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.
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-023-00269-x |
| _version_ | 1797849879244963840 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Xiaopu Zhou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T18:51:18Z |
| format | Article |
| id | doaj.art-1179b0a6657942b580123646d3f1699e |
| institution | Directory Open Access Journal |
| issn | 2730-664X |
| language | English |
| last_indexed | 2024-04-09T18:51:18Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj.art-1179b0a6657942b580123646d3f1699e2023-04-09T11:25:31ZengNature PortfolioCommunications Medicine2730-664X2023-04-013112010.1038/s43856-023-00269-xDeep learning-based polygenic risk analysis for Alzheimer’s disease predictionXiaopu Zhou0Yu Chen1Fanny C. F. Ip2Yuanbing Jiang3Han Cao4Ge Lv5Huan Zhong6Jiahang Chen7Tao Ye8Yuewen Chen9Yulin Zhang10Shuangshuang Ma11Ronnie M. N. Lo12Estella P. S. Tong13Alzheimer’s Disease Neuroimaging InitiativeVincent C. T. Mok14Timothy C. Y. Kwok15Qihao Guo16Kin Y. Mok17Maryam Shoai18John Hardy19Lei Chen20Amy K. Y. Fu21Nancy Y. Ip22Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDepartment of Computer Science and Engineering, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDepartment of Computer Science and Engineering, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyGuangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain ScienceGuangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen–Hong Kong Institute of Brain ScienceDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyGerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong KongTherese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Geriatrics, Department of Medicine and Therapeutics, The Chinese University of Hong KongDepartment of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDepartment of Neurodegenerative Disease, UCL Queen Square Institute of NeurologyHong Kong Center for Neurodegenerative Diseases, Hong Kong Science ParkDepartment of Computer Science and Engineering, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyDivision of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and TechnologyZhou 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.https://doi.org/10.1038/s43856-023-00269-x |
| spellingShingle | 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 Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction Communications Medicine |
| title | Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction |
| title_full | Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction |
| title_fullStr | Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction |
| title_full_unstemmed | Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction |
| title_short | Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction |
| title_sort | deep learning based polygenic risk analysis for alzheimer s disease prediction |
| url | https://doi.org/10.1038/s43856-023-00269-x |
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