Development, Optimization, and Application of a Meta-Dimensional Analysis Pipeline Using in Silico and Natural Data Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Holzinger:Thesis,
  author =       "Emily Rose Holzinger",
  title =        "Development, Optimization, and Application of a
                 Meta-Dimensional Analysis Pipeline Using in Silico and
                 Natural Data Sets",
  school =       "Human Genetics, Vanderbilt University",
  year =         "2013",
  address =      "Nashville, TN, USA",
  month =        "10 " # may,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, Biostatistics; Genetics",
  URL =          "http://gradworks.umi.com/35/74/3574009.html",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Holzinger_Thesis.pdf",
  size =         "178 pages",
  abstract =     "For this project, we develop, optimise, and implement
                 a novel analytical pipeline that combines a tree-based
                 variable selection method with an evolutionary
                 computation modelling method. The purpose of this
                 pipeline is to integrate high-throughput data from
                 different levels of biological regulation to identify
                 meta-dimensional models that predict a given outcome.
                 We suggest that by integrating different types of data
                 we will identify aspects of the genetic architecture
                 that would have been missed by single variable and/or
                 single data type study designs.

                 The development process consisted of rigorous
                 performance testing, method comparisons, and parameter
                 optimisations using in silico and biological data sets.
                 Next, we applied the analysis pipeline to a data set
                 with SNP genotypes, gene expression variables, and
                 quantitative low-density lipoprotein cholesterol
                 (LDL-C) trait outcomes.

                 Using our meta-dimensional analysis pipeline, we were
                 able to generate multi-variable models that explain a
                 proportion of the inter-individual variation in LDL-C
                 traits. Additionally, we were able to map these genetic
                 variants to biological units and pathways that would
                 not have been identified with single data type
                 analysis.",
  notes =        "includes some evaluations of multiple evolutionary
                 approaches, including evolved neural networks and
                 symbolic regression.

                 Supervisor: Marylyn Ritchie",
}

Genetic Programming entries for Emily Rose Holzinger

Citations