Journal of inflammation research

Using Machine Learning and Multi-Omics to Find Key Genes and Processes in Mitochondrial Recycling Linked to Non-Alcoholic Fatty Liver Disease

Updated

Abstract

Essence

A multi-omics machine learning analysis identifies five -related genes that may help distinguish NAFLD and explain immune-mitochondrial mechanisms.

Evidence

The evidence combines GEO transcriptomic and single-cell datasets, WGCNA, 11 machine learning algorithms, immune and pathway analyses, molecular docking, and in vitro NAFLD cell-model validation.

Caveat

The findings are biomarker and mechanism signals from public datasets plus cell models, not prospective clinical validation of diagnosis or therapy.

Simplified

Key numbers

0.974
Performance
Maximum achieved across multiple validation cohorts.
5
Core Genes Identified
Five core genes linked to and immune responses in NAFLD.

Full Text

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Funding

Competing interests

No financial or personal ties reported.
PubMed

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