Nucleic Acids Research Advance Access published online on June 6, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm368
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MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, P. R. China
*To whom correspondence should be addressed. Tel: +86-25-83793779; Fax: +86-25-83793779; Email: zhlu{at}seu.edu.cn
Received January 18, 2007. Revised April 26, 2007. Accepted April 26, 2007.
To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum of free energy (MFE) of the secondary structure and P-value of randomization test is used. Besides, a novel machine-learning algorithm, random forest (RF), is introduced. The results suggest that our method predicts at 98.21% specificity and 95.09% sensitivity. When compared with the previous study, Triplet-SVM-classifier, our RF method was nearly 10% greater in total accuracy. Further analysis indicated that the improvement was due to both the combined features and the RF algorithm. The MiPred web server is available at http://www.bioinf.seu.edu.cn/miRNA/. Given a sequence, MiPred decides whether it is a pre-miRNA-like hairpin sequence or not. If the sequence is a pre-miRNA-like hairpin, the RF classifier will predict whether it is a real pre-miRNA or a pseudo one.
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