Title: Multi-level clustering support vector machine trees for improved protein local structure prediction
Authors: Wei Zhong; Jieyue He; Xiujuan Chen; Yi Pan
Addresses: Division of Math and Computer Science, University of South Carolina Upstate, Spartanburg, SC 29303, USA ' School of Computer Science and Engineering, Southeast University, Nanjing 210096, China ' CollegeNET, Inc, 805 SW Broadway, Suite 1600, Portland, OR 97205, USA ' Department of Computer Science, Georgia State University, 34 Peachtree Street Room1417, Atlanta, GA 30303, USA
Abstract: Local protein structure prediction is one of important tasks for bioinformatics research. In order to further enhance the performance of local protein structure prediction, we propose the Multi-level Clustering Support Vector Machine Trees (MLSVMTs). Building on the multi-cluster tree structure, the MLSVMTs model uses multiple SVMs, each of which is customized to learn the unique sequence-to-structure relationship for one cluster. Both the combined 5 × 2 CV F test and the independent test show that the local structure prediction accuracy of MLSVMTs is significantly better than that of one-level K-means clustering, Multi-level clustering and Clustering Support Vector Machines.
Keywords: multi-level clustering; SVM trees; support vector machines; multiple SVMs; local structure prediction; parallel algorithm; protein structure prediction; bioinformatics.
International Journal of Data Mining and Bioinformatics, 2014 Vol.9 No.2, pp.172 - 198
Received: 01 Jan 2011
Accepted: 29 Dec 2011
Published online: 02 Oct 2013 *