Graph Structured Neural Networks (GSNN)

Overview

The GSNN method is a algorithm that integrates prior knowledge of latent variable interactions directly into neural architecture.

@article {Evans2024.02.28.582164,
	author = {Nathaniel J. Evans and Gordon B. Mills and Guanming Wu and Xubo Song and Shannon McWeeney},
	title = {Graph Structured Neural Networks for Perturbation Biology},
	elocation-id = {2024.02.28.582164},
	year = {2024},
	doi = {10.1101/2024.02.28.582164},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/02/29/2024.02.28.582164},
	eprint = {https://www.biorxiv.org/content/early/2024/02/29/2024.02.28.582164.full.pdf},
	journal = {bioRxiv}
}

The figures and analysis presented in the preprint can be run using the code available from this release. We have since migrated much of the analysis for the GSNN paper to this auxillary library.

Getting Started

Create the conda/mamba python environment and install the GSNN package:

$ mamba env create -f environment.yml 
$ conda activate gsnn 
(gsnn) $ pip install -e .