A Comparative Study of Machine Learning Methods for Detecting Promoters in Bacterial DNA Sequences

Authors:
Leonardo G. Tavares;Heitor S. Lopes;Carlos R. Erig Lima
Affiliations:
Bioinformatics Laboratory, Federal University of Technology Paraná (UTFPR), Curitiba (PR), Brazil 80230-901;Bioinformatics Laboratory, Federal University of Technology Paraná (UTFPR), Curitiba (PR), Brazil 80230-901;Bioinformatics Laboratory, Federal University of Technology Paraná (UTFPR), Curitiba (PR), Brazil 80230-901
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

Machine Learning methods have been widely used in bioinformatics, mainly for data classification and pattern recognition. The detection of genes in DNA sequences is still an open problem. Identifying the promoter region laying prior the gene itself is an important aid to detect a gene. This paper aims at applying several Machine Learning methods to the construction of classifiers for detection of promoters in the DNA of Escherichia coli. A thorough comparison of methods was done. In general, probabilistic and neural network-based methods were those that performed better regarding accuracy rate.