[Application of boosting-based decision tree ensemble classifiers for discrimination of thermophilic and mesophilic proteins].
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.
Zhang
Guang-Ya
GY
Institute of Industrial Biotechnology, Huaqiao University, Quanzhou 362021, China.
Fang
Bai-Shan
BS
D000465:Algorithms; D003663:Decision Trees; D008970:Molecular Weight; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry / Q000145:classification*
D011506:Proteins