title: Highly Efficient Framework for Predicting Interactions Between Proteins.
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.
authors.lastname: Zhu-Hong You
authors.lastname: MengChu Zhou
authors.lastname: Xin Luo
authors.lastname: Shuai Li
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D000069550:Machine Learning; D025941:Protein Interaction Mapping / Q000379:methods*; D060388:Support Vector Machine