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ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6623))

Abstract

Rapidly advancing technology has allowed for the generation of massive amounts data assessing variation across the human genome. One analysis method for this type of data is the genome-wide association study (GWAS) where each variation is assessed individually for association to disease. While these studies have elucidated novel etiology, much of the variation due to genetics remains unexplained. One hypothesis is that some of the variation lies in gene-gene interactions. An impediment to testing for interactions is the infeasibility of exhaustively searching all multi-locus models. Novel methods are being developed that perform a non-exhaustive search. Because these methods are new to genetic studies, rigorous parameter optimization is necessary. Here, we assess genotype encodings, function sets, and cross-over in two algorithms which use grammatical evolution to optimize neural networks or symbolic regression formulas in the ATHENA software package. Our results show that the effect of these parameters is highly dependent on the underlying disease model.

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Author information

Authors and Affiliations

  1. Center for Human Genetics Research, Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, USA

    Emily R. Holzinger, Scott M. Dudek, Eric C. Torstenson & Marylyn D. Ritchie

Authors
  1. Emily R. Holzinger
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  2. Scott M. Dudek
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  3. Eric C. Torstenson
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  4. Marylyn D. Ritchie
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Editor information

Editors and Affiliations

  1. Institute for High-Performance Computing and Networking (ICAR), Italian National Research Council (CNR), Via P. Bucci 41C, 87036 Rende, (CS), Italy

    Clara Pizzuti

  2. Center for Human Genetics Research, Vanderbilt University, 519 Light Hall, TN 37232, Nashville, USA

    Marylyn D. Ritchie

  3. Department of Animal Production Epidemiology and Ecology, University of Torino, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

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Holzinger, E.R., Dudek, S.M., Torstenson, E.C., Ritchie, M.D. (2011). ATHENA Optimization: The Effect of Initial Parameter Settings across Different Genetic Models. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-20389-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20388-6

  • Online ISBN: 978-3-642-20389-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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