ArticleFrontiers in genetics2022
Identification of biomarkers in nonalcoholic fatty liver disease: A machine learning method and experimental study.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- GATA-Biology · 2025Article
- Unraveling Metabolic Dysfunction-Associated Steatotic Liver Disease Through the Use of Omics Technologies.International journal of molecular sciences · 2025Review
- The evolution of artificial intelligence technology in non-alcoholic fatty liver disease.Frontiers in radiology · 2025Article
- Transgenerational inheritance of hepatic steatosis in mice: sperm methylome is largely reprogrammed and inherited but does not globally influence liver transcriptome.Environmental epigenetics · 2025Article
- Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators.Archives of Iranian medicine · 2024Article
- Development and validation of a machine learning-based framework for assessing metabolic-associated fatty liver disease risk.BMC public health · 2024Article
- Article
- Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.Frontiers in medicine · 2023Article
- An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers.Computational and structural biotechnology journal · 2023Article
- Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning.Frontiers in immunology · 2023Article
- A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.Annals of hepatologyReview
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease. However, the early diagnosis of NAFLD is challenging. Thus, the purpose of this study was to identify diagnostic biomarkers of NAFLD using machine learning algorithms. Differentially expressed genes between NAFLD and normal samples were identified separately from the GEO database. The key DEGs were selected through a protein‒protein interaction network, and their biological functions were analysed. Next, three machine learning algorithms were selected to construct models of NAFLD separately, and the model with the smallest sample residual was determined to be the best model. Then, logistic regression analysis was used to judge the accuracy of the five genes in predicting the risk of NAFLD. A single-sample gene set enrichment analysis algorithm was used to evaluate the immune cell infiltration of NAFLD, and the correlation between diagnostic biomarkers and immune cell infiltration was analysed. Finally, 10 pairs of peripheral blood samples from NAFLD patients and normal controls were collected for RNA isolation and quantitative real-time polymerase chain reaction for validation. Taken together, CEBPD, H4C11, CEBPB, GATA3, and KLF4 were identified as diagnostic biomarkers of NAFLD by machine learning algorithms and were related to immune cell infiltration in NAFLD. These key genes provide novel insights into the mechanisms and treatment of patients with NAFLD.
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