Predicting Toxicity from Gene Expression with Neural Networks

Peter Eastman; Vijay S. Pande

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arXiv · 1902.00060

Predicting Toxicity from Gene Expression with Neural Networks

Peter Eastman·Vijay S. Pande

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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.

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Peter Eastman, Vijay S. Pande. 2019-01-31. Predicting Toxicity from Gene Expression with Neural Networks. https://arxiv.org/abs/1902.00060

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