Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76078
Title: Detection of interactions between proteins by using legendre moments descriptor to extract discriminatory information embedded in PSSM
Authors: Wang, YB
You, ZH
Li, LP
Huang, YA 
Yi, HC
Keywords: Protein-protein interactions
Legendre moments
Position specific scoring matrix
Probabilistic classification vector machine
Issue Date: 2017
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Molecules, 2017, v. 22, no. 8, 1366 How to cite?
Journal: Molecules 
Abstract: Protein-protein interactions (PPIs) play a very large part in most cellular processes. Although a great deal of research has been devoted to detecting PPIs through high-throughput technologies, these methods are clearly expensive and cumbersome. Compared with the traditional experimental methods, computational methods have attracted much attention because of their good performance in detecting PPIs. In our work, a novel computational method named as PCVM-LM is proposed which combines the probabilistic classification vector machine (PCVM) model and Legendre moments (LMs) to predict PPIs from amino acid sequences. The improvement mainly comes from using the LMs to extract discriminatory information embedded in the position-specific scoring matrix (PSSM) combined with the PCVM classifier to implement prediction. The proposed method was evaluated on Yeast and Helicobacter pylori datasets with five-fold cross-validation experiments. The experimental results show that the proposed method achieves high average accuracies of 96.37% and 93.48%, respectively, which are much better than other well-known methods. To further evaluate the proposed method, we also compared the proposed method with the state-of-the-art support vector machine (SVM) classifier and other existing methods on the same datasets. The comparison results clearly show that our method is better than the SVM-based method and other existing methods. The promising experimental results show the reliability and effectiveness of the proposed method, which can be a useful decision support tool for protein research.
URI: http://hdl.handle.net/10397/76078
ISSN: 1420-3049
DOI: 10.3390/molecules22081366
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