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- W2013704002 abstract "Identification of disease-resistant genes in the rice is a tough work in various experimental studies. Xanthomonas oryzae pv. oryzae (Xoo) which causes bacterial blight are considered to be the most devastating diseases in most rice-growing regions. However, currently there is no existing method for the prediction of disease-resistant genes from sequence data. Accurate prediction of Xoo from protein sequences is illuminating for gene finding projects. We propose a novel machine-learning approach based on the method of support vector machine (SVM) and chaos game representation (CGR), to assess the chance of a protein in rice to be Xoo resistant. We choose 13 already cloned genes for positive data and 48 selective gene in rice for negative data, the average accuracy achieves 100% in resubstitution test, 95.08% in jackknife test, and the Matthews correlation coefficient achieves 0.8509. The successful application of SVM + CGR approach in this study suggests that it should be more useful in quantifying the protein sequence–structure relationship and predicting the structural property profiles from protein sequences." @default.
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- W2013704002 date "2010-08-01" @default.
- W2013704002 modified "2023-09-27" @default.
- W2013704002 title "Support vector machine method on predicting resistance gene against Xanthomonas oryzae pv. oryzae in rice" @default.
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- W2013704002 doi "https://doi.org/10.1016/j.eswa.2010.02.010" @default.
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