ArticleInternational journal of molecular sciences2025
Machine Learning on Toxicogenomic Data Reveals a Strong Association Between the Induction of Drug-Metabolizing Enzymes and Centrilobular Hepatocyte Hypertrophy in Rats.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- CYP1A1 as a conserved metabolic circuit linking environmental sensing to immune regulation.Archives of toxicology · 2026Review
- Molecular regulation of PGC-1α: from protein-protein interactions and post-translational modifications to pharmacological modulation.Journal of molecular medicine (Berlin, Germany) · 2026Review
- Exploring the Toxicological Effects of Acetyl Tributyl Citrate Exposure on Osteoarthritis Based on Machine Learning, Network Toxicology and Molecular Docking Analysis.Open access rheumatology : research and reviews · 2026Article
- Why did individuals seeking COVID-19 advice return to internet hospitals for follow-up in the early pandemic: Insights from machine learning.BMC public health · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Centrilobular hepatocyte hypertrophy is frequently observed in animal studies for chemical safety assessment. Although its toxicological significance and precise mechanism remain unknown, it is considered an adaptive response resulting from the induction of drug-metabolizing enzymes (DMEs). This study aimed to elucidate the association between centrilobular hepatocyte hypertrophy and DME induction using machine learning on toxicogenomic data. Utilizing publicly available gene expression data and pathological findings from rat livers of 134 compounds, we developed six different types of machine learning models to predict the occurrence of centrilobular hepatocyte hypertrophy based on gene expression data as explanatory variables. Among these, a LightGBM-based model demonstrated the best performance with an accuracy of approximately 0.9. With this model, we assessed each gene's contribution to predicting centrilobular hepatocyte hypertrophy using mean absolute SHAP values. The results revealed that
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Registered trials
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