diseases associated with Trop2 (Trop-2 is a transmembrane glycoprotein that is upregulated in many cancer types: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6402435/)\n",
    "\n",
    "**Key Python libraries that we will use:**\n",
    "\n",
    "* Pandas\n",
    "* NetworkX\n",
    "* Pyvis\n",
    "* BIKG"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b16367d7-8989-4f47-a51e-5d718ce43654",
   "metadata": {},
   "source": [
    "## <font color='green'>Background</font>\n",
    "\n",
    "**What are networks and graphs?**\n",
    "\n",
    "A network (or graph) is a colleciton of nodes and edges which allows us to model the relationships between nodes. \n",
    "In recent years, network science has seen a recent explosion in popularity thanks to its wide ranging applications.\n",
    "Some of the Networks' applications include social media or modelling biological systems.\n",
    "\n",
    "**What is a Knowledge Graph?**\n",
    "\n",
    "A Knowledge Graph is a set of data points connected by relations that describe a domain, for instance, a business, an organization, or a field of study. It is a powerful way of representing data because Knowledge Graphs can be built automatically and can then be explored to reveal new insights about the domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6b9209f7-abb5-4e33-b31b-f0933bca3651",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('/home/kvmm591/kvmm591/Python_training/Python_networks/Python_course/KG_pic.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61a1296-e263-444c-88f9-53b946eff8ac",
   "metadata": {},
   "source": [
    "**Applications of Graphs in Business**\n",
    "\n",
    "Raw data formats can be challenging for human brains to process. A graph visualisation can help with detecting unexpected connections, hidden patterns and outliers.\n",
    "\n",
    "Knowledge Graph value for business: \n",
    "* Quickly find relevant information \n",
    "* Find weak and emerging signals in the data\n",
    "* Get new insights by combining internal and public data \n",
    "* Solve emergent use cases/Create new hypothesis\n",
    "* Control access and visibility at multiple levels (graph, subgraph)\n",
    "* Perform cutting-edge analytics\n",
    "\n",
    "\n",
    "**Biological Insights Knowledge Graph (BIKG)**\n",
    "\n",
    "Recent years have seen rapid growth in the popularity of knowledge grahs in the life sciences domain. Knowledge graphs often serve as a backbone for data integration within organisation, providing a common representation structure which enables querying across data sources.\n",
    "\n",
    "BIKG is an AstraZeneca project aimed at building a knowledge graph combining both public and internal data to facilitate knowledge discovery using machine learning. BIKG integrates knowledge from heterogenous data sources including public databases like ChEMBL or Ensembl, information extracted from full-text publications using Natural Language Processing (NLP) techniques as well as diverse proprietary datasets collected as part of AstraZeneca drug development process and biological experimentation. \n",
    "\n",
    "To find out more please visit: \n",
    "* https://bikg.astrazeneca.net/\n",
    "* https://browser.bikg.astrazeneca.net/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cea38d0-6198-49bf-83d6-71247d8f36f4",
   "metadata": {},
   "source": [
    "## <font color='green'>Part 1: Simple Networks</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5f6a8cc-342f-43b3-96ae-10ce51406326",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install networkx\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b54a3ae9-68b7-4960-9cee-e21ba19123b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vertex set:  ['A', 'B', 'C', 'D', 'E']\n",
      "Edge set:  [('A', 'B'), ('A', 'C'), ('B', 'D'), ('B', 'E'), ('C', 'E')]\n"
     ]
    }
   ],
   "source": [
    "#Create an empty graph (initialize graph object)\n",
    "G = nx.Graph()\n",
    "\n",
    "#Adding nodes\n",
    "G.add_node('A') #add single node\n",
    "G.add_nodes_from(['B','C','D','E']) #add multiple nodes\n",
    "\n",
    "#Adding edges\n",
    "G.add_edge('A','B')\n",
    "G.add_edges_from([('A','C'), ('B','D'), ('B','E'), ('C', 'E')])\n",
    "\n",
    "#Accessing vertex and edge sets\n",
    "print(\"Vertex set: \",G.nodes())\n",
    "print(\"Edge set: \",G.edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad4b7434-196a-4ef3-847e-d25f11268e2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Drawing graph\n",
    "nx.draw(G)\n",
    "plt.show()\n",
    "\n",
    "#Add vertex labels\n",
    "nx.draw(G, with_labels=True, font_weight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a0486e-2493-49c5-b0c0-9f1432741938",
   "metadata": {},
   "source": [
    "By looking at this simple network you can get a good understanding how data are connected. B has the most connections with other nodes (3). A, E and C nodes have only 2 connections and D one connection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b25e45d3-5ad1-404a-9902-cd97ebd11a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating weighted graph\n",
    "G = nx.Graph() \n",
    "E = [('A', 'B', 2), ('A', 'C', 1), ('B', 'D', 5), ('B', 'E', 3), ('C', 'E', 2)]\n",
    "G.add_weighted_edges_from(E)\n",
    "\n",
    "pos=nx.spring_layout(G)\n",
    "nx.draw(G, pos, with_labels=True, font_weight='bold')\n",
    "edge_weight = nx.get_edge_attributes(G,'weight')\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_weight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df760265-60eb-410e-9ef1-1b2113257798",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating weighted graph (edges: labels, color, thickness; nodes: size based on degree)\n",
    "G = nx.Graph() \n",
    "G.add_edge('A', 'B', weight=13, color='r', relation='friend')\n",
    "G.add_edge('A', 'C', weight=9, color='b', relation='family')\n",
    "G.add_edge('B', 'D', weight=7, color='r', relation='friend')\n",
    "G.add_edge('E', 'B', weight=10, color='r', relation='friend')\n",
    "G.add_edge('E', 'A', weight=5, color='g', relation='enemy')\n",
    "G.add_edge('F', 'B', weight=13, color='b', relation='family')\n",
    "\n",
    "G.add_node(\"A\")#, size=800)\n",
    "G.add_node(\"B\")#, size=1000)\n",
    "G.add_node(\"C\")#, size=200)\n",
    "G.add_node(\"D\")#, size=200)\n",
    "G.add_node(\"E\")#, size=500)\n",
    "G.add_node(\"F\")#, size=200)\n",
    "\n",
    "colors = nx.get_edge_attributes(G,'color').values()\n",
    "weights = nx.get_edge_attributes(G,'weight').values()\n",
    "\n",
    "#Size of the node should correspond to its degree\n",
    "d = dict(G.degree)\n",
    "\n",
    "pos=nx.spring_layout(G)\n",
    "nx.draw(G, pos,\n",
    "        edge_color=colors,\n",
    "        width=list(weights),\n",
    "        with_labels=True, \n",
    "        font_weight='bold',\n",
    "        node_color=\"lightgreen\",\n",
    "        node_size=[v * 100 for v in d.values()]  # Scaling the size of the nodes by 100*degree\n",
    "       )\n",
    "\n",
    "edge_weight = nx.get_edge_attributes(G,'relation')\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_weight)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172eb32b-0d7d-4caa-825a-6b29753faa2a",
   "metadata": {},
   "source": [
    "### <font color='red'> First use case: drug-drug interactions </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a33489c8-2800-4b3c-ae10-ecbe82e54625",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pyvis.network import Network\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import Counter #Counter tool - you can count the key-value pairs in an object \n",
    "from  itertools import combinations #Intertools - to create iterations for efficient looping; combinations - all the combinations of list  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da29a066-c34f-4b43-9251-41953aafc4a4",
   "metadata": {},
   "source": [
    "**1) Data overview**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f072774d-08d1-497e-ba80-34a0b7eb71e5",
   "metadata": {},
   "source": [
    "* Every pairwise drug interaction is a relationship between two nodes (i.e. the two drug compounds).There are 222696 raws and 5 columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9df013d6-3911-4a6c-8774-672f7659e5e8",
   "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>drug1_id</th>\n",
       "      <th>drug2_id</th>\n",
       "      <th>drug1_name</th>\n",
       "      <th>drug2_name</th>\n",
       "      <th>interaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>DB00346</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Alfuzosin</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>DB13783</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Acemetacin</td>\n",
       "      <td>risk or severity of bleeding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>DB06605</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Apixaban</td>\n",
