基于特征选择和支持向量机的HIV-1型蛋白酶剪切位点预测

doi:10.13229/j.cnki.jdxbgxb201702040

Abstract

Abstract: In order to improve the prediction accuracy of the HIV-1 protease cleavage site, a shear prediction model based on feature selection and support vector machine is proposed. First, by analysis of the cleavage site dataset of 5830 samples, and using absorption minimum redundancy maximum relevance concept, the automatic termination method is employed to select the cleavage site feature vectors. Then, the feature vector is input to a support vector machine for learning and training to build the classification model of splice sites. Finally, simulation is carried out using MATLAB 2004 simulation toolbox. Results show that the proposed model has better prediction accuracy than that of the reference models and literature report. The selected features have good interpretability and biological significance.

Key words: biophysics, cleavage site prediction, feature selection, minimal redundancy maximal relevance(mRMR), support vector machine(SVM)

CLC Number:

YUAN Zhe-ming, ZHANG Hong-yang, CHEN Yuan. HIV-1 protease cleavage site prediction based on feature selection and support vector machine[J].吉林大学学报(工学版), 2017, 47(2): 639-646.

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HIV-1 protease cleavage site prediction based on feature selection and support vector machine

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