Sasagawa F, Tajima K
International Institute for Advanced Study of Social Information Science, Fujitsu Laboratories Ltd, Chiba, Japan.
Comput Appl Biosci. 1993 Apr;9(2):147-52. doi: 10.1093/bioinformatics/9.2.147.
We have studied the prediction of globular protein secondary structures by neural networks. Protein secondary structures are allocated to amino acid residues using Kabsch and Sander's dictionary of protein secondary structures and the neural network is taught the protein secondary structures. The input layer of the neural network allows sequences of residues including 20 amino acids, chain break, B, X and Z. We consider classifying secondary structures into groups of 3, 4 and 8. In each case, we calculate the percentage of correct predictions. We discuss the effect of overlearning on the protein secondary structure prediction. In addition, we include the application of a neural network with a modular architecture to prediction of protein secondary structures. We compare the results from neural networks with a modular architecture and with a simple three-layer structure.
我们利用神经网络研究了球状蛋白质二级结构的预测。蛋白质二级结构通过卡布斯和桑德的蛋白质二级结构字典分配给氨基酸残基，并将蛋白质二级结构传授给神经网络。神经网络的输入层允许包含20种氨基酸、链断裂、B、X和Z的残基序列。我们考虑将二级结构分类为3组、4组和8组。在每种情况下，我们计算正确预测的百分比。我们讨论了过度学习对蛋白质二级结构预测的影响。此外，我们还介绍了具有模块化架构的神经网络在蛋白质二级结构预测中的应用。我们比较了具有模块化架构的神经网络和简单三层结构的神经网络的结果。
