Highly Efficient Framework for Predicting Interactions Between Proteins

Document Type

Article

Publication Date

3-1-2017

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.

Identifier

85047726651 (Scopus)

Publication Title

IEEE Transactions on Cybernetics

External Full Text Location

https://doi.org/10.1109/TCYB.2016.2524994

e-ISSN

21682275

ISSN

21682267

PubMed ID

28113829

First Page

731

Last Page

743

Issue

3

Volume

47

Grant

CMMI-1162482

Fund Ref

National Natural Science Foundation of China

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