       "      <td>anticoagulant activities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>DB06695</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Dabigatran etexilate</td>\n",
       "      <td>anticoagulant activities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>DB09075</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Edoxaban</td>\n",
       "      <td>anticoagulant activities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drug1_id drug2_id   drug1_name            drug2_name  \\\n",
       "0  DB00006  DB00346  Bivalirudin             Alfuzosin   \n",
       "1  DB00006  DB13783  Bivalirudin            Acemetacin   \n",
       "2  DB00006  DB06605  Bivalirudin              Apixaban   \n",
       "3  DB00006  DB06695  Bivalirudin  Dabigatran etexilate   \n",
       "4  DB00006  DB09075  Bivalirudin              Edoxaban   \n",
       "\n",
       "               interaction_type  \n",
       "0           serum concentration  \n",
       "1  risk or severity of bleeding  \n",
       "2      anticoagulant activities  \n",
       "3      anticoagulant activities  \n",
       "4      anticoagulant activities  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import and view dataset\n",
    "DDI_data = pd.read_csv('/home/kvmm591/kvmm591/Python_training/Python_networks/Data/DDI_data.csv')\n",
    "DDI_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "911c45d5-c28b-46e4-abe7-401f35880e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(222696, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDI_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b36691b-797b-41a6-b8bf-e9c725deb977",
   "metadata": {},
   "source": [
    "**2) List types of interactions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cba8587d-ec02-4a5b-b3bf-c1d87758ed23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "risk or severity of adverse effects                              62767\n",
       "metabolism                                                       42811\n",
       "serum concentration                                              39294\n",
       "therapeutic efficacy                                             12807\n",
       "hypotensive activities                                           10051\n",
       "QTc-prolonging activities                                         5837\n",
       "antihypertensive activities                                       5638\n",
       "central nervous system depressant (CNS depressant) activities     5279\n",
       "anticoagulant activities                                          4344\n",
       "vasoconstricting activities                                       2577\n",
       "hypoglycemic activities                                           2461\n",
       "risk or severity of hypotension                                   1840\n",
       "sedative activities                                               1740\n",
       "hypokalemic activities                                            1561\n",
       "absorption                                                        1522\n",
       "orthostatic hypotensive activities                                1514\n",
       "bradycardic activities                                            1452\n",
       "neuroexcitatory activities                                        1392\n",
       "cardiotoxic activities                                            1286\n",
       "excretion rate of higher serum level                              1284\n",
       "Name: interaction_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List types of interactions (top 20)\n",
    "DDI_data['interaction_type'].value_counts()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bafd521c-57a0-4bd0-933b-cd49f611f85d",
   "metadata": {},
   "source": [
    "**3) List drugs with the most interactions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b53a1c1c-249a-4f6b-8ebd-b4ff3c639e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/kvmm591/ipykernel_259375/822163673.py:2: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  drugs = DDI_data['drug1_name'].append(DDI_data['drug2_name']).value_counts()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Amiodarone       1035\n",
       "Nefazodone        990\n",
       "Phenobarbital     984\n",
       "Venlafaxine       970\n",
       "Primidone         955\n",
       "Phenytoin         942\n",
       "Fluvoxamine       934\n",
       "Carbamazepine     933\n",
       "Curcumin          928\n",
       "Fosphenytoin      923\n",
       "Ziprasidone       922\n",
       "Pentobarbital     907\n",
       "Verapamil         882\n",
       "Clozapine         858\n",
       "Diltiazem         851\n",
       "Cyclosporine      844\n",
       "Vemurafenib       842\n",
       "Rifampicin        820\n",
       "Pitolisant        815\n",
       "Stiripentol       813\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List drugs with the most interactions\n",
    "drugs = DDI_data['drug1_name'].append(DDI_data['drug2_name']).value_counts()\n",
    "drugs[:20] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c09d1a2-93f4-4399-85b0-dd3ea343d986",
   "metadata": {},
   "source": [
    "**4) Types of interactions for each pair of drugs**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fab9ff63-b700-4011-a70f-9a5cd7225951",
   "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>Pair</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(Cyclosporine, Vincristine)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(Cyclosporine, Ledipasvir)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Cyclosporine, Doxorubicin)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(Cholecalciferol, Doxorubicin)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(Pravastatin, Vincristine)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(Fluvoxamine, Doxorubicin)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(Amphetamine, Bupropion)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(Nicotine, Tegafur)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(Bortezomib, Doxorubicin)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(Betaxolol, Doxorubicin)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Pair  Qty\n",
       "0     (Cyclosporine, Vincristine)    2\n",
       "1      (Cyclosporine, Ledipasvir)    2\n",
       "2     (Cyclosporine, Doxorubicin)    2\n",
       "3  (Cholecalciferol, Doxorubicin)    2\n",
       "4      (Pravastatin, Vincristine)    2\n",
       "5      (Fluvoxamine, Doxorubicin)    2\n",
       "6        (Amphetamine, Bupropion)    2\n",
       "7             (Nicotine, Tegafur)    2\n",
       "8       (Bortezomib, Doxorubicin)    2\n",
       "9        (Betaxolol, Doxorubicin)    2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print pairs of drugs that interact with each other and quantity of interactions (qty is either 1 or 2 in our case)\n",
    "n = 10\n",
    "df_poly_se_sm = DDI_data[['drug1_name', 'drug2_name']]\n",
    "L = Counter([y for x in df_poly_se_sm.values for y in combinations(x, 2)]).most_common(n) #Most_common: Return a list of the n most common elements and their counts from the most common to the least.\n",
    "\n",
    "combi_df = pd.DataFrame(L, columns=['Pair', 'Qty'])\n",
    "combi_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bdf5bfa8-2974-4d1f-925a-853295bfa143",
   "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>drug1_id</th>\n",
       "      <th>drug2_id</th>\n",
       "      <th>drug1_name</th>\n",
       "      <th>drug2_name</th>\n",
       "      <th>interaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>DB00091</td>\n",
       "      <td>DB00541</td>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>Vincristine</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>DB00091</td>\n",
       "      <td>DB00541</td>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>Vincristine</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drug1_id drug2_id    drug1_name   drug2_name     interaction_type\n",
       "628   DB00091  DB00541  Cyclosporine  Vincristine  serum concentration\n",
       "1032  DB00091  DB00541  Cyclosporine  Vincristine           metabolism"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#What number 2 means -> E.g. There are two types of interactions for the pair Cyclosporine and Vincristine \n",
    "#Select Cyclosporine Vincristine pair \n",
    "DDI_data.loc[(DDI_data['drug1_name'] == 'Cyclosporine') & (DDI_data['drug2_name'] == 'Vincristine')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c28432a7-9e12-45ee-b6e9-6396dd489f93",
   "metadata": {},
   "source": [
    "**5) Define possible edge attributes** (weight, color, interaction type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "677b4605-3828-4731-85e4-b751eb4be31e",
   "metadata": {},
   "source": [
