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Bioinformatics Advance Access published online on March 16, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl094
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received December 1, 2005
Revised February 21, 2006
Accepted March 9, 2006

Article

Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier machine learning for identification of microRNA genes

Malik Yousef 1, Michael Nebozhyn 1, Hagit Shatkay 2, Stathis Kanterakis 1, Louise C. Showe 1, and Michael K. Showe 1 *

1 The Wistar Institute, Philadelphia, PA 19104, USA
2 School of Computing, Queen's University, Kingston, Ontario

* To whom correspondence should be addressed.
Michael K. Showe, E-mail: showe{at}wistar.org


  Abstract

Motivation: Numerous computational methodologies utilize techniques based on sequence conservation and/or structural similarity for microRNA gene prediction. In this study we describe a new technique, which is applicable across several species, for predicting microRNA genes. This technique is based on machine learning, using the Naïve Bayes classifier. This computational procedure automatically generates a model from the input or training data, which is the sequence and structure of known microRNAs from a variety of species.

Results: This study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for microRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify microRNAs followed by a comparative analysis to decrease the number of false positives. The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms, which utilize low sensitivity and conserved genomic regions to decrease the rate of false positives.

Availability: The BayesMiRNAfind program is available at http://wotan.wistar.upenn.edu/miRNA.

Supplementary information:

Associate Editor: Keith A Crandall
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