GuiltyTargets Results

This repository contains the results of [1]:

[1]	Muslu, Ö., Hoyt, C. T., Hofmann-Apitius, M., & Fröhlich, H. (2019). GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning. bioRxiv, 1–14.

Due to licensing reasons, analyses that use TTD drug targets and Alzheimer's disease data sets have been removed from this reproduction.

Installation

You will need Python 3.7+ and R 3.6.0+ to run the program.

R Installation

On mac, install the latest version of R with:

$ brew install R

Install BioConductor with the instructions from https://www.bioconductor.org/install:

$ R -e 'install.packages("BiocManager")'
$ R -e 'BiocManager::install()'
$ R -e 'BiocManager::install(c("limma", "GEOquery", "Biobase"))'

Python Installation

To install the required Python libraries, you can run:

$ git clone https://github.com/GuiltyTargets/reproduction.git guiltytargets-results
$ cd guiltytargets-results
$ pip install -e .

Running

To run the code:

$ source run.sh

Output

You can find the output under reproduction/data. The results.csv file gives an overview of all AUROC values under different settings.