Integrated Bioinformatics and Machine Learning for Ascertainment and Validation of Biomarkers for Screening Breast Disease.

ABSTRACT: Background: This research sought to screen potential biomarkers in diagnosing breast diseases and elucidating their immune-related mechanisms. Methods: Three datasets were attained from the Gene Expression Omnibus (GEO) database. LIMMA package and weighted gene co-expression network analysis (WGCNA) were used to ascertain differentially expressed genes (DEGs) and key modules in benign breast disease (BBD) and breast cancer (BC). The intersecting genes underwent functional enrichment analysis. Three machine learning (ML) methods (encompassing LASSO regression, random forest, and support vector machine recursive feature elimination (SVM-RFE)) were implemented to select core genes. The diagnostic performance of the core genes was evaluated by comparing their expression levels, p

SUBMITTER: Wang Q 

PROVIDER: S-EPMC12652564 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature