ESCHERICHIA-COLI PROMOTERS - NEURAL NETWORKS DEVELOP DISTINCT DESCRIPTIONS IN LEARNING TO SEARCH FOR PROMOTERS OF DIFFERENT SPACING CLASSES

Back-propagation neural networks were trained to recognize promoter sequences of each of the three major spacing classes found in E.coli. These networks were trained with the object of maximizing their ability to generalize while maintaining the level of false positive identifications at a fraction of 1 percent. These objectives were generally met. Networks for the 16 base spacing class captured between 78 and 100% of previously unseen promoters in different tests; networks for the 17 base class identified 97% of the test promoters; networks for the 18 base class identified 79% of the test promoters. A tandem poll of networks for all three spacing classes produced a cumulative false positive level of less than 0.5%. In each case, the weight matrices used by the networks in their classification were analyzed to determine the relative weight assigned to the occurrence of a given base at a given position within the promoter. In this fashion, an approximate description of the network's definition of the promoter can be obtained.