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Report on the First Contest on Graph Matching Algorithms for Pattern Search in Biological Databases

  • Vincenzo CarlettiEmail author
  • Pasquale Foggia
  • Mario Vento
  • Xiaoyi Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

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.

Keywords

Large Graph Graph Match Biological Database Medium Graph Bioinformatics Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Aittokallio, T., Schwikowski, B.: Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics 7(3), 243–255 (2006)CrossRefGoogle Scholar
  2. 2.
    Berman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., Bourne, P.: The Protein Data Bank. Nucleic Acids Research 28, 235–242 (2000)CrossRefGoogle Scholar
  3. 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. 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. 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
  6. 6.
    Cordella, L., Foggia, P., Sansone, C., Vento, M.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1367–1372 (2004)CrossRefGoogle Scholar
  7. 7.
    Foggia, P., Vento, M., Jiang, X.: The biograph2014 contest dataset, http://biograph2014.unisa.it
  8. 8.
    Huan, J., Bandyopadhyay, D., Wang, W., Snoeyink, J., Prins, J., Tropsha, A.: Comparing graph representations of protein structure for mining family-specific residue-based packing motif. Journal of Computational Biology 12(6), 657–671 (2005)CrossRefGoogle Scholar
  9. 9.
    Kuhl, F.S., Crippen, G.M., Friesen, D.K.: A combinatorial algorithm for calculating ligand binding. Journal of Computational Chemistry 5(1), 24–34 (1984)CrossRefGoogle Scholar
  10. 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. 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
  12. 12.
    Raymond, J., Willett, P.: Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of Computer-Aided Molecular Design 16(7), 521–533 (2002)CrossRefGoogle Scholar
  13. 13.
    Solnon, C.: Alldifferent-based filtering for subgraph isomorphism. Artificial Intelligence 174(12-13), 850–864 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vincenzo Carletti
    • 1
    Email author
  • Pasquale Foggia
    • 1
  • Mario Vento
    • 1
  • Xiaoyi Jiang
    • 2
  1. 1.Department of Information Engineering, Electrical Engineering and Applied MathematicsUniversity of SalernoFiscianoItaly
  2. 2.Department of Computer ScienceUniversity of MünsterMünsterGermany

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