Merge pull request #46 from monarch-initiative/develop

merge in updated documentation

docs/source/Contact.rst

@@ -0,0 +1,11 @@
+Contact
+=======
+
+**Peter Robinson**
+
+Peter.Robinson@jax.org
+
+The Jackson Laboratory for Genomic Medicine
+
+Farmington, CT
+USA

docs/source/Install & running.rst

@@ -0,0 +1,17 @@
+org.monarchinitiative.loinc2hpo
+===============================
+Java library to map LOINC-encoded test results to Human Phenotype Ontology
+
+To build and run the GUI, use the following command.::
+
+  $ mvn clean package
+  $ java -jar loinc2hpogui/target/Loinc2HpoGui-jar-with-dependencies.jar
+
+
+To run the library code, enter the following ::
+
+  $ java -jar loinc2hpo-core/target/Loinc2HpoLib.jar download
+
+This will download the hp.obo file (You need to manually download the LOINC Core Table file). There is a demo function that currently doesn't do anything ::
+
+  $ java -jar loinc2hpo-core/target/Loinc2HpoLib.jar demo

docs/source/Intro to FHIR.rst

@@ -110,7 +110,7 @@ Interpretation from physicians: ::
       }
     ]
   },
-}
+  }
 
 ``Patient``
 
@@ -233,6 +233,6 @@ Interpretation from physicians: ::
     "reference": "Organization\/f001",
     "display": "Burgers University Medical Centre"
   }
-}
+  }
 
-``Patient`` resource is critical in cases when the interpretation of an observation, e.g. height or weight, dependents on the sex, age or other relevant information. In this case, the link to subject in ``Observation`` become critical as it allows retrieval of the related ``Patient`` resource.
+``Patient`` is essential in cases when the interpretation of an observation, e.g. height or weight, dependents on the sex, age or other relevant information of the patient. In this case, one can retrieve ``Patient`` with the link in the "subject" field of ``Observation``.

docs/source/Our Mission.rst

@@ -5,16 +5,16 @@ Our goal with this app is to extract patient phenotypes in HPO terms from Electr
 
   .. image:: images/mission.png
      :align: center
-     :scale: 50 %
+     :scale: 60 %
 
 
-To achieve our goal, we need to achieve the following:
+To achieve our goal, we need to do the following:
 
-  - *Map LOINC codes to candidate HPO terms*. This steps needs domain experts and will be done manually by our biocurators.
+- *Map LOINC codes to candidate HPO terms*. This steps needs domain experts and will be done manually by our biocurators.
 
-  - *Retrieve patient observations*. We will use RESTful API technology to achieve this. The hapi-fhir library provides such functionalities so this can be done trivially. We will also collaborate with other institutions and hospitals to gain access to large patient cohorts.
+- *Retrieve patient observations*. We will use RESTful API technology to achieve this. The hapi-fhir library provides such functionalities so this can be done trivially. We will also collaborate with other institutions and hospitals to gain access to large patient cohorts.
 
-  - *Convert patient observations into phenopackets*. Our app will analyze the relevant fields from each observation and then output the corresponding HPO term that describes the patient. By analyzing all observations related to one patient, we will be able to create a phenopacket for this patient. We are still refining our app for this process.
+- *Convert patient observations into phenopackets*. Our app will analyze the relevant fields from each observation and then output the corresponding HPO term that describes the patient. By analyzing all observations related to one patient, we will be able to create a phenopacket for this patient. We are still refining our app for this process.
 
 
 Having a phenopacket for an individual is essential for disease prediction and patient clustering. We hope to extend our app so that it can be used by physicians to assist patient diagnosis; we also hope that the phenopackets can help academic researchers in study disease mechanisms.

docs/source/Tutorial.rst

@@ -75,12 +75,16 @@ Consider this Loinc test: `Loinc 600-7` or `Bacteria identified in Blood by Cult
 
 1. Whether there are bacteria identified in the blood or not (alternatively, we say whether the patient is "positive" or "negative" for bacterial infection). In this case, we choose "Recurrent bacterial infections" for positive and the same term in negated form for negative result and annotate in the Basic Mode as described above.
 
-2. What type of bacteria is identified in the patient's blood. Here is from a real-world example:::
-
-   "coding":[{
-   "system": "http://snomed.info/sct",
-   "code": "3092008",
-   "display": "Staphylococcus aureus"}]
+2. What type of bacteria is identified in the patient's blood. Here is from a real-world example.
+::
+
+    "coding":[
+    {
+      "system": "http://snomed.info/sct",
+      "code": "3092008",
+      "display": "Staphylococcus aureus"
+    }
+    ]
 
 We can guess that the above lines indicate that the patient has *S. aureus* infection in his/her blood. In this case, our Basic Mode does not work well anymore because it only handles values that are too high, too low and intermediate. This is when Advanced Mode comes into play. To allow our app recognize this result, we need to assign a HPO term for **Snomed** code `3092008`. To do this,
 

docs/source/index.rst

@@ -1,35 +1,28 @@
 Loinc2hpo Documentation
 =======================
 
-.. toctree::
-   :maxdepth: 2
-   :caption: Contents:
-
-Indices and tables
-------------------
-
-
-:doc: `Intro to LOINC`
-:doc:`Intro to FHIR`
-:doc:`Our Mission`
-:doc:`Working logic`
-:doc:`Annotation logic`
-:doc:`Install & running`
-:doc:`Configuration`
-:doc:`Tutorial`
-:doc:`Contact`
-* :ref:`search`
-
-Loinc2hpo
----------
 Loinc2hpo is a JavaFX app designed to convert FHIR observations into HPO terms. The app has three core functionalities:
+
 - help mapping LOINC codes into candidate HPO terms
 - help retrieve patient observations
 - convert patient observations into HPO terms
 
-Mapping LOINC to HPO terms requires a significant effort from biocurators. Loinc2hpo tries to simply the process by automatically finding the best matching terms using Sparql query. Collaborators can easily split LOINC codes of interest into smaller subsets and share their annotations.
+Mapping LOINC to HPO terms requires a significant effort from biocurators. Loinc2hpo tries to simply the process by automatically finding the best matching terms using Sparql query. With this app, collaborators can easily split LOINC codes of interest into smaller tasks and share their annotations.
 
-For detailed instructions please refer to the documentation.
+.. toctree::
+   :maxdepth: 2
 
+   Intro to LOINC
+   Intro to FHIR
+   Our Mission
+   Working logic
+   Annotation logic
+   Install & running
+   Configuration
+   Tutorial
+   Contact
+
+GitHub repo
+-----------
 The source code of Loinc2hpo can be found at GitHub:
 https://github.com/monarch-initiative/loinc2hpo