ArticlemSystems2022
PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest.
Article in mSystems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 23 citations in OpenAlex.
- Using auxotrophic donor strains to explore pQBR57 plasmid host range among environmental soil bacterial isolates.Microbiology (Reading, England) · 2026Article
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- A Cautionary Case for Host Assignment Based on Broad Environmental blaEnvironmental microbiology reports · 2026Article
- Machine learning for prediction of histologic chorioamnionitis (stage ≥II) in parturients receiving labor analgesia: a retrospective multicentre cohort study.Frontiers in medicine · 2026Article
- ProFiT-SPEci-FISH: a novel approach for linking plasmids to hosts in complex microbial communities at the single-cell level.Microbiome · 2025Article
- A global soil plasmidome resource unveils functional and ecological roles of plasmids in soil microbiomes.Nature communications · 2025Article
- Towards the integration of antibiotic resistance gene mobility into environmental surveillance and risk assessment.npj antimicrobials and resistance · 2025Review
- Microbiome Single Cell Atlases Generated with a Commercial Instrument.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Metagenomics as a Transformative Tool for Antibiotic Resistance Surveillance: Highlighting the Impact of Mobile Genetic Elements with a Focus on the Complex Role of Phages.Antibiotics (Basel, Switzerland) · 2025Review
- MOSTPLAS: a self-correction multi-label learning model for plasmid host range prediction.Bioinformatics (Oxford, England) · 2025Article
- Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.Microbial genomics · 2025Article
- Klebsiella pneumoniae, a human-dog shuttle organism for the genes of CTX-M ESBL.Scientific reports · 2024Article
- Landscape of the metaplasmidome of deep-sea hydrothermal vents located at Arctic Mid-Ocean Ridges in the Norwegian-Greenland Sea: ecological insights from comparative analysis of plasmid identification tools.FEMS microbiology ecology · 2024Article
- HostNet: improved sequence representation in deep neural networks for virus-host prediction.BMC bioinformatics · 2023Article
- Article
- Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning.Bioinformatics (Oxford, England) · 2023Article
- Inference of the Life Cycle of Environmental Phages from Genomic Signature Distances to Their Hosts.Viruses · 2023Article
- HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer.Bioinformatics (Oxford, England) · 2023Article
- SourceFinder: a Machine-Learning-Based Tool for Identification of Chromosomal, Plasmid, and Bacteriophage Sequences from Assemblies.Microbiology spectrum · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning-specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/).
Indexed as
Identifiers
What OpenQuestion holds
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.