Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 47, Issue 3, Pages 731-743Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2016.2524994
Keywords
Big data; feature extraction; kernel extreme learning machine (K-ELM); low-rank approximation (LRA); protein-protein interactions (PPIs); support vector machine (SVM)
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Funding
- National Natural Science Foundation of China [61373086, 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]
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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.
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