title: Prediction of novel pre-microRNAs with high accuracy through boosting and SVM.
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.
  
  
  BACKGROUND
  miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.
authors: Department of Life Science, Hefei National Laboratory for Physical Sciences, Microscale and School of Life Sciences, University of Science and Technology of China, Hefei, China.
authors: Yuanwei
authors: Y
authors: Zhang
authors: Yifan
authors: Y
authors: Yang
authors: Huan
authors: H
authors: Zhang
authors: Xiaohua
authors: X
authors: Jiang
authors: Bo
authors: B
authors: Xu
authors: Yu
authors: Y
authors: Xue
authors: Yunxia
authors: Y
authors: Cao
authors: Qian
authors: Q
authors: Zhai
authors: Yong
authors: Y
authors: Zhai
authors: Mingqing
authors: M
authors: Xu
authors: Howard J
authors: HJ
authors: Cooke
authors: Qinghua
authors: Q
authors: Shi
mesh_terms: D000818:Animals; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D020224:Expressed Sequence Tags; D005260:Female; D005333:Fetus / Q000737:chemistry; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry / Q000235:genetics / Q000302:isolation & purification*; D010053:Ovary / Q000737:chemistry
chemical_list: D035683:MicroRNAs