Prediction of xylanase optimal temperature by support vector regression
Electron. j. biotechnol
;
15(1): 7-7, Jan. 2012. ilus, tab
Artigo
em Inglês
| LILACS
| ID: lil-640533
ABSTRACT
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, ξ, 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.
Texto completo:
DisponíveL
Índice:
LILACS (Américas)
Assunto principal:
Temperatura
/
Redes Neurais de Computação
/
Biologia Computacional
Tipo de estudo:
Estudo prognóstico
/
Fatores de risco
Idioma:
Inglês
Revista:
Electron. j. biotechnol
Assunto da revista:
Biotecnologia
Ano de publicação:
2012
Tipo de documento:
Artigo
/
Documento de projeto
País de afiliação:
China
Instituição/País de afiliação:
Huaqiao University/CN
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