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.
Jiang
Peng
P
State Key Laboratory of Bioelectronics, Department of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, PR China.
Wu
Haonan
H
Wang
Wenkai
W
Ma
Wei
W
Sun
Xiao
X
Lu
Zuhong
Z
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
D035683:MicroRNAs; D012333:RNA, Messenger