    "* We do not have data on interaction severity at this stage (such as mild, moderate, severe) to differentiate between different edges. Therefore just for the purpose of this exercise we will set color of the edge to red if the name of the interaction contains word \"severity\" and to blue if not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "600cad17-7d68-4c50-84b4-841aed342f73",
   "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>drug1_name</th>\n",
       "      <th>drug2_name</th>\n",
       "      <th>weight</th>\n",
       "      <th>color</th>\n",
       "      <th>interaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Alfuzosin</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Acemetacin</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>risk or severity of bleeding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Apixaban</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>anticoagulant activities</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    drug1_name  drug2_name  weight color              interaction_type\n",
       "0  Bivalirudin   Alfuzosin       1  blue           serum concentration\n",
       "1  Bivalirudin  Acemetacin       1   red  risk or severity of bleeding\n",
       "2  Bivalirudin    Apixaban       1  blue      anticoagulant activities"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since we do not have data on interaction severity at this stage, just for the purpose of this exercise we set color of the edge to red if the name of the interaction contains word \"severity\" and to blue if not.\n",
    "DDI_data['weight'] = 1\n",
    "DDI_data['color']='blue'\n",
    "DDI_data.loc[DDI_data['interaction_type'].str.contains('severity'), 'color']='red'\n",
    "\n",
    "# Keep only drug name columns\n",
    "DDI_data = DDI_data[['drug1_name', 'drug2_name', 'weight', 'color', 'interaction_type']]\n",
    "DDI_data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bc7ee7-5523-4fa8-a888-413b9572e639",
   "metadata": {},
   "source": [
    "**6) Retrieve basic summary of the network graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2fd051b7-11ee-4743-8e0c-9e3b29889e16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph named 'Drug Interactions Network' with 1868 nodes and 222271 edges\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/kvmm591/ipykernel_259375/762937952.py:8: DeprecationWarning: info is deprecated and will be removed in version 3.0.\n",
      "\n",
      "  print(nx.info(G))\n"
     ]
    }
   ],
   "source": [
    "# Generate a networkx graph object\n",
    "G = nx.from_pandas_edgelist(DDI_data, 'drug1_name', 'drug2_name', edge_attr='color')\n",
    "\n",
    "# Give the graph a name\n",
    "G.name = 'Drug Interactions Network'\n",
    "\n",
    "# Obtain general information of graph\n",
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ab6a4a-0795-4903-a93e-6f068cb5d526",
   "metadata": {},
   "source": [
    "**7) Get graph density**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d30636a-66a4-4680-b264-2900eae356a6",
   "metadata": {},
   "source": [
    "* Network density describes the proportion of potential connections in a network that are actual connections, with 0 representing a graph with zero edges, and 1 representing a complete graph where all nodes are interconnected.\n",
    "The low network density of 0.13 makes sense because we do not expect too many medications to have interactions with each other.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05aa72c6-6355-4b40-bd89-c77a1e537b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network density: 0.12746519339044304\n"
     ]
    }
   ],
   "source": [
    "# Get graph density\n",
    "density = nx.density(G)\n",
    "print(\"Network density:\", density)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75abee12-d1d9-412b-9e5c-88cab9212914",
   "metadata": {},
   "source": [
    "**8) Network degree**\n",
    "\n",
    "Degree: The degree of a node refers to the number of connections the node has/the number of edges it has.\n",
    "By calculating the degree of each node, we can get the top 20 drugs most frequently involved in drug interactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "14f38681-54ac-41d7-8a90-54cf81ac8dd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Amiodarone', 1028)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get most connected node (i.e. drug with most drug interactions)\n",
    "G.degree()\n",
    "max(dict(G.degree()).items(), key = lambda x : x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e3941ff-f3cb-4734-8ef5-2cf305889353",
   "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>drug1_name</th>\n",
       "      <th>drug2_name</th>\n",
       "      <th>interaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>Goserelin</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>QTc-prolonging activities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>Octreotide</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>QTc-prolonging activities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>Calcitriol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>Ergocalciferol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479</th>\n",
       "      <td>Cholecalciferol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3198</th>\n",
       "      <td>Pravastatin</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3452</th>\n",
       "      <td>Fluvoxamine</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4256</th>\n",
       "      <td>Valsartan</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4808</th>\n",
       "      <td>Ramipril</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>risk or severity of adverse effects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5422</th>\n",
       "      <td>Masoprocol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>antihypertensive activities</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5468</th>\n",
       "      <td>Flunisolide</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6156</th>\n",
       "      <td>Amphetamine</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6553</th>\n",
       "      <td>Nicotine</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6850</th>\n",
       "      <td>Cevimeline</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7376</th>\n",
       "      <td>Esmolol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>therapeutic efficacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7883</th>\n",
       "      <td>Bortezomib</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9470</th>\n",
       "      <td>Tramadol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9668</th>\n",
       "      <td>Betaxolol</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>therapeutic efficacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10327</th>\n",
       "      <td>Fluconazole</td>\n",
       "      <td>Amiodarone</td>\n",
       "      <td>metabolism</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            drug1_name  drug2_name                     interaction_type\n",
       "242          Goserelin  Amiodarone            QTc-prolonging activities\n",
       "908       Cyclosporine  Amiodarone                  serum concentration\n",
       "1451        Octreotide  Amiodarone            QTc-prolonging activities\n",
       "1669        Calcitriol  Amiodarone                           metabolism\n",
       "1967    Ergocalciferol  Amiodarone                           metabolism\n",
       "2479   Cholecalciferol  Amiodarone                           metabolism\n",
       "3198       Pravastatin  Amiodarone                  serum concentration\n",
       "3452       Fluvoxamine  Amiodarone                           metabolism\n",
       "4256         Valsartan  Amiodarone                           metabolism\n",
       "4808          Ramipril  Amiodarone  risk or severity of adverse effects\n",
       "5422        Masoprocol  Amiodarone          antihypertensive activities\n",
       "5468       Flunisolide  Amiodarone                           metabolism\n",
       "6156       Amphetamine  Amiodarone                           metabolism\n",
       "6553          Nicotine  Amiodarone                           metabolism\n",
       "6850        Cevimeline  Amiodarone                           metabolism\n",
       "7376           Esmolol  Amiodarone                 therapeutic efficacy\n",
       "7883        Bortezomib  Amiodarone                  serum concentration\n",
       "9470          Tramadol  Amiodarone                  serum concentration\n",
       "9668         Betaxolol  Amiodarone                 therapeutic efficacy\n",
       "10327      Fluconazole  Amiodarone                           metabolism"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show some interactions of Amiodarone\n",
    "amiodarone=DDI_data.loc[(DDI_data['drug1_name']=='Amiodarone')|(DDI_data['drug2_name']=='Amiodarone')]\n",
    "amiodarone[['drug1_name', 'drug2_name', 'interaction_type']].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "173f9782-2c4a-4866-a672-3fe277060186",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 drugs by degree:\n",
