Application of boosting-based decision tree ensemble classifiers for discrimination of thermophilic and mesophilic proteins / 生物工程学报
Chinese Journal of Biotechnology
; (12): 1026-1031, 2006.
Article
em Zh
| WPRIM
| ID: wpr-325431
Biblioteca responsável:
WPRO
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.
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Algoritmos
/
Árvores de Decisões
/
Proteínas
/
Química
/
Classificação
/
Redes Neurais de Computação
/
Peso Molecular
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Idioma:
Zh
Revista:
Chinese Journal of Biotechnology
Ano de publicação:
2006
Tipo de documento:
Article