Novel circling SWAT for deep learning based diagnostic classification of Alzheimer’s disease: Application to metabolome data

Recent advances in deep learning have allowed multi‐omics data to be used to detect early stages of Alzheimer's disease (AD). The metabolome, as the end‐product of biological cascades including the genome, transcriptome, and proteome, may have informative elements that could serve as potential AD biomarkers. We report a novel two‐step deep learning method to detect AD and progression using lipidomics data.