GraphPath: A graph attention model for molecular stratification with interpretability based on the pathway-pathway interaction network

Introduction

Achieving accurate and interpretable clinical predictions requires paramount attention to thoroughly characterizing patients at both the molecular and biological pathway levels. In this paper, we present GraphPath, a biological knowledge-driven graph neural network with multi-head self-attention mechanism that implements the pathway-pathway interaction network. We train GraphPath to classify the cancer status of patients with prostate cancer based on their multi-omics profiling.

Getting Started

1. Clone the repo

git clone https://github.com/amazingma/GraphPath.git

2. Create conda environment

conda env create --name GraphPath --file=environment.yml

Usage

1. Activate the created conda environment

source activate GraphPath

2. Train the model

python train.py

References

1. Ma T, Wang J. GraphPath: a graph attention model for molecular stratification with interpretability based on the pathway–pathway interaction network[J]. Bioinformatics, 2024, 40(4): btae165.
2. Elmarakeby H A, Hwang J, Arafeh R, et al. Biologically informed deep neural network for prostate cancer discovery[J]. Nature, 2021, 598(7880): 348-352.