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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Detalles Bibliográficos
Autores principales: Wang, Hao, Liu, Ruifeng, Schyman, Patric, Wallqvist, Anders
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Pharmacology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370634/
https://www.ncbi.nlm.nih.gov/pubmed/30804783
http://dx.doi.org/10.3389/fphar.2019.00042

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370634/
https://www.ncbi.nlm.nih.gov/pubmed/30804783
http://dx.doi.org/10.3389/fphar.2019.00042

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