Meta-Dimensional Analysis of Phenotypes Using the Analysis Tool for Heritable and Environmental Network Associations (ATHENA): Challenges with Building Large Networks

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@InCollection{Ritchie:2012:GPTP,
  author =       "Marylyn D. Ritchie and Emily R. Holzinger and 
                 Scott M. Dudek and Alex T. Frase and Prabhakar Chalise and 
                 Brooke Fridley",
  title =        "Meta-Dimensional Analysis of Phenotypes Using the
                 Analysis Tool for Heritable and Environmental Network
                 Associations (ATHENA): Challenges with Building Large
                 Networks",
  booktitle =    "Genetic Programming Theory and Practice X",
  year =         "2012",
  series =       "Genetic and Evolutionary Computation",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Marylyn D. Ritchie and Jason H. Moore",
  publisher =    "Springer",
  chapter =      "8",
  pages =        "103--115",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Neural networks, Data mining, Human
                 genetics, Systems biology, Meta-dimensional data",
  isbn13 =       "978-1-4614-6845-5",
  URL =          "http://dx.doi.org/10.1007/978-1-4614-6846-2_8",
  DOI =          "doi:10.1007/978-1-4614-6846-2_8",
  abstract =     "The search for the underlying heritability of complex
                 traits has led to an explosion of data generation and
                 analysis in the field of human genomics. With these
                 technological advances, we have made some progress in
                 the identification of genes and proteins associated
                 with common, complex human diseases. Still, our
                 understanding of the genetic architecture of complex
                 traits remains limited and additional research is
                 needed to illuminate the genetic and environmental
                 factors important for the disease process, much of
                 which will include looking at variation in DNA, RNA,
                 protein, etc. in a meta-dimensional analysis framework.
                 We have developed a machine learning technique, ATHENA:
                 Analysis Tool for Heritable and Environmental Network
                 Associations, to address this issue of integrating data
                 from multiple '-omics' technologies to identify models
                 that explain or predict the genetic architecture of
                 complex traits. In this chapter, we discuss the
                 challenges in handling meta-dimensional data using
                 grammatical evolution neural networks (GENN) which are
                 one modelling component of ATHENA, and a
                 characterisation of the models identified in simulation
                 studies to explore the ability of GENN to build
                 complex, meta-dimensional models. Challenges remain to
                 further understand the evolutionary process for GENN,
                 and an explanation of the simplicity of the models.
                 This work highlights potential areas for extension and
                 improvement of the GENN approach within ATHENA.",
  notes =        "part of \cite{Riolo:2012:GPTP} published after the
                 workshop in 2013",
}

Genetic Programming entries for Marylyn D Ritchie Emily Rose Holzinger Scott M Dudek Alex T Frase Prabhakar Chalise Brooke L Fridley

Citations