Identifying potential signatures for atherosclerosis in the context of predictive, preventive, and personalized medicine using integrative bioinformatics approaches and machine-learning strategies.

ABSTRACT:

Background

Atherosclerosis is a major contributor to morbidity and mortality worldwide. Although several molecular markers associated with atherosclerosis have been developed in recent years, the lack of robust evidence hinders their clinical applications. For these reasons, identification of novel and robust biomarkers will directly contribute to atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). This integrative analysis aimed to identify critical genetic markers of atherosclerosis and further explore the underlying molecular immune mechanism attributing to the altered biomarkers.

Methods

Gene Expression Omnibus (GEO) series datasets were downloaded from GEO. Firstly, differential expression analysis and functional analys

SUBMITTER: Xu J 

PROVIDER: S-EPMC9437201 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature