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.
Original language | English |
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Article number | 7444177 |
Pages (from-to) | 721-723 |
Number of pages | 3 |
Journal | IEEE Transactions on Cybernetics |
Volume | 47 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Mar 2017 |
Keywords
- Big data
- feature extraction
- kernel extreme learning machine (K-ELM)
- low-rank approximation (LRA)
- protein-protein interactions (PPIs)
- support vector machine (SVM)
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Information Systems
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering