Report on the First Contest on Graph Matching Algorithms for Pattern Search in Biological Databases
Graphs are a powerful data structure that can be applied to several problems in bioinformatics, and efficient graph matching is often a tool required for several applications that try to extract useful information from large databases of graphs. While graph matching is in general a NP-complete problem, several algorithms exist that can be fast enough on practical graphs. However, there is no single algorithm that is able to outperform the others on every kind of graphs, and so it is of paramount importance to assess the algorithms on graphs coming from the actual problem domain. To this aim, we have organized the first edition of the Contest on Graph Matching Algorithms for Pattern Search in Biological Databases, hosted by the ICPR2014 Conference, so as to provide an opportunity for comparing state-of-the-art matching algorithms on a new graph database built using several kinds of real-world graphs found in bioinformatics applications. The participating algorithms were evaluated with respect to both their computation time and their memory usage. This paper will describe the contest task and databases, will provide a brief outline of the participating algorithms, and will present the results of the contest.
KeywordsLarge Graph Graph Match Biological Database Medium Graph Bioinformatics Application
Unable to display preview. Download preview PDF.
- 3.Bolton, E., Wang, Y., Thyessen, P.A., Bryant, S.H.: PubChem: Integrated platform of small molecules and biological activities. Annual Reports in Computational Chemistry 4(12) (2008)Google Scholar
- 4.Bonnici, V., Giugno, R., Pulvirenti, A., Shasha, D., Ferro, A.: A subgraph isomorphism algorithm and its application to biochemical data. BMC Bioinformatics 14 (2013)Google Scholar
- 5.Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in Pattern Recognition. IJPRAI 18(3), 265–298 (2004)Google Scholar
- 7.Foggia, P., Vento, M., Jiang, X.: The biograph2014 contest dataset, http://biograph2014.unisa.it
- 10.Lacroix, V., Fernandez, C., Sagot, M.: Motif search in graphs: Application to metabolic networks. Transactions on Computational Biology and Bioinformatics (2006)Google Scholar
- 11.Nethercote, N., Seward, J.: Valgrind: A framework for heavyweight dynamic binary instrumentation. In: Proceedings of the 2007 ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI 2007, pp. 89–100. ACM (2007)Google Scholar
- 14.Vehlow, C., Stehr, H., Winkelmann, M., Duarte, J.M., Petzold, L., Dinse, J., Lappe, M.: CMView: Interactive contact map visualization and analysis. Bioinformatics (2011), doi:10.1093/bioinformatics/btr163Google Scholar