基于常用得分矩阵的神经网络法预测蛋白质的二级结构

本文用常用的得分矩阵代替传统的Qian编码作为神经网络的输入层预测了200个蛋白质二级结构。结果表明：以常用得分矩阵作为输入层的预测结果要优于Qian编码的预测性能。在200个蛋白质中，共有9个蛋白质的预测精度达到目前国际先进水平，即80％。这说明该方法具有一定的可行性。

Prediction of the protein secondary structure with common score matrix based neural network

The present paper describes the artificial neural network for the prediction of the protein secondary structure on the basis of common score matrix instead of Qian code as the input layer. Based on the predicted secondary structure of 200 proteins, it was found that the performance of the score matrix was a little better than that of Qian code. Among these 200 proteins, the predicted precision of 9 proteins was superior to 80%, the well-recognized upper limit in the field of predicting the protein secondary structure. Also,there were no significant difference among results based on a variety of score matrices.