MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. 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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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
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- Jiang, Peng; Wu, Haonan; Wang, Wenkai; Ma, Wei; Sun, Xiao; Lu, Zuhong: Mipred: Classification of real and pseudo microrna precursors using random forest prediction model with combined features. (2007) ioport