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

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 visualize our findings, we generated an insightful bioinformatics map of 10 Hub genes. We then conducted experimental validation on less-studied Hub genes, revealing significant differential mRNA expression of MYH9 and RHOQ in an AD cell model. Finally, we explored the biological significance of these two genes at the single-cell transcriptome level. This study not only introduces interactive SHAP panels for precise decision-making in AD but also offers novel insights into the identification of AD biomarkers through interpretable machine learning diagnostic models. Particularly, MYH9 has emerged as a promising new potential biomarker, pointing the way towards enhanced diagnostic accuracy and personalized therapeutic strategies for AD. Although the mRNA expression patterns of RHOQ are opposite in AD cell models and human brain tissue samples, the role of RHOQ in AD remains worthy of further exploration due to the diversity and complexity of biological molecular regulation.