title: Non-Alignment Features Based Enzyme/Non-Enzyme Classification Using an Ensemble Method.
abstract: As a growing number of protein structures are resolved without known functions, using computational methods to help predict protein functions from the structures becomes more and more important. Some computational methods predict protein functions by aligning to homologous proteins with known functions, but they fail to work if such homology cannot be identified. In this paper we classify enzymes/non-enzymes using non-alignment features. We propose a new ensemble method that includes three support vector machines (SVM) and two k-nearest neighbor algorithms (k-NN) and uses a simple majority voting rule. The test on a data set of 697 enzymes and 480 non-enzymes adapted from Dobson and Doig shows 85.59% accuracy in a 10-fold cross validation and 86.49% accuracy in a leave-one-out validation. The prediction accuracy is much better than other non-alignment features based methods and even slightly better than alignment features based methods. To our knowledge, our method is the first time to use ensemble methods to classify enzymes/non-enzymes and is superior over a single classifier.
authors.affiliation: Department of Mathematics, Boise State University, Boise, ID USA.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Davidson
authors.forename: Xueyi
authors.initials: X
authors.lastname: Wang