Machine learning methods for the prediction of antimicrobial resistance and identification of novel markers of resistance in Escherichia coli

Moat, Janice; University of Lethbridge. Faculty of Arts and Science

Machine learning methods for the prediction of antimicrobial resistance and identification of novel markers of resistance in Escherichia coli

dc.contributor.author	Moat, Janice
dc.contributor.author	University of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisor	Zovoilis, Athanasios
dc.contributor.supervisor	Laing, Chad
dc.date.accessioned	2024-01-22T22:59:17Z
dc.date.available	2024-01-22T22:59:17Z
dc.date.issued	2023
dc.degree.level	Masters
dc.description.abstract	Antimicrobial resistant strains of pathogenic Escherichia coli are a burden on the healthcare system, causing longer hospital stays and increased treatment costs compared to nonresistant strains. The proportion of E. coli infections in Canada caused by resistant strains producing extended-spectrum beta-lactamase rose from 3.4% in 2007 to 11.1% in 2017. Use of most antimicrobials in the treatment of Shiga-toxin producing E. coli infection is not recommended due to their propensity to increase toxin production. Rapid detection of resistant strains would improve both treatment and prevention of this pathogen. With whole genome sequencing (WGS) now ubiquitous in the analyses of outbreak and surveillance samples, in silico methods can be both faster and cheaper than traditional wet-lab methods. In this work, machine learning (ML) classification methods were used for the prediction of antimicrobial resistance and the identification of potentially novel genomic markers of resistance in E. coli. There are four supplementary files to accompany Chapter 3. Supplementary Figure 1 is the Phylogenetic tree of the 4300 E. coli isolates with Salmonella as an outgroup; it is coloured by country of origin and serotype and paired with legends for each. Supplementary Table 1 contains the list of publicly available genomes collected and their corresponding metadata. Supplementary Table 2 contains the top features extracted from trained machine learning models and their annotations. Supplementary Table 3 contains the accuracy data for training machine learning models across a range of 100 to 5000 features. Supplementary Table 4 contains the complete performance data for the 11-mer and 25-mer machine learning models.
dc.identifier.uri	https://hdl.handle.net/10133/6660
dc.language.iso	en
dc.proquest.subject	0715
dc.proquest.subject	0487
dc.proquest.subject	0369
dc.proquestyes	Yes
dc.publisher	Lethbridge, Alta. : University of Lethbridge, Dept. of Chemistry and Biochemistry
dc.publisher.department	Department of Biochemistry
dc.publisher.faculty	Arts and Science
dc.relation.ispartofseries	Thesis (University of Lethbridge. Faculty of Arts and Science)
dc.subject	Bioinformatics
dc.subject	Machine learning
dc.subject	Antimicrobial resistance
dc.subject	Bacteria
dc.subject	E. coli
dc.subject.lcsh	Escherichia coli
dc.subject.lcsh	Anti-infective agents
dc.subject.lcsh	Dissertations, Academic
dc.title	Machine learning methods for the prediction of antimicrobial resistance and identification of novel markers of resistance in Escherichia coli
dc.type	Thesis