Optimal Gene Selection and Machine Learning Framework for Alzheimer’s Disease Prediction Using Transcriptomic Data (Statistics, Optimization & Information Computing, 2025)
Accurate Alzheimer’s Disease (AD) prediction using gene expression data is significantly challenged by ultra-highdimensionality (38,319 genes) and class imbalance (697 AD vs. 460 controls). To overcome these barriers, we developedan end-to-end machine learning framework integrating advanced feature engineering with optimized classification. Ourstudy leveraged 1,157 post-mortem dorsolateral prefrontal cortex samples from multi-cohort repositories, selected fortheir established relevance to Alzheimer’s disease (AD) pathology, and employed adaptive linear interpolation withbiological replicates to impute sparse missing values (