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semmed

Code to create a hetnet from the data in SemmedDB and perform a machine learning alorithm on the resulting network.

The primary data source for the hetnet is SemMedDB. The gold standard used for machine learning is derived from DrugCentral.

To use much of this repo, a UTS liscence/account is required. You can apply for one here.

This repo is divided into several steps. First, the python enviornment should be set up and activated

Setting up the python environment.

Use anaconda with enviornment.yml to run this code. After installing anaconda use conda env create envionment.yml to install the enviornment. Then use source activate ml to start the enviornmnet.

Running the Pipeline

1. prepare

The prepare folder contains the scripts and notebooks needed to download semmedDB and other mapping files used for this repo, as well as some pre-processing steps.

2. build

The build folder contains the notebooks that build the hetnet version of semmedDB

3. ml

The ml folder contains scripts to run the machine learning pipeline on the generated hetnet, as well as notebooks for the analysis of these results.

Other Sections

tools

The tools folder cotains some helpful scripts, useful for this project.

data

All data will be downloaded and written to this folder. Contains a couple small starter files important for running the pipeline.

Requirements

In addition to the anaconda environment, the following programs and package required to run all scripts in this repo

  • pv - progress viewer for opening text

  • psql - PostgreSQL database for Drugcentral Data Dump parsing

  • hetnet-ml - For feature extraction in machine learning pipeline. must be downloaded to the same parent directory as this repo

      $ cd ..
  $ git clone https://github.com/mmayers12/hetnet-ml.git

  • disease-ontology - For some ID cross references. Must be downloaded to the same parent directory as this repo

      $ cd ..
  $ git clone https://github.com/mmayers12/disease-ontology.git

  • UMLS Account - Required for downloading the UMLS Metathesaurs data.

Database References

SemMedDB

Kilicoglu, H, Shin D, Fiszman M, Rosemblat G, Rindflesch TC. (2012) SemMedDB: A PubMed-scale repository of biomedical semantic predications. Bioinformatics, 28(23), 3158-60.

DrugCentral

Oleg Ursu Jayme Holmes Jeffrey Knockel Cristian G. Bologa Jeremy J. Yang Stephen L. Mathias Stuart J. Nelson Tudor I. Oprea. (2017) DrugCentral: online drug compendium. Nucleic Acids Research, 45(D1) D932–D939. https://doi.org/10.1093/nar/gkw993