Skip Navigation



Bioinformatics Advance Access published online on October 8, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm484
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary data
Right arrow Supplementary Data
Right arrowOA All Versions of this Article:
23/22/2987    most recent
btm484v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Yousef, M.
Right arrow Articles by Showe, M. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Yousef, M.
Right arrow Articles by Showe, M. K.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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 +, Andrew V. Kossenkov , Louise C. Showe and Michael K. Showe *

The Wistar Institute, Philadelphia, PA 19104, USA

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


  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 don't 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: showe{at}wistar.org

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

Associate Editor: Dr. Limsoon Wong

+ 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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
S. Bandyopadhyay and R. Mitra
TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples
Bioinformatics, October 15, 2009; 25(20): 2625 - 2631.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
F. Xiao, Z. Zuo, G. Cai, S. Kang, X. Gao, and T. Li
miRecords: an integrated resource for microRNA-target interactions
Nucleic Acids Res., January 1, 2009; 37(suppl_1): D105 - D110.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.