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Bioinformatics Advance Access originally published online on March 16, 2006
Bioinformatics 2006 22(11):1325-1334; 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

Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier

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, Canada

*To whom correspondence should be addressed.

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

Results: Our 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 miRNA 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 miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.

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

Contact: showe{at}wistar.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on December 1, 2005; revised on February 21, 2006; accepted on March 9, 2006

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