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

Yuanwei Zhang, Yifan Yang, Huan Zhang, Xiaohua Jiang, Bo Xu, Yu Xue, Yunxia Cao, Qian Zhai, Yong Zhai, Mingqing Xu, Howard J Cooke, Qinghua Shi

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1436-7
Number of pages2
JournalBioinformatics
Volume27
Issue number10
DOIs
Publication statusPublished - 15 May 2011

Keywords

  • Animals
  • Artificial Intelligence
  • Base Sequence
  • Computational Biology
  • Expressed Sequence Tags
  • Female
  • Fetus
  • Humans
  • MicroRNAs
  • Ovary

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