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Highly Efficient Framework for Predicting Interactions Between Proteins
You, Zhu-Hong1; Zhou, MengChu2,3; Luo, Xin4; Li, Shuai5
2017-03-01
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.
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)
DOI10.1109/TCYB.2016.2524994
发表期刊IEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
卷号47期号:3页码:731-743
通讯作者Zhou, MC (reprint author), Macau Univ Sci & Technol, Inst Syst Engn, Macau 999078, Peoples R China.
收录类别SCI
WOS记录号WOS:000396395400016
语种英语