Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Journal of Computational Chemistry
3D Part Segmentation Using Simulated Electrical Charge Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Cluster validity methods: part I
ACM SIGMOD Record
Partitioning 3D Surface Meshes Using Watershed Segmentation
IEEE Transactions on Visualization and Computer Graphics
A Developer's Survey of Polygonal Simplification Algorithms
IEEE Computer Graphics and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D Molecular Surface Representation Supporting Neighborhood Queries
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Molecular shape analysis based upon the morse-smale complex and the connolly function
Proceedings of the nineteenth annual symposium on Computational geometry
Statistical classification and segmentation of biomolecular surfaces
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Finding Patterns on Protein Surfaces: Algorithms and Applications to Protein Classification
IEEE Transactions on Knowledge and Data Engineering
Protein classification by matching and clustering surface graphs
Pattern Recognition
OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees
Expert Systems with Applications: An International Journal
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Though most approaches to protein comparison are based on their structure, several studies produced evidence of a strict correlation between the surface characteristics of proteins and the way they interact. Surface-based techniques for protein comparison typically require applying clustering algorithms to the punctual 3D description of the surface in order to produce a compact surface representation, capable of effectively condensing its description. In this paper, we propose a formalization of the requirements for surface clustering in the biochemical context and present two different clustering techniques that meet them, based, respectively, on region-growing and on an original template matching algorithm. We discuss the validity of these techniques with the support of tests performed on a set of about one hundred protein models generated by punctual mutations of four structurally characterized proteins. Finally, an analysis is made of how different factors impact on the effectiveness of clustering in capturing surface similarities.