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Comparison of Neural Network Optimization Approaches for Studies of Human Genetics

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

A major goal of human genetics is the identification of susceptibility genes associated with common, complex diseases. The preponderance of gene-gene and gene-environment interactions comprising the genetic architecture of common diseases presents a difficult challenge. To address this, novel computational approaches have been applied to studies of human disease. These novel approaches seek to capture the complexity inherent in common diseases. Previously, we developed a genetic programming neural network (GPNN) to optimize network architecture for the detection of disease susceptibility genes in association studies. While GPNN was a successful endeavor, we wanted to address the limitations in its flexibility and ease of development. To this end, we developed a grammatical evolution neural network (GENN) approach that accounts for the drawbacks of GPNN. In this study we show that this new method has high power to detect gene-gene interactions in simulated data. We also compare the performance of GENN to GPNN, a traditional back-propagation neural network (BPNN) and a random search algorithm. GENN outperforms both BPNN and the random search, and performs at least as well as GPNN. This study demonstrates the utility of using GE to evolve NN in studies of complex human disease.

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References

  1. Kardia, S., Rozek, L., Hahn, L., Fingerlin, T., Moore, J.: Identifying multilocus genetic risk profiles: a comparison of the multifactor data reduction method and logistic regression. Genetic Epidemiology (2000)

    Google Scholar 

  2. Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Ann. Med. 34, 88–95 (2002)

    Article  Google Scholar 

  3. Culverhouse, R., Klein, T., Shannon, W.: Detecting epistatic interactions contributing to quantitative traits. Genet. Epidemiol. 27, 141–152 (2004)

    Article  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Kooperberg, C., Ruczinski, I., LeBlanc, M.L., Hsu, L.: Sequence analysis using logic regression. Genet. Epidemiol. 21(suppl. 1), S626–S631 (2001)

    Google Scholar 

  6. Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56, 73–82 (2003)

    Article  Google Scholar 

  7. Nelson, M.R., Kardia, S.L., 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)

    Article  Google Scholar 

  8. Ritchie, M.D., Hahn, L.W., Roodi, N., Bailey, L.R., Dupont, W.D., Parl, F.F., et al.: Multifactordimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J. Hum. Genet. 69, 138–147 (2001)

    Article  Google Scholar 

  9. 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. Genet. Epidemiol. 24, 150–157 (2003)

    Article  Google Scholar 

  10. 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. 13, 1952–1960 (2003)

    Google Scholar 

  11. Zhu, J., Hastie, T.: Classification of gene microarrays by penalized logistic regression. Biostatistics 5, 427–443 (2004)

    Article  MATH  Google Scholar 

  12. Schalkoff, R.: Artificial Neural Networks. McGraw-Hill Companies Inc., New York (1997)

    MATH  Google Scholar 

  13. Bhat, A., Lucek, P.R., Ott, J.: Analysis of complex traits using neural networks. Genet. Epidemiol. 17, S503–S507 (1999)

    Google Scholar 

  14. Curtis, D., North, B.V., Sham, P.: Use of an artificial neural network to detect association between a disease and multiple marker genotypes. Annals of Human Genetics 65, 95–107 (2001)

    Article  Google Scholar 

  15. Li, W., Haghighi, F., Falk, C.: Design of artificial neural network and its applications to the analysis of alcoholism data. Genet. Epidemiol. 17, S223–S228 (1999)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Lucek, P.R., Ott, J.: Neural network analysis of complex traits. Genet. Epidemiol. 14, 1101–1106 (1997)

    Article  Google Scholar 

  18. Marinov, M., Weeks, D.: The complexity of linkage analysis with neural networks. Human Heredity 51, 169–176 (2001)

    Article  Google Scholar 

  19. Ott, J.: Neural networks and disease association. American Journal of Medical Genetics (Neuropsychiatric Genetics) 105(60), 61 (2001)

    Google Scholar 

  20. Saccone, N.L., Downey, T.J., Meyer, D.J., Neuman, R.J., Rice, J.P.: Mapping genotype to phenotype for linkage analysis. Genet Epidemiol 17(suppl.), 703–708 (1999)

