title: Using Bagging classifier to predict protein domain structural class.
abstract: Classification and prediction of protein domain structural class is one of the important topics in the molecular biology. We introduce the Bagging (Bootstrap aggregating), one of the bootstrap methods, for classifying and predicting protein structural classes. By a bootstrap aggregating procedure, the Bagging can improve a weak classifier, for instance the random tree method, to a significant step towards optimality. In this research, it is demonstrated that the Bagging performed at least as well as LogitBoost and Support vector machines in predicting the structural classes for a given protein domain dataset by 10 cross-validation test, which indicate that the Bagging method is promising and anticipated that it could be potentially further improved on predicting protein structural classes as well as other bio-macromolecular attributes, if the bagging method and other existing methods can be effectively complemented with each other.
authors.affiliation: Dept of Combinatorics and Geometry, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China 200031.
authors.forename: Liuhuan
authors.initials: L
authors.lastname: Dong
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yuan
authors.forename: Yudong
authors.initials: Y
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification*
chemical_list: D011506:Proteins