Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
NeMoFinder: dissecting genome-wide protein-protein interactions with meso-scale network motifs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
FANMOD: a tool for fast network motif detection
Bioinformatics
Efficient Detection of Network Motifs
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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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.