Highly Efficient Framework for Predicting Interactions Between Proteins

• Published in: IEEE transactions on cybernetics 2017-03, Vol.47 (3), p.731-743
• Main Authors: You, Zhu-Hong, Zhou, MengChu, Luo, Xin, Li, Shuai
• Format: Article
• Language: English
• Subjects: Amino acids; Approximation; Artificial neural networks; Big data; Biological activity; Computational modeling; Feature extraction; Kernel; kernel extreme learning machine (K-ELM); Kernels; low-rank approximation (LRA); Mapping; Mathematical analysis; Predictive models; Protein sequence; Proteins; protein–protein interactions (PPIs); Representations; support vector machine (SVM)
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2016.2524994

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.