• Log In
  • New issue alert
  • Submit a manuscript
  • Register
  • Home
  • About
  • Editorial Board
  • Search
  • Archives
  • Current
  • Forthcoming

Share

Article Panel


Vol 15, No 1 (2012)
»Table of Contents
Reading Tools
  • About the author
  • How to cite this article
  • Indexing metadata
  • Print version
  • Look up terms
  • Finding References
  • Review policy

Related items
  • Author's work


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International.
Prediction of xylanase optimal temperature by support vector regression | Zhang | Electronic Journal of Biotechnology
doi: 10.2225/vol15-issue1-fulltext-8
Electronic Journal of Biotechnology, Vol 15, No 1 (2012)

Prediction of xylanase optimal temperature by support vector regression

Guangya Zhang, Huihua Ge



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, 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.




Full Text: | Full Text | Reprint PDF |

ISSN:  0717-3458

Contact: edbiotec@pucv.cl

Pontificia Universidad Católica de Valparaíso
Av. Brasil 2950, Valparaíso, Chile
Copyright © 1997- 2023 by Electronic Journal of Biotechnology