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Bioinformatics Advance Access originally published online on October 8, 2007
Bioinformatics 2007 23(22):2987-2992; doi:10.1093/bioinformatics/btm484
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Naïve Bayes for microRNA target predictions—machine learning for microRNA targets

Malik Yousef , Segun Jung {dagger}, Andrew V. Kossenkov , Louise C. Showe and Michael K. Showe *

The Wistar Institute, Philadelphia, PA 19104, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Most computational methodologies for miRNA:mRNA target gene prediction use the seed segment of the miRNA and require cross-species sequence conservation in this region of the mRNA target. Methods that do not rely on conservation generate numbers of predictions, which are too large to validate. We describe a target prediction method (NBmiRTar) that does not require sequence conservation, using instead, machine learning by a naïve Bayes classifier. It generates a model from sequence and miRNA:mRNA duplex information from validated targets and artificially generated negative examples. Both the ‘seed’ and ‘out-seed’ segments of the miRNA:mRNA duplex are used for target identification.

Results: The application of machine-learning techniques to the features we have used is a useful and general approach for microRNA target gene prediction. Our technique produces fewer false positive predictions and fewer target candidates to be tested. It exhibits higher sensitivity and specificity than algorithms that rely on conserved genomic regions to decrease false positive predictions.

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

Contact: yousef{at}wistar.org

Supplementary information: http://wotan.wistar.upenn.edu/NBmiRTar/

Associate Editor: Limsoon Wong

{dagger}Present address: The Sackler Institute of Biomedical Sciences, NYU, USA.

Received on July 16, 2007; revised on September 11, 2007; accepted on September 14, 2007

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