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

Medicine and Health

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

N. Beebe-wang, S. Celik, et al.

Explore the groundbreaking multi-task deep learning framework MD-AD, developed by Nicasia Beebe-Wang and colleagues, which dives deep into heterogeneous Alzheimer's Disease datasets to reveal complex non-linear relationships and subtle disease signals, showcasing its remarkable versatility across species and tissues.

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Playback language: English
Abstract
This paper introduces MD-AD, a multi-task deep learning framework designed to analyze heterogeneous Alzheimer's Disease (AD) datasets. MD-AD leverages the synergy between deep neural networks and multi-cohort settings to uncover complex, non-linear relationships between gene expression and AD neuropathologies, overcoming limitations of conventional methods. The framework identifies subtle disease signals, reveals sex-specific relationships between microglial immune response and neuropathology, and demonstrates generalizability across species and tissues.
Publisher
Nature Communications
Published On
Sep 10, 2021
Authors
Nicasia Beebe-Wang, Safiye Celik, Ethan Weinberger, Pascal Sturmfels, Philip L. De Jager, Sara Mostafavi, Su-In Lee
Tags
Alzheimer's Disease
multi-task deep learning
gene expression
neuropathology
sex-specific relationships
microglial immune response
generalizability

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