Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65379
Title: Construction of reliable protein–protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation features
Authors: Huang, YA
You, ZH
Li, X
Chen, X
Hu, P
Li, S 
Luo, X 
Keywords: Protein sequence
Protein-protein interaction networks
Substitution matrix representation
Weighted sparse representation
Issue Date: 2016
Publisher: Elsevier
Source: Neurocomputing, 2016, v. 218, p. 131-138 How to cite?
Journal: Neurocomputing 
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.
URI: http://hdl.handle.net/10397/65379
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2016.08.063
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