Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction

Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk.

Bibliographic Details
Main Authors: Xiaopu Zhou, Yu Chen, Fanny C. F. Ip, Yuanbing Jiang, Han Cao, Ge Lv, Huan Zhong, Jiahang Chen, Tao Ye, Yuewen Chen, Yulin Zhang, Shuangshuang Ma, Ronnie M. N. Lo, Estella P. S. Tong, Alzheimer’s Disease Neuroimaging Initiative, Vincent C. T. Mok, Timothy C. Y. Kwok, Qihao Guo, Kin Y. Mok, Maryam Shoai, John Hardy, Lei Chen, Amy K. Y. Fu, Nancy Y. Ip
Format: Article
Language:English
Published: Nature Portfolio 2023-04-01
Series:Communications Medicine
Online Access:https://doi.org/10.1038/s43856-023-00269-x
Description
Summary:Zhou et al. utilise deep learning to improve polygenic risk analysis for Alzheimer’s disease. Their computational approach outperforms existing statistical methods and helps to identify potential biological mechanisms of Alzheimer’s disease risk.
ISSN:2730-664X