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EDA-Based Logistic Regression Applied to Biomarkers Selection in Breast Cancer

  • Santiago González
  • Victor Robles
  • Jose Maria Peña
  • Oscar Cubo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5518)

Abstract

Logistic regression (LR) is a simple and efficient supervised learning algorithm for estimating the probability of an outcome variable. This algorithm is widely accepted and used in medicine for classification of diseases using DNA microarray data. Classical LR does not perform well for microarrays when applied directly, because the number of variables exceeds the number of samples. However, by reducing the number of genes and selecting specific variables (using filtering methods) great results can be obtained with this algorithm. On this contribution we propose a novel approach for fitting the (penalized) LR models based on EDAs. Breast Cancer dataset has been proposed to compare both accuracy and gene selection.

Keywords

Breast Cancer Biomarker Feature Selection Logistic Regression Evolutionary Algorithms Estimation of Distribution Algorithms 
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References

  1. 1.
    Armananzas, R., Inza, I., Santana, R., Saeys, Y., Flores, J.L., Lozano, J.A., Van de Peer, Y., Blanco, R., Robles, V., Bielza, C., Larranaga, P.: A review of estimation of distribution algorithms in bioinformatics. BioData mining 1(1) (September 2008)Google Scholar
  2. 2.
    Efron, B., Tibshirani, R.: Improvements on cross-validation: The 0.632+ bootstrap method. JASA (92), 548–560 (1997)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989)zbMATHGoogle Scholar
  4. 4.
    Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression, 2nd edn. John Wiley and Sons, New York (2000)CrossRefzbMATHGoogle Scholar
  5. 5.
    Inza, I., Larrañaga, P.: Learning bayesian networks in the space of structures by estimation of distribution algorithms. International Journal of Intelligent Systems (18), 205–220 (2003)CrossRefzbMATHGoogle Scholar
  6. 6.
    Inza, I., Larrañaga, P., Etxeberria, R., Sierra, B.: Feature subset selection by bayesian network-based optimization. Artif. Intell. 123(1-2), 157–184 (2000)CrossRefzbMATHGoogle Scholar
  7. 7.
    Larrañaga, P., Etxeberria, R., Lozano, J.A., Peña, J.M.: Combinatonal optimization by learning and simulation of bayesian networks. In: Boutilier, C., Goldszmidt, M. (eds.) UAI, pp. 343–352. Morgan Kaufmann, San Francisco (2000)Google Scholar
  8. 8.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation. Kluwer Academic Publisher, Dordrecht (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E.: Towards a New Evolutionary Computation. In: Advances in the Estimation of Distribution Algorithms. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Minka, T.P.: A comparison of numerical optimizers for logistic regression. Technical report, Carnegie Mellon University (2003)Google Scholar
  11. 11.
    Ng, A., Jordan, M.: On discriminative versus generative classifiers: A comparison of logistic regression and naive bayes. In: Proceedings of NIPS, vol. 14, pp. 605–610 (2001)Google Scholar
  12. 12.
    Quackenbush, J.: Microarray data normalization and transformation - nature geneticsGoogle Scholar
  13. 13.
    Romero, T., Larrañaga, P., Sierra, B.: Learning bayesian networks in the space of orderings with estimation of distribution algorithms. International Journal of Pattern Recognition and Artificial Intelligence 4(18), 607–625 (2004)CrossRefGoogle Scholar
  14. 14.
    Shen, L., Tan, E.C.: Dimension reduction-based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2(2), 166–175 (2005)CrossRefGoogle Scholar
  15. 15.
    Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for dna microarrays. Bioinformatics 17(6), 520–525 (2001)CrossRefGoogle Scholar
  16. 16.
    van ’t Veer, L.J., Dai, H., van de Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., Schreiber, G.J., Kerkhoven, R.M., Roberts, C., Linsley, P.S., Bernards, R., Friend, S.H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871), 530–536 (2002)CrossRefGoogle Scholar
  17. 17.
    Verleysen, M., François, D.: The curse of dimensionality in data mining and time series prediction. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 758–770. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Vinterbo, S., Ohno-Machado, L.: A genetic algorithm to select variables in logistic regression: Example in the domain of myocardial infarction. In: Proceedings of the AMIA Symposium, pp. 984–988 (1999)Google Scholar
  19. 19.
    Winker, P., Gilli, M.: Applications of optimization heuristics to estimation and modelling problems. Computational Statistics and Data Analysis (47), 211–223 (2004)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Santiago González
    • 1
  • Victor Robles
    • 1
  • Jose Maria Peña
    • 1
  • Oscar Cubo
    • 1
  1. 1.Department of Computer ArchitectureUniversidad Politécnica de MadridMadridSpain

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