title: Using LogitBoost classifier to predict protein structural classes.
abstract: Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.
authors.affiliation: Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai 200436, China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000818:Animals; D019295:Computational Biology; D008956:Models, Chemical; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D011506:Proteins / Q000145:classification*; D012984:Software
chemical_list: D011506:Proteins