元胞自动机图的蛋白质二级结构类型预测  

Secondary structure prediction by GLCM of CAI

作　　者：胡鸿豪[1] 宋丽平[1] 肖绚[1] 

机构地区：[1]景德镇陶瓷学院机电学院,江西景德镇333001

出　　处：《计算机工程与应用》2010年第15期226-229,共4页Computer Engineering and Applications

基　　金：国家自然科学基金No.60661001;江西省自然科学基金No.0611060~~

摘　　要：蛋白质结构预测是后基因组时代的一项重要任务,蛋白质二级结构预测是蛋白质结构预测的关键步骤。利用氨基酸数字编码模型生成蛋白质序列的元胞自动机图(Cellular Automata Image,CAI),提出了一种基于灰度共生矩阵(Gray Level Co-occurrence Matrix,GLCM)提取纹理图像特征的方法。用扩大的协方差算法进行预测,仿真结果显示有较好的分类效果,Jackknife检验的预测成功率达到94.61%。One of the important tasks of the post genome project is protein structure prediction,the key step of which is protein secondary structure prediction.This paper makes use of a model of digital coding for amino acid,and uses cellular automata to generate image representation for protein sequences.A protein sequence can be represented by a unique image,and the image takes into account the interactional actions between amino acids.By the Gray Level Co-occurrence Matrix（GLCM） deriving from the CAI,a novel method has been developed that can be used to predict the secondary structure of the protein.The result thus obtained is quite promising.

关 键 词：蛋白质二级结构 元胞自动机 灰度共生矩阵 K近邻算法 Jackknife测试 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]