Skip to main content

Biclusters Evaluation Based on Shifting and Scaling Patterns

  • Conference paper
Intelligent Data Engineering and Automated Learning - IDEAL 2007 (IDEAL 2007)

Abstract

Microarray techniques have motivated the develop of different methods to extract useful information from a biological point of view. Biclustering algorithms obtain a set of genes with the same behaviour over a group of experimental conditions from gene expression data. In order to evaluate the quality of a bicluster, it is useful to identify specific tendencies represented by patterns on data. These patterns describe the behaviour of a bicluster obtained previously by an adequate biclustering technique from gene expression data. In this paper a new measure for evaluating biclusters is proposed. This measure captures a special kind of patterns with scaling trends which represents quality patterns. They are not contemplated with the previous evaluating measure accepted in the literature. This work is a first step to investigate methods that search biclusters based on the concept of shift and scale invariance. Experimental results based on the yeast cell cycle and the human B-cell lymphoma datasets are reported. Finally, the performance of the proposed technique is compared with an optimization method based on the Nelder-Mead Simplex search algorithm.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ben-Dor, A., Shamir, R., Yakhini, Z.: Clustering Gene Expression Patterns. Journal of Computational Biology 6, 281–297 (1999)

    Article  Google Scholar 

  2. Wang, H., Wang, W., Yang, J., Yu, P.S.: Clustering by Pattern Similarity in Large Data Sets. In: ACM SIGMOD International Conference on Management of Data, pp. 394–405 (2002)

    Google Scholar 

  3. Tanay, A., Sharan, R., Shamir, R.: Discovering Statistically Significant Biclusters in Gene Expression Data. Bioinformatics 18, 196–205 (2002)

    Article  Google Scholar 

  4. Madeira, S.C., Oliveira, A.L.: Biclustering algorithms for biological data analysis: a survey. IEEE/ACM Transactions on Computational Biology and Bioinformatics 1, 24–45 (2004)

    Article  Google Scholar 

  5. Divina, F., Aguilar-Ruiz, J.S.: Biclustering of Expression Data with Evolutionary Computation. IEEE Transactions on Knowledge & Data Engineering 18(5), 590–602 (2006)

    Article  Google Scholar 

  6. Bryan, K., Cunningham, P., Bolshakova, N.: Biclustering of Expression Data Using Simulated Annealing. In: IEEE Symposium on Computer-Based Medical Systems, pp. 383–388 (2005)

    Google Scholar 

  7. Mitra, S., Banka, H.: Multi-objective evolutionary biclustering of gene expression data. Pattern Recognition 39(12), 2464–2477 (2006)

    Article  MATH  Google Scholar 

  8. Cheng, Y., Church, G.M.: Biclustering of Expression Data. In: Proceedings of the 8th International Conference on Itelligent. Systems for Molecular Biology, La Jolla, CA, pp. 93–103 (2000)

    Google Scholar 

  9. Aguilar-Ruiz, J.S.: Shifting and Scaling Patterns from Gene Expression Data. Bioinformatics 21(20), 3840–3845 (2005)

    Article  Google Scholar 

  10. Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Computer J. 7, 308–313 (1965)

    Google Scholar 

  11. Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programing: Theory and Algorithms. John Wiley and sons, Chichester (1993)

    MATH  Google Scholar 

  12. Fletcher, R.: A New Approach to Variable Metric Algorithms. Computer Journal 13, 317–322 (1970)

    Article  MATH  Google Scholar 

  13. Shanno, D.F.: Conditioning of Quasi-Newton Methods for Function Minimization. Mathematics of Computing 24, 647–656 (1970)

    Article  MathSciNet  Google Scholar 

  14. Cho, R., et al.: A Genome-Wide Transcriptional Analysis of the Mitotic Cell Cycle. Molecular Cell 2, 65–73 (1998)

    Article  Google Scholar 

  15. Alizadeh, A.A., et al.: Distinct Types of Diffuse Large b-cell Lymphoma Identified by Gene Expression Profiling. Nature 403, 503–511 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Hujun Yin Peter Tino Emilio Corchado Will Byrne Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nepomuceno, J.A., Troncoso Lora, A., Aguilar–Ruiz, J.S., García–Gutiérrez, J. (2007). Biclusters Evaluation Based on Shifting and Scaling Patterns. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_84

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77226-2_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77225-5

  • Online ISBN: 978-3-540-77226-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics