ArticleInternational journal of molecular sciences2021
Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Pooled it
- Prediction of antimicrobial susceptibility of pneumococci based on whole-genome sequencing data: a direct comparison of two genomic tools to conventional antimicrobial susceptibility testing.Journal of clinical microbiology · 2025Article
- Evaluating the Performance ofCombinatorial chemistry & high throughput screening · 2025Article
- Article
- The Impact of Artificial Intelligence on Microbial Diagnosis.Microorganisms · 2024Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis.BMC microbiology · 2023Article
- Review
- Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples.Antibiotics (Basel, Switzerland) · 2023Article
- Validation and Application of Long-Read Whole-Genome Sequencing for Antimicrobial Resistance Gene Detection and Antimicrobial Susceptibility Testing.Antimicrobial agents and chemotherapy · 2023Article
- Acinetobacter baumannii Genomic Sequence-Based Core Genome Multilocus Sequence Typing Using Ridom SeqSphere+ and Antimicrobial Susceptibility Prediction in ARESdb.Journal of clinical microbiology · 2022Article
- Automated antimicrobial susceptibility testing and antimicrobial resistance genotyping using Illumina and Oxford Nanopore Technologies sequencing data amongFrontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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
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