ArticleFrontiers in bioengineering and biotechnology2020
Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed, 64 citations in OpenAlex.
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Apoptotic signatures allow early and rapid screening of drug-induced liver injury to accelerate drug discovery.Communications medicine · 2025Article
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Variability vs. phenotype: Multimodal analysis of Dravet syndrome brain organoids powered by deep learning.iScience · 2025Article
- Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug response.Journal of experimental & clinical cancer research : CR · 2025Article
- Comprehensive analysis of high-throughput transcriptomics to distinguish drug-induced liver injury (DILI) phenotypes.Archives of toxicology · 2025Review
- Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.PLoS computational biology · 2025Article
- Gene expression inference based on graph neural networks using L1000 data.Briefings in bioinformatics · 2025Article
- Deep learning-based discovery of compounds for blood pressure lowering effects.Scientific reports · 2025Article
- Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.Drug design, development and therapy · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- Bridging organ transcriptomics for advancing multiple organ toxicity assessment with a generative AI approach.NPJ digital medicine · 2024Article
- A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques.BMC bioinformatics · 2024Article
- Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.Biomolecules · 2024Article
- A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Investigation of the General Molecular Mechanisms of Gallic Acid via Analyses of Its Transcriptome Profile.International journal of molecular sciences · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- New approach methodologies in human regulatory toxicology - Not if, but how and when!Environment international · 2023Review
- Computational models for predicting liver toxicity in the deep learning era.Frontiers in toxicology · 2023Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.