【摘要】 运用加入竞争层的BP网络 ,研究了基于蛋白质二级结构内容的域结构类预测问题。在BP网络中嵌入一竞争 ,层显著提高了网络预测性能。仅使用了一个小的训练集和简单的网络结构 ,获得了很高的预测精度 :自支持精度 97 6 2 % ,jack -knife测试精度 97 6 2 % ,及平均外推精度 90 74 %。在建立更完备的域结构类特征向量和更有代表性的训练集的基础上 ,所述方法将为蛋白质域结构分类领域提供新的分类基准。更多还原

【Abstract】 In this paper,the problem of prediction of protein/domain structural class based on their secondary structure contents has been investigated via BP neural networks (Multiple Layer perceptron with Back-Propogation algorithm) plus a competitive layer.By embedding a competitive layer into the network,the prediction accuracy can be significantly improved.With a small training set and a simple network architecture,a high prediction accuracy has been achieved,i.e.,self-consistence accuracy 97.62%,jack-knife test accuracy 97.62% and extrapolating accuracy 90.74% on average.It is believed that the neural networks of this paper can provide a more appropriate protein/domain structural class assignment criterion with a complete classification attribute vector and a bigger and more representative training set built up.更多还原