Cellular automata and genetic algorithms for parallel problem solving in human genetics

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2439, Issue , 2002, Pages 821-830

  Moore, Jason H ^a   Hahn, Lance W ^a  

^a VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR AUTOMATA; GENES; GENETIC ALGORITHMS; KNOWLEDGE REPRESENTATION;

ATTRIBUTE INTERACTIONS; GENE-GENE INTERACTION; GENETIC VARIATION; HUMAN GENETICS; MULTIFACTORIAL DISEASE; PARALLEL CA; REAL-WORLD; SIMULATION STUDIES;

DATA MINING;

EID: 0344837170     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45712-7_79     Document Type: Conference Paper

References (30)

1. Moore, J.H., Williams, S.M.: New strategies for identifying gene-gene interactions in hypertension. Annals of Medicine 34 (2002) 88-95
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)
3. Schlichting, C.D., Pigliucci, M.: Phenotypic Evolution: A Reaction Norm Perspective. Sinauer Assocuiates, Inc., Sunderland (1998)
4. Freitas, A.A.: Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Reviews 16 (2001) 177-199
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
6. Mitchell, M., Crutchfield, J.P., Hraber, P.T.: Evolving cellular automata to perform computations: Mechanisms and impediments. Physica D 75 (1994) 361-391
7. Wolfram, S.: Cellular automata as models of complexity. Nature 311 (1984) 419-424
8. Gardner, M.: The fantastic combinations of John Conway’s new solitaire game “Life”. Scientific American 223 (1970) 120-123
9. Sipper, M.: Evolution of Parallel Cellular Machines. Springer, New York (1997)
10. Langley, P.: Elements of Machine Learning. Morgan Kaufmann Publishers, Inc., San Francisco (1996)
11. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
12. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
13. Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming: An Introduction. Morgan Kaufmann Publishers, San Francisco (1998)
14. Cantu-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Boston (2000)
15. Wright, S.: Isolation by distance. Genetics 28 (1943) 114-138
16. http://garage.cps.msu.edu
17. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
18. 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
19. Frankel, W.N., Schork, N.J.: Who’s afraid of epistasis? Nature Genetics 14 (1996) 371-373
20. Li, W., Reich, J.: A complete enumeration and classification of two-locus disease models. Human Heredity 50 (2000) 334-349
21. Busch, C.P., Hegele, R.A.: Genetic determinants of type 2 diabetes mellitus. Clinical Genetics 60 (2001) 243-54
22. Sipper, M.: Co-evolving non-uniform cellular automata to perform computations. Physica D 92 (1996) 193-208
23. 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
24. 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)
25. Tapadar, P., Ghosh, S., Majumder, P.P.: Haplotyping in pedigrees via a genetic algorithm. Human Heredity 50 (2000) 43-56
26. 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).
27. 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)
28. 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)
29. 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
30. 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