Multi-level clustering support vector machine trees for improved protein local structure prediction

Authors:
Wei Zhong;Jieyue He;Xiujuan Chen;Yi Pan
Affiliations:
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
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2014

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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.