A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

Array-based epigenome-wide association studies only test about 2% of the CpG sites in the genome. Here, the authors describe EWASplus, a supervised machine learning strategy that extends EWAS coverage to the entire genome, and use it to identify novel brain CpGs associated with Alzheimer’s disease....

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Autores principales:	Yanting Huang, Xiaobo Sun, Huige Jiang, Shaojun Yu, Chloe Robins, Matthew J. Armstrong, Ronghua Li, Zhen Mei, Xiaochuan Shi, Ekaterina Sergeevna Gerasimov, Philip L. De Jager, David A. Bennett, Aliza P. Wingo, Peng Jin, Thomas S. Wingo, Zhaohui S. Qin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/65c87e8eceb04148ada622868111db36
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spelling	oai:doaj.org-article:65c87e8eceb04148ada622868111db362021-12-02T17:56:56ZA machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease10.1038/s41467-021-24710-82041-1723https://doaj.org/article/65c87e8eceb04148ada622868111db362021-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24710-8https://doaj.org/toc/2041-1723Array-based epigenome-wide association studies only test about 2% of the CpG sites in the genome. Here, the authors describe EWASplus, a supervised machine learning strategy that extends EWAS coverage to the entire genome, and use it to identify novel brain CpGs associated with Alzheimer’s disease.Yanting HuangXiaobo SunHuige JiangShaojun YuChloe RobinsMatthew J. ArmstrongRonghua LiZhen MeiXiaochuan ShiEkaterina Sergeevna GerasimovPhilip L. De JagerDavid A. BennettAliza P. WingoPeng JinThomas S. WingoZhaohui S. QinNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yanting Huang Xiaobo Sun Huige Jiang Shaojun Yu Chloe Robins Matthew J. Armstrong Ronghua Li Zhen Mei Xiaochuan Shi Ekaterina Sergeevna Gerasimov Philip L. De Jager David A. Bennett Aliza P. Wingo Peng Jin Thomas S. Wingo Zhaohui S. Qin A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
description	Array-based epigenome-wide association studies only test about 2% of the CpG sites in the genome. Here, the authors describe EWASplus, a supervised machine learning strategy that extends EWAS coverage to the entire genome, and use it to identify novel brain CpGs associated with Alzheimer’s disease.
format	article
author	Yanting Huang Xiaobo Sun Huige Jiang Shaojun Yu Chloe Robins Matthew J. Armstrong Ronghua Li Zhen Mei Xiaochuan Shi Ekaterina Sergeevna Gerasimov Philip L. De Jager David A. Bennett Aliza P. Wingo Peng Jin Thomas S. Wingo Zhaohui S. Qin
author_facet	Yanting Huang Xiaobo Sun Huige Jiang Shaojun Yu Chloe Robins Matthew J. Armstrong Ronghua Li Zhen Mei Xiaochuan Shi Ekaterina Sergeevna Gerasimov Philip L. De Jager David A. Bennett Aliza P. Wingo Peng Jin Thomas S. Wingo Zhaohui S. Qin
author_sort	Yanting Huang
title	A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
title_short	A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
title_full	A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
title_fullStr	A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
title_full_unstemmed	A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease
title_sort	machine learning approach to brain epigenetic analysis reveals kinases associated with alzheimer’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/65c87e8eceb04148ada622868111db36
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A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer’s disease

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