Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis.

ABSTRACT:

Background

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

Methods

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core ge

SUBMITTER: Zhao X 

PROVIDER: S-EPMC10638835 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature