کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
386092 | 660877 | 2010 | 5 صفحه PDF | دانلود رایگان |

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.
Journal: Expert Systems with Applications - Volume 37, Issue 8, August 2010, Pages 5946–5950