Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

Abstract Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we pr...

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Autores principales:	Pu Wang, Ruiquan Ge, Liming Liu, Xuan Xiao, Ye Li, Yunpeng Cai
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0946ddd40db14a0cb6dc44b493799109
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id	oai:doaj.org-article:0946ddd40db14a0cb6dc44b493799109
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spelling	oai:doaj.org-article:0946ddd40db14a0cb6dc44b4937991092021-12-02T12:32:19ZMulti-label Learning for Predicting the Activities of Antimicrobial Peptides10.1038/s41598-017-01986-92045-2322https://doaj.org/article/0946ddd40db14a0cb6dc44b4937991092017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01986-9https://doaj.org/toc/2045-2322Abstract Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.Pu WangRuiquan GeLiming LiuXuan XiaoYe LiYunpeng CaiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pu Wang Ruiquan Ge Liming Liu Xuan Xiao Ye Li Yunpeng Cai Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
description	Abstract Antimicrobial peptides (AMPs) are peptide antibiotics with a broad spectrum of antimicrobial activities. Activity prediction of AMPs from their amino acid sequences is of great therapeutic importance but imposes challenges on prediction methods due to label interactions. In this paper we propose a novel multi-label learning model to address this problem. A weighted K-nearest neighbor classifier is adopted for efficient representation learning of the sequence data. A multiple linear regression model is then employed to learn a mapping from the classifier score vectors to the target labels, with label correlations considered. Several popular multi-label learning algorithms and feature extraction methods were tested on a comprehensive, up-to-date AMP dataset with twelve biological activities covered and its filtered version with five activities covered. The experimental results showed that our proposed method has competitive performance with previous works and could be used as a powerful engine for activity prediction of AMPs.
format	article
author	Pu Wang Ruiquan Ge Liming Liu Xuan Xiao Ye Li Yunpeng Cai
author_facet	Pu Wang Ruiquan Ge Liming Liu Xuan Xiao Ye Li Yunpeng Cai
author_sort	Pu Wang
title	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
title_short	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
title_full	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
title_fullStr	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
title_full_unstemmed	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
title_sort	multi-label learning for predicting the activities of antimicrobial peptides
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/0946ddd40db14a0cb6dc44b493799109
work_keys_str_mv	AT puwang multilabellearningforpredictingtheactivitiesofantimicrobialpeptides AT ruiquange multilabellearningforpredictingtheactivitiesofantimicrobialpeptides AT limingliu multilabellearningforpredictingtheactivitiesofantimicrobialpeptides AT xuanxiao multilabellearningforpredictingtheactivitiesofantimicrobialpeptides AT yeli multilabellearningforpredictingtheactivitiesofantimicrobialpeptides AT yunpengcai multilabellearningforpredictingtheactivitiesofantimicrobialpeptides
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Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

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