MVP predicts the pathogenicity of missense variants by deep learning

Accurate prediction of variant pathogenicity is essential to understanding genetic risks in disease. Here, the authors present a deep neural network method for prediction of missense variant pathogenicity, MVP, and demonstrate its utility in prioritizing de novo variants contributing to developmenta...

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Autores principales:	Hongjian Qi, Haicang Zhang, Yige Zhao, Chen Chen, John J. Long, Wendy K. Chung, Yongtao Guan, Yufeng Shen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/00b2a98c75c843c688e0cc486648e40e
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id	oai:doaj.org-article:00b2a98c75c843c688e0cc486648e40e
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spelling	oai:doaj.org-article:00b2a98c75c843c688e0cc486648e40e2021-12-02T10:49:21ZMVP predicts the pathogenicity of missense variants by deep learning10.1038/s41467-020-20847-02041-1723https://doaj.org/article/00b2a98c75c843c688e0cc486648e40e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20847-0https://doaj.org/toc/2041-1723Accurate prediction of variant pathogenicity is essential to understanding genetic risks in disease. Here, the authors present a deep neural network method for prediction of missense variant pathogenicity, MVP, and demonstrate its utility in prioritizing de novo variants contributing to developmental disorders.Hongjian QiHaicang ZhangYige ZhaoChen ChenJohn J. LongWendy K. ChungYongtao GuanYufeng ShenNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Hongjian Qi Haicang Zhang Yige Zhao Chen Chen John J. Long Wendy K. Chung Yongtao Guan Yufeng Shen MVP predicts the pathogenicity of missense variants by deep learning
description	Accurate prediction of variant pathogenicity is essential to understanding genetic risks in disease. Here, the authors present a deep neural network method for prediction of missense variant pathogenicity, MVP, and demonstrate its utility in prioritizing de novo variants contributing to developmental disorders.
format	article
author	Hongjian Qi Haicang Zhang Yige Zhao Chen Chen John J. Long Wendy K. Chung Yongtao Guan Yufeng Shen
author_facet	Hongjian Qi Haicang Zhang Yige Zhao Chen Chen John J. Long Wendy K. Chung Yongtao Guan Yufeng Shen
author_sort	Hongjian Qi
title	MVP predicts the pathogenicity of missense variants by deep learning
title_short	MVP predicts the pathogenicity of missense variants by deep learning
title_full	MVP predicts the pathogenicity of missense variants by deep learning
title_fullStr	MVP predicts the pathogenicity of missense variants by deep learning
title_full_unstemmed	MVP predicts the pathogenicity of missense variants by deep learning
title_sort	mvp predicts the pathogenicity of missense variants by deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/00b2a98c75c843c688e0cc486648e40e
work_keys_str_mv	AT hongjianqi mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT haicangzhang mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT yigezhao mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT chenchen mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT johnjlong mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT wendykchung mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT yongtaoguan mvppredictsthepathogenicityofmissensevariantsbydeeplearning AT yufengshen mvppredictsthepathogenicityofmissensevariantsbydeeplearning
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MVP predicts the pathogenicity of missense variants by deep learning

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