title: Multi-level clustering support vector machine trees for improved protein local structure prediction.
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 x 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.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhong
authors.forename: Jieyue
authors.initials: J
authors.lastname: He
authors.forename: Xiujuan
authors.initials: X
authors.lastname: Chen
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D060388:Support Vector Machine
chemical_list: D011506:Proteins