基于岭回归和SVM的高维特征选择与肽QSAR建模  

Feature Selection for High-Dimensional Data Based on Ridge Regression and SVM and Its Application in Peptide QSAR Modeling

作　　者：王志明[1,2,3] 韩娜[1,2] 袁哲明[1,2] 伍朝华[3] 

机构地区：[1]湖南省作物种质创新与资源利用重点实验室,长沙410128 [2]湖南省植物病虫害生物学与防控重点实验室,长沙410128 [3]湖南农业大学理学院,长沙410128

出　　处：《物理化学学报》2013年第3期498-507,共10页Acta Physico-Chimica Sinica

基　　金：湖南省杰出青年科学基金(10JJ1005);教育部博士点基金(20124320110002)资助项目~~

摘　　要：岭回归估计权重绝对值在一定程度上体现了对应特征作用大小,据此发展了基于岭回归(RR)和支持向量机(SVM)的高维特征选择算法.对苦味二肽(BTT)和细胞毒性T淋巴细胞(CTL)表位9肽两个肽体系,以氨基酸的531个物理化学性质参数直接表征肽结构,各获得1062、4779个初始特征;对训练集,初始特征以岭回归排序后序贯引入,当SVM留一法交叉测试(LOOCV)的均方误差(MSE)显著上扬时终止,最后以多轮末尾淘汰进一步精筛,分别获得7、18个物理化学意义明确的保留特征.基于保留特征与支持向量回归(SVR),对训练集建立定量构效关系(QSAR)模型,预测独立测试集,其拟合精度、留一法交叉测试精度、独立预测精度均优于现有文献报道结果.新方法运行速度快,选取的特征物理化学意义明确,解释性强,在肽、蛋白质定量构效关系建模等高维数据回归预测领域有较广泛应用前景.Absolute weight values estimated from test data by ridge regression （RR） can reflect the significance of corresponding features.Based on RR and support vector machine （SVM）,a new feature selection algorithm for high-dimensional data is proposed.Examples from bitter tasting thresholds （BTT） and cytotoxic T lymphocyte （CTL） epitopes are presented.All 531 physicochemical property parameters were employed to express each residue of one peptide,thus 1062 and 4779 descriptors were obtained for BTT and CTL,respectively.Each sample was divided into training and test sets,and weight estimates of all training set descriptors were generated by RR.According to the descending order of the weights,corresponding features were gradually selected until the mean square error （MSE） of leave-one-out cross validation （LOOCV） increased significantly.Based on smaller training datasets obtained from the previous step,the reserved features were available from multiple elimination rounds.7 and 18 descriptors were selected by the new method for BTT and CTL,respectively.A quantitative structure-activity relationship （QSAR） model based on support vector regression （SVR） was established on extracted data with the reserved descriptors,and was then used for test data prediction.The fitting,LOOCV,and external prediction accuracies were significantly improved with respect to reported literature values.Because of the calculation speed,clear physicochemical meaning,and ease of interpretation,the new method is widely applicable to regression forecasting of high-dimensional data such as QSAR modeling of peptide or proteins.

关 键 词：定量构效关系 岭回归 支持向量机 特征选择 高维特征 

分 类 号：Q51[生物学—生物化学]