基于一种新型马尔科夫模型的预测蛋白质亚细胞位点的方法(英文)  

Prediction of protein subcellular locations based on an improved Markov chain model

作　　者：赵娟[1] 秦玉芳[2] 刘太岗[3] 王军[1] 

机构地区：[1]上海师范大学数理学院,上海200234 [2]上海海洋大学信息学院,上海201306 [3]山东农业大学信息科学与工程学院,泰安271018

出　　处：《上海师范大学学报（自然科学版）》2011年第2期125-131,共7页Journal of Shanghai Normal University(Natural Sciences)

基　　金：supported by Shanghai Leading Academic Discipline Project(S30405);Innovation Program of Shanghai Municipal Education Commission(09zz134)

摘　　要：亚细胞位点是蛋白质很重要的功能特征.找到一种有效的、可信度高的预测蛋白质位点的方法是很必要的.提出了一种基于马尔科夫模型的改进预测方法.首先,对于一条给定的蛋白质序列,通过计算在马尔科夫模型下20个氨基酸残基的状态转移矩阵,建立一个420维的特征向量,然后利用支持向量机进行训练和预测,最后夹克刀检验证实了该方法的预测精度与以前的马尔科夫模型相比得到了一定的提高.Subcellular localization is a key functional characteristic of proteins.An effective and reliable prediction system for protein subcellular localizations is necessary.In the present paper,a method based on an improved Markov chain model is proposed.First,a 420-dimentional feature vector is constructed based on the state transition matrix of 20 residues under a Markov model of protein sequences.Then the support vector machine（SVM） is used to train the model and predict the subcellular location of a given peptide sequence.Finally,the jackknife cross-validation test on the dataset confirms that our prediction accuracy is improved compared with the original Markov-based method[1].

关 键 词：蛋白质亚细胞位点 马尔科夫模型 支持向量机 夹克刀检验 序列特征 

分 类 号：O211.62[理学—概率论与数理统计]