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Machine Learning Methods for the Prediction of Antimicrobial Resistance and Identification of Novel Markers of Resistance in Escherichia coli.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Machine Learning Methods for the Prediction of Antimicrobial Resistance and Identification of Novel Markers of Resistance in Escherichia coli./
Author:	Moat, Janice.
Description:	1 online resource (152 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-08.
Contained By:	Masters Abstracts International85-08.
Subject:	Microbiology. -
Online resource:	click for full text (PQDT)
ISBN:	9798381686241

Machine Learning Methods for the Prediction of Antimicrobial Resistance and Identification of Novel Markers of Resistance in Escherichia coli.
Moat, Janice.

Machine Learning Methods for the Prediction of Antimicrobial Resistance and Identification of Novel Markers of Resistance in Escherichia coli. - 1 online resource (152 pages)

Source: Masters Abstracts International, Volume: 85-08.

Thesis (M.Sc.)--University of Lethbridge (Canada), 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381686241Subjects--Topical Terms:

591510
Microbiology.
Subjects--Index Terms:

Antimicrobial resistanceIndex Terms--Genre/Form:

554714
Electronic books.

Machine Learning Methods for the Prediction of Antimicrobial Resistance and Identification of Novel Markers of Resistance in Escherichia coli.
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520 $a 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.
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