| Mechanics & Control Technology |
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| Splicing-site recognition of rice (Oryza sativa L.)DNA sequences by support vector machines |
| PENG Si-hua, FAN Long-jiang, PENG Xiao-ning, ZHUANG Shu-lin, DU Wei, CHEN Liang-biao |
| Department of Control Science and Engineering, College of Information Science and Engineering,Zhejiang University, Hangzhou 310027, China; Institute of Bioinformatics, Zhejiang University, Hangzhou 310029, China; Verna and Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine,1 Baylor Plaza, Houston, Texas, TX 77030, USA; College of Science, Zhejiang University, Hangzhou 310027, China |
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Abstract 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.
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Received: 07 September 2002
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