An assessment of neural network and statistical approaches for prediction of E. coli promoter sites.
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.
Horton
P B
PB
Institute for Chemical Research, Kyoto University, Japan.
Kanehisa
M
M
D001483:Base Sequence; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004926:Escherichia coli / Q000235:genetics*; D005069:Evaluation Studies as Topic; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011401:Promoter Regions, Genetic / Q000235:genetics*