title: MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features.
abstract: 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.
authors.affiliation: State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
authors.forename: Peng
authors.initials: P
authors.lastname: Jiang
authors.forename: Haonan
authors.initials: H
authors.lastname: Wu
authors.forename: Wenkai
authors.initials: W
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Ma
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
authors.forename: Zuhong
authors.initials: Z
authors.lastname: Lu
mesh_terms: D000818:Animals; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005189:False Positive Reactions; D016678:Genome; D023281:Genomics; D006801:Humans; D035683:MicroRNAs / Q000145:classification* / Q000235:genetics*; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software; D013045:Species Specificity
chemical_list: D035683:MicroRNAs; D012333:RNA, Messenger