We train a neural network to predict chemical toxicity based on gene expression data. The input to the network is a full expression profile collected either in vitro from cultured cells or in vivo from live animals. The output is a set of fine grained predictions for the presence of a variety of pathological effects in treated animals. When trained on the Open TG-GATEs database it produces good results, outperforming classical models trained on the same data. This is a promising approach for efficiently screening chemicals for toxic effects, and for more accurately evaluating drug candidates based on preclinical data.
@article{arxiv.1902.00060,
title = {Predicting Toxicity from Gene Expression with Neural Networks},
author = {Peter Eastman and Vijay S. Pande},
journal= {arXiv preprint arXiv:1902.00060},
year = {2019}
}