ArticleScientific reports2019
Antimicrobial Resistance Prediction for Gram-Negative Bacteria via Game Theory-Based Feature Evaluation.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine Learning Prediction of Multidrug Resistance in Swine-DerivedVeterinary sciences · 2025Article
- Empirical evidence of resource dependent evolution of payoff matrices in Saccharomyces cerevisiae populations.Journal of evolutionary biology · 2025Article
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Review
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- The Pseudomonas aeruginosa Resistome: Permanent and Transient Antibiotic Resistance, an Overview.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Editorial: Machine learning approaches to antimicrobials: discovery and resistance.Frontiers in bioinformatics · 2024Article
- Artificial intelligence tools for the identification of antibiotic resistance genes.Frontiers in microbiology · 2024Review
- PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.Bioinformatics (Oxford, England) · 2023Article
- BPAGS: a web application for bacteriocin prediction via feature evaluation using alternating decision tree, genetic algorithm, and linear support vector classifier.Frontiers in bioinformatics · 2023Article
- Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome.Microbiome · 2022Article
- The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.Current genetics · 2021Review
- A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions.Frontiers in microbiology · 2021Review
- Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.Scientific reports · 2020Article
- BacEffluxPred: A two-tier system to predict and categorize bacterial efflux mediated antibiotic resistance proteins.Scientific reports · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing prevalence of antimicrobial-resistant bacteria drives the need for advanced methods to identify antimicrobial-resistance (AMR) genes in bacterial pathogens. With the availability of whole genome sequences, best-hit methods can be used to identify AMR genes by differentiating unknown sequences with known AMR sequences in existing online repositories. Nevertheless, these methods may not perform well when identifying resistance genes with sequences having low sequence identity with known sequences. We present a machine learning approach that uses protein sequences, with sequence identity ranging between 10% and 90%, as an alternative to conventional DNA sequence alignment-based approaches to identify putative AMR genes in Gram-negative bacteria. By using game theory to choose which protein characteristics to use in our machine learning model, we can predict AMR protein sequences for Gram-negative bacteria with an accuracy ranging from 93% to 99%. In order to obtain similar classification results, identity thresholds as low as 53% were required when using BLASTp.
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