Investigating the use of classification models to study microbial community associations with bacterial vaginosis

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

@PhdThesis{Beck:thesis,
  author =       "Daniel Beck",
  title =        "Investigating the use of classification models to
                 study microbial community associations with bacterial
                 vaginosis",
  school =       "University of Idaho",
  year =         "2014",
  address =      "USA",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, grammatical evolution, Linear GP,
                 Push",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/daniel_beck_dissertation.pdf",
  size =         "82 pages",
  abstract =     "Microbial communities are highly complex, often
                 composed of hundreds or thousands of different microbe
                 types. They are found nearly everywhere; in soil,
                 water, and in close association with other organisms.
                 Microbial communities are difficult to study. Many
                 microbes are not easily grown in laboratory conditions.
                 Interactions between microbes may limit the
                 applicability of observations collected using isolated
                 taxa. However, new sequencing technology is allowing
                 researchers to study microbial communities in novel
                 ways. Among these new techniques is 16S rRNA
                 fingerprinting, which enables researchers to estimate
                 the relative abundance of most microbes in the
                 community.

                 These techniques are often used to study microbial
                 communities living on or in the human body. These
                 microbiomes are found at many different body sites and
                 have been linked to the health of their human host. In
                 particular, the vagina microbiome has been linked to
                 bacterial vaginosis (BV). BV is highly prevalent with
                 symptoms including odour, discharge, and irritation.
                 While no single microbe has been shown to cause BV, the
                 structure of the microbial community as a whole is
                 associated with BV.

                 In this thesis, I explore methods that may be used to
                 discover associations between microbial communities and
                 phenotypes of those communities. I focus on
                 associations between the vagina microbiome and BV. The
                 first two chapters of this thesis describe software
                 tools used to explore and visualise ecological
                 datasets. In the last two chapters, I explore the use
                 of machine learning techniques to model the
                 relationships between the vagina microbiome and BV.
                 Machine learning techniques are able to produce complex
                 models that classify microbial communities by BV
                 characteristics. These models may capture interactions
                 that simpler models miss.",
  notes =        "Supervisor James A Foster",
}

Genetic Programming entries for Daniel Beck

Citations