Interpretable deep learning framework for understanding molecular changes in human brains with Alzheimer's disease: implications for microglia activation and sex differences.

ABSTRACT: The utilization of artificial intelligence in studying the dysregulation of gene expression in Alzheimer's disease (AD) affected brain tissues remains underexplored, particularly in delineating common and specific transcriptomic signatures across different brain regions implicated in AD-related cellular and molecular processes, which could help illuminate novel disease biology for biomarker and target discovery. Herein we developed a deep learning framework, which consisted of multi-layer perceptron (MLP) models to classify neuropathologically confirmed AD versus controls, using bulk tissue RNA-seq data from the RNAseq Harmonization Study of the Accelerating Medicines Project for Alzheimer's Disease (AMP-AD) consortium. The models were trained based on data from three distinct brain region

SUBMITTER: Trivedi MR 

PROVIDER: S-EPMC12267417 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature