Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning

Chiara Condorelli, Emanuele Nicitra, Nicolò Musso, Dafne Bongiorno, Stefania Stefani, Lucia Valentina Gambuzza, Vincenza Carchiolo and Mattia Frasca

PLOS ONE, 2024, vol. 19, issue 9, 1-19

Abstract: Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medicines. This resistance renders antibiotics and other antimicrobial drugs ineffective, making infections challenging or impossible to treat. This escalation in drug resistance heightens the risk of disease spread, severe illness, disability, and mortality. With datasets now containing hundreds or even thousands of pathogen genomes, machine learning techniques are on the rise for predicting antibiotic resistance in pathogens, prediction based on gene content and genome composition. Aim of this work is to combine and incorporate machine learning methods on bacterial genomic data to predict antimicrobial resistance, we will focus on the case of Klebsiella pneumoniae in order to support clinicians in selecting appropriate therapy.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0309333

DOI: 10.1371/journal.pone.0309333

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