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Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses
Improving the accuracy of toxicity prediction models for liver injuries is a key element in evaluating the safety of drugs and chemicals. Mechanism-based information derived from expression (transcriptomic) data, in combination with machine-learning methods, promises to improve the accuracy and robu...
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| Veröffentlicht in: | Front Pharmacol |
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| Hauptverfasser: | , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Frontiers Media S.A.
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6370634/ https://ncbi.nlm.nih.gov/pubmed/30804783 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fphar.2019.00042 |
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