MiPred swMATH ID: 29580 Software Authors: Jiang, P., Wu, H., Wang, W., Ma, W., Sun, X., Lu, Z. Description: 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 Homepage: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1933124/ Related Software: MiRFinder; miRBase; starBase; ROAST; Velvet; ARACHNE; SpliceTrap; DWE; CLIPZ; mirTools; miRExpress; TargetSpy; miRNAkey; PatMaN; SeqBuster; ProMiR II; MiRonTop; miRanalyzer; DIANA-mirExTra; HPeak Cited in: 3 Publications all top 5 Cited by 10 Authors 1 Aransay, Ana M. 1 Chuang, Mao-Te 1 Hackenberg, Michael 1 Han, Ping 1 Hu, Ya-Han 1 Jiang, Bin 1 Lo, Chia-Lun 1 Rodríguez-Ezpeleta, Naiara 1 Song, Xiaofeng 1 Wang, Minghao Cited in 2 Serials 1 International Transactions in Operational Research 1 Journal of Theoretical Biology Cited in 4 Fields 3 Biology and other natural sciences (92-XX) 2 Computer science (68-XX) 1 General and overarching topics; collections (00-XX) 1 Statistics (62-XX) Citations by Year