title: [Random forest for classification of thermophilic and psychrophilic proteins based on amino acid composition distribution].
abstract: We used amino acid composition distribution (AACD) to discriminate thermophilic and psychrophilic proteins. We used 10-fold cross-validation and independent testing with other dataset to evaluate the models. The results showed that when the segment was 1, the overall accuracy reached 92.9% and 90.2%, respectively. The AACD method improved the prediction accuracy when support vector machine was used as the classifier. When six new features were introduced, the overall accuracy of random forest improved to 93.2% and 92.2%, the areas under the receiver operation characteristic curve were 0.9771 and 0.9696, which was better than other ensemble classifiers and comparable with that of SVM.
authors.affiliation: Key Laboratory of Industrial Biotechnology, Huaqiao University, Quanzhou 362021, China.
authors.forename: Guangya
authors.initials: G
authors.lastname: Zhang
authors.forename: Baishan
authors.initials: B
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001419:Bacteria / Q000235:genetics; D001426:Bacterial Proteins / Q000737:chemistry* / Q000145:classification*; D003198:Computer Simulation; D016002:Discriminant Analysis; D008956:Models, Chemical; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein / Q000379:methods*; D013696:Temperature
chemical_list: D001426:Bacterial Proteins