Year: 2013
Type: dissertation
Abstract: Hundreds of studies have addressed whether the presence or absence of certain bacteria are linked with a particular phenotype. However, it is plausible that the causative agent (or the consequence) of a given phenotype is not a single type of microbe, but groups of them, perhaps in specific combinations. Rule Induction is a commonly used machine learning method to infer structure within observational data, and build rules to represent these structures. In this thesis I introduce the application of a method, Rule Induction, to infer co-occurrence patterns in microbial data. First, I benchmark the methods within Rule Induction, to assess how rules are generated with regards to several parameters such as table density, support and confidence. I then subsample data over multiple iterations to understand the robustness of the rules being produced to verify due to sampling. Next, I provide insight into different biological variables and examine their effect on rules produced. I compare 16S rRNA region, specifically V1-3 and V3-5 regions. I compare different sequencingtechnology, specifically 454 and Illumina. I finally compare time, specifically looking over a time frame of 400 ays. Within all these comparisons I aim to understand the differentces, but more importantly what is conserved when these samples are stratified by these variables in terms of the generated rules. Finally, I explore Rule Induction using two microbial datasets, and compare the rules to already-known associations. The first dataset I interpret identifies a correlation between HIV and the Gut Microbiome. The second data set distinguishes the Gut Microbiome over varyuing geographical lovations. I link each of these rules produced from each data set with taxonomic information and consolidate those rules to give rise to the underlying structure within the biological data.
Author: Kumar Thurimella
Language: English
Topic: Gut microbiota and health
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2260022321