Construction of reliable protein–protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features

Yu An Huang, Zhu Hong You, Xiao Li, Xing Chen, Pengwei Hu, Shuai Li, Xin Luo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)

Abstract

Protein-protein interactions (PPIs) networks play an important role in most of biological processes. Although much effort has been devoted to using high-throughput biological technologies to identify PPIs of various kinds of organisms, the experimental methods are expensive, time-consuming, and tedious. Therefore, developing computational methods for predicting PPIs is of great significance in this post-genomic era. In recent years, the exponential increase of available protein sequence data leads to the urgent need for sequence-based prediction model. In this paper, we report a highly efficient method for constructing PPIs networks. The main improvements come from a novel protein sequence representation called pseudo-SMR, and from adopting weighted sparse representation based classifier (WSRC). When predicting the PPIs of Yeast, Human and H. pylori datasets, the 5-fold cross-validation accuracies performed by the proposed method achieve as high as 97.09%, 96.71% and 91.15% respectively, significantly better than previous methods. To further evaluate the performance of the proposed method, extensive experiments are performed to compare the proposed method with state-of-the-art Support Vector Machine (SVM) classifier. Promising results obtained show that the proposed method is feasible, robust and powerful.
Original languageEnglish
Pages (from-to)131-138
Number of pages8
JournalNeurocomputing
Volume218
DOIs
Publication statusPublished - 19 Dec 2016

Keywords

  • Protein sequence
  • Protein-protein interaction networks
  • Substitution matrix representation
  • Weighted sparse representation

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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