TY - GEN
T1 - Initialization parameter sweep in ATHENA
T2 - 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
AU - Holzinger, Emily R.
AU - Buchanan, Carrie C.
AU - Dudek, Scott M.
AU - Torstenson, Eric C.
AU - Turner, Stephen D.
AU - Ritchie, Marylyn D.
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, genegene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multilocus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.
AB - Recent advances in genotyping technology have led to the generation of an enormous quantity of genetic data. Traditional methods of statistical analysis have proved insufficient in extracting all of the information about the genetic components of common, complex human diseases. A contributing factor to the problem of analysis is that amongst the small main effects of each single gene on disease susceptibility, there are non-linear, genegene interactions that can be difficult for traditional, parametric analyses to detect. In addition, exhaustively searching all multilocus combinations has proved computationally impractical. Novel strategies for analysis have been developed to address these issues. The Analysis Tool for Heritable and Environmental Network Associations (ATHENA) is an analytical tool that incorporates grammatical evolution neural networks (GENN) to detect interactions among genetic factors. Initial parameters define how the evolutionary process will be implemented. This research addresses how different parameter settings affect detection of disease models involving interactions. In the current study, we iterate over multiple parameter values to determine which combinations appear optimal for detecting interactions in simulated data for multiple genetic models. Our results indicate that the factors that have the greatest influence on detection are: input variable encoding, population size, and parallel computation.
UR - http://www.scopus.com/inward/record.url?scp=77955883431&partnerID=8YFLogxK
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U2 - 10.1145/1830483.1830519
DO - 10.1145/1830483.1830519
M3 - Conference contribution
AN - SCOPUS:77955883431
SN - 9781450300728
T3 - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
SP - 203
EP - 210
BT - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10
Y2 - 7 July 2010 through 11 July 2010
ER -