ATHENA optimization: The effect of initial parameter settings across different genetic models

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

Volumn 6623 LNCS, Issue , 2011, Pages 48-58

  Holzinger, Emily R ^a   Dudek, Scott M ^a   Torstenson, Eric C ^a   Ritchie, Marylyn D ^a  

^a VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS METHOD; CROSS-OVER; DISEASE MODELS; ENCODINGS; FUNCTION SETS; GENE-GENE INTERACTION; GENETIC MODELS; GENETIC STUDIES; GENOME-WIDE ASSOCIATION; GRAMMATICAL EVOLUTION; HUMAN GENOMES; INITIAL PARAMETER; NOVEL METHODS; PARAMETER OPTIMIZATION; RAPIDLY ADVANCING TECHNOLOGY; SYMBOLIC REGRESSION;

BIOINFORMATICS; DATA MINING; EVOLUTIONARY ALGORITHMS; GENES; LEARNING SYSTEMS; NEURAL NETWORKS; OPTIMIZATION;

BIOLOGY;

EID: 79955779552     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20389-3_5     Document Type: Conference Paper

References (28)

1. Hindorff, L.A., Sethupathy, P., Junkins, H.A., et al.: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U.S.A 106(23), 9362-9367 (2009)
2. Nicolae, D.L., Gamazon, E., Zhang, W., et al.: Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6(4), e1000888 (2010)
3. Manolio, T.A., Collins, F.S., Cox, N.J., et al.: Finding the missing heritability of complex diseases. Nature 461(7265), 747-753 (2009)
4. Maher, B.: Personal genomes: The case of the missing heritability. Nature 456(7218), 18-21 (2008)
5. Motsinger, A.A., Ritchie, M.D., Reif, D.M.: Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics 8(9), 1229-1241 (2007)
6. Bateson, W.: Mendel's Principles of Heredity. Cambridge University Press, Cambridge (1909)
7. Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Proc 6th Intl.Congress of Genetics, vol. 1, pp. 356-366 (1932)
8. Moore, J.H.: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered. 56(1-3), 73-82 (2003)
9. Moore, J.H., Barney, N., Tsai, C.T., et al.: Symbolic modeling of epistasis. Hum. Hered. 63(2), 120-133 (2007)
10. Bishop, C.M.: Neural Networks for Pattern Recognition, pp. 1-482. Oxford University Press, London (1995)
11. Krogh, A.: What are artificial neural networks? Nat. Biotechnol. 26(2), 195-197 (2008)
12. Koza, J.R., Rice, J.P.: Genetic generation of both the weights and architecture for a neural network. IEEE Transactions II (1991)
13. Ritchie, M.D., White, B.C., Parker, J.S., et al.: Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases. BMC Bioinformatics 4(1), 28 (2003)
14. Koza, J.: Genetic Programmming: On the Programming of Computers by Means of Natural Selection, pp. 1-819. MIT Press, Cambridge (1992)
15. Schmidt, M., Lipson, H.: Distilling free-form natural laws from experimental data. Science 324(5923), 81-85 (2009)
16. Moore, J.H., Parker, J.S., Olsen, N.J., et al.: Symbolic discriminant analysis of microarray data in autoimmune disease. Genet. Epidemiol. 23, 57-69 (2002)
17. Motsinger-Reif, A.A., Dudek, S.M., Hahn, L.W., et al.: Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genetic Epidemiology 32(4), 325-340 (2008)
18. O'Neill, M., Ryan, C.: Grammatical Evolution. IEEE Transactions on Evolutionary Computation 5(4) (2001)
19. Holzinger, E.R., Buchanan, C.C., Dudek, S.M., et al.: Initialization Parameter Sweep in ATHENA: Optimizing Neural Networks for Detecting Gene-Gene Interactions in the Presence of Small Main Effects. In: Genet. Evol. Comput. Conf., pp. 203-210 (2010)
20. Reif, D.M., White, B.C., Olsen, N.J., et al.: Complex function sets improve symbolic discriminant analysis of microarray data. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O'Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2724. Springer, Heidelberg (2003)
21. Dudek, S.M., Motsinger, A.A., Velez, D.R., et al.: Data simulation software for whole-genome association and other studies in human genetics. In: Pac. Symp. Biocomput., vol. 11, pp. 499-510 (2006)
22. Edwards, T.L., Bush, W.S., Turner, S.D., et al.: Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA, LNCS, vol. 4793. pp. 24-35 (2008)
23. Ott, J.: Neural networks and disease association studies. American Journal of Medical Genetics (Neuropsychiatric Genetics) 105(60), 61 (2001)
24. O'Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language, First. Kluwer Academic Publishers, Norwell (2003)
25. Turner, S.D., Dudek, S.M., Ritchie, M.D.: Grammatical Evolution of Neural Networks for Discovering Epistasis among Quantitative Trait Loci. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2010. LNCS, vol. 6023, pp. 86-97. Springer, Heidelberg (2010)
26. Motsinger, A.A., Hahn, L.W., Dudek, S.M., et al.: Alternative Cross-Over Strategies and Selection Techniques for Grammatical Evolution Optimized Neural Networks. In: Genet. Evol. Comput. Conf., pp. 947-948 (2006)
27. Turner, S.D., Dudek, S.M., Ritchie, M.D.: ATHENA: A knowledge-based hybrid back-propagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci. BioData. Min. 3(1), 5 (2010)
28. Bush, W.S., Dudek, S.M., Ritchie, M.D.: Biofilter: a knowledge- integration system for the multi-locus analysis of genome-wide association studies. In: Pac. Symp. Biocomput., pp. 368-379 (2009)