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,
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author = "Marylyn D. Ritchie and Emily R. Holzinger and
Scott M. Dudek and Alex T. Frase and Prabhakar Chalise and
Brooke Fridley",
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title = "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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booktitle = "Genetic Programming Theory and Practice X",
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year = "2012",
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series = "Genetic and Evolutionary Computation",
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editor = "Rick Riolo and Ekaterina Vladislavleva and
Marylyn D. Ritchie and Jason H. Moore",
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publisher = "Springer",
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chapter = "8",
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pages = "103--115",
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address = "Ann Arbor, USA",
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month = "12-14 " # may,
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keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Neural networks, Data mining, Human
genetics, Systems biology, Meta-dimensional data",
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isbn13 = "978-1-4614-6845-5",
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URL = "http://dx.doi.org/10.1007/978-1-4614-6846-2_8",
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DOI = "doi:10.1007/978-1-4614-6846-2_8",
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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.",
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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