Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction.

ABSTRACT:

Background

To investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction.

Methods

Raw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI.

Results

The six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, whi

SUBMITTER: Dong H 

PROVIDER: S-EPMC10052851 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature