计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (20): 17-19.

• 博士论坛 • 上一篇    下一篇

基于径向基神经网络的蛋白质二级结构预测

王菲露1,2,3,宋 杨1,4   

  1. 1.安徽建筑工业学院 电子与信息工程学院,合肥 230601
    2.中国科学技术大学 自动化系,合肥 230026
    3.中国科学院 合肥智能机械研究所,合肥 230031
    4.安徽大学 电子信息工程学院,合肥 230039
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-07-11 发布日期:2011-07-11

Protein secondary structure prediction based on radial basis neural network

WANG Feilu1,2,3,SONG Yang1,4   

  1. 1.School of Electronics and Information Engineering,Anhui University of Architecture,Hefei 230601,China
    2.Department of Automation,University of Science and Technology of China,Hefei 230026,China
    3.Institute of Intelligence Machines,Chinese Academy of Sciences,Hefei 230031,China
    4.School of Electronics and Information Engineering,Anhui University,Hefei 230039,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-07-11 Published:2011-07-11

摘要: 为提高蛋白质二级结构预测的精确度,提出并构建精确的径向基神经网络、广义回归神经网络,并基于5位编码和Profile编码,采用不同大小的滑动窗口,利用交叉检证法构建多个径向基网络预测器,分别对蛋白质二级结构进行预测,得到了较好的实验结果,其中aveQ3提高到70.96%。结果表明,径向基神经网络模型能有效提高预测精确度,也证明了实验方法的有效性和可行性。

关键词: 径向基神经网络, 蛋白质二级结构, 预测器

Abstract: In order to improve the prediction accuracy of protein secondary structure,exact radial basis function neural network and generalized regression neural network are presented.Multi-radial basis network predictors are established based on 5 encoding,Profile encoding,different sliding windows and cross validation method.These predictors are used to predict protein secondary structure,and it gets preferable results.Thereinto,aveQ3 is improved to 70.96%.The results not only show radial basis network models can increase the prediction accuracy efficiently,but also prove the validity and feasibility of these motheds.

Key words: radial basis neural network, protein secondary structure, predictor