Abstract
A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. Identifying gene-gene and gene-environment interactions which comprise the genetic architecture for a majority of common diseases is a difficult challenge. To this end, novel computational approaches have been applied to studies of human disease. Previously, a GP neural network (GPNN) approach was employed. Although the GPNN method has been quite successful, a clear comparison of GPNN and GP alone to detect genetic effects has not been made. In this paper, we demonstrate that using NN evolved by GP can be more powerful than GP alone. This is most likely due to the confined search space of the GPNN approach, in comparison to a free form GP. This study demonstrates the utility of using GP to evolve NN in studies of the genetics of common, complex human disease.
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References
Kardia, S.L.R.: Context-dependent genetic effects in hypertension. Curr. Hypertens. Reports 2, 32–38 (2000)
Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Ann. Med. 34, 88–95 (2002)
Kooperberg, C., Ruczinski, I., LeBlanc, M.L., Hsu, L.: Sequence Analysis using Logic Regression. Genetic Epidemiology S1, 626–631 (2001)
Zhu, J.: Classification of gene microarrays by penalized logistic regression. Biostatistics 5, 427–443 (2003)
Tahri-Daizadeh, N., Tregouet, D.A., Nicaud, V., Manuel, N., Cambien, F., Tiret, L.: Automated detection of informative combined effects in genetic association studies of complex traits. Genome Res. 8, 1952–1960 (2003)
Nelson, M., Kardia, S.L.R., Ferrell, R.E., Sing, C.F.: A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res. 11, 458–470 (2001)
Culverhouse, R., Klein, T., Shannon, W.: Detecting epistatic interactions contributing to quantitative traits. Genet Epidemiol. 27, 141–152 (2004)
Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138–147 (2001)
Hahn, L.W., Ritchie, M.D., Moore, J.H.: Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19, 376–382 (2003)
Ritchie, M.D., Hahn, L.W., Moore, J.H.: Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Gen. Epi. 24, 150–157 (2003)
Marinov, M., Weeks, D.: The complexity of linkage analysis with neural networks. Hum. Hered. 51, 169–176 (2001)
Lucek, P., Hanke, J., Reich, J., Solla, S.A., Ott, J.: Multi-locus nonparametric linkage analysis of complex trait loci with neural networks. Hum. Hered. 48, 275–284 (1998)
Koza, J.R., Rice, J.P.: Genetic generation of both the weights and architecture for a neural network, vol. II. IEEE Press, Los Alamitos (1991)
Gruau, F.C.: Cellular encoding of genetic neural networks. Master’s thesis Ecole Normale Superieure de Lyon, pp. 1–42 (1992)
Ritchie, M.D., White, B.C., Parker, J.S., Hahn, L.W., Moore, J.H.: Optimization of neural network architecture using genetic programming improves detection of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4, 28 (2003)
Moore, J.H., Parker, J.S., Olsen, N.J., Aune, T.S.: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet. Epidemiol. 23, 57–69 (2002)
Moore, J.H., Parker, J.S., Hahn, L.W.: Symbolic Discriminant Analysis for Mining Gene Expression Patterns. In: Flach, P.A., De Raedt, L. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 372–381. Springer, Heidelberg (2001)
Ritchie, M.D., Coffey, C.S., Moore, J.H.: Genetic Programming Neural Networks: A Bioinformatics Tool for Human Genetics. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 438–448. Springer, Heidelberg (2004)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
Moore, J.H.: Cross validation consistency for the assessment of genetic programming results in microarray studies. In: Raidl, G., et al. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 99–106. Springer, Heidelberg (2003)
Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., Moore, J.H.: Multifactor dimensionality reduction reveals high-order interactions among estrogen metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69, 138–147 (2001)
Koza, J.R.: Genetic Programming: On the programming of computers by means of natural selection. MIT Press, Cambridge (1993)
Koza, J.R.: Genetic Programming II: Automatic discovery of reusable programs. MIT Press, Cambridge (1998)
Koza, J.R., Bennett, F.H., Andre, D., Keane, M.A.: Genetic Programming III: Automatic programming and automatic circuit synthesis. MIT Press, Cambridge (1999)
Culverhouse, R., Suarez, B.K., Lin, J., Reich, T.: A Perspective on Epistasis: Limits of Models Displaying No Main Effect. Am. J. Hum. Genet. 70, 461–471 (2002)
Moore, J.H., Hahn, L.W., Ritchie, M.D., Thornton, T.A., White, B.C.: Application of genetic algorithms to the discovery of complex genetic models for simulations studies in human genetics. In: Langdon, W.B., et al. (eds.) Proceedings of the Genetic and Evolutionary Algorithm Conference, pp. 1150–1155. Morgan Kaufman Publishers, San Francisco (2002)
Ott, J.: Neural networks and disease association. Am. J. Med. Genet. 105, 60–61 (2001)
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Bush, W.S., Motsinger, A.A., Dudek, S.M., Ritchie, M.D. (2005). Can Neural Network Constraints in GP Provide Power to Detect Genes Associated with Human Disease?. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_5
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DOI: https://doi.org/10.1007/978-3-540-32003-6_5
Publisher Name: Springer, Berlin, Heidelberg
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