Initialization parameter sweep in ATHENA: Optimizing neural networks for detecting gene-gene interactions in the presence of small main effects

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 203-210

  Holzinger, Emily R ^a   Buchanan, Carrie C ^a   Dudek, Scott M ^a   Torstenson, Eric C ^a   Turner, Stephen D ^a   Ritchie, Marylyn D ^a  

^a VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Epistasis; Grammatical evolution; Machine learning; Neural networks

Indexed keywords

AFFECT DETECTION; ANALYSIS TOOLS; ANALYTICAL TOOL; CONTRIBUTING FACTOR; DISEASE MODELS; DISEASE SUSCEPTIBILITY; EPISTASIS; EVOLUTIONARY PROCESS; GENE-GENE INTERACTION; GENETIC DATA; GENETIC FACTORS; GENETIC MODELS; GENOTYPING; GRAMMATICAL EVOLUTION; HUMAN DISEASE; INITIAL PARAMETER; INPUT VARIABLES; MACHINE-LEARNING; MAIN EFFECT; MULTIPLE PARAMETERS; NON-LINEAR; NOVEL STRATEGIES; PARALLEL COMPUTATION; PARAMETER SETTING; PARAMETRIC ANALYSIS; POPULATION SIZES; SIMULATED DATA; STATISTICAL ANALYSIS;

ASSOCIATION REACTIONS; BIOLOGY; COMPUTATIONAL GRAMMARS; COMPUTER SIMULATION; EVOLUTIONARY ALGORITHMS; LEARNING SYSTEMS; OPTIMIZATION; POPULATION STATISTICS;

NEURAL NETWORKS;

EID: 77955883431     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830519     Document Type: Conference Paper

References (19)

1. Bishop, C.M. Neural Networks for Pattern Recognition Oxford University Press, London, 1995.
2. Bush,W.S., Dudek, S.M., andRitchie, M.D. Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac. Symp. Biocomput., (2009), 368-379.
3. Cordell, H. J. Genome-wide association studies: Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet., 10 (2009), 392-404.
4. Dudek. SM, Motsinger AA, Velez DR, Williams SM, Ritchie MD. Data Simulation Software for Whole-Genome Association and Other Studies in Human Genetics. Pac Symp BioComput, (2006), 499-510.
5. Edwards,T.L., Bush,W.S., Turner, S.D., Dudek, S.M., Torstenson, E.S., Schmidt, M., Martin, E. andRitchie, M.D. Generating Linkage Disequilibrium Patterns in Data Simulations Using genomeSIMLA. Lecture Notes in Computer Science, 4793 (2008), 24-35.
6. Koza, J.R. andRice, J.P. Genetic generation of both the weights and architecture for a neural network. Proceeeding of the IEEE Joint Conference on Neural Networks, (1991), 397-404.
7. Moore, T.H. andWilliams, S.M. New strategies for identifying gene-gene interactions in hypertension. Ann. Med., 34 (2002), 88-95.
8. Moore, J.H. The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum. Hered,. 56 (2003), 73-82.
9. Motsinger-Reif, A. A., Reif, D. M., Fanelli, T. J., andRitchie, M. D. A comparison of analytical methods for genetic association studies. Genet.Epidemiol. 32 (2008), 767-778.
10. Motsinger-Reif, A.A., Dudek, S.M., Hahn, L.W., and Ritchie.M.D. Comparison of approaches for machinelearning optimization of neural networks for detecting genegene interactions in genetic epidemiology. Genetic Epidemiology 32 (2008) 325-340.
11. Motsinger-Reif, A. A. andRitchie, M. D. Neural networks for genetic epidemiology: past, present, and future. BioData.Min., 1 (2008), 3.
12. Motsinger, A.A., Dudek, S.M., Hahn, L.W., andRitchie, M.D. Comparison of neural network optimization approaches for studies of human genetics. Lecture Notes in Computer Science, 3907(2006), 103-114.
13. O'Neill ,M. andRyan, C. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Norwell, MA, 2003.
14. O'Neill, M. and Ryan C. Grammatical Evolution. IEEE Transactions on Evolutionary Computation, 5 (2001), 349-358.
15. Ott, J. Neural networks and disease association studies. American Journal of Medical Genetics (Neuropsychiatric Genetics), 105 (2001), 60-61.
16. Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L. R., Dupont, W. D., Pari, F. F., andMoore, J. H. Multifactordimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Gen., 69 (2001), 138-147.
17. Ritchie, M. D., White, B. C., Parker, J. S., Hahn, L. W., andMoore, 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 (2003) 28.
18. Skapura, D.M. Building neural networks. ACM Press, New York, 1995.
19. Yao, Xin. Evolving Aritficial Neural Networks. Proceedings of the IEEE, (1999), 1423-1447.