Computational target inference by mining transcriptional data using a novel Siamese spectral-based graph convolutional network

This repository is a PyTorch version rewritten from https://github.com/boyuezhong/SSGCN/.

Code for "Drug target inference by mining transcriptional data using a novel graph convolutional network framework"

System requirements

Operating systems requirements

This package is supported for Linux.

Software Dependencies

The SSGCN model was implemented in the PyTorch framework (version 1.12.1) in Python 3.7.16.

Hardware requirements

The SSGCN requires a computer with a GPU.

Installation guide:

Environmental setup

conda create -n SSGCN python=3.7
conda activate SSGCN
cd SSGCN
pip install -r requirements.txt

or

cd SSGCN
conda env create -n SSGCN -f environment.yaml

Dataset

All data files are released on https://drive.google.com/drive/folders/1yHB_gE1e0cNJJeLj74ij1dtPLJiQ3fmu?usp=sharing, and the pickle form data of PC3 cell can be downloaded from https://drive.google.com/drive/folders/14odFgnwwbUhTpbExIM7L4aJWM1YYpE6-?usp=drive_link.

Demo

cd SSGCN
python ./code/ssgcn_pytorch.py

Expected output

The expected output can be found in ./saved_model

Reproduction instructions

The results of benchmark can be reproduced.

License

This code is licensed under the Apache 2.0 License.