基于柔性神经树的蛋白质结构预测  

Protein Structural Prediction Based on Flexible Neural Tree

作　　者：黄秀[1] 陈月辉[1] 曹毅[1] 

机构地区：[1]济南大学信息科学与工程学院,济南250022

出　　处：《计算机工程》2011年第1期159-160,163,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60573065);山东省自然科学基金资助项目(Y2007G33)

摘　　要：提出一种基于柔性神经树的蛋白质结构预测方法,将近似熵和蛋白质序列的疏水特性作为伪氨基酸组成的特征。对数据集中的每一条蛋白质进行特征提取。对于一个蛋白质样本,用一个27-D伪氨基酸组成作为其特征,伪氨基酸组成特征作为输入数据,柔性神经树作为预测工具,分类方法采用M-ary方法,数据集选用640数据集。仿真结果表明,该方法具有较好的优化性能,提高了预测的准确率。This paper proposes a method of protein structural prediction classes based on flexible neural tree. The approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the Pseudo-Amino Acid（PseAA） components. It extracts features of protein in data set. For a given protein sequence sample, a 27-D PseAA composition is gen^rated as its descriptor. PseAA composition features as input data, the flexible neural tree is adopted as the prediction engine. A classification method named M-ary classifier is introduced. The 640 protein sequence is used as the dataset. Experimental result shows the method has better optimization of performance and improves the predictive accuracy rate.

关 键 词：蛋白质结构分类 伪氨基酸组成 近似熵 疏水性 柔性神经树 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]