A novel deep learning model with transformer architectures to enable multi‐scale whole genome sequence analysis for Alzheimer's disease dementia prediction

Abstract

Background: The importance of early prediction of Alzheimer's disease (AD) is emerging, and a genomic approach provides a promising path to this goal. One limitation is the high dimensionality of genomic data, which remains incompletely understood. Deep learning (DL) models hold potential for processing and interpreting such complex data.