Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies.

ABSTRACT: Deep neural networks (DNNs) capture complex relationships among variables, however, because they require copious samples, their potential has yet to be fully tapped for understanding relationships between gene expression and human phenotypes. Here we introduce an analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimer's Disease neuropathology), which leverages an unexpected synergy between DNNs and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which require "harmonized" phenotypes and tend to capture cohort-level variations, obscuring subtler true disease signals. Instead, MD-AD incorporates related phenotypes sparsely measured across cohorts, and learns interactions between genes and phenotypes not dis

SUBMITTER: Beebe-Wang N 

PROVIDER: S-EPMC8433314 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature