Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction.

ABSTRACT: The present study aimed to identify genes associated with increased risk of myocardial infarction (MI) and construct an early diagnosis model based on support vector machine (SVM) learning. The gene expression profile data of GSE34198, containing 97 human blood samples including 49 patients with MI and 48 healthy individuals, were obtained from the Gene Expression Omnibus database. Differentially expressed gene (DEG) screening, DEG enrichment analysis, protein‑protein interaction (PPI) network investigation and clustering analysis were performed. The feature genes were identified using the neighboring score algorithm. Furthermore, a recursive feature elimination (RFE) algorithm was employed to screen risk factors among feature genes. The SVM prediction model was constructed and validated u

SUBMITTER: Fang HZ 

PROVIDER: S-EPMC7411293 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature