Skip to main content
SpringerLink
Log in

Biological Knowledge Integration in DNA Microarray Gene Expression Classification Based on Rough Set Theory

  • Conference paper
6th International Conference on Practical Applications of Computational Biology & Bioinformatics

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 154))

Abstract

DNA microarrays have contributed to the exponential growth of genetic data from years. One of the possible applications of this large amount of gene expression data diagnosis of diseases like cancer using classification methods. In turn, explicit biological knowledge about gene functions has also grown tremendously over the last decade. This work integrates explicit biological knowledge in classification process using Rough Set Theory, making it more effective. In addition, the proposed model is able to indicate which part of biological knowledge has been used building the model and classifing new samples.

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 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.

References

  1. McLachlan, G.J., Do, K.A., Ambroise, C.: Analyzing Microarray Gene Expression Data. John Wiley & Sons, Inc., Chichester (2004)

    Book  MATH  Google Scholar 

  2. Dudoit, S., Fridlyand, J., Speed, T.P.: Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97(457), 77–87 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Furey, Cristianini, Duffy, Bednarski, Schummer, Haussler: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16, 906–914 (2000)

    Article  Google Scholar 

  4. Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Angelo, M., Ladd, C., Reich, M., Mesirov, P., Poggio, T., Gerald, W., Loda, M., Lander, E.S., Golub, T.R.: Multi-class cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America 98, 15149–15154 (2001)

    Article  Google Scholar 

  5. Meltzer, P.S., Khan, J., Wei, J.S., Ringnér, M., Saal, L.H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine 7, 673–679 (2001)

    Article  Google Scholar 

  6. Díaz-Uriarte, R., de Andrés, S.A.: Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7, 3 (2006)

    Article  Google Scholar 

  7. Demichelis, F., Magni, P., Piergiorgi, P., Rubin, M.A., Bellazzi, R.: A hierarchical naïve bayes model for handling sample heterogeneity in classification problems: an application to tissue microarrays. BMC Bioinformatics 7, 514 (2006)

    Article  Google Scholar 

  8. Pawlak, Z.: Rough Sets, Theoretical aspects of reasoning about data. Kluwer Academic Publishers (1991)

    Google Scholar 

  9. Chen, X., Wang, L.: Integrating biological knowledge with gene expression profiles for survival prediction of cancer. Computational Biology 16(2), 265–278 (2009)

    Article  Google Scholar 

  10. Wei, Z., Li, H.: Nonparametric pathway-based regression models for analysis of genomic data. Biostatistics 8, 265–284 (2007)

    Article  MATH  Google Scholar 

  11. Tai, F., Pan, W.: Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data. Bioinformatics 23(23), 3170–3177 (2007)

    Article  Google Scholar 

  12. Tai, F., Pan, W.: Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms. Bioinformatics 23(14), 1775–1782 (2007)

    Article  Google Scholar 

  13. Galvez, J.F., Diaz, F., Carrion, P., Garcia, A.: An Application for Knowledge Discovery Based on a Revision of VPRS Model. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 296–303. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  14. Ziarko, W.: Variable precision rough set model. Computer and System Sciences 46, 39–59 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  15. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  16. Fodor, I.: A survey of dimension reduction techniques. tech. rep., Lawrence Livermore National Laboratory (May 2002)

    Google Scholar 

  17. Glez-Pena, D.: Modelo para la integratión de conocimiento biológico explícito en técnicas de clasificación aplicadas a datos procedentes de microarrays de ADN. PhD thesis, University of Vigo (2009)

    Google Scholar 

  18. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2, 559–572 (1901)

    Google Scholar 

  19. Glez-Pena, D., Alvarez, R., Diaz, F., Fdez-Riverola, F.: Dfp: a bioconductor package for fuzzy profile identification and gene reduction of microarray data. BMC Bioinformatics 10(1), 37 (2009)

    Article  Google Scholar 

  20. Maji, P., Paul, S.: Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data. Int. J. Approx. Reasoning 52(3), 408–426 (2011)

    Article  Google Scholar 

  21. Galvez, J.F., Olivieri, D., Carrion, P.: An improved algorithm for determining reducts in rough set models (2003)

    Google Scholar 

  22. Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods — Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)

    Google Scholar 

  23. Fix, E., Hodges, J.L.: Discriminatory analysis – nonparametric discrimination: Consistency properties. Tech. Rep. Project 21-49-004, Report No. 4, 261-279, USAF School of Aviation Medicine, Randolph Field, Texas (1951)

    Google Scholar 

  24. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  25. Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., Nordgren, H., Farmer, P., Praz, V., Haibe-Kains, B., Desmedt, C., Larsimont, D., Cardoso, F., Peterse, H., Nuyten, D., Buyse, M., Van de Vijver, M.J., Bergh, J., Piccart, M., Delorenzi, M.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute 98(4), 262–272 (2006)

    Article  Google Scholar 

  26. Edgar, R., Domrachev, M., Lash, A.E.: Gene expression omnibus: Ncbi gene expression and hybridization array data repository. Nucleic Acids Research 30(1), 207–210 (2002)

    Article  Google Scholar 

  27. Wang, Y., Klijn, J., Zhang, Y., Sieuwerts, A., Look, M., Yang, F., Talantov, D., Timmermans, M., Meijervangelder, M., Yu, J.: Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. The Lancet 365, 671–679 (2005)

    Google Scholar 

  28. Amberger, J.S., Bocchini, C.A., Scott, A.F., Hamosh, A.: Mckusick’s online mendelian inheritance in man (OMIM®). Nucleic Acids Research 37(Database-Issue), 793–796 (2009)

    Article  Google Scholar 

  29. Ben-David, A.: Comparison of classification accuracy using cohen’s weighted kappa. Expert Syst. Appl. 34(2), 825–832 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ESEI: Escuela Superior de Enxeñería Informática, University of Vigo, Ed. Politécnico, Campus Universitario As Lagoas s/n, 32004, Ourense, Spain

    D. Calvo-Dmgz, J. F. Galvez, Daniel Glez-Peña & Florentino Fdez-Riverola

Authors
  1. D. Calvo-Dmgz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. F. Galvez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel Glez-Peña
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Florentino Fdez-Riverola
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Calvo-Dmgz .

Editor information

Editors and Affiliations

  1. , Dep. Informática / CCTC, Universidade do Minho, Largo do Paco, Braga, 4710 - 057, Portugal

    Miguel P. Rocha

  2. European Bioinformatics Institute, Wellcome Trust Genome Campus, EMBL Outstation - Hinxton, Hinxton, CB10 1SD, United Kingdom

    Nicholas Luscombe

  3. , Department of Informatics, University of Vigo, Edificio Politécnico. Campus Universitario As Lagoas s/n, Ourense, 32004, Spain

    Florentino Fdez-Riverola

  4. Faculty of Science, Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan M. Corchado Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Calvo-Dmgz, D., Galvez, J.F., Glez-Peña, D., Fdez-Riverola, F. (2012). Biological Knowledge Integration in DNA Microarray Gene Expression Classification Based on Rough Set Theory. In: Rocha, M., Luscombe, N., Fdez-Riverola, F., Rodríguez, J. (eds) 6th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent and Soft Computing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28839-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28839-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28838-8

  • Online ISBN: 978-3-642-28839-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation