Abstract
Our ability to simultaneously measure the expression levels of thousands of genes in biological samples is providing important new opportunities for improving the diagnosis, prevention, and treatment of common diseases. However, new technologies such as DNA microarrays are generating new challenges for variable selection and statistical modeling. In response to these challenges, a genetic programming-based strategy called symbolic discriminant analysis (SDA) for the automatic selection of gene expression variables and mathematical functions for statistical modeling of clinical endpoints has been developed. The initial development and evaluation of SDA has focused on a function set consisting of only the four basic arithmetic operators. The goal of the present study is to evaluate whether adding more complex operators such as square root to the function set improves SDA modeling of microarray data. The results presented in this paper demonstrate that adding complex functions to the terminal set significantly improves SDA modeling by reducing model size and, in some cases, reducing classification error and runtime. We anticipate SDA will be an important new evolutionary computation tool to be added to the repertoire of methods for the analysis of microarray data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schena, M., Shalon, D., Davis, R.W., Brown, P.O.: Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270 (1995) 467–470
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)
Moore, J.H., Parker, J.S., Olsen, N., Aune, T. Symbolic discriminant analysis of microarray data in autoimmune disease. Genetic Epidemiology 23 (2002) 57–69
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge London (1992)
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)
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)
Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine 34 (2002) 88–95
Moore, J.H.: Cross validation consistency for the assessment of genetic programming results in microarray studies. In: Raidl, G. et al. (eds) Lecture Notes in Computer Science 2611, in press, Springer-Verlag, Berlin (2003).
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2001)
Devroye, L., Gyorfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer-Verlag, New York (1996)
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) 138–147
Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Ann. Eugen. 7 (1936) 179–188
Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. Prentice Hall, Upper Saddle River (1998)
Huberty, C.J.: Applied Discriminant Analysis. John Wiley & Sons, Inc., New York (1994)
Neter, J., Wasserman, W., Kutner, M.H.: Applied Linear Statistical Models, Regression, Analysis of Variance, and Experimental Designs. 3rd edn. Irwin, Homewood (1990)
Langley, P.: Elements of Machine Learning. Morgan Kaufmann Publishers, Inc., San Francisco (1996)
Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Boston (2000)
http://garage.cps.msu.edu/software/software-index.html
Fogel, G.B., Corne, D.W.: Evolutionary Computation in Bioinformatics. Morgan Kaufmann Publishers, Inc., San Francisco (2003)
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) 5–9
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reif, D.M., White, B.C., Olsen, N., Aune, T., Moore, J.H. (2003). Complex Function Sets Improve Symbolic Discriminant Analysis of Microarray Data. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_121
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_121
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive