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BioMed Research International
Volume 2015, Article ID 902198, 10 pages
http://dx.doi.org/10.1155/2015/902198
Research Article

Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
2School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
3Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, Jiangsu 215163, China
4School of Electronic and Information Engineering, Soochow University, Suzhou, Jiangsu 215123, China

Received 13 August 2015; Accepted 4 October 2015

Academic Editor: Alok Sharma

Copyright © 2015 Yu-An Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets.