ArticleScientific reports2025
Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Exploring Toxicological Mechanisms of Typical PFAS-Induced NAFLD through Integrated Computational Toxicology and AOP Frameworks.Environment & health (Washington, D.C.) · 2026Article
- High-Fat-Diet-Induced Hyperglycemia Alters Liver Extracellular Matrix Composition in Mice Model.Cells · 2026Article
- Protein lactylation in metabolic dysfunction-associated steatotic liver disease: a mechanistic review.Diabetology & metabolic syndrome · 2026Review
- Development and validation of an interpretable machine learning model for early prediction of postoperative atrial fibrillation following on-pump cardiac surgery.Frontiers in surgery · 2026Article
- The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.Frontiers in radiology · 2025Article
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Authors and funding
5 authors.
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Abstract
Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our study, we analyzed data from multiple sources to find crucial genes linked to NASH fibrosis. We built diagnostic models using 103 machine learning algorithms and validated them with two external datasets. All models performed well, with the best one (RF + Enet[alpha = 0.6]) achieving an average AUC of 0.822. This model used five key genes: LUM, COL1A2, THBS2, COL5A2, and NTS. Our findings show that these genes are important in collagen and extracellular matrix pathways, shedding light on how NASH progresses to liver fibrosis. We also found that certain immune cells, like M1 macrophages, are involved in this process. This study provides a reliable diagnostic tool for assessing fibrosis risk in NASH patients and suggests potential for immunotherapy, laying a foundation for future treatments.
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