Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses

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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Bibliographic Details
Main Authors: Hao Wang, Ruifeng Liu, Patric Schyman, Anders Wallqvist
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-02-01
Series:Frontiers in Pharmacology
Subjects:
machine leaning
classification model
toxicity prediction
artificial neural network
biliary hyperplasia
liver fibrosis
Online Access:https://www.frontiersin.org/article/10.3389/fphar.2019.00042/full

Internet

https://www.frontiersin.org/article/10.3389/fphar.2019.00042/full

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