The Influence of E. Coli Promoter Feature Elements on Promoter Recognition

In order to research the recognition method for E. coli promoter, E. coli promoter feature elements are researched combining with molecubiology theory and statistical facts of E. coli gene promoters. Two-structure neural network methods were applied to analysis the promoter sequence elements in E. coli gene promoters by selecting different promoter conservative sequences. As a result, we found that the recognition rate of the positives and negatives have the best performance when the canonical elements and the non-canonical sequence elements are all included, which is 77.67% and 88.45% respectively. This result can provide help to the feature selection and the recognition algorithm research of the promoters.