Abstract
Protein–protein interaction plays a fundamental role in many biological processes and diseases. Characterizing protein interaction sites is crucial for the understanding of the mechanism of protein–protein interaction and their cellular functions. In this paper, we proposed a method based on integrated support vector machine (SVM) with a hybrid kernel to predict protein interaction sites. First, a number of features of the protein interaction sites were extracted. Secondly, the technique of sliding window was used to construct a protein feature space based on the influence of the adjacent residues. Thirdly, to avoid the impact of imbalance of the data set on prediction accuracy, we employed boost-strap to re-sample the data. Finally, we built a SVM classifier, whose hybrid kernel comprised a Gaussian kernel and a Polynomial kernel. In addition, an improved particle swarm optimization (PSO) algorithm was applied to optimize the SVM parameters. Experimental results show that the PSO-optimized SVM classifier outperforms existing methods.
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Acknowledgments
This work was funded by the Natural Science Foundation of Fujian Province (2012J05114, 2013N5006), Special Project on the Integration of Industry, Fuzhou City Science Foundation (2012G106). We also want to thank for the help from Dr. Zhibin Fu.
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Guo, H., Liu, B., Cai, D. et al. Predicting protein–protein interaction sites using modified support vector machine. Int. J. Mach. Learn. & Cyber. 9, 393–398 (2018). https://doi.org/10.1007/s13042-015-0450-6
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DOI: https://doi.org/10.1007/s13042-015-0450-6