Nearest Neighbor Classification in 3D Protein Databases
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Three-Dimensional Shape-Structure Comparison Method for Protein Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Protein Classification by Matching 3D Structures
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
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In this paper, a novel 3D structure-based approach is presented for fast and accurate classification of protein molecules. We have used our voxel and ray based descriptors for feature extraction of protein structures. By using these descriptors, in this paper we propose a novel approach for classifying protein molecules, named Supervised Growing Neural Gas (SGNG). It combines the Growing Neural Gas (GNG) as a hidden layer, and Radial Basis Function (RBF) as an output layer. GNG and its supervised version SGNG have not yet been applied for protein retrieval and classification. Our approach was evaluated according to the SCOP method. The results show that our approach achieves more than 83,5% by using the voxel descriptor and 98,4% classification accuracy by using the ray descriptor, while it is simpler and faster than the SCOP method. We provide some experimental results.