TY - GEN
T1 - TopEVM: Using co-occurrence and topology patterns of enzymes in metabolic networks to construct phylogenetic trees
AU - Zhou, Tingting
AU - Chan, Chun Chung
AU - Wang, Zhenghua
PY - 2008/12/5
Y1 - 2008/12/5
N2 - 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.
AB - 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.
KW - Co-occurrence pattern
KW - Degree centrality
KW - Document clustering
KW - Evolutionary distance
KW - Metabolic network
KW - Phylogenetic analysis
KW - TopEVM
KW - Topology pattern
UR - http://www.scopus.com/inward/record.url?scp=57049154558&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-88436-1-20
DO - 10.1007/978-3-540-88436-1-20
M3 - Conference article published in proceeding or book
SN - 3540884343
SN - 9783540884347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 225
EP - 236
BT - 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
T2 - 3rd IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2008
Y2 - 15 October 2008 through 17 October 2008
ER -