Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning.

ABSTRACT:

Background

Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.

Methods

We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KE

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11390589 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature