Data/hora: 01/06/2024 14:42:34

Provedor de dados: 	69	
País: 	Chile	
Título: 	Prediction of xylanase optimal temperature by support vector regression	
Autores: 	Zhang,Guangya; Ge,Huihua.	
Data: 	2012-01-01	
Ano: 	2012	
Palavras-chave: 	Amino acid composition; Optimum temperature; Support vector machine; Uniform design; Xylanase.	
Resumo: 	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, &amp;#958;, and &amp;#947; 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.	
Tipo: 	Journal article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-34582012000100007	
Editor: 	Pontificia Universidad Católica de Valparaíso	
Formato: 	text/html	
Fonte: 	Electronic Journal of Biotechnology  v.15 n.1 2012