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

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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