Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines.
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.
Peng
Si-hua
SH
Department of Control Science and Engineering, College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, China. pengsihua@zju.edu.cn
Fan
Long-jiang
LJ
Peng
Xiao-ning
XN
Zhuang
Shu-lin
SL
Du
Wei
W
Chen
Liang-biao
LB
D000465:Algorithms; D004247:DNA / Q000235:genetics*; D005822:Genetic Vectors; D007438:Introns; D008962:Models, Theoretical; D012275:Oryza / Q000235:genetics*; D012326:RNA Splicing
D004247:DNA