Barlow T W
Physical Chemistry Laboratory, Oxford, England.
J Mol Graph. 1995 Jun;13(3):175-83. doi: 10.1016/0263-7855(95)00016-y.
A feed-forward neural network has been employed for protein secondary structure prediction. Attempts were made to improve on previous prediction accuracies using a hierarchical mixture of experts (HME). In this method input data are clustered and used to train a series of different networks. Application of an HME to the prediction of protein secondary structure is shown to provide no advantages over a single network. We have also tried various new input representations, chosen to incorporate the effect of residues a long distance away in the one-dimensional amino acid chain. Prediction accuracy using these methods is comparable to that achieved by other neural networks.
前馈神经网络已被用于蛋白质二级结构预测。人们尝试使用专家分层混合（HME）来提高先前的预测准确率。在这种方法中，输入数据被聚类并用于训练一系列不同的网络。结果表明，将HME应用于蛋白质二级结构预测并没有比单个网络更具优势。我们还尝试了各种新的输入表示方法，这些方法旨在纳入一维氨基酸链中远距离残基的影响。使用这些方法的预测准确率与其他神经网络所达到的准确率相当。
