The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Multi-Level Approach to SCOP Fold Recognition
BIBE '05 Proceedings of the Fifth IEEE Symposium on Bioinformatics and Bioengineering
Three-Dimensional Shape-Structure Comparison Method for Protein Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient protein tertiary structure retrievals and classifications using content based comparison algorithms
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The tertiary structure of a protein molecule is the main factor which determines its function. All information required for a protein to be folded in its natural structure, is coded in its amino acid sequence. The way this sequence folds in the 3D space can be used for determining its function. With the technology innovations, the number of determined protein structures increases every day, so improving the efficiency of protein structure retrieval and classification methods becomes an important research issue. In this paper, we propose a novel protein classifier. Our classifier considers the conformation of protein structure in the 3D space. Namely, our voxel based protein descriptor is used for representing the protein structures. Then, the Support Vector Machine method (SVM) is used for classifying protein structures. The results show that our classifier achieves 78.83% accuracy, while it is faster than other algorithms with comparable accuracy.