Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detection and diagnosis to improve intervention outcomes. This study integrates various bioinformatics methodologies with interpretable machine learning to identify reliable biomarkers for AD diagnosis and treatment. By leveraging differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and construction of Protein-Protein Interaction (PPI) Networks, we meticulously analyzed the AD dataset from the GEO database to pinpoint Hub genes. Subsequently, various machine learning algorithms were employed to construct diagnostic models, which were then elucidated using SHapley Additive exPlanations (SHAP). To vis

SUBMITTER: Wang F 

PROVIDER: S-EPMC11680850 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature