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Gene Expression Array Exploration Using \(\mathcal{K}\)-Formal Concept Analysis

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6628)

Abstract

DNA micro-arrays are a mechanism for eliciting gene expression values, the concentration of the transcription products of a set of genes, under different chemical conditions. The phenomena of interest—up-regulation, down-regulation and co-regulation—are hypothesized to stem from the functional relationships among transcription products.

In [1,2,3] a generalisation of Formal Concept Analysis was developed with data mining applications in mind, \(\mathcal{K}\)-Formal Concept Analysis, where incidences take values in certain kinds of semirings, instead of the usual Boolean carrier set. In this paper, we use (\(\overline{\mathbb{R}}_{min, +}\))- and (\(\overline{\mathbb{R}}_{max, +}\)) to analyse gene expression data for Arabidopsis thaliana. We introduce the mechanism to render the data in the appropriate algebra and profit by the wealth of different Galois Connections available in Generalized Formal Concept Analysis to carry different analysis for up- and down-regulated genes.

Keywords

Gene Expression Data Structural Lattice Concept Analysis Concept Lattice Formal Context 
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.
    Valverde-Albacete, F.J., Peláez-Moreno, C.: Towards a generalisation of Formal Concept Analysis for data mining purposes. In: Missaoui, R., Schmidt, J. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3874, pp. 161–176. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Valverde-Albacete, F.J., Peláez-Moreno, C.: Further Galois connections between semimodules over idempotent semirings. In: Diatta, J., Eklund, P. (eds.) Proceedings of the 4th Conference on Concept Lattices and Applications (CLA 2007), Montpellier, pp. 199–212 (2007)Google Scholar
  3. 3.
    Valverde-Albacete, F.J., Peláez-Moreno, C.: Extending conceptualisation modes for generalised Formal Concept Analysis. Information Sciences (in press)Google Scholar
  4. 4.
    Stoughton, R.: Applications of DNA microarrays in biology. Biochemistry 74, 53 (2005)CrossRefGoogle Scholar
  5. 5.
    Yevtushenko, S.A.: System of data analysis “Concept Explorer”. In: [17], pp. 127–134 (in Russian), http://sourceforge.net/projects/conexp
  6. 6.
    Van Hoewyk, D., Takahashi, H., Inoue, E., Hess, A., Tamaoki, M., Pilon-Smits, E.A.H.: Transcriptome analyses give insights into Selenium-stress responses and Selenium tolerance mechanisms in arabidopsis. Physiologia Plantarum 132, 236–253 (2008)Google Scholar
  7. 7.
    Affymetrix. Statistical algorithms description document, Santa Clara, Ca (2002)Google Scholar
  8. 8.
    Gentleman, R., Carey, V., Huber, W., Irizarry, R., Dudoit, S. (eds.): Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  9. 9.
    Irizarry, R.A.: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Research 31, 15e–15 (2003)Google Scholar
  10. 10.
    Pensa, R., Besson, J., Boulicaut, J.: A methodology for biologically relevant pattern discovery from gene expression data. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS (LNAI), vol. 3245, pp. 230–241. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Motameny, S., Versmold, B., Schmutzler, R.: Formal Concept Analysis for the identification of combinatorial biomarkers in breast cancer. In: Medina, R., Obiedkov, S. (eds.) ICFCA 2008. LNCS (LNAI), vol. 4933, pp. 229–240. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Gebert, J., Motameny, S., Faigle, U., Forst, C., Schrader, R.: Identifying genes of gene regulatory networks using Formal Concept Analysis. Journal of Computational Biology 15, 185–194 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Kaytoue, M., Duplessis, S., Kuznetsov, S.O., Napoli, A.: Two FCA-based methods for mining gene expression data. In: Ferré, S., Rudolph, S. (eds.) ICFCA 2009. LNCS, vol. 5548, pp. 251–266. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Pensa, R., Boulicaut, J.: Towards Fault-Tolerant Formal Concept Analysis. In: Bandini, S., Manzoni, S. (eds.) AI*IA 2005. LNCS (LNAI), vol. 3673, pp. 212–223. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Kaytoue, M., Kuznetsov, S., Napoli, A., Duplessis, S.: Mining gene expression data with pattern structures in Formal Concept Analysis. In: Information Sciences (2011)Google Scholar
  16. 16.
    Ganter, B., Kuznetsov, S.: 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
  17. 17.
    ACM: Proceedings of the 7th National Conference on Artificial Intelligence KII 2000, Russia, ACM (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Dpto. de Teoría de la Señal y de las ComunicacionesUniversidad Carlos III de MadridLeganésSpain

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