Identification of risk genes associated with myocardial infarction based on the recursive feature elimination algorithm and support vector machine classifier.

ABSTRACT: The aim of the present study was to identify risk genes in myocardial infarction. Microarray data GSE34198, containing data from the peripheral blood of 49 myocardial infarction samples and 48 corresponding control samples, were downloaded from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). The DEGs were used to construct a protein‑protein interaction (PPI) network of patient samples, from which the feature genes were identified using the neighboring score method. The recursive feature elimination (RFE) algorithm was employed to select the risk genes among feature genes, which were subsequently applied to perform a support vector machine (SVM) classifier to identify the specific signature in myocardial infarction samples. Another dataset, GSE61144

SUBMITTER: Yang X 

PROVIDER: S-EPMC5780094 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature