Machine learning-based colistin resistance marker screening and phenotype prediction in Escherichia coli from whole genome sequencing data

Tian, Yingxin; Zhang, Di; Chen, Fangyuan; Rao, Guanhua; Zhang, Ying

doi:10.1016/j.jinf.2023.11.009

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Moreover, successfully developed novel machine learning models for predicting resistance of Escherichia coli to colistin have identified some previously unknown colistinresistant biomarkers [34]. Supervised machine learning classifiers identified known and unknown resistance-associated mutations and genes related to resistance to 28 antimicrobials in Escherichia coli and Salmonella enterica [35].…”

Section: Genome Analysis For Prediction Of Resistant Strains and Susc...mentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

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Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs’ kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

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Section: Genome Analysis For Prediction Of Resistant Strains and Susc...mentioning

confidence: 99%

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Rusic,

Kumric,

Seselja Perisin

et al. 2024

Microorganisms

View full text Add to dashboard Cite

show abstract

Machine learning-based colistin resistance marker screening and phenotype prediction in Escherichia coli from whole genome sequencing data

Cited by 1 publication

References 9 publications

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

Tackling the Antimicrobial Resistance “Pandemic” with Machine Learning Tools: A Summary of Available Evidence

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