Spacefilling curves and the planar travelling salesman problem
Journal of the ACM (JACM)
Instance-Based Learning Algorithms
Machine Learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Towards Index-based Similarity Search for Protein Structure Databases
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
PSIST: Indexing Protein Structures Using Suffix Trees
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Alternate Representation of Distance Matrices for Characterization of Protein Structure
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
PiQA: an algebra for querying protein data sets
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Proceedings of the ACM workshop on 3D object retrieval
Efficient Approaches for Retrieving Protein Tertiary Structures
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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The ability to retrieve molecules based on structural similarity has use in many applications, from disease diagnosis and treatment to drug discovery and design. In this paper, we present a method to represent protein molecules that allows for the fast, flexible and efficient retrieval of similar structures, based on either global or local attributes. We begin by computing the pair-wise distance between amino acids, transforming each 3D structure into a 2D distance matrix. We normalize this matrix to a specific size and apply a 2D wavelet decomposition to generate a set of approximation coefficients, which serves as our global feature vector. This transformation reduces the overall dimensionality of the data while still preserving spatial features and correlations. We test our method by running queries on three different protein data sets that have been used previously in the literature, basing our comparisons on labels taken from the SCOP database. We find that our method significantly outperforms existing approaches, in terms of retrieval accuracy, memory utilization and execution time. Specifically, using a k-d tree and running a 10-nearest-neighbor search on a dataset of 33,000 proteins against itself, we see an average accuracy of 89% at the SCOP SuperFamily level and a total query time that is up to 350 times faster than previously published techniques. In addition to processing queries based on global similarity, we also propose innovative extensions to effectively match proteins based solely on shared local substructures, allowing for a more flexible query interface.