%0 Journal Article
%T CLASSIFICATION OF QUATERNARY STRUCTURE USING SUPPORT VECTOR MACHINES AND BAYES METHODS<br>基于支持向量机和贝叶斯方法的蛋白质四级结构分类研究
%A ZHANG Shao-wu
%A PAN Quan
%A ZHANG Hong-cai
%A ZHANG Yun-long
%A WANG Hai-yu
%A <br>张绍武
%A 潘泉
%A 张洪才
%A 张云龙
%A 王海瑜
%J 生物物理学报
%D 2003
%I 
%X The quaternary structure was classified using support vector machine method and Bayes method. It was found that the result of using support vector machine is the best, using 10-fold cross-validation test, the overall accuracy, true positive rate, Mattew's correlation coefficient and false negative rate are 74.2%, 84.6%, 0.474, 38.9% respectively; the result of Bayes method is not so good as that of the support vector machine method, the false negative rate of using 10-fold cross-validation test is the smallest. Those results show that the primary sequences of homo-oligomeric proteins contain quaternary information. The feature vectors appear to capture essential information about the composition and hydrophobicity of the residues in the surface patches that are buried in the interfaces of associated subunits. And they also show that the support vector machines is a specially effective method.
%K Support vector machines
%K Bayes
%K Protein quaternary structure
%K Subunits<br>支持向量机
%K 贝叶斯方法
%K 蛋白质
%K 四级结构
%K 分类
%K 亚基
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=7D3A3E33621FB77D&yid=D43C4A19B2EE3C0A&vid=2A8D03AD8076A2E3&iid=0B39A22176CE99FB&sid=73579BC9CFB2D787&eid=A58CF3BAE79427D0&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=6&reference_num=18