Highly Efficient Framework for Predicting Interactions Between Proteins

doi:10.1109/TCYB.2016.2524994

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	Highly Efficient Framework for Predicting Interactions Between Proteins
	You, Zhu-Hong 1; Zhou, MengChu 2,3; Luo, Xin4 ; Li, Shuai 5
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2017-03-01
卷号	47 期号:3 页码:731-743
关键词	Big data feature extraction kernel extreme learning machine (K-ELM) low-rank approximation (LRA) protein-protein interactions (PPIs) support vector machine (SVM)
ISSN号	2168-2267
DOI	10.1109/TCYB.2016.2524994
通讯作者	Zhou, MC (reprint author), Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China.
英文摘要	Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i. e., Low-rank approximationkernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.
资助项目	National Natural Science Foundation of China[61373086] ; National Natural Science Foundation of China[61401385] ; US National Natural Science Foundation[CMMI-1162482] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences ; Young Scientist Foundation of Chongqing[cstc2014kjrc-qnrc40005] ; Fundamental Research Funds for the Central Universities[106112015CDJXY180005]
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000396395400016
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/3352]
专题	大数据挖掘及应用中心
通讯作者	Zhou, MengChu
作者单位	1.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China 3.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China 5.Hong Kong Polytech Univ, Dept Comp, Hong Kong 999077, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	You, Zhu-Hong,Zhou, MengChu,Luo, Xin,et al. Highly Efficient Framework for Predicting Interactions Between Proteins[J]. IEEE TRANSACTIONS ON CYBERNETICS,2017,47(3):731-743.
APA	You, Zhu-Hong,Zhou, MengChu,Luo, Xin,&Li, Shuai.(2017).Highly Efficient Framework for Predicting Interactions Between Proteins.IEEE TRANSACTIONS ON CYBERNETICS,47(3),731-743.
MLA	You, Zhu-Hong,et al."Highly Efficient Framework for Predicting Interactions Between Proteins".IEEE TRANSACTIONS ON CYBERNETICS 47.3(2017):731-743.