Skip to main content
SpringerLink
Log in

Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters

  • Conference paper
  • First Online:
Model and Data Engineering (MEDI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12732))

Included in the following conference series:

Abstract

Formal Concept Analysis has been widely applied to identify differently expressed genes among microarray data. Top-K Formal Concepts are identified as efficient in generating most important Formal Concepts. To the best of our knowledge, no currently available algorithm is able to perform this challenging task. Therefore, we introduce Top-BicMiner, a new method for mining biclusters from gene expression data through Top-k Formal Concepts. It performs the extraction of the sets of both positive and negative correlations biclusters. Top-BicMiner relies on Formal concept analysis as well as a specific discretization method. Extensive experiments, carried out on real-life datasets, shed light on Top-BicMiner’s ability to identify statistically and biologically significant biclusters.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The extraction of the formal concepts is carried out through the invocation of the efficient LCM algorithm [23].

  2. 2.

    In fact, coherent formal concepts having an intersection size above or equal to the given threshold \(\alpha 1\) belong to the same bicluster, while those with an intersection value below it, do not.

    .

  3. 3.

    Available at https://github.com/mehdi-kaytoue/trimax.

  4. 4.

    Available at http://arep.med.harvard.edu/biclustering/.

  5. 5.

    Available at http://arep.med.harvard.edu/biclustering/.

  6. 6.

    Available at http://llama.mshri.on.ca/funcassociate/.

  7. 7.

    The best biclusters have an adjusted p-value less than 0.001%.

  8. 8.

    Available at https://www.yeastgenome.org/goTermFinder.

  9. 9.

    http://geneontology.org/.

References

  1. Ayadi, W., Elloumi, M., Hao, J.K.: A biclustering algorithm based on a bicluster enumeration tree: application to DNA microarray data. BioData Mining 2, 9 (2009)

    Article  Google Scholar 

  2. Ayadi, W., Elloumi, M., Hao, J.K.: Bicfinder: a biclustering algorithm for microarray data analysis. Knowl. Inf. Syst. 30(2), 341–358 (2012)

    Article  Google Scholar 

  3. Ayadi, W., Hao, J.: A memetic algorithm for discovering negative correlation biclusters of DNA microarray data. Neurocomputing 145, 14–22 (2014). https://doi.org/10.1016/j.neucom.2014.05.074

    Article  Google Scholar 

  4. Ben-Dor, A., Chor, B., Karp, R.M., Yakhini, Z.: Discovering local structure in gene expression data: the order-preserving submatrix problem. J. Comput. Biol. 10(3/4), 373–384 (2003)

    Article  Google Scholar 

  5. Bergmann, S., Ihmels, J., Barkai, N.: Defining transcription modules using large-scale gene expression data. Bioinformatics 20(13), 1993–2003 (2004)

    Article  Google Scholar 

  6. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE Trans. Comput. Biol. Bioinf. 1, 24–45 (2004)

    Article  Google Scholar 

  7. Cheng, Y., Church, G.M.: Biclustering of expression data. In: Proceedings of ISMB, UC San Diego, California, pp. 93–103 (2000)

    Google Scholar 

  8. Ganter, B., Wille, R.: Formal Concept Analysis - Mathematical Foundations. Springer, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    Book  MATH  Google Scholar 

  9. Henriques, R., Antunes, C., Madeira, S.C.: Methods for the efficient discovery of large item-indexable sequential patterns. In: New Frontiers in Mining Complex Patterns - Second International Workshop, NFMCP 2013, Held in Conjunction with ECML-PKDD 2013, Prague, Czech Republic, 27 September 2013, Revised Selected Papers, pp. 100–116 (2013). https://doi.org/10.1007/978-3-319-08407-7_7

  10. Henriques, R., Madeira, S.C.: Bicspam: flexible biclustering using sequential patterns. BMC Bioinf. 15, 130 (2014). https://doi.org/10.1186/1471-2105-15-130

    Article  Google Scholar 

  11. Henriques, R., Madeira, S.C.: Bic2pam: constraint-guided biclustering for biological data analysis with domain knowledge. Algorithms Molec. Biol. 11, 23 (2016). https://doi.org/10.1186/s13015-016-0085-5

    Article  Google Scholar 

  12. Houari, A., Ayadi, W., Yahia, S.B.: Discovering low overlapping biclusters in gene expression data through generic association rules. In: Bellatreche, L., Manolopoulos, Y. (eds.) MEDI 2015. LNCS, vol. 9344, pp. 139–153. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23781-7_12

    Chapter  Google Scholar 

  13. Houari, A., Ayadi, W., Yahia, S.B.: Mining negative correlation biclusters from gene expression data using generic association rules. In: Zanni-Merk, C., Frydman, C.S., Toro, C., Hicks, Y., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 21st International Conference KES-2017, Marseille, France, 6–8 September 2017, Procedia Computer Science, vol. 112, pp. 278–287. Elsevier (2017). https://doi.org/10.1016/j.procs.2017.08.262