      "('Amiodarone', 1028)\n",
      "('Nefazodone', 986)\n",
      "('Phenobarbital', 976)\n",
      "('Venlafaxine', 969)\n",
      "('Primidone', 947)\n",
      "('Phenytoin', 935)\n",
      "('Fluvoxamine', 933)\n",
      "('Curcumin', 924)\n",
      "('Ziprasidone', 920)\n",
      "('Carbamazepine', 917)\n",
      "('Fosphenytoin', 916)\n",
      "('Pentobarbital', 899)\n",
      "('Verapamil', 878)\n",
      "('Clozapine', 855)\n",
      "('Diltiazem', 849)\n",
      "('Vemurafenib', 842)\n",
      "('Cyclosporine', 841)\n",
      "('Pitolisant', 815)\n",
      "('Stiripentol', 813)\n",
      "('Rifampicin', 811)\n"
     ]
    }
   ],
   "source": [
    "# Create dictionary to store degrees of nodes\n",
    "degree_dict = dict(G.degree(G.nodes()))\n",
    "nx.set_node_attributes(G, degree_dict, 'degree')\n",
    "\n",
    "# Generate sorted list of tuples of drug entity and corresponding degree\n",
    "sorted_degree = sorted(degree_dict.items(), key=lambda x: x[1], reverse=True) \n",
    "\n",
    "print(\"Top 20 drugs by degree:\")\n",
    "for d in sorted_degree[:20]:\n",
    "    print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e7f2a64-5ecd-4f26-89ba-3e8a4c69736f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237.97751605995717"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The average degree of an undirected graph is the sum of the degrees of all its nodes divided by the number of nodes in the graph. It is a measure of the connectedness of a graph.\n",
    "#The average degree is 238, meaning that each drug typically interacts with 238 other drugs on average. \n",
    "\n",
    "# Get all degree values\n",
    "sorted_degree_values = [pair[1] for pair in sorted_degree]\n",
    "\n",
    "nnodes = G.number_of_nodes()\n",
    "avrg_degree = sum(sorted_degree_values) / nnodes\n",
    "avrg_degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d986b9df-15b0-4afd-899a-bff6cd679711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Sometimes, the average degree does not reveal enough about the structure of a graph. Examining the distribution of degrees through the nodes of a graph is a much more useful measure. For instance, it would be helpful to know if almost all the nodes have degrees near the average degree, or if there are a few nodes with significantly higher degrees in the graph. \n",
    "\n",
    "# Set fixed bin size\n",
    "bins = np.arange(0, 1050, 2) # fixed bin size\n",
    "\n",
    "# Plot histogram\n",
    "plt.xlim([min(sorted_degree_values)-15, max(sorted_degree_values)+15])\n",
    "plt.hist(sorted_degree_values, bins=bins, alpha=0.5)\n",
    "plt.title('Node degree distribution (fixed bin size)')\n",
    "plt.xlabel('degree')\n",
    "plt.ylabel('count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8a78c4-c021-416d-b726-ecd4e90fd3ac",
   "metadata": {},
   "source": [
    "**9) Network visualisation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9be391c0-6dc3-41b9-99f7-1eb3141d434c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The sequence to creating a Pyvis visualization from a DataFrame is as such:\n",
    "\n",
    "# Define function to generate Pyvis visualization\n",
    "def generate_network_viz(df, source_col, target_col, edge_attr='color',#weights, \n",
    "                         layout='repulsion',#'barnes_hut',\n",
    "                         central_gravity=0.15, #The gravity attractor to pull the entire network to the center.\n",
    "                         node_distance=420, #This is the range of influence for the repulsion.\n",
    "                         spring_length=100, #length of the edges\n",
    "                         spring_strength=0.15, # strong the edges springs are\n",
    "                         damping=0.96 #A value ranging from 0 to 1 of how much of the velocity from theprevious physics simulation iteration carries over to the next iteration.\n",
    "                         ):\n",
    "    \n",
    "    # Generate a networkx graph\n",
    "    G = nx.from_pandas_edgelist(df, source_col, target_col, edge_attr='color') #weights, 'interaction_type'\n",
    "        \n",
    "    bgcolor, font_color = '#222222', 'white'\n",
    "    \n",
    "    # Initiate PyVis network object\n",
    "    drug_net = Network(\n",
    "                       height='700px', \n",
    "                       width='100%',\n",
    "                       bgcolor=bgcolor, \n",
    "                       font_color=font_color, \n",
    "                       notebook=True\n",
    "                      )\n",
    "    \n",
    "    # Take Networkx graph and translate it to a PyVis graph format\n",
    "    drug_net.from_nx(G)\n",
    "    \n",
    "    # Create different network layout (repulsion or Barnes Hut)\n",
    "    drug_net.repulsion(\n",
    "                            node_distance=node_distance, \n",
    "                            central_gravity=central_gravity, \n",
    "                            spring_length=spring_length, \n",
    "                            spring_strength=spring_strength, \n",
    "                            damping=damping\n",
    "                          )     \n",
    "    return drug_net"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f4d3a8-746c-4e41-8931-60d908ddfe88",
   "metadata": {},
   "source": [
    "* It would take very long to load entire network for all drug interactions. Therefore we will work on a subset of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "844e367b-be95-4e02-8dd3-08a6977bd916",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample drugs for diabetes (Metformin), hyperlipidemia (Simvastatin), and hypertension (Lisinopril)\n",
    "subset_meds = ['Metformin', 'Simvastatin', 'Lisinopril']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be7713e7-cf17-4a9d-a50c-ffc7bef2e210",
   "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>drug1_name</th>\n",
       "      <th>drug2_name</th>\n",
       "      <th>weight</th>\n",
       "      <th>color</th>\n",
       "      <th>interaction_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>Simvastatin</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Goserelin</td>\n",
       "      <td>Metformin</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>therapeutic efficacy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Daptomycin</td>\n",
       "      <td>Simvastatin</td>\n",
       "      <td>1</td>\n",
       "      <td>red</td>\n",
       "      <td>risk or severity of adverse effects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>Simvastatin</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cyclosporine</td>\n",
       "      <td>Lisinopril</td>\n",
       "      <td>1</td>\n",
       "      <td>blue</td>\n",
       "      <td>serum concentration</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drug1_name   drug2_name  weight color  \\\n",
       "0   Bivalirudin  Simvastatin       1  blue   \n",
       "1     Goserelin    Metformin       1  blue   \n",
       "2    Daptomycin  Simvastatin       1   red   \n",
       "3  Cyclosporine  Simvastatin       1  blue   \n",
       "4  Cyclosporine   Lisinopril       1  blue   \n",
       "\n",
       "                      interaction_type  \n",
       "0                  serum concentration  \n",
       "1                 therapeutic efficacy  \n",
       "2  risk or severity of adverse effects  \n",
       "3                  serum concentration  \n",
       "4                  serum concentration  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Focus on subset meds\n",
    "df_db_int_sm = DDI_data.loc[DDI_data['drug1_name'].isin(subset_meds) | DDI_data['drug2_name'].isin(subset_meds)]\n",
    "df_db_int_sm = df_db_int_sm.reset_index(drop=True)\n",
    "df_db_int_sm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8a48f9-3d60-4376-aa9e-d0df32fcab74",
   "metadata": {},
   "source": [
    "* Network visualization with Repulsion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "281ed026-3bc5-41f9-b532-2731525319a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"700px\"\n",
       "            src=\"drug_interactions_network_subset_repulsion.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2b45f362ea90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a networkx graph based on subset data\n",
    "db_subset_net = generate_network_viz(df_db_int_sm, 'drug1_name', 'drug2_name', edge_attr='color', layout='repulsion') \n",
    "\n",
    "# Display interactive graph\n",
    "db_subset_net.show('drug_interactions_network_subset_repulsion.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbfa9e7c-b395-445a-a02c-ac23081d0705",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Summary of the first use case: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b3c77bd-1cd3-465e-8a54-6a745ecc9f87",
   "metadata": {},
   "source": [
