Article ID Journal Published Year Pages File Type
386092 Expert Systems with Applications 2010 5 Pages PDF
Abstract

MotivationIdentification 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.ResultsWe 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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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