  14. Houari, A., Ayadi, W., Yahia, S.B.: NBF: an fca-based algorithm to identify negative correlation biclusters of DNA microarray data. In: Barolli, L., Takizawa, M., Enokido, T., Ogiela, M.R., Ogiela, L., Javaid, N. (eds.) 32nd IEEE International Conference on Advanced Information Networking and Applications, AINA 2018, Krakow, Poland, 16–18 May 2018, pp. 1003–1010. IEEE Computer Society (2018). https://doi.org/10.1109/AINA.2018.00146

  15. Houari, A., Ayadi, W., Yahia, S.B.: A new fca-based method for identifying biclusters in gene expression data. Int. J. Mach. Learn. Cybern. 9(11), 1879–1893 (2018). https://doi.org/10.1007/s13042-018-0794-9

    Article  Google Scholar 

  16. Hwang, C.L., Yoon, K.: Methods for multiple attribute decision making. In: Multiple Attribute Decision Making, pp. 58–191. Springer, Heidelberg (1981). https://doi.org/10.1007/978-3-642-48318-9_3

  17. Kaytoue, M., Kuznetsov, S.O., Macko, J., Napoli, A.: Biclustering meets triadic concept analysis. Ann. Math. Artif. Intell. 70(1–2), 55–79 (2014). https://doi.org/10.1007/s10472-013-9379-1

    Article  MathSciNet  MATH  Google Scholar 

  18. Kuznetsov, S.O.: Stability as an estimate of degree of substantiation of hypotheses derived on the basis of operational similarity. Nauchno-Tekhnichekaya Informatisiya Seriya 2-Informatsionnye Protsessy I Sistemy (12), 21–29 (1990)

    Google Scholar 

  19. Li, X., Shao, M.-W., Zhao, X.-M.: Constructing lattice based on irreducible concepts. Int. J. Mach. Learn. Cybern. 8(1), 109–122 (2016). https://doi.org/10.1007/s13042-016-0587-y

    Article  Google Scholar 

  20. Mouakher, A., Ben Yahia, S.: Qualitycover: efficient binary relation coverage guided by induced knowledge quality. Inf. Sci. 355, 58–73 (2016)

    Article  Google Scholar 

  21. Nepomuceno, J.A., Troncoso, A., Aguilar-Ruiz, J.S.: Scatter search-based identification of local patterns with positive and negative correlations in gene expression data. Appl. Soft Comput. 35, 637–651 (2015). https://doi.org/10.1016/j.asoc.2015.06.019

    Article  Google Scholar 

  22. Prelic, A., et al.: A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinformatics 22(9), 1122–1129 (2006)

    Article  Google Scholar 

  23. Uno, T., Asai, T., Uchida, Y., Arimura, H.: An efficient algorithm for enumerating closed patterns in transaction databases. In: Discovery Science, 7th International Conference, DS 2004, Padova, Italy, 2–5 October 2004, Proceedings, pp. 16–31 (2004). https://doi.org/10.1007/978-3-540-30214-8_2

  24. Zanakis, S.H., Solomon, A., Wishart, N., Dublish, S.: Multi-attribute decision making: a simulation comparison of select methods. Eur. J. Oper. Res. 107(3), 507–529 (1998)

    Article  Google Scholar 

  25. Zhao, Y., Yu, J., Wang, G., Chen, L., Wang, B., Yu, G.: Maximal subspace coregulated gene clustering. IEEE Trans. Knowl. Data Eng. 20(1), 83–98 (2008). https://doi.org/10.1109/TKDE.2007.190670

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University Mustapha STAMBOULI of Mascara, Mascara, Algeria

    Amina Houari

  2. Faculty of Siences of Tunis, LIPAH-LR11ES14, University of Tunis El Manar, 2092, Tunis, Tunisia

    Sadok Ben Yahia

  3. Department of Software Science, Tallinn University of Technology, Tallinn, Estonia

    Sadok Ben Yahia

Authors
  1. Amina Houari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadok Ben Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amina Houari .

Editor information

Editors and Affiliations

  1. University of Nantes, Nantes, France

    Christian Attiogbé

  2. Tallinn University of Technology, Tallinn, Estonia

    Sadok Ben Yahia

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Houari, A., Ben Yahia, S. (2021). Top-K Formal Concepts for Identifying Positively and Negatively Correlated Biclusters. In: Attiogbé, C., Ben Yahia, S. (eds) Model and Data Engineering. MEDI 2021. Lecture Notes in Computer Science(), vol 12732. Springer, Cham. https://doi.org/10.1007/978-3-030-78428-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78428-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78427-0

  • Online ISBN: 978-3-030-78428-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation