Using Pattern Structures for Analyzing Ontology-Based Annotations of Biomedical Data

  • Adrien Coulet
  • Florent Domenach
  • Mehdi Kaytoue
  • Amedeo Napoli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7880)

Abstract

Annotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations, i.e., data-concept relationships, are useful for data integration whereas the reference ontology can guide the analysis of integrated data. Then the analysis of annotations can provide relevant knowledge units to consider for extracting and understanding possible correlations between data. Formal Concept Analysis (FCA) which builds from a binary context a concept lattice can be used for such a knowledge discovery task. However annotated biomedical data are usually not binary and a scaling procedure for using FCA is required as a preprocessing, leading to problems of expressivity, ranging from loss of information to the generation of a large number of additional binary attributes. By contrast, pattern structures offer a general FCA-based framework for building a concept lattice from complex data, e.g., a set of objects with partially ordered descriptions. In this paper, we show how to instantiate this general framework when descriptions are ordered by an ontology. We illustrate our approach with the analysis of annotations of drug related documents, and we show the capabilities of the approach for knowledge discovery.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baader, F., Brandt, S., Lutz, C.: Pushing the el envelope. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI, pp. 364–369. Professional Book Center (2005)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)Google Scholar
  3. 3.
    Baader, F., Küsters, R., Molitor, R.: Computing least common subsumers in description logics with existential restrictions. In: IJCAI, pp. 96–103 (1999)Google Scholar
  4. 4.
    Barbut, M., Monjardet, B. (eds.): Ordres et classification: Algèbre et combinatoire (tome II). Hachette, Paris (1970)Google Scholar
  5. 5.
    Bodenreider, O.: The Unified Medical Language System (UMLS): Integrating biomedical terminology. Nucleic Acids Research 32(Database-Issue), 267–270 (2004)CrossRefGoogle Scholar
  6. 6.
    Butte, A.J.: Viewpoint paper: Translational bioinformatics: Coming of age. JAMIA 15(6), 709–714 (2008)Google Scholar
  7. 7.
    Ganter, B., Kuznetsov, S.O.: Pattern Structures and Their Projections. In: Delugach, H.S., Stumme, G. (eds.) ICCS 2001. LNCS (LNAI), vol. 2120, pp. 129–142. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical foundations edition. Springer (1999)Google Scholar
  9. 9.
    Jonquet, C., LePendu, P., Falconer, S.M., Coulet, A., Noy, N.F., Musen, M.A., Shah, N.H.: NCBO Resource Index: Ontology-based search and mining of biomedical resources. J. Web Sem. 9(3), 316–324 (2011)CrossRefGoogle Scholar
  10. 10.
    Kaytoue, M., Kuznetsov, S.O., Napoli, A.: Revisiting numerical pattern mining with formal concept analysis. In: IJCAI, pp. 1342–1347 (2011)Google Scholar
  11. 11.
    Kaytoue, M., Kuznetsov, S.O., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in formal concept analysis. Inf. Sci. 181(10), 1989–2001 (2011)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., Djoumbou, Y., Eisner, R., Guo, A., Wishart, D.S.: DrugBank 3.0: A comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Research 39(Database-Issue), 1035–1041 (2011)CrossRefGoogle Scholar
  13. 13.
    Kuznetsov, S.O.: A fast algorithm for computing all intersections of objects in a finite semi-lattice. Automatic Documentation and Mathematical Linguistics 27(5), 400–412 (2004)Google Scholar
  14. 14.
    Kuznetsov, S.O., Samokhin, M.V.: Learning closed sets of labeled graphs for chemical applications. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, pp. 190–208. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    McCray, A.T.: An upper level ontology for the biomedical domain. Comp. Funct. Genom. 4, 80–84 (2003)CrossRefGoogle Scholar
  16. 16.
    Outrata, J., Vychodil, V.: Fast algorithm for computing fixpoints of galois connections induced by object-attribute relational data. Inf. Sci. 185(1), 114–127 (2012)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Pesquita, C., Faria, D., Falcão, A.O., Lord, P., Couto, F.M.: Semantic similarity in biomedical ontologies. PLoS Computational Biology 5(7) (2009)Google Scholar
  18. 18.
    Rubin, D.L., Shah, N., Noy, N.F.: Biomedical ontologies: A functional perspective. Briefings in Bioinformatics 9(1), 75–90 (2008)CrossRefGoogle Scholar
  19. 19.
    Sioutos, N., de Coronado, S., Haber, M.W., Hartel, F.W., Shaiu, W.-L., Wright, L.W.: NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information. Journal of Biomedical Informatics 40(1), 30–43 (2007)CrossRefGoogle Scholar
  20. 20.
    Whetzel, P.L., Noy, N.F., Shah, N.H., Alexander, P.R., Nyulas, C., Tudorache, T., Musen, M.A.: BioPortal: Enhanced functionality via new web services from the national center for biomedical ontology to access and use ontologies in software applications. Nucleic Acids Research 39(Web-Server-Issue), 541–545 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Adrien Coulet
    • 1
  • Florent Domenach
    • 2
  • Mehdi Kaytoue
    • 3
  • Amedeo Napoli
    • 1
  1. 1.LORIA (Université de Lorraine – CNRS – Inria Nancy Grand Est, UMR 7503)Vandoeuvre-lès-NancyFrance
  2. 2.Computer Science DepartmentUniversity of NicosiaNicosiaCyprus
  3. 3.CNRS, INSA-Lyon, LIRIS, UMR5205Université de LyonFrance

Personalised recommendations