RNA secondary structure prediction using deep learning with thermodynamic integration

Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.

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Detalles Bibliográficos
Autores principales:	Kengo Sato, Manato Akiyama, Yasubumi Sakakibara
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/862e961ec971484fbb3f7229d26241f6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:862e961ec971484fbb3f7229d26241f62021-12-02T12:14:48ZRNA secondary structure prediction using deep learning with thermodynamic integration10.1038/s41467-021-21194-42041-1723https://doaj.org/article/862e961ec971484fbb3f7229d26241f62021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21194-4https://doaj.org/toc/2041-1723Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.Kengo SatoManato AkiyamaYasubumi SakakibaraNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kengo Sato Manato Akiyama Yasubumi Sakakibara RNA secondary structure prediction using deep learning with thermodynamic integration
description	Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms.
format	article
author	Kengo Sato Manato Akiyama Yasubumi Sakakibara
author_facet	Kengo Sato Manato Akiyama Yasubumi Sakakibara
author_sort	Kengo Sato
title	RNA secondary structure prediction using deep learning with thermodynamic integration
title_short	RNA secondary structure prediction using deep learning with thermodynamic integration
title_full	RNA secondary structure prediction using deep learning with thermodynamic integration
title_fullStr	RNA secondary structure prediction using deep learning with thermodynamic integration
title_full_unstemmed	RNA secondary structure prediction using deep learning with thermodynamic integration
title_sort	rna secondary structure prediction using deep learning with thermodynamic integration
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/862e961ec971484fbb3f7229d26241f6
work_keys_str_mv	AT kengosato rnasecondarystructurepredictionusingdeeplearningwiththermodynamicintegration AT manatoakiyama rnasecondarystructurepredictionusingdeeplearningwiththermodynamicintegration AT yasubumisakakibara rnasecondarystructurepredictionusingdeeplearningwiththermodynamicintegration
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RNA secondary structure prediction using deep learning with thermodynamic integration

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