Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction

Májek, Peter; Lüftinger, Lukas; Beisken, Stephan; Rattei, Thomas; Materna, Arne

doi:10.3390/ijms222313049

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Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction

¹,

^1,2

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and

^1,*

Ares Genetics GmbH, Vienna 1030, Austria

Department of Computational Systems Biology, University of Vienna, Vienna 1030, Austria

Centre for Microbiology and Environmental Systems Science, Division of Computational Systems Biology, University of Vienna, Vienna 1030, Austria

Author to whom correspondence should be addressed.

Academic Editors: Hiromi Nishida and Hiroshi Toda

Int. J. Mol. Sci. 2021, 22(23), 13049; https://doi.org/10.3390/ijms222313049

Received: 15 October 2021 / Revised: 25 November 2021 / Accepted: 29 November 2021 / Published: 2 December 2021

(This article belongs to the Special Issue Microbioinformatics)

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Abstract

The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the functional impact of observed mutations with PROVEAN to use the predicted impact as a new feature for each protein in an organism’s proteome. The addition of the new features was tested on a total of 19,521 isolates across nine clinically relevant pathogens and 30 different antibiotics. The new features significantly improved the predictive performance of trained AMR models for Pseudomonas aeruginosa, Citrobacter freundii, and Escherichia coli. The balanced accuracy of the respective models of those three pathogens improved by 6.0% on average. View Full-Text

Keywords: machine learning; genomics; antimicrobial resistance; antibiotics; WGS; genome-wide mutation scoring

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Májek, P.; Lüftinger, L.; Beisken, S.; Rattei, T.; Materna, A. Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction. Int. J. Mol. Sci. 2021, 22, 13049. https://doi.org/10.3390/ijms222313049

AMA Style

Májek P, Lüftinger L, Beisken S, Rattei T, Materna A. Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction. International Journal of Molecular Sciences. 2021; 22(23):13049. https://doi.org/10.3390/ijms222313049

Chicago/Turabian Style

Májek, Peter, Lukas Lüftinger, Stephan Beisken, Thomas Rattei, and Arne Materna. 2021. "Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction" International Journal of Molecular Sciences 22, no. 23: 13049. https://doi.org/10.3390/ijms222313049

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Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction

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