Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae
Author(s)
Type
Journal Article
Abstract
Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.
Date Issued
2024-03-01
Date Acceptance
2024-03-07
Citation
Microbial Genomics, 2024, 10 (3)
ISSN
2057-5858
Publisher
Microbiology Society
Journal / Book Title
Microbial Genomics
Volume
10
Issue
3
Copyright Statement
© 2024 The Authors This is an open-access article distributed under the terms of the Creative Commons Attribution License.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/38529944
Subjects
AMR
antibiotic resistance
bacterial genomics
Genetics & Heredity
GWAS
Klebsiella pneumoniae
Life Sciences & Biomedicine
machine learning
MIC
Microbiology
MODELS
REGULARIZATION
Science & Technology
SELECTION
Publication Status
Published
Coverage Spatial
England
Article Number
001222
Date Publish Online
2024-03-26