Prediction of novel pre-microRNAs with high accuracy through boosting and SVM

Bioinformatics. 2011 May 15;27(10):1436-7. doi: 10.1093/bioinformatics/btr148. Epub 2011 Mar 23.


High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species.

Availability: miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Artificial Intelligence
  • Base Sequence
  • Computational Biology / methods*
  • Expressed Sequence Tags
  • Female
  • Fetus / chemistry
  • Humans
  • MicroRNAs / chemistry
  • MicroRNAs / genetics
  • MicroRNAs / isolation & purification*
  • Ovary / chemistry


  • MicroRNAs