Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach.

ABSTRACT: Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress of AMI involves immune cell infiltration. However, suitable clinical diagnostic biomarkers and the roles of immune cells in AMI remain unknown. Three datasets (GSE61145, GSE34198, and GSE66360) were used from Gene Expression Omnibus. Dysregulated expression of genes was screened and functionally analyzed. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify significant module genes associated with AMI. Machine learning algorithms (Support Vector Machine (SVM), Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO)) were applied to identify hub genes. Subsequently, receiver operating characteristic curves (ROC) were generated to evaluate the risk o

SUBMITTER: Jiang H 

PROVIDER: S-EPMC12277416 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature