Deep learning-based brain transcriptomic signatures associated with the neuropathological and clinical severity of Alzheimer's disease.

ABSTRACT: Brain tissue gene expression from donors with and without Alzheimer's disease has been used to help inform the molecular changes associated with the development and potential treatment of this disorder. Here, we use a deep learning method to analyse RNA-seq data from 1114 brain donors from the Accelerating Medicines Project for Alzheimer's Disease consortium to characterize post-mortem brain transcriptome signatures associated with amyloid-β plaque, tau neurofibrillary tangles and clinical severity in multiple Alzheimer's disease dementia populations. Starting from the cross-sectional data in the Religious Orders Study and Memory and Aging Project cohort (n = 634), a deep learning framework was built to obtain a trajectory that mirrors Alzheimer's disease progression. A severity ind

SUBMITTER: Wang Q 

PROVIDER: S-EPMC8728025 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature