Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning

RNA-binding protein (RBP) is a total name of a class of proteins that bind to RNA (ribonucleic acid) along with the process of RNA??s regulation metabolic. An RBP may have multiple target RNAs, and its defective expression may cause various diseases. Existing methods are mostly designed for a specif...

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Autor principal:	YANG Haitao, DENG Zhaohong, WANG Shitong
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	multi-view deep feature learning multi-label feature learning optimal multi-label chain learning rna-binding proteins recognition Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/84146c58a5f54cacbf1864caa7cf090f
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id	oai:doaj.org-article:84146c58a5f54cacbf1864caa7cf090f
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spelling	oai:doaj.org-article:84146c58a5f54cacbf1864caa7cf090f2021-11-10T08:27:47ZRecognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning10.3778/j.issn.1673-9418.20060961673-9418https://doaj.org/article/84146c58a5f54cacbf1864caa7cf090f2021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2958.shtmlhttps://doaj.org/toc/1673-9418RNA-binding protein (RBP) is a total name of a class of proteins that bind to RNA (ribonucleic acid) along with the process of RNA??s regulation metabolic. An RBP may have multiple target RNAs, and its defective expression may cause various diseases. Existing methods are mostly designed for a specific RBP binary classification model to predict whether an RNA can bind to it. But these methods do not take into account the similarity and association between different RBPs. Therefore, iDeepM uses multi-label deep learning methods to improve it. This method fuses multi-label technology and long short term memory (LSTM) network, learns the similarity between different RBPs, and predicts the binding of a given RNA to multiple RBPs. However, this method fails to perform sufficient feature learning and multi-label learning on RNA sequences, and the prediction accuracy is low. This paper continues the research ideas of iDeepM multi-label, and proposes a new method RNA-RBP multiview learning (RRMVL). For the first time, the RNA sequence view, the amino acid sequence view, the RNA sequence semantic view and the multi-gap dipeptide component view are used to compose multi-view data to deal with multi-label RBP recognition. In order to use the different learning advantages of multi-view data, this paper fuses the deep features extracted from four views and uses the principle of logistic regression to learn multi-label features from them. After that, the learnt weighted feature vectors are fed to the multi-label classifier chain to achieve the optimal multi-label chain learning effect. Experimental studies show that the prediction accuracy of the RNA-binding protein recognition model combining multi-view and multi-label learning has been significantly improved compared with the previous single-view method.YANG Haitao, DENG Zhaohong, WANG ShitongJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlemulti-view deep feature learningmulti-label feature learningoptimal multi-label chain learningrna-binding proteins recognitionElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2193-2205 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	multi-view deep feature learning multi-label feature learning optimal multi-label chain learning rna-binding proteins recognition Electronic computers. Computer science QA75.5-76.95
spellingShingle	multi-view deep feature learning multi-label feature learning optimal multi-label chain learning rna-binding proteins recognition Electronic computers. Computer science QA75.5-76.95 YANG Haitao, DENG Zhaohong, WANG Shitong Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
description	RNA-binding protein (RBP) is a total name of a class of proteins that bind to RNA (ribonucleic acid) along with the process of RNA??s regulation metabolic. An RBP may have multiple target RNAs, and its defective expression may cause various diseases. Existing methods are mostly designed for a specific RBP binary classification model to predict whether an RNA can bind to it. But these methods do not take into account the similarity and association between different RBPs. Therefore, iDeepM uses multi-label deep learning methods to improve it. This method fuses multi-label technology and long short term memory (LSTM) network, learns the similarity between different RBPs, and predicts the binding of a given RNA to multiple RBPs. However, this method fails to perform sufficient feature learning and multi-label learning on RNA sequences, and the prediction accuracy is low. This paper continues the research ideas of iDeepM multi-label, and proposes a new method RNA-RBP multiview learning (RRMVL). For the first time, the RNA sequence view, the amino acid sequence view, the RNA sequence semantic view and the multi-gap dipeptide component view are used to compose multi-view data to deal with multi-label RBP recognition. In order to use the different learning advantages of multi-view data, this paper fuses the deep features extracted from four views and uses the principle of logistic regression to learn multi-label features from them. After that, the learnt weighted feature vectors are fed to the multi-label classifier chain to achieve the optimal multi-label chain learning effect. Experimental studies show that the prediction accuracy of the RNA-binding protein recognition model combining multi-view and multi-label learning has been significantly improved compared with the previous single-view method.
format	article
author	YANG Haitao, DENG Zhaohong, WANG Shitong
author_facet	YANG Haitao, DENG Zhaohong, WANG Shitong
author_sort	YANG Haitao, DENG Zhaohong, WANG Shitong
title	Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
title_short	Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
title_full	Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
title_fullStr	Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
title_full_unstemmed	Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning
title_sort	recognition of rna-binding protein by fusion of multi-view and multi-label learning
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/84146c58a5f54cacbf1864caa7cf090f
work_keys_str_mv	AT yanghaitaodengzhaohongwangshitong recognitionofrnabindingproteinbyfusionofmultiviewandmultilabellearning
_version_	1718440391938670592

Recognition of RNA-Binding Protein by Fusion of Multi-view and Multi-label Learning

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