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- W2918291250 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." @default.
- W2918291250 created "2019-03-11" @default.
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- W2918291250 date "2019-01-31" @default.
- W2918291250 modified "2023-09-27" @default.
- W2918291250 title "Predicting Toxicity from Gene Expression with Neural Networks" @default.
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