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Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines
Si-hua Peng1, Long-jiang Fan, Xiao-ning Peng
1Department of Control Science and Engineering, College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, China. pengsihua@zju.edu.cn
Motivation:
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.
Method:
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.