Highly Efficient Framework for Predicting Interactions Between Proteins

Zhu Hong You, Meng Chu Zhou, Xin Luo, Shuai Li

科研成果: 期刊稿件文章同行评审

103 引用 (Scopus)

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 approximation-kernel 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.

源语言英语
文章编号7444177
页(从-至)731-743
页数13
期刊IEEE Transactions on Cybernetics
47
3
DOI
出版状态已出版 - 3月 2017
已对外发布

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