A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs

Predicting the functional consequences of non-coding genetic variants is a challenge. Here, He et al. present GenoNet, a semi-supervised method that combines information from experimentally confirmed regulatory variants with cell type- and tissue specific annotation for function prediction.

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Detalles Bibliográficos
Autores principales:	Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/684d88cf45c54d75a24c417383281dee
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spelling	oai:doaj.org-article:684d88cf45c54d75a24c417383281dee2021-12-02T14:39:24ZA semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs10.1038/s41467-018-07349-w2041-1723https://doaj.org/article/684d88cf45c54d75a24c417383281dee2018-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07349-whttps://doaj.org/toc/2041-1723Predicting the functional consequences of non-coding genetic variants is a challenge. Here, He et al. present GenoNet, a semi-supervised method that combines information from experimentally confirmed regulatory variants with cell type- and tissue specific annotation for function prediction.Zihuai HeLinxi LiuKai WangIuliana Ionita-LazaNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Zihuai He Linxi Liu Kai Wang Iuliana Ionita-Laza A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
description	Predicting the functional consequences of non-coding genetic variants is a challenge. Here, He et al. present GenoNet, a semi-supervised method that combines information from experimentally confirmed regulatory variants with cell type- and tissue specific annotation for function prediction.
format	article
author	Zihuai He Linxi Liu Kai Wang Iuliana Ionita-Laza
author_facet	Zihuai He Linxi Liu Kai Wang Iuliana Ionita-Laza
author_sort	Zihuai He
title	A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
title_short	A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
title_full	A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
title_fullStr	A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
title_full_unstemmed	A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs
title_sort	semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using mpras
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/684d88cf45c54d75a24c417383281dee
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A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs

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