用级联相关神经网络预测蛋白质二级结构。
Predicting protein secondary structure by cascade-correlation neural networks.
作者信息
Wood Matthew J, Hirst Jonathan D
机构信息
School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK.
出版信息
Bioinformatics. 2004 Feb 12;20(3):419-20. doi: 10.1093/bioinformatics/btg423. Epub 2004 Jan 22.
Abstract
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.
摘要
反向传播神经网络算法是预测蛋白质二级结构的常用方法。虽然这种方法很流行，但学习速度可能较慢，在此我们将其与另一种方法：级联相关架构进行比较。级联相关使用一种构造性算法，能在更短时间内达到与反向传播相当的预测准确率。
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