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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; eggNOG; KEGG; DEGseq; starBase; BSMAP; ROAST; Velvet; ARACHNE; SpliceTrap; DWE; CLIPZ; mirTools; miRExpress; TargetSpy; miRNAkey; PatMaN; SeqBuster; ProMiR II
Cited in: 3 Documents

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