Congruence, similarity and symmetries of geometric objects
Discrete & Computational Geometry - ACM Symposium on Computational Geometry, Waterloo
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
SCG '92 Proceedings of the eighth annual symposium on Computational geometry
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Intelligent systems and interfaces
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Structural Alignment and Core Detection by Geometric Hashing
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Improved Simulated Annealing, Boltzmann Machine, and Attributed Graph Matching
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Proceedings of the 2004 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Multiple Graph Alignment for the Structural Analysis of Protein Active Sites
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiple alignment by aligning alignments
Bioinformatics
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Kernels For Structured Data
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Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.