Prediction of Drought Resistance Gene with Clustered Amino Acid Features
Автор: Xia Jingbo, Shi Feng, Hu Xuehai, Li Zhi, Song Chaohong, Xiong Huijuan
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 11 vol.4, 2012 года.
Бесплатный доступ
Drought resistant gene plays important role in molecular breeding while little is known for its genetic mechanism. By extracting the clustered amino acids features, crucial numerical features are inferred for the resistance property of the given gene. Support vector machine algorithm is used to testify the reliability of feature extraction method. After carefully parameters choosing, the accuracy of the predictor achieves 79.36% in Jack-knife test, and the Mathews correlation coefficient achieves 0.5636.
Support Vector Machine, Classifier, Amino Acid Composition, K-Means
Короткий адрес: https://sciup.org/15010332
IDR: 15010332
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