Review of machine learning methods for RNA secondary structure prediction.

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Qi Zhao, Zheng Zhao, Xiaoya Fan, Zhengwei Yuan, Qian Mao, Yudong Yao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e660cd4533794647a001cf8ba2f46d01
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e660cd4533794647a001cf8ba2f46d01
record_format	dspace
spelling	oai:doaj.org-article:e660cd4533794647a001cf8ba2f46d012021-12-02T19:58:00ZReview of machine learning methods for RNA secondary structure prediction.1553-734X1553-735810.1371/journal.pcbi.1009291https://doaj.org/article/e660cd4533794647a001cf8ba2f46d012021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009291https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.Qi ZhaoZheng ZhaoXiaoya FanZhengwei YuanQian MaoYudong YaoPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009291 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Qi Zhao Zheng Zhao Xiaoya Fan Zhengwei Yuan Qian Mao Yudong Yao Review of machine learning methods for RNA secondary structure prediction.
description	Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.
format	article
author	Qi Zhao Zheng Zhao Xiaoya Fan Zhengwei Yuan Qian Mao Yudong Yao
author_facet	Qi Zhao Zheng Zhao Xiaoya Fan Zhengwei Yuan Qian Mao Yudong Yao
author_sort	Qi Zhao
title	Review of machine learning methods for RNA secondary structure prediction.
title_short	Review of machine learning methods for RNA secondary structure prediction.
title_full	Review of machine learning methods for RNA secondary structure prediction.
title_fullStr	Review of machine learning methods for RNA secondary structure prediction.
title_full_unstemmed	Review of machine learning methods for RNA secondary structure prediction.
title_sort	review of machine learning methods for rna secondary structure prediction.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e660cd4533794647a001cf8ba2f46d01
work_keys_str_mv	AT qizhao reviewofmachinelearningmethodsforrnasecondarystructureprediction AT zhengzhao reviewofmachinelearningmethodsforrnasecondarystructureprediction AT xiaoyafan reviewofmachinelearningmethodsforrnasecondarystructureprediction AT zhengweiyuan reviewofmachinelearningmethodsforrnasecondarystructureprediction AT qianmao reviewofmachinelearningmethodsforrnasecondarystructureprediction AT yudongyao reviewofmachinelearningmethodsforrnasecondarystructureprediction
_version_	1718375812744347648

Review of machine learning methods for RNA secondary structure prediction.

Ejemplares similares