Journal
BIOINFORMATICS
Volume 23, Issue 22, Pages 2987-2992Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btm484
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Funding
- NCI NIH HHS [U01 CA85060, P30 CA010815, U01 CA085060, R01 CA106553] Funding Source: Medline
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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 nave 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@wistar.org Supplementary information: http://wotan.wistar.upenn.edu/NBmiRTar/.
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