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

Frontiers in Pharmacology
Hao WangAnders Wallqvist

Abstract

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 robustness of current toxicity prediction models. Deep neural networks (DNNs) have the advantage of automatically assembling the relevant features from a large number of input features. This makes them especially suitable for modeling transcriptomic data, which typically contain thousands of features. Here, we gaged gene- and pathway-level feature selection schemes using single- and multi-task DNN approaches in predicting chemically induced liver injuries (biliary hyperplasia, fibrosis, and necrosis) from whole-genome DNA microarray data. The single-task DNN models showed high predictive accuracy and endpoint specificity, with Matthews correlation coefficients for the three endpoints on 10-fold cross validation ranging from 0.56 to 0.89, with an average of 0.74 in the best feature sets. The DNN models outperformed Random Forest models in cross validation and showed better performance than Support Vector Machine models when tested in the ...Continue Reading

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Citations

Dec 18, 2020·Frontiers in Bioengineering and Biotechnology·Ting LiShraddha Thakkar
Jan 14, 2021·Biology Direct·Joaquim Aguirre-PlansBaldo Oliva
Dec 29, 2020·Chemical Research in Toxicology·Ting LiShraddha Thakkar
Feb 27, 2021·Frontiers in Pharmacology·Patric SchymanAnders Wallqvist
Oct 19, 2019·Chemical Research in Toxicology·Andy H VoMohan S Rao

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Datasets Mentioned

BETA
GSE70559
GSE56740
GSE87696
GSE72076
GSE49631
GSE95470

Software Mentioned

DrugMatrix
TG
Keras
GATEs
Open TG - GATEs
learn
Scikit
TensorFlow

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