    "* Network has a total of 1868 drug entities (nodes) with 222,271 interactions (edges). \n",
    "* Network density is 0.13 meaning that some nodes are interconnected.\n",
    "* The average degree is 238, meaning that each drug typically interacts with 238 other drugs on average. \n",
    "* The output above reveals that the drug most frequently involved in drug interactions is Amiodarone, with a node degree of 1028. \n",
    "* The data clusters in the visualisation shown above correspond to groups of drugs that interact with one, two or all of the 3 selected drugs (metformin, simvastatin or lisinopril)\n",
    "* From 3 drugs selected for network visualisation, lisinopril has the most edges that have a word \"severity\" as part of interaction name (shown in red). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f7602fc-b78d-4ca9-b27a-3b3a8ef1fe0f",
   "metadata": {},
   "source": [
    "## <font color='green'>Part 2: BIKG Subgraphs</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b3b1231-cc28-4275-9af8-4740ddfc448a",
   "metadata": {},
   "source": [
    "### <font color='red'> Second use case: Trop-2 </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bea4b1d-2b23-48d8-a163-cb2cdd018ffc",
   "metadata": {},
   "source": [
    "### 1) Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b87e3bf0-1326-4df9-95c7-5de76101da82",
   "metadata": {},
   "outputs": [],
   "source": [
    "## install packages and modules\n",
    "#import sys, os\n",
    "\n",
    "#sys.path.insert(0, os.path.join(os.environ[\"MODULESHOME\"], \"init\"))\n",
    "#from env_modules_python import module\n",
    "\n",
    "#module(\n",
    "#    \"load\", \"graphviz/0.14-GCCcore-8.2.0-Python-3.7.2-Java-11.0.2\"\n",
    "#)  # load the graphviz module\n",
    "\n",
    "#!{sys.executable} -m pip install pandas --user\n",
    "#!{sys.executable} -m pip install networkx --user\n",
    "#!{sys.executable} -m pip install matplotlib --user\n",
    "#!{sys.executable} -m pip install seaborn --user\n",
    "#!{sys.executable} -m pip install holoviews xarray datashader --user\n",
    "#!{sys.executable} -m pip install scikit-image --user\n",
    "#!{sys.executable} -m pip install bikg --user --index-url https://artifactory.astrazeneca.net/api/pypi/pypi-virtual/simple\n",
    "#!{sys.executable} -m pip install numpy==1.20.0 --user\n",
    "##!{sys.executable} -m pip install pandas_profiling --user\n",
    "\n",
    "#!{sys.executable} -m pip install bikg --upgrade --index-url https://artifactory.astrazeneca.net/api/pypi/pypi-virtual/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "071b2e51-cdb3-4cb7-9ad8-06db19348e6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTH_TYPE = \"email_password\"\n",
    "GRAPH_TYPE = \"full\"\n",
    "RENDER_ENGINE = \"pyvis\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c30eab7c-e05d-4ed9-8efd-498e61e42c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "####################\n",
      "❤️ Please don't forget to credit BIKG if you use it in your work. For example, internal postcards, presentations and any publications.\n",
      "####################\n"
     ]
    }
   ],
   "source": [
    "import bikg\n",
    "import pandas as pd\n",
    "from pyvis import network as net"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da2bb21-2de2-4f69-8e1a-fe0e2d391696",
   "metadata": {},
   "source": [
    "### 2) Authenticate \n",
    "* To download BIKG data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1c676a39-ddbd-4926-a31c-a0d71343fed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter your prid (e.g kabc123): kvmm591\n",
      "Enter your password: ··············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-07 14:51:22,361 - INFO - download - Authenticating as kvmm591@astrazeneca.net\n",
      "2023-06-07 14:51:22,362 - INFO - download - Checking your credentials work...\n",
      "2023-06-07 14:51:23,329 - INFO - download - Valid\n"
     ]
    }
   ],
   "source": [
    "credentials = bikg.download.authenticate(auth_type=\"email_password\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dca1a8e-fdfd-4d9c-a1a0-d0c3b22bc135",
   "metadata": {},
   "source": [
    "### 3) Setup graph configuration\n",
    "* Preview the full BIKG schema - to understand what data is available "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "358f00ab-beb6-45b7-a059-aee3cc69151e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-07 14:53:02,525 - INFO - download - Checking for the latest graph version...\n",
      "2023-06-07 14:53:02,847 - INFO - download - latest graph version: 2023-05-10-4.32.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"800\"\n",
       "            height=\"500\"\n",
       "            src=\"graph_schema.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2b4bcb0f7220>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pip install -U pyvis==0.1.9\n",
    "\n",
    "remote_graph = bikg.download.RemoteGraph(credentials, graph_version=\"latest\")\n",
    "full_schema = remote_graph.get_remote_schema()\n",
    "\n",
    "full_schema.print_schema(engine=\"pyvis\", width=800, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a868de1-6c66-42d4-a953-a1114aa90c8e",
   "metadata": {},
   "source": [
    "* The full schema is very busy.\n",
    "* Filters can be used to preview smaller parts of the schema. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "45428e50-3e6b-4fd8-a3fe-4b515774f81a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"500px\"\n",
       "            height=\"300px\"\n",
       "            src=\"graph_schema.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2b45f31856a0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_schema.print_schema(\n",
    "        engine=\"pyvis\",\n",
    "        source_filters=[\"Gene Target\"],\n",
    "        relation_filters=[\"co_occurs\",\"interacts\"]\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58a4166c-5da1-4bf3-98dd-dac050a0900f",
   "metadata": {},
   "source": [
    "* List available edges and node types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de3a9a50-7a13-484b-9757-998d6f3efb30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node types: ['Pathway', 'Variant', 'Protein Variant', 'Cellular Component', 'Cell Type', 'Biological Process', 'Patient', 'Drug Combination', 'Protein Complex', 'Treatment', 'Symptom', 'Disease', 'Pharmacologic Class', 'Molecular Function', 'Mechanism of Action', 'Cell Line', 'Compound', 'Biomarker', 'Assay', 'Side Effect', 'Phenotype', 'Sample', 'Anatomy', 'Gene Target'] \n",
      "\n",
      "Edge types: ['co_occurs', 'develops_into', 'has_inferred_link', 'has_link', 'has_negative_inferred_link', 'has_negative_link', 'part_of', 'rdfs:subClassOf', 'has_assay_cell_line', 'has_assay_target', 'non_responsive_to', 'response_additive', 'response_antagonistic', 'response_has_benefit', 'response_no_benefit', 'response_synergistic', 'responsive_to', 'go:happens_during', 'go:negatively_regulates', 'go:occurs_in', 'go:positively_regulates', 'go:regulates', 'involved_in', 'for_treatment', 'includes', 'related_to_disease', 'has_screen_effect', 'resistance_hit_in_screen', 'sensitive_hit_in_screen', 'treated_with_compound', 'localizes', 'produces', 'interacts', 'affects', 'causes', 'has_assay', 'has_intended_target', 'has_mechanism', 'influences', 'resembles', 'treats', 'downregulates', 'modelled_in', 'presents', 'upregulates', 'altered_in', 'associated_with_variant', 'associates', 'co_expressed_with', 'downregulated_in', 'has_side_effect', 'has_variant', 'is_expressed_in', 'is_overexpressed', 'is_underexpressed', 'modifies', 'not_expressed_in', 'participates', 'regulates', 'upregulated_in', 'has_mechanism_target', 'has_comorbidity', 'has_disease', 'has_sample', 'has_treatment_response', 'is_hospitalized_because_of', 'under_treatment', 'observed_in', 'has_model'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Node types:\", full_schema.node_types, \"\\n\")\n",
    "print(\"Edge types:\", full_schema.edge_types, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ce1359-3194-4670-9da5-06f5d8cc35e1",
   "metadata": {},
   "source": [
    "* List the possible triple combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e8c49de-2ec3-49e7-809b-94ed86e36671",
   "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>source_type</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_type</th>\n",
       "      <th>provenances</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>co_occurs</td>\n",
       "      <td>Anatomy</td>\n",
       "      <td>[BIKG_SCIENTIFIC_LITERATURE]</td>\n",
       "      <td>112126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>co_occurs</td>\n",
       "      <td>Biological Process</td>\n",
       "      <td>[BIKG_SCIENTIFIC_LITERATURE]</td>\n",
       "      <td>4617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>co_occurs</td>\n",
       "      <td>Cell Type</td>\n",
       "      <td>[BIKG_SCIENTIFIC_LITERATURE]</td>\n",
       "      <td>6615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>co_occurs</td>\n",
