Journal
ELECTRONIC JOURNAL OF BIOTECHNOLOGY
Volume 15, Issue 1, Pages -Publisher
UNIV CATOLICA DE VALPARAISO
DOI: 10.2225/vol15-issue1-fulltext-8
Keywords
amino acid composition; optimum temperature; support vector machine; uniform design; xylanase
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Funding
- National Natural Science Foundation of China [20806031]
- Huaqiao University [JB-GJ1006]
- Program for New Century Excellent Talents in Universities of Fujian Province [07176C02]
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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, epsilon, and gamma was 50, 0.001 and 1.5, respectively. The average root-mean-square errors (RMSE) of the LOO cross-validation were 9.53 degrees C, while the RMSE of the back propagation neural network (BPNN), was 11.55 degrees C. 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.
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