Advertisement

Cross Validation Consistency for the Assessment of Genetic Programming Results in Microarray Studies

  • Jason H. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

DNA microarray technology has made it possible to measure the expression levels of thousands of genes simultaneously in a particular cell or tissue. The challenge for computational biologists and bioinformaticists will be to develop methods that are able to identify subsets of gene expression variables and features that classify cells and tissues into meaningful biological and clinical groups. Genetic programming (GP) has emerged as a machine learning tool for variable and feature selection in microarray data analysis. However, a limitation of GP is a lack of cross validation strategies for the assessment of GP results. This is partly due to the inherent complexity of GP due to its stochastic properties. Here, we introduce and review cross validation consistency (CVC) as a new modeling strategy for use with GP. We review the application of CVC to symbolic discriminant analysis (SDA), a GP-based analytical strategy for mining gene expression patterns in DNA microarray data.

Keywords

Systemic Lupus Erythematosus Acute Myeloid Leukemia Linear Discriminant Analysis Multifactor Dimensionality Reduction Gene Expression Variable 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270 (1995) 467–470CrossRefGoogle Scholar
  2. 2.
    Velculesco, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W.: Serial analysis of gene expression. Science 270 (1995) 484–487CrossRefGoogle Scholar
  3. 3.
    Caprioli, R.M., Farmer, T.B., Gile, J.: Molecular imaging of biological samples: Localization of peptides and proteins using MALDI-TOF MS. Analyt. Chem. 69 (1997) 4751–4760CrossRefGoogle Scholar
  4. 4.
    Bradley, J.V.: Distribution-free statistical tests. Prentice-Hall, Englewood Cliffs (1968)zbMATHGoogle Scholar
  5. 5.
    Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Reviews 16 (2001) 177–199zbMATHCrossRefGoogle Scholar
  6. 6.
    Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine 34 (2002) 88–95CrossRefGoogle Scholar
  7. 7.
    Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Plummer, W.D., Parl, F.F. and Moore, J.H.: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. American Journal of Human Genetics 69 (2001) 138–147CrossRefGoogle Scholar
  8. 8.
    Templeton, A.R.: Epistasis and complex traits. In: Wade, M., Brodie III, B., Wolf, J. (eds.): Epistasis and Evolutionary Process. Oxford University Press, New York (2000)Google Scholar
  9. 9.
    Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Ann. Eugen. 7 (1936) 179–188Google Scholar
  10. 10.
    Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River (1998)Google Scholar
  11. 11.
    Huberty, C.J.: Applied Discriminant Analysis. John Wiley & Sons, Inc., New York Chichester Bisbane Toronto Singapore (1994)zbMATHGoogle Scholar
  12. 12.
    Neter, J., Wasserman, W., Kutner, M.H.: Applied Linear Statistical Models, Regression, Analysis of Variance, and Experimental Designs. 3rd edn. Irwin, Homewood (1990)Google Scholar
  13. 13.
    Moore, J.H., Parker, J.S., Hahn, L.W.: Symbolic discriminant analysis for mining gene expression patterns. In: De Raedt, L., Flach, P. (eds) Lecture Notes in Artificial Intelligence 2167, pp 372–81, Springer-Verlag, Berlin (2001)Google Scholar
  14. 14.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge London (1992)zbMATHGoogle Scholar
  15. 15.
    Moore, J.H., Parker, J.S.: Evolutionary computation in microarray data analysis. In: Lin, S. and Johnson, K. (eds): Methods of Microarray Data Analysis. Kluwer Academic Publishers, Boston (2001)Google Scholar
  16. 16.
    Moore, J.H., Parker, J.S., Olsen, N., Aune, T. Symbolic discriminant analysis of microarray data in autoimmune disease. Genetic Epidemiology 23 (2002) 57–69CrossRefGoogle Scholar
  17. 17.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)zbMATHGoogle Scholar
  18. 18.
    Devroye, L., Gyorfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New York (1996)zbMATHGoogle Scholar
  19. 19.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531–537CrossRefGoogle Scholar
  20. 20.
    Maas, K., Chan, S., Parker, J., Slater, A., Moore, J.H., Olsen, N., and Aune, T.M.: Cutting edge: molecular portrait of human autoimmunity. Journal of Immunology 169 (2002) 5–9Google Scholar
  21. 21.
    Gilbert, R.J., Rowland, J.J., Kell, D.B.: Genomic computing: explanatory modelling for functional genomics. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds): Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  22. 22.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences USA 95 (1998) 14863–68Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jason H. Moore
    • 1
  1. 1.Program in Human Genetics, Department of Molecular Physiology and BiophysicsVanderbilt UniversityNashvilleUSA

Personalised recommendations