Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

Journal: Journal of translational medicine

PMID: 40275274

Abstract

BACKGROUND: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD is impeded by the lack of reliable diagnostic biomarkers and effective therapeutic strategies.

Authors

Weiliang Zhang
Weirong Lu

Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China.
Yaqi Jiao

Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China.
Tianhao Li

Tianjin Union Medical Center, Tianjin Medical University, Tianjin, China.
Haining Wang

Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, 518045, China.
Chunhua Wan

Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, Nantong, Jiangsu, 226019, China. chwan@ntu.edu.cn.

Keywords

Animals Biomarkers Disease Progression Fatty Liver Gene Expression Profiling Gene Expression Regulation Gene Ontology Gene Regulatory Networks Humans Machine Learning Nomograms Protein Interaction Maps Reproducibility of Results ROC Curve

External Resources

View on PubMed Access via DOI PubMed (40275274)

Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning.

Abstract

Authors

Keywords

External Resources

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