c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.
Journal:
BMC medical genomics
Published Date:
Oct 13, 2023
Abstract
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based biomarker discovery in Alzheimer's can facilitate less-invasive, routine diagnostic tests to aid early intervention. Therefore, we propose "c-Diadem" (constrained dual-input Alzheimer's disease model), a novel deep learning classifier which incorporates KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway constraints on the input genotyping data to predict disease, i.e., mild cognitive impairment (MCI)/AD or cognitively normal (CN). SHAP (SHapley Additive exPlanations) was used to explain the model and identify novel, potential blood-based genetic markers of MCI/AD.