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.