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Electronic Journal of Biotechnology
Universidad Católica de Valparaíso
ISSN: 0717-3458
Vol. 15, No. 1, 2012
Bioline Code: ej12007
Full paper language: English
Document type: Research Article
Document available free of charge

Electronic Journal of Biotechnology, Vol. 15, No. 1, 2012

 en Prediction of xylanase optimal temperature by support vector regression
Zhang, Guangya & Ge, Huihua


Background: Support vector machine (SVM), a novel powerful machine learning technology, was used to develop the non-linear quantitative structure-property relationship (QSPR) model of the G/11 xylanase based on the amino acid composition. The uniform design (UD) method was applied to optimize the running parameters of SVM for the first time.
Results: Results showed that the predicted optimum temperature of leave-one-out (LOO) cross-validation fitted the experimental optimum temperature very well, when the running parameter C, Ɛ, and γ was 50, 0.001 and 1.5, respectively. The average root-mean-square errors (RMSE) of the LOO cross-validation were 9.53ºC, while the RMSE of the back propagation neural network (BPNN), was 11.55ºC. The predictive ability of SVM is a minor improvement over BPNN, but it is superior to the reported method based on stepwise regression. Two experimental examples proved the validation of the model for predicting the optimal temperature of xylanase.
Conclusion: The results indicated that UD might be an effective method to optimize the parameters of SVM, which could be used as an alternative powerful modeling tool for QSPR studies of xylanase.

amino acid composition, optimum temperature, support vector machine, uniform design, xylanase

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