ArticleFrontiers in pharmacology2019
Deep Neural Network Models for Predicting Chemically Induced Liver Toxicity Endpoints From Transcriptomic Responses.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Review
- BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.Quantitative biology (Beijing, China) · 2026Article
- Comprehensive analysis of high-throughput transcriptomics to distinguish drug-induced liver injury (DILI) phenotypes.Archives of toxicology · 2025Review
- VitroBert: modeling DILI by pretraining BERT on in vitro data.Journal of cheminformatics · 2025Article
- Capturing Unanticipated Drug Toxicities Using an Ensemble Machine Learning Approach.Research square · 2025Article
- Computational models for predicting liver toxicity in the deep learning era.Frontiers in toxicology · 2023Review
- Artificial Intelligence-Assisted Image Analysis of Acetaminophen-Induced Acute Hepatic Injury in Sprague-Dawley Rats.Diagnostics (Basel, Switzerland) · 2022Article
- Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination.Chinese medicine · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Supervised Methods for Biomarker Detection from Microarray Experiments.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Leveraging high-throughput screening data, deep neural networks, and conditional generative adversarial networks to advance predictive toxicology.PLoS computational biology · 2021Article
- The toxicity of SiOJournal of environmental health science & engineering · 2021Article
- An ensemble learning approach for modeling the systems biology of drug-induced injury.Biology direct · 2021Article
- Detecting Novel Ototoxins and Potentiation of Ototoxicity by Disease Settings.Frontiers in neurology · 2021Review
- TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries.Frontiers in pharmacology · 2021Article
- Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment.Nanomaterials (Basel, Switzerland) · 2020Review
- Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes.PloS one · 2020Article
- Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.Frontiers in bioengineering and biotechnology · 2020Article
- In silico toxicology: From structure-activity relationships towards deep learning and adverse outcome pathways.Wiley interdisciplinary reviews. Computational molecular scienceReview
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
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 external validation datasets. In the cross validation studies, the effect of the feature selection scheme was negligible among the studied feature sets. Further evaluation of the models on their ability to predict the injury phenotype
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