Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Whole-genome prokaryotic phylogeny
Bioinformatics
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Graph descriptors from B-matrix representation
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
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Topological properties of metabolic networks may reflect systematic differences between evolutionary distinct groups of organisms. Indeed, the mean shortest path length between metabolites is, on average, longer in eukaryotes than in bacteria. We show that not only the averages of groups differ, but the organisms can be successfully clustered, based on network properties, into categories corresponding to taxonomic groups. We use the fact that in metabolic networks of different organisms, correspondence between vertices is available. We compare our approach with several graph indices employed previously to analyse metabolic networks, and show that they fail at achieving level of clustering similar to ours. Finally, we show that the phylogenetic tree constructed using network-based approach agrees in most cases with gene-based phylogeny.