Nearest Neighbor Classification in 3D Protein Databases
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
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ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Image analysis by Krawtchouk moments
IEEE Transactions on Image Processing
Capri/MR: exploring protein databases from a structural and physicochemical point of view
Proceedings of the VLDB Endowment
SGNG Protein Classifier by Matching 3D Structures
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
A 3D Shape Retrieval Framework Supporting Multimodal Queries
International Journal of Computer Vision
A protein classifier based on SVM by using the voxel based descriptor
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Learning context-sensitive similarity by shortest path propagation
Pattern Recognition
Efficient Approaches for Retrieving Protein Tertiary Structures
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SHREC'10 track: protein model classification
EG 3DOR'10 Proceedings of the 3rd Eurographics conference on 3D Object Retrieval
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In this paper, a 3D shape-based approach is presented for the efficient search, retrieval, and classification of protein molecules. The method relies primarily on the geometric 3D structure of the proteins, which is produced from the corresponding PDB files and secondarily on their primary and secondary structure. After proper positioning of the 3D structures, in terms of translation and scaling, the Spherical Trace Transform is applied to them so as to produce geometry-based descriptor vectors, which are completely rotation invariant and perfectly describe their 3D shape. Additionally, characteristic attributes of the primary and secondary structure of the protein molecules are extracted, forming attribute-based descriptor vectors. The descriptor vectors are weighted and an integrated descriptor vector is produced. Three classification methods are tested. A part of the FSSP/DALI database, which provides a structural classification of the proteins, is used as the ground truth in order to evaluate the classification accuracy of the proposed method. The experimental results show that the proposed method achieves more than 99 percent classification accuracy while remaining much simpler and faster than the DALI method.