XGDAG

This is the official repository for XGDAG: eXplainable Gene-Disease Associations via Graph Neural Networks.

The repository contains scripts and notebook to run the code and reproduce the experiments in the paper.

Data

The data generated and provided in this repository are based on PPI data from BioGRID and Gene-Disease Associations from DisGeNET. The original data can be dowloaded from the related websites. Part of the analysis relies on the set of all disease associations from DisGeNET. Given the size of this file, it needs to be manually downloaded from here and placed in the Datasets folder.

Using the aformentioned data we built graphs available for use in the Graphs folder. The script CreateGraph.py was used for this purpose.

Prerequisites

XGDAG relies on PyTorch 1.12 and PyTorch Geometric 2.1.

In the CondaEnvs folder we provide several Conda environment configurations compatible with XGDAG.

Train the model (optional)

Pretrained models for the diseases analized in the paper are available in the Models folder. However, it is possible to train a custom GraphSAGE model by running:

 python TrainerScript.py

The module contains parameters that can be adjusted at will.

Explanation phase

To run XGDAG and additional explainers (SubgraphX, GraphSVX, and GNNExplainer):

 python ComputeRankingScript.py

The script will use the explainers to explain the models for the specified diseases. Gene rankings will be saved in the Rankings folder. The latter containts also rankings from additional methods (see paper) used for comparison.

Analysis and comparison

After having computed the rankings (or using the precompued ones), it is possible to use the provided notebooks to analyze the results and generate plots like the ones shown in the paper. The notebooks comparison_plots_disgenet.ipynb and comparison_plots_omim.ipynb analyze the results on DisGeNET and OMIM+PheI datasets, respectively. Finally, comparison_plots_omim_vs_disgenet.ipynb provides a comparison of the results on both datasets.

Citation

If you use our work, please cite our paper 😄

Andrea Mastropietro, Gianluca De Carlo, Aris Anagnostopoulos, XGDAG: explainable gene–disease associations via graph neural networks, Bioinformatics, Volume 39, Issue 8, August 2023, btad482, https://doi.org/10.1093/bioinformatics/btad482

Special thanks to Simone Fiacco for creating the XGDAG logo.

Contacts

For any further question, do not hesitate to drop an email here or here.