title: An algorithm for network motif discovery in biological networks.
abstract: Network motif discovery is a key problem in analysis of biological networks. In this paper, we present an efficient algorithm for detecting consensus motifs. First, we extend subgraph searching algorithm Enumerate Subgraphs (ESU) to efficiently search non-treelike subgraphs of which the probability of occurrence in random networks is small. Then, we classify isomorphic subgraphs into different groups. Finally, we use hierarchical clustering method to cluster subgraphs, and derive a consensus motif from the clusters. Our algorithm is applied to the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae. The experiment results show that the algorithm can efficiently discover motifs, which are consistent with current biology knowledge. And, it can also detect several consensus motifs with a given size, which may help biologists go further into cellular process.
authors.affiliation: School of Computer Science and Technology, Xidian University Xi'an, Shaanxi 710071, China. gmqin@mail.xidian.edu.cn
authors.forename: Guimin
authors.initials: G
authors.lastname: Qin
authors.forename: Lin
authors.initials: L
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins