gNetDGP

An End-to-End Graph Neural Network for Disease Gene Prioritization.

Table of contents

• Installation
  • Docker
  • Conda
• Usage
  • Train the generic model
  • Predict using the generic model
  • Train the specific model
  • Predict using the specific model
• Additional material

Installation

Using docker

We provide a Dockerfile to setup a runtime. To use it run

docker build -t gNetDGP .

Using Conda

conda env create -f environment.yml
conda activate gnetdgp_env

Usage

To get an overview of available commands use

python main.py --help

To list the available options on a specific command use

python main.py [COMMAND] --help

Train the generic model

To train a new generic model use

python main.py generic-train --training_data_path ./data/training/genes_diseases.tsv

For available options run

python main.py generic-train --help

Predict using the generic model

Provide an input file of gene, disease tuples like in the test/example_input_generic.tsv

Then run the command

python main.py generic-predict test/example_input_generi.tsv

This will score the provided disease, gene tuples and return a augmented version of the input file with added scores. The result is stored in --out_file, the default is ./generic_predict_results.tsv

The result is sorted by the predicted score by default. If you want to preserve the input order add the option --sort_result_by_score False

To use a specific pre-trained model for the prediction add the option --model_path /path/to/model.ptm.

To get a list of available genes in the model run

python main.py generic-predict --get_available_genes

To get a list of available diseases in the model run

python main.py generic-predict --get_available_diseases

Train the specific model

To train the specific model run

main.py specific-train --training_disease_genes_path ./data/training/genes_diseases.tsv --training_disease_class_assignments_path ./data/training/extracted_disease_class_assignments.tsv

For available options run

python main.py specific-train --help

Predict using the specific model

To predict disease scores for the specific mode, provide a input_file with entrez gene IDs (one per line) like e.g. in test/example_input_specific.tsv. Use the --model_path flag to provide a pretrained specific model.

main.py specific-predict ./test/example_input_specific.tsv --model_path /path/to/choosen/pretrained/specific/model.ptm

This will score the provided gene IDs to be associated with the disease the model was trained on and return a augmented version of the input file with added scores.

The result is stored in --out_file, the default is ./generic_predict_results.tsv

The result is sorted by the predicted score by default. If you want to preserve the input order add the option --sort_result_by_score False

Additional material

The process of the reported experiments is documented in

• results/disease_gene_prioritization.ipynb for the disease gene prioritization task.
• results/disease_gene_classification.ipynb for the disease type classification.