{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["53(5)"],"submitter":["Wu X"],"pubmed_abstract":["<h4>Objectives</h4>To explore the pathogenesis and potential biomarkers of atrial fibrillation based on bioinformatics.<h4>Methods</h4>Differentially expressed genes and module genes related to atrial fibrillation were obtained from GSE41177 and GSE79768 datasets (Chinese-origin tissue samples) through differential expression analysis and weighted gene co-expression network analysis. Candidate hub genes were obtained by taking intersections, and hub genes were obtained after gender stratification. Subsequently, functional enrichment analysis and immune infiltration analysis were performed. Four machine learning models were constructed based on the hub genes, and the optimal model was selected to construct a prediction nomogram. The prediction ability of the nomogram was verified using cali"],"journal":["Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences"],"pagination":["593-603"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11528137"],"repository":["biostudies-literature"],"pubmed_title":["[Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics]."],"pmcid":["PMC11528137"],"pubmed_authors":["Pan X","Li Y","Pan J","Xue C","Gong L","Kang J","Wu X"],"additional_accession":[]},"is_claimable":false,"name":"[Pathogenesis and potential diagnostic biomarkers of atrial fibrillation in Chinese population: a study based on bioinfor-matics].","description":"<h4>Objectives</h4>To explore the pathogenesis and potential biomarkers of atrial fibrillation based on bioinformatics.<h4>Methods</h4>Differentially expressed genes and module genes related to atrial fibrillation were obtained from GSE41177 and GSE79768 datasets (Chinese-origin tissue samples) through differential expression analysis and weighted gene co-expression network analysis. Candidate hub genes were obtained by taking intersections, and hub genes were obtained after gender stratification. Subsequently, functional enrichment analysis and immune infiltration analysis were performed. Four machine learning models were constructed based on the hub genes, and the optimal model was selected to construct a prediction nomogram. The prediction ability of the nomogram was verified using cali","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-07-15T09:34:26.148Z","creation":"2025-04-06T09:31:59.013Z"},"accession":"S-EPMC11528137","cross_references":{"pubmed":["39319462"],"doi":["10.3724/zdxbyxb-2024-0027"]}}