This paper presents new techniques for biosequence classi cation, with a focus on recognizing E. Coli promoters in DNA. Speci cally, given an unlabeled DNA sequence S, we want to determine whether or not S is an E. Coli promoter. We use an expectation-maximization (EM) algorithm to locate the -35 and -10 binding sites in an E. Coli promoter sequence. The EM algorithm di ers from previously published EM algorithms in that, instead of assuming a uniform distribution for the lengths of the spacer between the -35 binding site and the -10 binding site as well as the spacer between the -10 binding site and the transcriptional start site, our algorithm deduces the probability distribution for these lengths. Based on the located binding sites, we select features in each E. Coli promoter sequence according to their information contents and represent the features using an orthogonal encoding method. We then feed the features to a neural network for promoter recognition. Empirical studies show that the proposed approach achieves good performance on di erent datasets. 2
Moreover, REF uses neural networks and expectation maximization to classify E. Coli promoters.
DNA Sequence Classification via an Expectation Maximization Algorithm and Neural Networks: A Case Study
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Computer Science