Multi-label Learning for Predicting the Activities of Antimicrobial Peptides

	Multi-label Learning for Predicting the Activities of Antimicrobial Peptides
	Pu Wang; Ruiquan Ge; Liming Liu; Xuan Xiao; Ye Li; Yunpeng Cai
刊名	Scientific Reports
	2017
文献子类	期刊论文
英文摘要	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.
URL标识	查看原文
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/12602]
专题	深圳先进技术研究院_数字所
作者单位	Scientific Reports
推荐引用方式 GB/T 7714	Pu Wang,Ruiquan Ge,Liming Liu,et al. Multi-label Learning for Predicting the Activities of Antimicrobial Peptides[J]. Scientific Reports,2017.
APA	Pu Wang,Ruiquan Ge,Liming Liu,Xuan Xiao,Ye Li,&Yunpeng Cai.(2017).Multi-label Learning for Predicting the Activities of Antimicrobial Peptides.Scientific Reports.
MLA	Pu Wang,et al."Multi-label Learning for Predicting the Activities of Antimicrobial Peptides".Scientific Reports (2017).