The prediction of protein secondary structure with a cascade correlation learning architecture of neural networks

被引：5

作者：

Vivarelli, F ^{[1
]}

Fariselli, P ^{[1
]}

Casadio, R ^{[1
]}

机构：

[1] UNIV BOLOGNA, DEPT BIOL, BIOPHYS LAB, I-40126 BOLOGNA, ITALY

来源：

NEURAL COMPUTING & APPLICATIONS | 1997年 / 6卷 / 01期

关键词：

cascade correlation learning algorithm; neural networks; pattern recognition; predictive methods; protein secondary structure prediction;

D O I：

10.1007/BF01670152

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards rite global minimum of the error function more efficiently than BPNN. However; only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (C-alpha, and C-beta and C-coil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system.

引用

页码：57 / 62

页数：6

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