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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Project partially supported by the Start-up Funding of Zhejiang University to Chen Liang-biao
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Si-hua, P., Long-jiang, F., Xiao-ning, P. et al. Splicing-site recognition of rice (Oryza sativa L.) DNA sequences by support vector machines. J. Zhejiang Univ. Sci. A 4, 573–577 (2003). https://doi.org/10.1631/jzus.2003.0573
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DOI: https://doi.org/10.1631/jzus.2003.0573