基于级联神经网络的蛋白质二级结构预测  

Protein Secondary Structure Prediction Based on Cascade Neural Networks

作　　者：王艳春[1,2] 何东健[3] 王守志[4] 

机构地区：[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

出　　处：《计算机工程》2010年第4期22-24,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(30471138)

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6122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%。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%.

关 键 词：神经网络 蛋白质 二级结构预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]