Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70270
Title: TopEVM : using occurrence and topology patterns of enzymes in metabolic networks to construct phylogenetic trees
Authors: Zhou, T
Chan, KCC 
Wang, Z
Keywords: TopEVM
Phylogenetic analysis
Metabolic network
Co-occurrence pattern
Dcument clustering
Topology pattern
Degree centrality
Evolutionary distance
Issue Date: 2008
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2008, v. 5265, p. 225-236 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Network-based phylogenetic analysis typically involves representing metabolic networks as graphs and analyzing the characteristics of vertex sets using set theoretic measures. Such approaches, however, fail to take into account the structural characteristics of graphs. In this paper we propose a new pattern recognition technique, TopEVM, to help representing metabolic networks as weighted vectors. We assign weights according to co-occurrence patterns and topology patterns of enzymes, where the former are determined in a manner similar to the Tf-Idf approach used in document clustering, and the latter are determined using the degree centrality of enzymes. By comparing the weighted vectors of organisms, we determine the evolutionary distances and construct the phylogenetic trees. The resulting TopEVM trees are compared to the previous NCE trees with the NCBI Taxonomy trees as reference. It shows that TopEVM can construct trees much closer to the NCBI Taxonomy trees than the previous NCE methods.
Description: The 3rd IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB 2008),Melbourne, Australia, Oct 15-17, 2008
URI: http://hdl.handle.net/10397/70270
ISBN: 978-3-540-88434-7
978-3-540-88436-1
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-88436-1_20
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