Application of boosting-based decision tree ensemble classifiers for discrimination of thermophilic and mesophilic proteins.
- Author:
Guang-Ya ZHANG
1
;
Bai-Shan FANG
Author Information
1. Institute of Industrial Biotechnology, Huaqiao University, Quanzhou 362021, China.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Decision Trees;
Molecular Weight;
Neural Networks (Computer);
Proteins;
chemistry;
classification
- From:
Chinese Journal of Biotechnology
2006;22(6):1026-1031
- CountryChina
- Language:Chinese
-
Abstract:
In this paper, the Boosting-based decision tree ensemble classifiers were applied to discriminate thermophilic and mesophilic proteins. Three methods, namely, self-consistency test, 5-fold cross-validation and independent testing with other dataset, were used to evaluate the performance and robust of the models. Logitboost, as a novel classifier in Boosting algorithm, performed better than Adaboost. The overall accuracy of the three methods was 100%, 88.4% and 89.5%, respectively. It was demonstrated that LogitBoost performed comparably or even better than that of neural network, a very powerful classifier widely used in biological literatures. The influence of protein size on discrimination was addressed. It is anticipated that the power in predicting many bio-macromolecular attributes will be further strengthened if the Boosting and some other existing algorithms can be effectively complemented with each other.