한빛사논문
- 조회406
Nicasia Beebe-Wang1, Safiye Celik2, Ethan Weinberger1, Pascal Sturmfels1, Philip L. De Jager3, Sara Mostafavi1,4,5,* & Su-In Lee1,5,*
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA. 2Recursion Pharmaceuticals, Salt Lake City, UT, USA. 3Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA. 4Department of Statistics, University of British Columbia, Vancouver, BC, Canada. 5These authors jointly supervised this work: Sara Mostafavi & Su-In Lee.
*Corresponding author.
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 discovered using linear models, identifying subtler signals than cohort-level variations which can be uniquely recapitulated in animal models and across tissues. We show that MD-AD exploits sex-specific relationships between microglial immune response and neuropathology, providing a nuanced context for the association between inflammatory genes and Alzheimer’s Disease.
논문정보
- Research article
- 2021년 09월 (BRIC 등록일 2021-09-24)
- 국외 연구진
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