TopEVM: Using Co-occurrence and Topology Patterns of Enzymes in Metabolic Networks to Construct Phylogenetic Trees

Authors:
Tingting Zhou;Keith C. Chan;Zhenghua Wang
Affiliations:
National Laboratory for Paralleling and Distributed Processing, National University of Defense Technology, Changsha, P.R. China 410073 and Department of computing, The Hong Kong Polytechnic Univer ...;Department of computing, The Hong Kong Polytechnic University, Hong Kong, China;National Laboratory for Paralleling and Distributed Processing, National University of Defense Technology, Changsha, P.R. China 410073
Venue:
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2008

Citing 4
Cited 0

An information-theoretic perspective of tf—idf measures

Information Processing and Management: an International Journal
Nodal Distance Algorithm: Calculating a Phylogenetic Tree Comparison Metric

BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
TOPD/FMTS

Bioinformatics
An approach for determining evolutionary distance in network-based phylogenetic analysis

ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications

Quantified Score

Hi-index	0.00

Visualization

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-Idfapproach 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 TopEVMtrees are compared to the previous NCEtrees with the NCBI Taxonomy trees as reference. It shows that TopEVMcan construct trees much closer to the NCBI Taxonomy trees than the previous NCEmethods.