生物信息学2012，Vol.10Issue(4)：274-279,6.DOI:10.3969/j.issn.1672-5565.2012.04.10
基于二层特征筛选的HIV-1蛋白酶特异位点预测
HIV-1 protease cleavage site prediction based on two stage feature selection method
摘要
Abstract
The HIV - 1 protease inhibitor plays an important role in the therapy of AIDS. The research on HIV - 1 protease' s cleavage site will be useful to found new therapeutic targets. To predict the HIV — 1 protease specific site , we apply Amino Acid Index( AAIndex) ' s 531 amino acid' s parameter of chemical and physical to present the structure of peptide sample. And based on two stage feature selection method , 57 features are selected from original 4248 features. By using four kernel function of support vector machine(SVM) , HIV - 1 protease specific site' s model is built. Our research showed the modeling by the kernel function of Normalize Poly Kernel had the higher prediction rate than other three kernel function. As a result, the accuracy rate of prediction achieves 93. 947% and 93.684% for corss validation test and an independent set test, respectively.关键词
变量筛选/支持向量机/10折交叉验证/预测模型/HIV-1蛋白酶Key words
Feature selection/ SVM (support vector machine) / 10 - crossvalidation/ Prediction model/ HIV - 1 protease分类
化学化工引用本文复制引用
袁啸尘,钮冰,尹京苑..基于二层特征筛选的HIV-1蛋白酶特异位点预测[J].生物信息学,2012,10(4):274-279,6.基金项目
上海市优秀青年教师基金(SHU10022) （SHU10022）
国家自然科学基金(20973108). （20973108）
