Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.

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Autores principales:	Zitao Song, Daiyun Huang, Bowen Song, Kunqi Chen, Yiyou Song, Gang Liu, Jionglong Su, João Pedro de Magalhães, Daniel J. Rigden, Jia Meng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/2013910e98b9461eaf0247a35dedc43a
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spelling	oai:doaj.org-article:2013910e98b9461eaf0247a35dedc43a2021-12-02T18:19:01ZAttention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications10.1038/s41467-021-24313-32041-1723https://doaj.org/article/2013910e98b9461eaf0247a35dedc43a2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24313-3https://doaj.org/toc/2041-1723RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.Zitao SongDaiyun HuangBowen SongKunqi ChenYiyou SongGang LiuJionglong SuJoão Pedro de MagalhãesDaniel J. RigdenJia MengNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Zitao Song Daiyun Huang Bowen Song Kunqi Chen Yiyou Song Gang Liu Jionglong Su João Pedro de Magalhães Daniel J. Rigden Jia Meng Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
description	RNA modifications appear to play a role in determining RNA structure and function. Here, the authors develop a deep learning model that predicts the location of 12 RNA modifications using primary sequence, and show that several modifications are associated, which suggests dependencies between them.
format	article
author	Zitao Song Daiyun Huang Bowen Song Kunqi Chen Yiyou Song Gang Liu Jionglong Su João Pedro de Magalhães Daniel J. Rigden Jia Meng
author_facet	Zitao Song Daiyun Huang Bowen Song Kunqi Chen Yiyou Song Gang Liu Jionglong Su João Pedro de Magalhães Daniel J. Rigden Jia Meng
author_sort	Zitao Song
title	Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
title_short	Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
title_full	Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
title_fullStr	Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
title_full_unstemmed	Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications
title_sort	attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring rna modifications
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2013910e98b9461eaf0247a35dedc43a
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Attention-based multi-label neural networks for integrated prediction and interpretation of twelve widely occurring RNA modifications

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