E. coli promoter prediction using feed-forward neural networks

Abstract

E. coli promoter recognition is an area of great interest in bioinformatics. In this paper, we describe the implementation of a feed forward neural network to predict the E. coli promoter. According to the sequence conservation, some sequences with 60 bases are selected as positive samples and some corresponding non-promoters from E. coli coding areas are selected as negative samples, and a classifier based on feed forward neural network is trained. Results show that feed forward neural networks can extract the statistical characteristics of promoters more effectively, and that coding with four dimensions for nucleic acid data is superior to two dimensions. Another result demonstrated here is that the number of hidden layers seems to have no significant effect on E. coli promoter prediction precision. The research results in this paper can provide reference for promoter recognition research.