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Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6023))

Abstract

A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such interactive models present an important analytical challenge, requiring that methods perform both variable selection and statistical modeling to generate testable genetic model hypotheses. Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interactive effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. Currently, we introduce the Grammatical Evolution Decision Trees (GEDT) method, and demonstrate that GEDT has power to detect interactive models in a range of simulated data, revealing GEDT to be a promising new approach for human genetics.

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Author information

Authors and Affiliations

  1. Department of Computer Science, North Carolina State University, Raleigh, NC, USA, 27695

    Sushamna Deodhar

  2. Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, NC, USA, 27695

    Alison Motsinger-Reif

Authors
  1. Sushamna Deodhar
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  2. Alison Motsinger-Reif
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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. Department of Molecular Physiology and Biophysics, Vanderbilt University, Center for Human Genetics Research, 519 Light Hall, 37232, Nashville, TN, USA

    Marylyn D. Ritchie

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

    Mario Giacobini

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Deodhar, S., Motsinger-Reif, A. (2010). Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2010. Lecture Notes in Computer Science, vol 6023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12211-8_9

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  • DOI: https://doi.org/10.1007/978-3-642-12211-8_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12210-1

  • Online ISBN: 978-3-642-12211-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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