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计算机工程 ›› 2010, Vol. 36 ›› Issue (4): 22-24. doi: 10.3969/j.issn.1000-3428.2010.04.008

• 博士论文 • 上一篇    下一篇

基于级联神经网络的蛋白质二级结构预测

王艳春1,2,何东健3,王守志4   

  1. (1. 西北农林科技大学机械与电子工程学院,杨陵 712100;2. 青岛农业大学信息科学与工程学院,青岛 266109; 3. 西北农林科技大学信息工程学院,杨陵 712100;4. 威海职业学院机电工程系,威海 264210)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-02-20 发布日期:2010-02-20

Protein Secondary Structure Prediction Based on Cascade Neural Networks

WANG Yan-chun1,2, HE Dong-jian3, WANG Shou-zhi4   

  1. (1. College of Mechanical and Electronic Engineering, Northwest A & F University, Yangling 712100; 2. College of Information Science and Engineering, Qingdao Agricultural University, Qingdao 266109; 3. College of Information Engineering, Northwest A & F University, Yangling 712100; 4. Department of Mechanical and Electronic Engineering, Weihai Vocational College, Weihai 264210)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-02-20 Published:2010-02-20

摘要: 为提高蛋白质二级结构预测的精度,提出一种由两层网络构成的级联神经网络模型。第1层网络采用具有差异度的5个子网构成的网络模型,对第2层网络的输入编码进行改进。对PDBSelect25中的36条蛋白质共6 122个残基进行测试,结果表明,该模型能有效预测蛋白质二级结构,其预测精度分别比SNN, DSC, PREDSATOR方法提高5.31%, 1.21%和0.92%,平均预测精度提高到69.61%。

关键词: 神经网络, 蛋白质, 二级结构预测

Abstract: In order to improve the prediction accuracy of protein secondary structure, a cascade neural networks composed of two-level network is presented. The first level is composed of five subnets with different structure, and the coding method of the second-level is studied and improved. The model is employed to predict 36 nonhomologous protein sequences with 6 122 residues in PDBSelect25. Results show that the proposed model can efficiently improve the prediction accuracy, increasing the prediction accuracy by 5.31%, 1.21% and 0.92% respectively compared with SNN, DSC and PREDSATOR method, improving the average prediction accuracy to 69.61%.

Key words: neural networks, protein, secondary structure prediction

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