生物信息学2011，Vol.9Issue(3)：224-228,234,6.DOI:10.3969/j.issn.1672-5565.2011.03.012
基于RBF神经网络的蛋白质二级结构预测
Protein Secondary Structure Prediction Based on Radial Basis Function Neural Network
张斌 1尹京苑 2薛丹1
作者信息
- 1. 上海大学生命科学院,上海,200444
- 2. 上海大学计算机工程与科学学院,上海,200072
- 折叠
摘要
Abstract
Protein secondary structure is important to its function research. In this paper, principal component analysis was used to reduce the dimensions and noises in the basic physical and chemical properties of amino acids and secondary structure orientation. Then radial basis neural networks was used to predict the protein secondary structure. Principal component analysis, which turned the previous 20 x 12 matrix into a 20 x 4 matrix, greatly reduced the input dimensions of the network. During the simulating process, the prediction accuracy was reached 71. 81% when training window was 21 and spread was 7. The results showed that the RBF neural network was an effective method in protein secondary structure prediction.关键词
RBF神经网络/蛋白质二级结构预测/主成分分析Key words
Radial Basis Function Neural Network/Protein Secondary Structure Prediction/principal component a-nalysis分类
生物科学引用本文复制引用
张斌,尹京苑,薛丹..基于RBF神经网络的蛋白质二级结构预测[J].生物信息学,2011,9(3):224-228,234,6.
