This repository contains the code and resources of the following paper:

PRS-Net: Interpretable polygenic risk scores via geometric learning

PRS-Net is an interpretable genomic deep learning-based approach designed to effectively model the nonlinearity of the biological system and deliver more precise PRS predictions.

Setup the required environment using environment.yml with Anaconda. While in the project directory run:

conda env create

Activate the environment

conda activate PRS-Net

TBD!

We upload an example dataset at [https://figshare.com/s/8bbb8cad9ac644bf9caa].

Unzip the file and put it in example_dataset/.

python train.py --data_path ../example_dataset/ --dataset ad_eur

PRS-Net is licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0.