Predicting Toxicity from Gene Expression with Neural Networks

TL;DR

This paper presents a neural network model that predicts chemical toxicity from gene expression data, outperforming classical models and aiding in chemical screening and drug evaluation.

Contribution

Introduces a neural network approach for toxicity prediction from gene expression profiles, demonstrating improved accuracy over traditional models.

Findings

Neural network outperforms classical models on TG-GATEs data.

Effective in predicting a variety of pathological effects.

Offers a promising tool for chemical screening and drug evaluation.

Abstract

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.