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

吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 639-646.doi: 10.13229/j.cnki.jdxbgxb201702040

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

袁哲明^{1, 2}, 张弘杨^{1, 2}, 陈渊¹

1.湖南农业大学湖南省作物种质创新与资源利用重点实验室,长沙410128;
2.湖南农业大学植物病虫害生物学与防控湖南省重点实验室,长沙410128

收稿日期:2015-12-15 出版日期:2017-03-20 发布日期:2017-03-20
通讯作者: 陈渊(1987-),男,讲师.研究方向:大数据分析.E-mail:chenyuan0510@126.com
作者简介:袁哲明(1971-),男,教授,博士生导师.研究方向:大数据分析,生物信息学.E-mail:zhmyuan@sina.com
基金资助:
高等学校博士学科点专项科研基金项目(20124320110002); 长沙市科技计划项目(K1406018-21).

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

YUAN Zhe-ming^{1, 2}, ZHANG Hong-yang^{1, 2}, CHEN Yuan¹

1.Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Hunan Agricultural University, Changsha 410128, China;
2.Hunan Provincial Key Laboratory for Biology and Control of Plant Diseases and Insect Pests, Hunan Agricultural University, Changsha 410128, China

Received:2015-12-15 Online:2017-03-20 Published:2017-03-20

摘要/Abstract

摘要： 为了提高HIV-1型蛋白酶剪切位点的预测准确性,提出一种基于特征选择和支持向量机的剪切位点预测模型。首先,通过对5830个样本的HIV-1型蛋白酶剪切位点数据集进行分析,根据最小冗余最大相关理念,采用可自动终止法选择剪切位点的特征向量;然后,将特征向量输入到支持向量机进行学习和训练,建立HIV-1型蛋白酶剪切位点的分类模型;最后,采用Matlab 2014的仿真工具箱进行仿真测试。实验结果表明:本文模型在特征最少的条件下,剪切位点预测精度优于参比模型及文献报道,且所选择的特征向量具有较好的可解释性及生物学意义。

关键词: 生物物理学, 剪切位点预测, 特征选择, 最小冗余最大相关, 支持向量机

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)

中图分类号:

袁哲明, 张弘杨, 陈渊. 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[J]. 吉林大学学报(工学版), 2017, 47(2): 639-646.

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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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

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

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