ReviewTrends in microbiology2021
Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.
Review in Trends in microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Approaches for Microorganism Identification, Virulence Assessment, and Antimicrobial Susceptibility Evaluation Using DNA Sequencing Methods: A Systematic Review.Molecular biotechnology · 2025Pooled it
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- Soil Acidification Enriches Antibiotic Resistome.Global change biology · 2026Article
- Integrated Genome-Wide Association Study and Machine Learning Approach for Characterizing the Determinants of Biofilm Formation in Staphylococcus aureus.Interdisciplinary sciences, computational life sciences · 2026Article
- Defining thebioRxiv : the preprint server for biology · 2026Article
- Resolving competing evolutionary histories in joint ancestral state reconstruction.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Interpretable learning algorithms enable pathogenic potential assessment and virulence-associated gene discovery ofFrontiers in microbiology · 2026Article
- Effects of predatory mite biocontrols on the dispersal of antibiotic resistomes and virulence factors in tea garden soils.Microbiome · 2025Article
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- Uncertainty in joint Ancestral State Reconstruction: Improving accuracy and biological interpretability of ancestral state prediction.bioRxiv : the preprint server for biology · 2025Article
- Climate warming fuels the global antibiotic resistome by altering soil bacterial traits.Nature ecology & evolution · 2025Article
- Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics.Bioinformatics (Oxford, England) · 2025Article
- VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts.Briefings in bioinformatics · 2025Article
- aurora: a machine learning gwas tool for analyzing microbial habitat adaptation.Genome biology · 2025Article
- Accelerating eucalypt clone selection pipeline via cloned progeny trials and molecular data.Plant methods · 2025Article
- Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Immunosenescence: How Aging Increases Susceptibility to Bacterial Infections and Virulence Factors.Microorganisms · 2024Review
- Step-by-Step Bacterial Genome Comparison.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Integrating Genomic Data with the Development of CRISPR-Based Point-of-Care-Testing for Bacterial Infections.Current clinical microbiology reports · 2024Review
- Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen.PLoS pathogens · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.
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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 Socratic graph.