Int. J. Mol. Sci. 2014, 15(2), 3220-3233; doi:10.3390/ijms15023220
Article

Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine

1,2,* email, 1email and 1email
Received: 1 January 2014; in revised form: 27 January 2014 / Accepted: 29 January 2014 / Published: 21 February 2014
(This article belongs to the Special Issue Molecular Science for Drug Development and Biomedicine)
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Abstract: Protein–protein interactions (PPIs) play a key role in many cellular processes. Unfortunately, the experimental methods currently used to identify PPIs are both time-consuming and expensive. These obstacles could be overcome by developing computational approaches to predict PPIs. Here, we report two methods of amino acids feature extraction: (i) distance frequency with PCA reducing the dimension (DFPCA) and (ii) amino acid index distribution (AAID) representing the protein sequences. In order to obtain the most robust and reliable results for PPI prediction, pairwise kernel function and support vector machines (SVM) were employed to avoid the concatenation order of two feature vectors generated with two proteins. The highest prediction accuracies of AAID and DFPCA were 94% and 93.96%, respectively, using the 10 CV test, and the results of pairwise radial basis kernel function are considerably improved over those based on radial basis kernel function. Overall, the PPI prediction tool, termed PPI-PKSVM, which is freely available at http://159.226.118.31/PPI/index.html, promises to become useful in such areas as bio-analysis and drug development.
Keywords: amino acid distance frequency; amino acid index distribution; protein–protein interaction; pairwise kernel function; support vector machine
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.

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MDPI and ACS Style

Zhang, S.-W.; Hao, L.-Y.; Zhang, T.-H. Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine. Int. J. Mol. Sci. 2014, 15, 3220-3233.

AMA Style

Zhang S-W, Hao L-Y, Zhang T-H. Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine. International Journal of Molecular Sciences. 2014; 15(2):3220-3233.

Chicago/Turabian Style

Zhang, Shao-Wu; Hao, Li-Yang; Zhang, Ting-He. 2014. "Prediction of Protein–Protein Interaction with Pairwise Kernel Support Vector Machine." Int. J. Mol. Sci. 15, no. 2: 3220-3233.


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