rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Application of Bayesian Hierarchical Models in Genetic Data Analysis
  name:
    name part: Zhang, Lin
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Mallick, Bani K.
    name part: 1965-
    role:
      role term: thesis advisor.
  name:
    name part: Baladandayuthapani, Veerabhadran
    name part: 1976-
    role:
      role term: thesis advisor.
  name:
    name part: Texas A & M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Genetic data analysis has been capturing a lot of attentions for understanding the mechanism of the development and progressing of diseases like cancers, and is crucial in discovering genetic markers and treatment targets in medical research. This dissertation focuses on several important issues in genetic data analysis, graphical network modeling, feature selection, and covariance estimation. First, we develop a gene network modeling method for discrete gene expression data, produced by technologies such as serial analysis of gene expression and RNA sequencing experiment, which generate counts of mRNA transcripts in cell samples. We propose a generalized linear model to fit the discrete gene expression data and assume that the log ratios of the mean expression levels follow a Gaussian distribution. We derive the gene network structures by selecting covariance matrices of the Gaussian distribution with a hyper-inverse Wishart prior. We incorporate prior network models based on Gene Ontology information, which avails existing biological information on the genes of interest. Next, we consider a variable selection problem, where the variables have natural grouping structures, with application to analysis of chromosomal copy number data. The chromosomal copy number data are produced by molecular inversion probes experiments which measure probe-specific copy number changes. We propose a novel Bayesian variable selection method, the hierarchical structured variable se- lection (HSVS) method, which accounts for the natural gene and probe-within-gene architecture to identify important genes and probes associated with clinically relevant outcomes. We propose the HSVS model for grouped variable selection, where simultaneous selection of both groups and within-group variables is of interest. The HSVS model utilizes a discrete mixture prior distribution for group selection and group-specific Bayesian lasso hierarchies for variable selection within groups. We further provide met…
  note: by Lin Zhang.
  note: "Major Subject: Statistics"
  note: Includes vita.
  note: Doctor of Philosophy Texas A & M University 2012 http://hdl.handle.net/1969.1/148056
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Statistics
  subject:
    topic: covariance estimation
  subject:
    topic: feature selection
  subject:
    topic: graphical network modeling
  subject:
    topic: genetic data analysis
  subject:
    topic: Bayesian hierarchical model
  identifier: http://hdl.handle.net/1969.1/148056
  record info:
    record content source: TXA
    record creation date: 131219
    record change date: 20151206090655.0
    record identifier: in00003421213
    language of cataloging:
      language term: eng