ArticlePloS one2013
QuateXelero: an accelerated exact network motif detection algorithm.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Machine Learning Inference of Gene Regulatory Networks in DevelopingPlants (Basel, Switzerland) · 2024Article
- Review of tools and algorithms for network motif discovery in biological networks.IET systems biology · 2020Review
- Controllability in cancer metabolic networks according to drug targets as driver nodes.PloS one · 2013Article
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5 authors.
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Abstract
Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks' structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.
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