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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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

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