       "      <td>Cellular Component</td>\n",
       "      <td>[BIKG_SCIENTIFIC_LITERATURE]</td>\n",
       "      <td>2596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>co_occurs</td>\n",
       "      <td>Compound</td>\n",
       "      <td>[BIKG_SCIENTIFIC_LITERATURE]</td>\n",
       "      <td>51543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>Variant</td>\n",
       "      <td>associates</td>\n",
       "      <td>Anatomy</td>\n",
       "      <td>[OPENTARGETS_GENETICS_D2V2G]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>Variant</td>\n",
       "      <td>associates</td>\n",
       "      <td>Biological Process</td>\n",
       "      <td>[OPENTARGETS_GENETICS_D2V2G]</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Variant</td>\n",
       "      <td>associates</td>\n",
       "      <td>Compound</td>\n",
       "      <td>[OPENTARGETS_GENETICS_D2V2G]</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>Variant</td>\n",
       "      <td>associates</td>\n",
       "      <td>Disease</td>\n",
       "      <td>[OMIM_DATA, OPENTARGETS_GENETICS_D2V2G]</td>\n",
       "      <td>232431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>Variant</td>\n",
       "      <td>associates</td>\n",
       "      <td>Phenotype</td>\n",
       "      <td>[OMIM_DATA, OPENTARGETS_GENETICS_D2V2G]</td>\n",
       "      <td>3079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>567 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    source_type    relation         target_type  \\\n",
       "0       Anatomy   co_occurs             Anatomy   \n",
       "1       Anatomy   co_occurs  Biological Process   \n",
       "2       Anatomy   co_occurs           Cell Type   \n",
       "3       Anatomy   co_occurs  Cellular Component   \n",
       "4       Anatomy   co_occurs            Compound   \n",
       "..          ...         ...                 ...   \n",
       "562     Variant  associates             Anatomy   \n",
       "563     Variant  associates  Biological Process   \n",
       "564     Variant  associates            Compound   \n",
       "565     Variant  associates             Disease   \n",
       "566     Variant  associates           Phenotype   \n",
       "\n",
       "                                 provenances   count  \n",
       "0               [BIKG_SCIENTIFIC_LITERATURE]  112126  \n",
       "1               [BIKG_SCIENTIFIC_LITERATURE]    4617  \n",
       "2               [BIKG_SCIENTIFIC_LITERATURE]    6615  \n",
       "3               [BIKG_SCIENTIFIC_LITERATURE]    2596  \n",
       "4               [BIKG_SCIENTIFIC_LITERATURE]   51543  \n",
       "..                                       ...     ...  \n",
       "562             [OPENTARGETS_GENETICS_D2V2G]       1  \n",
       "563             [OPENTARGETS_GENETICS_D2V2G]     809  \n",
       "564             [OPENTARGETS_GENETICS_D2V2G]     239  \n",
       "565  [OMIM_DATA, OPENTARGETS_GENETICS_D2V2G]  232431  \n",
       "566  [OMIM_DATA, OPENTARGETS_GENETICS_D2V2G]    3079  \n",
       "\n",
       "[567 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(full_schema.triple_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83159f30-6d90-41dd-b9b2-83b1895af2d4",
   "metadata": {},
   "source": [
    "### 4) Create your graph configuration and load your graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64f747f2-376b-4ba4-9958-8ab849ec049e",
   "metadata": {},
   "source": [
    "* Do it when you want to use a subgraph of BIKG\n",
    "* Below is Gene-Disease-Compound_Pharmacologic Class subgraph.\n",
    "* Interactive tool to build a subgraph: https://config.bikg.astrazeneca.net/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f1f4cd33-8a80-46df-8150-25737935449f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-07 14:51:24,196 - INFO - download - Checking for the latest graph version...\n",
      "2023-06-07 14:51:24,399 - INFO - download - latest graph version: 2023-05-10-4.32.0\n",
      "2023-06-07 14:51:28,649 - INFO - graph - Loading graph...\n",
      "2023-06-07 14:52:08,313 - INFO - graph - Done!\n"
     ]
    }
   ],
   "source": [
    "#GraphConfig - defines the subgraph to be used; enables you to create complex combinations and leverage include and exclude conditions\n",
    "#GraphConfigHelper - to help create your own graph configuration\n",
    "#Edge filters - to load edges that match certain column values \n",
    "#The edge_filters settings apply edge filters\n",
    "\n",
    "gc_helper = bikg.GraphConfigHelper(credentials, graph_version=\"latest\")\n",
    "graph_config = bikg.GraphConfig(\n",
    "    graph_version=gc_helper.graph_version,\n",
    "    include_filters=gc_helper.generate_triple_filters([\n",
    "        [\"Gene Target\", (\n",
    "            {\n",
    "                \"associates\": {\n",
    "                    \"prov\": [\n",
    "                        \"CTD\",\n",
    "                        \"HETIONET\",\n",
    "                        \"HPOA\",\n",
    "                        \"LOUNKINE_2012\",\n",
    "                        \"OPENTARGETS\",\n",
    "                        \"ORPHANET\",\n",
    "                        \"PHARMAPROJECTS\",\n",
    "                        \"TCRD\"\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        \n",
    "        ), \"Disease\"], \n",
    "        [\"Compound\", (\n",
    "            {\n",
    "                \"affects\": {\n",
    "                    \"prov\": [\n",
    "                        \"AZ_COMPOUND_SIGNATURES\",\n",
    "                        \"AZ_ONCOLOGY_DRUG_TABLE\",\n",
    "                        \"BIOGRID\",\n",
    "                        \"DGI_DB\",\n",
    "                        \"HETIONET\",\n",
    "                        \"LOUNKINE_2012\",\n",
    "                        \"METABASE\"\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        , \"has_intended_target\"), \"Gene Target\"], \n",
    "        [\"Compound\", (\"resembles\"), \"Compound\"], \n",
    "        [\"Compound\", (\"influences\", \n",
    "            {\n",
    "                \"treats\": {\n",
    "                    \"prov\": [\n",
    "                        \"PHARMAPROJECTS\"\n",
    "                    ]\n",
    "                }\n",
    "            }\n",
    "        \n",
    "        ), \"Disease\"], \n",
    "        [\"Pharmacologic Class\", \"*\", \"Compound\"], \n",
    "        [\"Disease\", (\"rdfs:subClassOf\", \"resembles\", \"presents\"), \"Disease\"], \n",
    "\n",
    "    ]),\n",
    ")\n",
    "\n",
    "dataset_path = \"/projects/it/bikg/downloads/%s\" % gc_helper.graph_version\n",
    "\n",
    "#Create the graph and load it into memory\n",
    "subgraph = bikg.Graph.load_bikg(\n",
    "    local_path=dataset_path,\n",
    "    credentials=credentials,\n",
    "    graph_config=graph_config,\n",
    "    download=False #By selected download=True it will download the data to disk then load it into memory. If you set download=False it will only use pre-downloaded data.\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48560e22-0217-424d-8371-e69df62841fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BIKG subgraph stats:\n",
      "\tNum edges: 2017567\n",
      "\tNum nodes: 567445\n"
     ]
    }
   ],
   "source": [
    "print(\"BIKG subgraph stats:\")\n",
    "print(\"\\tNum edges:\", len (subgraph.edges) )\n",
    "print(\"\\tNum nodes:\", len (subgraph.nodes) )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5121860-6626-4949-8ed1-0d8ecf4e7be8",
   "metadata": {},
   "source": [
    "### 5) Visualise your graph schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e5dc72fc-d27a-465e-a832-0daf7a1e5e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"600\"\n",
       "            height=\"500\"\n",
       "            src=\"graph_schema.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2b4bcd39d370>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_config.visualize(width=600, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f57affa-6fb4-47ee-bd62-97aca6e52a49",
   "metadata": {},
   "source": [
    "* The edges and nodes data are stored in pandas dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1e2173bd-0737-4564-851d-67c30203a8cd",
   "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>source_label</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26_PzauqnQm7W7hMwIJGyx</th>\n",
       "      <td>CHEMBL409764</td>\n",
       "      <td>resembles</td>\n",
       "      <td>CHEMBL276038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8O8yITFvyV95UQamM7ElBJ</th>\n",
       "      <td>GGTLC2</td>\n",
       "      <td>associates</td>\n",
       "      <td>metaphyseal acroscyphodysplasia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6T2mDR5g0Vu6OZl9XX4vfV</th>\n",
       "      <td>CHEMCONNECT:259114647</td>\n",
       "      <td>resembles</td>\n",
       "      <td>CHEMBL2403620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5R4H65X1XOe75jN6cWpatc</th>\n",
