Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

ABSTRACT:

Background

Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease development; therefore, a classifier based on gene expression might contribute to CAD diagnosis. This study aimed to construct genetic classification models for CAD using gene expression data, which may provide new insight into the understanding of its pathogenesis.

Methods

All statistical analysis was completed by R 3.4.4 software. Three raw gene expression datasets (GSE12288, GSE7638 and GSE66360) related to CAD were downloaded from the Gene Expression Omnibus database and included for analysis. Limma package was performed to iden

SUBMITTER: Peng W 

PROVIDER: S-EPMC8840658 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature