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MicroRNA target prediction based on second-order
Hidden Markov Model |
| Song GAO1,Diangang QIN1,Tienan FENG1,Yifei WANG1,Liangsheng ZHANG2, |
| 1.Department of Mathematics,
School of Sciences, Shanghai University, Shanghai 200444, China; 2.Department of Mathematics,
School of Sciences, Shanghai University, Shanghai 200444, China；School of Life Sciences,
Institute of Plant Biology, Fudan University, Shanghai 200433, China; |
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Abstract MicroRNAs are one class of small single-stranded RNA of about 22nt serving as important negative gene regulators. In animals, miRNAs mainly repress protein translation by binding itself to the 3’ UTR regions of mRNAs with imperfect complementary pairing. Although bioinformatics investigations have resulted in a number of target prediction tools, all of these have a common shortcoming—a high false positive rate. Therefore, it is important to further filter the predicted targets. In this paper, based on miRNA:target duplex, we construct a second-order Hidden Markov Model, implement Baum-Welch training algorithm and apply this model to further process predicted targets. The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%, specificity of 55.10% and accuracy of 69.62% by 10-fold cross-validation experiments. In order to further verify the applicability of the algorithm, previously collected datasets, including 195 positive and 38 negative, are chosen to test it, with consistent results. We believe that our method will provide some guidance for experimental biologists, especially in choosing miRNA targets for validation.
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| Keywords
microRNA
target gene
experimentally supported targets
second-order Hidden Markov Model
forward algorithm
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Issue Date: 01 April 2010
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