ArticleFrontiers in microbiology2022
A Practical Approach for Predicting Antimicrobial Phenotype Resistance in
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
31 citing papers in PubMed.
- Climate change impact on disease emergence and antimicrobial resistance: A perspective on transmission and detection.iScience · 2026Review
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Can Artificial Intelligence Transform Early Warning for Antimicrobial-Resistant Outbreak Clones? Approaches, Gaps, and Opportunities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2026Review
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Integrating artificial intelligence with genome sequencing against antimicrobial resistance: a narrative review.Frontiers in public health · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- Gene copy-number features generalize better than SNPs for antimicrobial resistance prediction in Staphylococcus aureus.npj antimicrobials and resistance · 2025Article
- HKDE-LACM: a hybrid model for lactic acid bacteria classification via k-mer and DNABERT-2 embedding fusion with cyclic DE-BO optimization.BMC genomics · 2025Article
- Prediction of antimicrobial resistance inMicrobiology spectrum · 2025Article
- Review
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
- Antimicrobial minimum inhibitory concentrations can be imputed from phenotypic data using a random forest approach.American journal of veterinary research · 2025Article
- Advancements in AI-driven drug sensitivity testing research.Frontiers in cellular and infection microbiology · 2025Review
- The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.Computational and structural biotechnology journal · 2025Review
- Generative AI in microbial evolution and resistance: toward robust, explainable, and equitable predictions.Frontiers in microbiology · 2025Article
- Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.PLoS computational biology · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Accessory genes define species-specific routes to antibiotic resistance.Life science alliance · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the reduction in sequencing price and acceleration of sequencing speed, it is particularly important to directly link the genotype and phenotype of bacteria. Here, we firstly predicted the minimum inhibitory concentrations of ten antimicrobial agents for
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.