| Splicing-site recognition of rice (Oryza sativa L.)DNA sequences |
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| 作者姓名： | 彭司华 樊龙江 彭小宁 庄树林 杜维 陈良标 |
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| 作者单位： | Department of Control Science and Engineering，College of Information Science and Engineering，Zhejiang University，Institute of Bioinformatics，Zhejiang University，Verna and Mclean Department of Biochemistry and Molecular Biology，Baylor College of Medicine，1 Baylor Plaza，College of Science，Zhejiang University，Department of Control Science and Engineering，College of Information Science and Engineering，Zhejiang University，Institute of Bioinformatics，Zhejiang University Hangzhou 310027，China，Hangzhou 310029，China，Houston，Texas，TX 77030，USA，Hangzhou 310027，China，Hangzhou 310027，China，Hangzhou 310029，China |
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| 基金项目： | ProjectpartiallysupportedbytheStartupFundingofZhejiangUniversitytoChenLiang biao |
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| 摘 要： | INTRODUCTIONCorrectlypinpointingsplicing sitesingenomicDNAsequencesisnotaneasytask ,whichisofgreatimportancetothegenomeannotationandgenefinding .Intronsaregenerallydividedinto3classes,namelycalssI,classIIandcommonnu cleuspre mRNA .IntronofclassIandIIcango…
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Splicing-site recognition of rice (Oryza sativa L.)DNA sequences |
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| PENG Si-hua,FAN Long-jiang,PENG Xiao-ningZHUANG Shu-lin,DU Wei,CHEN Liang-biao.Splicing-site recognition of rice (Oryza sativa L.)DNA sequences[J].Journal of Zhejiang University Science,2003(5). |
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| Authors: | PENG Si-hua FAN Long-jiang PENG Xiao-ningZHUANG Shu-lin DU Wei CHEN Liang-biao |
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| Abstract: | Motivation: It was found that high accuracy splicing-site recognitio n of rice (Oryza sativa L.) DNA sequence is especially difficult. We describe d a new method for the splicing-site recognition of rice DNA sequences. Method: Bas e d on the intron in eukaryotic organisms conforming to the principle of GT-AG,w e used support vector machines (SVM) to predict the splicing sites. By machine l earning,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 wa s 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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| Keywords: | Support vector machines Machine learning Intron Spli cing site Oryza sativa |
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