Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64497
Title: Highly efficient framework for predicting interactions between proteins
Authors: You, Z
Zhou, M
Luo, X
Li, S 
Keywords: Big data
Feature extraction
Kernel extreme learning machine (K-ELM)
Low-rank approximation (LRA)
Protein–protein interactions (PPIs)
Support vector machine (SVM)
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, 2017, v. 47, no. 3, p. 731-743 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: 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.
URI: http://hdl.handle.net/10397/64497
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2016.2524994
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