title: Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines.
abstract: BACKGROUND
  It was found that high accuracy splicing-site recognition of rice (Oryza sativa L.) DNA sequence is especially difficult. We described a new method for the splicing-site recognition of rice DNA sequences.
  
  
  METHODS
  Based on the intron in eukaryotic organisms conforming to the principle of GT-AG, we used support vector machines (SVM) to predict the splicing sites. By machine learning, we built a model and used it to test the effect of the test data set of true and pseudo splicing sites.
  
  
  RESULTS
  The prediction accuracy we obtained was 87.53% at the true 5' end splicing site and 87.37% at the true 3' end splicing sites. The results suggested that the SVM approach could achieve higher accuracy than the previous approaches.
authors.affiliation: Department of Control Science and Engineering, College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, China. pengsihua@zju.edu.cn
authors.forename: Si-hua
authors.initials: SH
authors.lastname: Peng
authors.forename: Long-jiang
authors.initials: LJ
authors.lastname: Fan
authors.forename: Xiao-ning
authors.initials: XN
authors.lastname: Peng
authors.forename: Shu-lin
authors.initials: SL
authors.lastname: Zhuang
authors.forename: Wei
authors.initials: W
authors.lastname: Du
authors.forename: Liang-biao
authors.initials: LB
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics*; D005822:Genetic Vectors; D007438:Introns; D008962:Models, Theoretical; D012275:Oryza / Q000235:genetics*; D012326:RNA Splicing
chemical_list: D004247:DNA