The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models.

ABSTRACT:

Background

This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease.

Methods

Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based

SUBMITTER: Cai L 

PROVIDER: S-EPMC10614158 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature