ArticleFrontiers in microbiology2020
PARMAP: A Pan-Genome-Based Computational Framework for Predicting Antimicrobial Resistance.
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 11 papers.
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Who cites it
11 citing papers in PubMed.
- Pan-genome analysis reveals the triangle mechanisms of antibiotic resistance, pathogenicity, and biofilm formation in Enterococcus faecalis.Molecular genetics and genomics : MGG · 2026Article
- Interred mechanisms of resistance and host immune evasion revealed through network-connectivity analysis ofmSystems · 2025Article
- Article
- Evolutionary genomic analyses of canineApplied and environmental microbiology · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Machine Learning of the Whole Genome Sequence ofMicroorganisms · 2023Review
- Using bacterial pan-genome-based feature selection approach to improve the prediction of minimum inhibitory concentration (MIC).Frontiers in genetics · 2023Article
- Prediction of antimicrobial minimal inhibitory concentrations forSaudi journal of biological sciences · 2022Article
- Bioinformatics tools used for whole-genome sequencing analysis of Neisseria gonorrhoeae: a literature review.Briefings in functional genomics · 2022Review
- Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing.Briefings in bioinformatics · 2022Article
- Paving the way for precise diagnostics of antimicrobial resistant bacteria.Frontiers in molecular biosciences · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) has emerged as one of the most urgent global threats to public health. Accurate detection of AMR phenotypes is critical for reducing the spread of AMR strains. Here, we developed PARMAP (Prediction of Antimicrobial Resistance by MAPping genetic alterations in pan-genome) to predict AMR phenotypes and to identify AMR-associated genetic alterations based on the pan-genome of bacteria by utilizing machine learning algorithms. When we applied PARMAP to 1,597
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Registered trials
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