ArticleFrontiers in microbiology2020
Evaluation of Machine Learning Models for Predicting Antimicrobial Resistance of
Article in Frontiers in microbiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- The Application of artificial intelligence in periprosthetic joint infection.Journal of advanced research · 2026Review
- Machine learning-based lineage prediction from antimicrobial susceptibility testing phenotypes forMicrobial genomics · 2026Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- A data privacy and deep learning based AMR dashboard for rural and regional veterinary practices in Texas.Frontiers in veterinary science · 2025Article
- Predicting antimicrobial resistance inComputational and structural biotechnology journal · 2024Article
- Using interpretable machine learning to predict bloodstream infection and antimicrobial resistance in patients admitted to ICU: Early alert predictors based on EHR data to guide antimicrobial stewardship.PLOS digital health · 2024Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes.Briefings in bioinformatics · 2024Article
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.PloS one · 2024Article
- Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods.Epidemiology and health · 2024Article
- From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry.FEMS microbiology reviews · 2023Review
- Article
- Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation.Antibiotics (Basel, Switzerland) · 2023Review
- Antimicrobial resistance and machine learning: past, present, and future.Frontiers in microbiology · 2023Article
- Machine Learning Techniques for Antimicrobial Resistance Prediction ofComputational intelligence and neuroscience · 2023Article
- Deciphering complex antibiotic resistance patterns inFrontiers in cellular and infection microbiology · 2023Article
- A comparison of various feature extraction and machine learning methods for antimicrobial resistance prediction inFrontiers in antibiotics · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is becoming a huge problem in countries all over the world, and new approaches to identifying strains resistant or susceptible to certain antibiotics are essential in fighting against antibiotic-resistant pathogens. Genotype-based machine learning methods showed great promise as a diagnostic tool, due to the increasing availability of genomic datasets and AST phenotypes. In this article, Support Vector Machine (SVM) and Set Covering Machine (SCM) models were used to learn and predict the resistance of the five drugs (Tetracycline, Ampicillin, Sulfisoxazole, Trimethoprim, and Enrofloxacin). The SVM model used the number of co-occurring k-mers between the genome of the isolates and the reference genes to learn and predict the phenotypes of the bacteria to a specific antimicrobial, while the SCM model uses a greedy approach to construct conjunction or disjunction of Boolean functions to find the most concise set of k-mers that allows for accurate prediction of the phenotype. Five-fold cross-validation was performed on the training set of the SVM and SCM model to select the best hyperparameter values to avoid model overfitting. The training accuracy (mean cross-validation score) and the testing accuracy of SVM and SCM models of five drugs were above 90% regardless of the resistant mechanism of which were acquired resistant or point mutation in the chromosome. The results of correlation between the phenotype and the model predictions of the five drugs indicated that both SVM and SCM models could significantly classify the resistant isolates from the sensitive isolates of the bacteria (
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