MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

Nucleic Acids Res. 2007 Jul;35(Web Server issue):W339-44. doi: 10.1093/nar/gkm368. Epub 2007 Jun 6.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Base Sequence
  • Computational Biology / methods*
  • Computer Simulation
  • False Positive Reactions
  • Genome
  • Genomics
  • Humans
  • MicroRNAs / classification*
  • MicroRNAs / genetics*
  • Molecular Sequence Data
  • Nucleic Acid Conformation*
  • RNA, Messenger / metabolism
  • Software
  • Species Specificity


  • MicroRNAs
  • RNA, Messenger