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:1.7630

@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",
• broken = "http://gradworks.umi.com/35/74/3574009.html",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Holzinger_Thesis.pdf",
• URL = "https://search.proquest.com/docview/1444626517/4BBFCADE1E4F4C51PQ/11",
• 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

  ProQuest Dissertations Publishing, 2013. 3574009",

}

Genetic Programming entries for Emily Rose Holzinger

Citations