基于支持向量机的蛋白质同源寡聚体分类研究  

Classification of Protein Homo-oligomers Using Support Vector Machine

作　　者：张绍武[1] 潘泉[1] 陈润生[2] 张洪才[1] 

机构地区：[1]西北工业大学自动控制系,西安710072 [2]中国科学院生物物理研究所,北京100101

出　　处：《生物化学与生物物理进展》2003年第6期879-883,共5页Progress In Biochemistry and Biophysics

基　　金：西北工业大学博士创新基金资助~~

摘　　要：基于支持向量机和贝叶斯方法 ,从蛋白质一级序列出发对蛋白质同源二聚体、同源三聚体、同源四聚体、同源六聚体进行分类研究 ,结果表明 :基于支持向量机 ,采用“一对多”和“一对一”策略 ,其分类总精度分别为 77 3 6%和 93 43 % ,分别比基于贝叶斯协方差判别法的分类总精度 50 64%提高 2 6 72和 42 79个百分点 .从而说明支持向量机可用于蛋白质同源寡聚体分类 ,且是一种非常有效的方法 .对于多类蛋白质同源寡聚体分类 ,基于相同的机器学习方法 (如支持向量机 ) ,采用“一对一”策略比“一对多”效果好 .The homo-dimer, homo-trimer, homo-tetramer and homo-hexamer of protein were classified using both of support vector machine and Bayes covariant discriminant methods. It was found that the total accuracies of 'one-versus-rest' and 'all-versus-all' are 77.36% and 93.43% respectively using support vector machine in jackknife test, which are 26.72 and 42.79 percentile higher respectively than that of Bayes covariant discriminant method in the same test. These results show that the support vector machine is a specially effective method for classifying the higher protein homo-oligomers from protein primary sequences. Using 'all-versus-all' policy is better than 'one-versus-rest' policy for classifying homo-oligomers; based on the same machine learning method ( such as support vector machine). And it was also indicated that the primary sequences of homo-oligomeric proteins contain quaternary information.

关 键 词：支持向量机 蛋白质 同源寡聚体 分类 贝叶斯协方差判别法 

分 类 号：Q51[生物学—生物化学] Q617