Decoding Pathogenic Mutational Landscapes in Alzheimer's Disease Through Integrated Transcriptomics.

Abstract excerpt

Alzheimer's disease (AD) is increasingly understood as a disorder driven not only by amyloid and tau pathology but also fundamentally shaped by underlying genetic mutations. By integrating multiple AD gene expression datasets with machine learning approaches-including random forest, XGBoost, LASSO, and SVM-we identified 172 differentially expressed genes, with TUBB2A, RTN4, and YWHAZ emerging as top...

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