Horton P B, Kanehisa M
Institute for Chemical Research, Kyoto University, Japan.
Nucleic Acids Res. 1992 Aug 25;20(16):4331-8. doi: 10.1093/nar/20.16.4331.
We have constructed a perceptron type neural network for E. coli promoter prediction and improved its ability to generalize with a new technique for selecting the sequence features shown during training. We have also reconstructed five previous prediction methods and compared the effectiveness of those methods and our neural network. Surprisingly, the simple statistical method of Mulligan et al. performed the best amongst the previous methods. Our neural network was comparable to Mulligan's method when false positives were kept low and better than Mulligan's method when false negatives were kept low. We also showed the correlation between the prediction rates of neural networks achieved by previous researchers and the information content of their data sets.
我们构建了一个用于大肠杆菌启动子预测的感知器型神经网络，并采用一种新的技术来选择训练期间显示的序列特征，从而提高了其泛化能力。我们还重构了之前的五种预测方法，并比较了这些方法与我们的神经网络的有效性。令人惊讶的是，Mulligan等人的简单统计方法在之前的方法中表现最佳。当误报率较低时，我们的神经网络与Mulligan的方法相当；当漏报率较低时，我们的神经网络比Mulligan的方法更好。我们还展示了之前研究人员所实现的神经网络预测率与其数据集信息含量之间的相关性。
