DNA sequence classification via an expectation maximization algorithm and neural networks: A case study

Document Type

Article

Publication Date

11-1-2001

Abstract

This paper presents new techniques for biosequence classification, with a focus on recognizing E. Coli promoters in DNA. Specifically, 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 differs 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 (NN) for promoter recognition. Empirical studies show that the proposed approach achieves good performance on different datasets.

Identifier

0035521109 (Scopus)

Publication Title

IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews

External Full Text Location

https://doi.org/10.1109/5326.983930

ISSN

10946977

First Page

468

Last Page

475

Issue

4

Volume

31

Grant

IIS-9988345

Fund Ref

National Science Foundation

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