Open AccessPredicting protein secondary structure by cascade-correlation neural networks
Author(s) -
Matthew J. A. Wood,
Jonathan D. Hirst
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btg423
Subject(s) - cascade , constructive , artificial neural network , correlation , computer science , backpropagation , artificial intelligence , algorithm , pattern recognition (psychology) , mathematics , process (computing) , chemistry , chromatography , geometry , operating system
The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorter time.
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