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International Conference on Parallel Problem Solving from Nature

PPSN 2002: Parallel Problem Solving from Nature — PPSN VII pp 821–830Cite as

Cellular Automata and Genetic Algorithms for Parallel Problem Solving in Human Genetics

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2439))

Abstract

An important goal of human genetics is to identify variations in genes that are associated with risk of disease. This goal is complicated by the fact that, for common multifactorial diseases such as hypertension, interactions between genetic variations are likely to be more important than the independent effects of any single genetic variation. Attribute interaction is a well-known problem in data mining and is a complicating factor in genetic data analysis. We have previously addressed this problem by developing a parallel approach to problem solving that utilizes one-dimensional cellular automata (CA) for knowledge representation and genetic algorithms (GA) for optimization. In this study, we evaluate the power of this parallel CA approach by simulating genegene interactions and adding noise from several common real-world sources. These simulation studies document the strengths of the CA approach and document a weakness that needs to be addressed.

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References

  1. Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine 34 (2002) 88–95

    Article  Google Scholar 

  2. 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 

  3. Schlichting, C.D., Pigliucci, M.: Phenotypic Evolution: A Reaction Norm Perspective. Sinauer Assocuiates, Inc., Sunderland (1998)

    Google Scholar 

  4. Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Reviews 16 (2001) 177–199

    Article  MATH  Google Scholar 

  5. Moore, J.H., Hahn, L.W.: A cellular automata approach to detecting interactions among single-nucleotide polymorphisms in complex multifactorial diseases. Pacific Symposium on Biocomputing 2002 (2002) 53–64

    Google Scholar 

  6. Mitchell, M., Crutchfield, J.P., Hraber, P.T.: Evolving cellular automata to perform computations: Mechanisms and impediments. Physica D 75 (1994) 361–391

    Google Scholar 

  7. Wolfram, S.: Cellular automata as models of complexity. Nature 311 (1984) 419–424

    Article  Google Scholar 

  8. Gardner, M.: The fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American 223 (1970) 120–123

    Article  Google Scholar 

  9. Sipper, M.: Evolution of Parallel Cellular Machines. Springer, New York (1997)

    Google Scholar 

  10. Langley, P.: Elements of Machine Learning. Morgan Kaufmann Publishers, Inc., San Francisco (1996)

    Google Scholar 

  11. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  12. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  13. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann Publishers, San Francisco (1998)

    MATH  Google Scholar 

  14. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Boston (2000)

    MATH  Google Scholar 

  15. Wright, S.: Isolation by distance. Genetics 28 (1943) 114–138

    Google Scholar 

  16. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. Frankel, W.N., Schork, N.J.: Who’s afraid of epistasis? Nature Genetics 14 (1996) 371–373

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Busch, C.P., Hegele, R.A.: Genetic determinants of type 2 diabetes mellitus. Clinical Genetics 60 (2001) 243–54

    Article  Google Scholar 

  21. Sipper, M.: Co-evolving non-uniform cellular automata to perform computations. Physica D 92 (1996) 193–208

    MathSciNet  Google Scholar 

  22. Carlborg, O., Andersson, L., Kinghorn, B.: The use of a genetic algorithm for simultaneous mapping of multiple interacting quantitative trait loci. Genetics 155 (2000) 2003–10

    Google Scholar 

  23. Congdon, C.B., Sing, C.F., Reilly, S.L.: Genetic algorithms for identifying combinations of genes and other risk factors associated with coronary artery disease. In: Proceedings of the Workshop on Artificial Intelligence and the Genome. Chambery (1993)

    Google Scholar 

  24. Tapadar, P., Ghosh, S., Majumder, P.P.: Haplotyping in pedigrees via a genetic algorithm. Human Heredity 50 (2000) 43–56

    Article  Google Scholar 

  25. 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. Springer-Verlag, Berlin (2001).

    Google Scholar 

  26. 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 

  27. 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 simulation studies in human genetics. In: W.B. Langdon, E. Cantu-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska eds: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  28. Schork, N.J., Fallin, D., Thiel, B., Xu, X., Broeckel, U., Jacob, H.J., Cohen, D: The future of genetic case-control studies. Advances in Genetics 42 (2001) 191–212

    Article  Google Scholar 

  29. Shannon, W.D., Province, M.A., Rao, D.C: Tree-based recursive partitioning methods for subdividing sibpairs into relatively more homogeneous subgroups. Genetic Epidemiology 20 (2001) 293–306

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Program in Human Genetics, Department of Molecular Physiology and Biophysics, Vanderbilt University, 519 Light Hall, 37232-0700, Nashville, TN, USA

    Jason H. Moore & Lance W. Hahn

Authors
  1. Jason H. Moore
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  2. Lance W. Hahn
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Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingeniería Informática, 18071, Granada, Spain

    Juan Julián Merelo Guervós

  2. Department of Informatics, Technological Educational Institute of Thessaloniki, 54101, Thessaloniki, Greece

    Panagiotis Adamidis

  3. Department of Informatics XI, University of Dortmund, 44221, Dortmund, Germany

    Hans-Georg Beyer  & Hans-Paul Schwefel  & 

  4. Department of Signal Theory and Communications, Universidad Carlos III, Madrid, Spain

    José-Luis Fernández-Villacañas

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© 2002 Springer-Verlag Berlin Heidelberg

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Moore, J.H., Hahn, L.W. (2002). Cellular Automata and Genetic Algorithms for Parallel Problem Solving in Human Genetics. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_79

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  • DOI: https://doi.org/10.1007/3-540-45712-7_79

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44139-7

  • Online ISBN: 978-3-540-45712-1

  • eBook Packages: Springer Book Archive

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