    Google Scholar 

  21. Sherriff, A., Ott, J.: Applications of neural networks for geen finding. Advances in Genetics 42, 287–297 (2001)

    Article  Google Scholar 

  22. 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 and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4, 28 (2003)

    Article  Google Scholar 

  23. Koza, J., Rice, J.: Genetic generation of both the weights and architecture for a neural network. IEEE Transactions, II (1991)

    Google Scholar 

  24. Motsinger, A.A., Lee, S., Mellick, G., Ritchie, M.D.: GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease. BMC Bioinformatics (2005) (in press)

    Google Scholar 

  25. Bush, W.S., Motsinger, A.A., Dudek, S.M., Ritchie, M.D.: Can neural network constraints in GP provide power to detect genes associated with human disease? In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 44–53. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  26. Ritchie, M.D., Coffey, C.S., Moore, J.H.: Genetic programming neural networks as a bioinformatics tool for human genetics. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 438–448. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  27. O’Neill, M., Ryan, C.: Grammatical Evolution. IEEE Transactions on Evolutionary Computation 5, 349–357 (2001)

    Article  Google Scholar 

  28. O’Neill, M., Ryan, C.: Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Kluwer Academic Publishers, Boston (2003)

    MATH  Google Scholar 

  29. Moore, J.H., Hahn, L.W.: Petri net modeling of high-order genetic systems using grammatical evolution. BioSystems 72, 177–186 (2003)

    Article  Google Scholar 

  30. Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  31. Cantu-Paz, E.: Efficient and accurate parallel genetic algorithms. Kluwer Academic Publishers, Boston (2000)

    MATH  Google Scholar 

  32. Utans, J., Moody, J.: Selecting neural network architectures via the prediction risk application to corporate bond rating prediction. In: Conference Proceedings on the First International Conference on Artificial Intelligence Applications on Wall Street. IEEE Press, Los Alamitos (1991)

    Google Scholar 

  33. Moody, J.: Prediction risk and architecture selection for neural networks. In: Cherkassky, V., Friedman, J.H., Wechsler, H. (eds.) From Statistics to Nerual Networks: Theory and Pattern Recognition Applications. NATO ASI Series F, Springer, Heidelberg (1994)

    Google Scholar 

  34. Fahlman, S.E., Lebiere, C.: The Cascade-Correlation Learning Architecture. Masters from School of Computer Science. Carnegie Mellon University (1991)

    Google Scholar 

  35. Templeton, A.: Epistasis and complex traits. In: Wade, M., Broadie III, B., Wolf, J. (eds.) Epistasis and the Evolutionary Process, pp. 41–57. Oxford University Press, Oxford (2000)

    Google Scholar 

  36. Li, W., Reich, J.: A complete enumeration and classification of two-locus disease models. Hum.Hered. 50, 334–349 (2000)

    Article  Google Scholar 

  37. Moore, J., Hahn, L., Ritchie, M., Thornton, T., White, B.: Application of genetic algorithms to the discovery of complex models for simulation studies in human genetics. In: Langdon, W.B., Cantu-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) Proceedings of the Genetic and Evolutionary Algorithm Conference, pp. 1150–1155. Morgan Kaufman Publishers, San Francisco (2002)

    Google Scholar 

  38. Anderson, J.: An Introduction to Neural Networks. MIT Press, Cambridge, Massachusetts (1995)

    MATH  Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. Frankel, W., Schork, N.: Who’s afraid of epistasis? Nat.Genet. 14, 371–373 (1996)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37232, USA

    Alison A. Motsinger, Scott M. Dudek, Lance W. Hahn & Marylyn D. Ritchie

Authors
  1. Alison A. Motsinger
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  2. Scott M. Dudek
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  3. Lance W. Hahn
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  4. Marylyn D. Ritchie
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

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© 2006 Springer-Verlag Berlin Heidelberg

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Motsinger, A.A., Dudek, S.M., Hahn, L.W., Ritchie, M.D. (2006). Comparison of Neural Network Optimization Approaches for Studies of Human Genetics. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_10

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  • DOI: https://doi.org/10.1007/11732242_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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