- 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, Bioinformatics, Biology",
-
URL = "
https://www.lib.uidaho.edu/digital/etd/items/beck_idaho_0089e_10212.html",
-
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 = "Beck_idaho_0089E_10212 Supervisor James A Foster",