       "      <td>RSPH14</td>\n",
       "      <td>associates</td>\n",
       "      <td>Kidney Neoplasms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8OkajTMUyoY6IXI67693NA</th>\n",
       "      <td>CHEMCONNECT:206118570</td>\n",
       "      <td>resembles</td>\n",
       "      <td>CHEMBL3197762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 source_label    relation  \\\n",
       "edge_id                                                     \n",
       "26_PzauqnQm7W7hMwIJGyx           CHEMBL409764   resembles   \n",
       "8O8yITFvyV95UQamM7ElBJ                 GGTLC2  associates   \n",
       "6T2mDR5g0Vu6OZl9XX4vfV  CHEMCONNECT:259114647   resembles   \n",
       "5R4H65X1XOe75jN6cWpatc                 RSPH14  associates   \n",
       "8OkajTMUyoY6IXI67693NA  CHEMCONNECT:206118570   resembles   \n",
       "\n",
       "                                           target_label  \n",
       "edge_id                                                  \n",
       "26_PzauqnQm7W7hMwIJGyx                     CHEMBL276038  \n",
       "8O8yITFvyV95UQamM7ElBJ  metaphyseal acroscyphodysplasia  \n",
       "6T2mDR5g0Vu6OZl9XX4vfV                    CHEMBL2403620  \n",
       "5R4H65X1XOe75jN6cWpatc                 Kidney Neoplasms  \n",
       "8OkajTMUyoY6IXI67693NA                    CHEMBL3197762  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgraph.edges[[\"source_label\",\"relation\",\"target_label\"]].sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1e8ee373-95df-4b13-a4e4-435fca81fc66",
   "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>default_label</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CHEMBL:CHEMBL454240</th>\n",
       "      <td>GLABRACIN A</td>\n",
       "      <td>Compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMCONNECT:208910922</th>\n",
       "      <td>CHEMCONNECT:208910922</td>\n",
       "      <td>Compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL:CHEMBL3349365</th>\n",
       "      <td>CHEMBL3349365</td>\n",
       "      <td>Compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHEMBL:CHEMBL196635</th>\n",
       "      <td>CHEMBL196635</td>\n",
       "      <td>Compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSEMBL:ENSG00000136869</th>\n",
       "      <td>TLR4</td>\n",
       "      <td>Gene Target</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 default_label         type\n",
       "default_id                                                 \n",
       "CHEMBL:CHEMBL454240                GLABRACIN A     Compound\n",
       "CHEMCONNECT:208910922    CHEMCONNECT:208910922     Compound\n",
       "CHEMBL:CHEMBL3349365             CHEMBL3349365     Compound\n",
       "CHEMBL:CHEMBL196635               CHEMBL196635     Compound\n",
       "ENSEMBL:ENSG00000136869                   TLR4  Gene Target"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subgraph.nodes.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "354f4d07-bfad-499c-bea7-5ba1e0439304",
   "metadata": {},
   "source": [
    "### 6) TROP2 subgraph \n",
    "\n",
    "* Subgraphs are pandas objects\n",
    "* Some convenient variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "94dc0a2e-a033-4e82-97ef-02fca04761bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "edges = subgraph.edges\n",
    "nodes = subgraph.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9bdc4b74-f0dd-42e5-8289-776f6094fe83",
   "metadata": {},
   "outputs": [],
   "source": [
    "node2label = nodes.default_label.to_dict()\n",
    "node2type = nodes.type.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6025c47e-a9d4-4538-a037-6860838cfd32",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_label = \"TACSTD2\" #Trop2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64a94c4-749d-4138-9334-b3952bc9bce6",
   "metadata": {},
   "source": [
    "* Find compounds and diseases connecting to TROP-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b90e516b-b417-4e3a-886b-677bf6ee7e8e",
   "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>default_label</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSEMBL:ENSG00000184292</th>\n",
       "      <td>TACSTD2</td>\n",
       "      <td>Gene Target</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        default_label         type\n",
       "default_id                                        \n",
       "ENSEMBL:ENSG00000184292       TACSTD2  Gene Target"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes[nodes.default_label == gene_label] #to get a node by a mapped ID/label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b9a69b9e-74a6-494e-9125-a0c362603ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "trop2_gene_id = \"ENSEMBL:ENSG00000184292\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01709aa9-b8c1-4087-b631-b78a0dc96029",
   "metadata": {},
   "source": [
    "* Get 1-hop Compound and Disease neighbours of TROP-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cf689f5b-f7e0-491b-87c0-534a470e50a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source_id</th>\n",
       "      <th>source_label</th>\n",
       "      <th>source_type</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_id</th>\n",
       "      <th>target_label</th>\n",
       "      <th>target_type</th>\n",
       "      <th>prov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_id</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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AhyaIzLkhZ7y098Cb7DET</th>\n",
       "      <td>CHEMCONNECT:325463774</td>\n",
       "      <td>CDK2_5968</td>\n",
       "      <td>Compound</td>\n",
       "      <td>affects</td>\n",
       "      <td>ENSEMBL:ENSG00000184292</td>\n",
       "      <td>TACSTD2</td>\n",
       "      <td>Gene Target</td>\n",
       "      <td>AZ_ONCOLOGY_DRUG_TABLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6ZOhd1kQEMu4q2_Af4ABiK</th>\n",
       "      <td>CHEMBL:CHEMBL3301610</td>\n",
       "      <td>ABEMACICLIB</td>\n",
       "      <td>Compound</td>\n",
       "      <td>affects</td>\n",
       "      <td>ENSEMBL:ENSG00000184292</td>\n",
       "      <td>TACSTD2</td>\n",
       "      <td>Gene Target</td>\n",
       "      <td>AZ_ONCOLOGY_DRUG_TABLE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    source_id source_label source_type  \\\n",
       "edge_id                                                                  \n",
       "AhyaIzLkhZ7y098Cb7DET   CHEMCONNECT:325463774    CDK2_5968    Compound   \n",
       "6ZOhd1kQEMu4q2_Af4ABiK   CHEMBL:CHEMBL3301610  ABEMACICLIB    Compound   \n",
       "\n",
       "                       relation                target_id target_label  \\\n",
       "edge_id                                                                 \n",
       "AhyaIzLkhZ7y098Cb7DET   affects  ENSEMBL:ENSG00000184292      TACSTD2   \n",
       "6ZOhd1kQEMu4q2_Af4ABiK  affects  ENSEMBL:ENSG00000184292      TACSTD2   \n",
       "\n",
       "                        target_type                    prov  \n",
       "edge_id                                                      \n",
       "AhyaIzLkhZ7y098Cb7DET   Gene Target  AZ_ONCOLOGY_DRUG_TABLE  \n",
       "6ZOhd1kQEMu4q2_Af4ABiK  Gene Target  AZ_ONCOLOGY_DRUG_TABLE  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop2_edges = edges[\n",
    "    (edges.source_id == trop2_gene_id) |\n",
    "    (edges.target_id == trop2_gene_id) \n",
    "].copy()\n",
    "trop2_edges.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9addfa5e-b0ae-4e78-ab7e-43b628baadcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'DOID:0050155',\n",
       " 'DOID:0050687',\n",
       " 'EFO:0000182',\n",
       " 'EFO:0000335',\n",
       " 'EFO:0000365',\n",
       " 'EFO:0000384',\n",
       " 'EFO:0001361',\n",
       " 'EFO:0002517',\n",
       " 'EFO:0004252',\n",
       " 'EFO:0004266',\n",
       " 'EFO:0004268',\n",
       " 'EFO:0005842',\n",
       " 'EFO:1001999',\n",
       " 'MONDO:0003825',\n",
       " 'MONDO:0003834',\n",
       " 'MONDO:0004953',\n",
       " 'MONDO:0004992',\n",
       " 'MONDO:0004993',\n",
       " 'MONDO:0005005',\n",
       " 'MONDO:0005023',\n",
       " 'MONDO:0005075',\n",
       " 'MONDO:0005101',\n",
       " 'MONDO:0005133',\n",
       " 'MONDO:0005138',\n",
       " 'MONDO:0005140',\n",
       " 'MONDO:0005192',\n",
       " 'MONDO:0005221',\n",
       " 'MONDO:0005265',\n",
       " 'MONDO:0005282',\n",
       " 'MONDO:0005328',\n",
       " 'MONDO:0005401',\n",
       " 'MONDO:0006156',\n",
       " 'MONDO:0006498',\n",
       " 'MONDO:0006863',\n",
       " 'MONDO:0007034',\n",
       " 'MONDO:0008449',\n",
       " 'MONDO:0008777',\n",
       " 'MONDO:0009061',\n",
       " 'MONDO:0009239',\n",
       " 'MONDO:0009807',\n",
       " 'MONDO:0010671',\n",
       " 'MONDO:0010765',\n",
       " 'MONDO:0011849',\n",
       " 'MONDO:0016281',\n",
       " 'MONDO:0016553',\n",
       " 'MONDO:0017967',\n",
       " 'MONDO:0018301',\n",
