title: LogitBoost classifier for discriminating thermophilic and mesophilic proteins.
abstract: A novel classifier, the so-called LogitBoost classifier, was introduced to discriminate the thermophilic and mesophilic proteins according to their primary structures. When the 20-amino acid composition was chosen as the feature vector, the overall accuracy of the self-consistency check and a five-fold cross-validation procedure was 97.0% and 86.6%, respectively. To test if the method was also applicable to a wide range of biological targets, an independent testing dataset was also used. The method based on LogitBoost algorithm has achieved an overall classification accuracy of 88.9%. According to the three different validation check approaches, it was demonstrated that LogitBoost outperformed AdaBoost and performed comparably with RBF neural network and support vector machine. The influence of protein size on discrimination was addressed.
authors.affiliation: Key Laboratory of Industrial Biotechnology (Hua Qiao University), Fujian Province University, Quanzhou, 362021 Fujian, PR China. zhgyghh@hqu.edu.cn
authors.forename: Guangya
authors.initials: G
authors.lastname: Zhang
authors.forename: Baishan
authors.initials: B
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins