Machine learning techniques for predicting bacillus subtilis promoters

Authors:
Meika I. Monteiro;Marcilio C. P. de Souto;Luiz M. G. Gonçalves;Lucymara F. Agnez-Lima
Affiliations:
Department of Computing and Automation, Federal University of Rio Grande do Norte;Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte;Department of Computing and Automation, Federal University of Rio Grande do Norte;Department of Cellular Biology and Genetics, Federal University of Rio Grande do Norte, Natal, RN, Brazil
Venue:
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Year:
2005

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Abstract

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this paper, we present an empirical comparison of machine learning techniques such as Naive Bayes, Decision Trees, Support Vector Machines and Neural Networks to the task of predicting Bacillus subtilis promoters. In order to do so, we first built a data set of promoter and nonpromoter sequences for this organism.