       " 'MONDO:0018393',\n",
       " 'ORPHANET:363741',\n",
       " 'ORPHANET:983'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#COmpounds that connect to Trop-2\n",
    "trop2_compounds = set(trop2_edges[trop2_edges.source_type == \"Compound\"].source_id.tolist())\n",
    "\n",
    "#Diseases that connect to Trop-2\n",
    "trop2_diseases = set(trop2_edges[trop2_edges.target_type == \"Disease\"].target_id.tolist())\n",
    "trop2_diseases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2157c100-4ac1-4f79-8e4d-dfcb3d5d4111",
   "metadata": {},
   "source": [
    "* How does the compound connect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bee9886f-9a0a-4865-9170-91ae776932f6",
   "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>source_id</th>\n",
       "      <th>source_label</th>\n",
       "      <th>source_type</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_id</th>\n",
       "      <th>target_label</th>\n",
       "      <th>target_type</th>\n",
       "      <th>prov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_id</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [source_id, source_label, source_type, relation, target_id, target_label, target_type, prov]\n",
       "Index: []"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop2_cmp_cmp = edges[\n",
    "    edges.source_id.isin(trop2_compounds) &\n",
    "    edges.target_id.isin(trop2_compounds) \n",
    "]\n",
    "trop2_cmp_cmp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a050a09c-92f4-4549-b571-41f7c98325c4",
   "metadata": {},
   "source": [
    "They don't!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1521f708-b8db-43c3-a6dd-5bab8a141a33",
   "metadata": {},
   "source": [
    "* How does the diseases connect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa39e205-82da-4c5b-90fb-8ec97d38f5ba",
   "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>source_id</th>\n",
       "      <th>source_label</th>\n",
       "      <th>source_type</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_id</th>\n",
       "      <th>target_label</th>\n",
       "      <th>target_type</th>\n",
       "      <th>prov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_id</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>8czVlJ23OGI4bB9pFjmVIO</th>\n",
       "      <td>MONDO:0004993</td>\n",
       "      <td>carcinoma</td>\n",
       "      <td>Disease</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MONDO:0004992</td>\n",
       "      <td>cancer</td>\n",
       "      <td>Disease</td>\n",
       "      <td>MONDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1fypz5HOIaleC1WzcS_wZ</th>\n",
       "      <td>MONDO:0005138</td>\n",
       "      <td>lung carcinoma</td>\n",
       "      <td>Disease</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MONDO:0004993</td>\n",
       "      <td>carcinoma</td>\n",
       "      <td>Disease</td>\n",
       "      <td>MONDO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5ElkPwuZOo_6cjdg73az8l</th>\n",
       "      <td>MONDO:0005401</td>\n",
       "      <td>colonic neoplasm</td>\n",
       "      <td>Disease</td>\n",
       "      <td>resembles</td>\n",
       "      <td>MONDO:0005101</td>\n",
       "      <td>ulcerative colitis</td>\n",
       "      <td>Disease</td>\n",
       "      <td>HETIONET</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            source_id      source_label source_type  \\\n",
       "edge_id                                                               \n",
       "8czVlJ23OGI4bB9pFjmVIO  MONDO:0004993         carcinoma     Disease   \n",
       "1fypz5HOIaleC1WzcS_wZ   MONDO:0005138    lung carcinoma     Disease   \n",
       "5ElkPwuZOo_6cjdg73az8l  MONDO:0005401  colonic neoplasm     Disease   \n",
       "\n",
       "                               relation      target_id        target_label  \\\n",
       "edge_id                                                                      \n",
       "8czVlJ23OGI4bB9pFjmVIO  rdfs:subClassOf  MONDO:0004992              cancer   \n",
       "1fypz5HOIaleC1WzcS_wZ   rdfs:subClassOf  MONDO:0004993           carcinoma   \n",
       "5ElkPwuZOo_6cjdg73az8l        resembles  MONDO:0005101  ulcerative colitis   \n",
       "\n",
       "                       target_type      prov  \n",
       "edge_id                                       \n",
       "8czVlJ23OGI4bB9pFjmVIO     Disease     MONDO  \n",
       "1fypz5HOIaleC1WzcS_wZ      Disease     MONDO  \n",
       "5ElkPwuZOo_6cjdg73az8l     Disease  HETIONET  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop2_dis_dis = edges[\n",
    "    edges.source_id.isin(trop2_diseases) &\n",
    "    edges.target_id.isin(trop2_diseases) \n",
    "]\n",
    "trop2_dis_dis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cedaf218-2e79-4862-814f-31275125ff0e",
   "metadata": {},
   "source": [
    "* How does compounds and diseases connect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "15b5c5b5-5603-44e3-87a5-5022bdbafd8f",
   "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>source_id</th>\n",
       "      <th>source_label</th>\n",
       "      <th>source_type</th>\n",
       "      <th>relation</th>\n",
       "      <th>target_id</th>\n",
       "      <th>target_label</th>\n",
       "      <th>target_type</th>\n",
       "      <th>prov</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edge_id</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [source_id, source_label, source_type, relation, target_id, target_label, target_type, prov]\n",
       "Index: []"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trop2_cmp_dis = edges[\n",
    "    (edges.source_id.isin(trop2_compounds) &\n",
    "    edges.target_id.isin(trop2_diseases) ) |\n",
    "    (edges.source_id.isin(trop2_diseases) &\n",
    "    edges.target_id.isin(trop2_compounds) )\n",
    "    \n",
    "]\n",
    "trop2_cmp_dis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1bd55d3-ef9a-48fe-aeb0-9ad00c2a49e1",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Collect the subgraphs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "81bcb2c6-d672-41bd-9ccf-0bda96b077df",
   "metadata": {},
   "outputs": [],
   "source": [
    "vis_edges = pd.concat([\n",
    "    trop2_edges, \n",
    "    trop2_cmp_cmp, \n",
    "    trop2_dis_dis,\n",
    "    trop2_cmp_dis\n",
    "])[[\"source_id\", \"target_id\"]].to_records(index=False)\n",
    "vis_nodes = set(vis_edges.source_id.tolist() + vis_edges.target_id.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4bb5f0-21c3-48f2-8ab2-f6a07ca6af39",
   "metadata": {
    "tags": []
   },
   "source": [
    "* Visualize (compound in blue, diesease in red, gene target in yellow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "39b7039d-fbc4-41f0-9dae-1bfde93014ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"1000\"\n",
       "            height=\"800\"\n",
       "            src=\"trop2.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x2b4bcaaef400>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors = {\n",
    "    \"Compound\": \"#00cec9\", #blue\n",
    "    \"Disease\": \"#e17055\", #red\n",
    "    \"Gene Target\": \"#fdcb6e\" #yellow\n",
    "}\n",
    "\n",
    "g = net.Network(notebook=True, directed=True, width=1000, height=800) #\n",
    "for node_id in vis_nodes:\n",
    "    node_label = node2label[node_id]\n",
    "    if node_label:\n",
    "        node_type = node2type[node_id]\n",
    "        color = colors[node_type]\n",
    "        g.add_node(node_label, color=color, title=node_id)\n",
    "\n",
    "for edge in vis_edges:\n",
    "    \n",
    "    src_label = node2label[edge[0]]\n",
    "    tgt_label = node2label[edge[1]]\n",
    "    g.add_edge(src_label, tgt_label)\n",
    "\n",
    "    \n",
    "g.set_options(\n",
    "\"\"\"\n",
    "var options = {\n",
    "  \"physics\": {\n",
    "    \"barnesHut\": {\n",
    "      \"gravitationalConstant\": -11000\n",
    "    },\n",
    "    \"minVelocity\": 0.75\n",
    "  }\n",
    "}\n",
    "\"\"\"\n",
    ")  \n",
    "\n",
    "g.show(\"trop2.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da87dd9-c4a9-4208-9fce-811679b685dd",
   "metadata": {},
   "source": [
    "### Summary of the second use case: \n",
    "\n",
    "* The full schema is very busy. Therefore it is best to preview smaller parts of the schema.\n",
    "* We limited our data to focus on a use case arund Trop2 gene target. \n",
    "* For this limited dataset we observed that Trop2 has many edges with both compounds and diseases. There are no compound-compound edges and compound-diease edges. There are however disease-disease edges. \n",
    "* Next we could add more data modalities to this graph to explore what internal/external chemistry/biology do we have about Trop2. \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50dec074-fe8a-4f12-979d-96b91947e451",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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