ArticleScientific reports2020
Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Progress in toxicogenomics to protect human health.Nature reviews. Genetics · 2025Review
- Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects.Heliyon · 2024Review
- PFAS and their association with the increased risk of cardiovascular disease in postmenopausal women.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.Archives of toxicology · 2024Article
- AI-driven Discovery of Morphomolecular Signatures in Toxicology.bioRxiv : the preprint server for biology · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets.Archives of toxicology · 2023Article
- Identification of metabolic pathways contributing to ERScientific reports · 2023Article
- New approach methodologies: A quantitative in vitro to in vivo extrapolation case study with PFASs.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Article
- Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques.Metabolites · 2021Article
- Computational Strategies for the Identification of a Transcriptional Biomarker Panel to Sense Cellular Growth States inSensors (Basel, Switzerland) · 2021Article
- Downregulation of CYP39A1 Serves as a Novel Biomarker in Hepatocellular Carcinoma with Worse Clinical Outcome.Oxidative medicine and cellular longevity · 2021Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the supervised methods are involved in metabolism and detoxification (Car3, Crat, Cyp39a1, Dcd, Lbp, Scly, Slc23a1, and Tkfc) and transcriptional regulation (Ablim3). Several of these genes are also implicated in liver carcinogenesis, including Crat, Car3 and Slc23a1. Our biomarker gene signature provides high statistical accuracy and a manageable number of genes to study as indicators to potentially accelerate toxicity testing based on their ability to induce liver necrosis and, eventually, liver cancer.
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Registered trials
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