Theoretical Computer Science
Multiple Alignment of Biological Networks: A Flexible Approach
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
Algorithmic aspects of heterogeneous biological networks comparison
COCOA'11 Proceedings of the 5th international conference on Combinatorial optimization and applications
Computer Science Review
Algorithms for subnetwork mining in heterogeneous networks
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
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Motivation: Modern comparative genomics does not restrict to sequence but involves the comparison of metabolic pathways or protein--protein interactions as well. Central in this approach is the concept of neighbourhood between entities (genes, proteins, chemical compounds). Therefore there is a growing need for new methods aiming at merging the connectivity information from different biological sources in order to infer functional coupling. Results: We present a generic approach to merge the information from two or more graphs representing biological data. The method is based on two concepts. The first one, the correspondence multigraph, precisely defines how correspondence is performed between the primary data-graphs. The second one, the common connected components, defines which property of the multigraph is searched for. Although this problem has already been informally stated in the past few years, we give here a formal and general statement together with an exact algorithm to solve it. Availability: The algorithm presented in this paper has been implemented in C. Source code is freely available for download at: http://www.inrialpes.fr/helix/people/viari/cccpart Contact: Alain.Viari@inrialpes.fr