Highly Efficient Framework for Predicting Interactions Between Proteins
文献类型:期刊论文
作者 | You, Zhu-Hong1; Zhou, MengChu2,3; Luo, Xin4![]() |
刊名 | IEEE TRANSACTIONS ON CYBERNETICS
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出版日期 | 2017-03-01 |
卷号 | 47期号:3页码:731-743 |
ISSN号 | 2168-2267 |
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) |
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 |
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. |
入库方式: OAI收割
来源:重庆绿色智能技术研究院
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