ArticleProceedings of the National Academy of Sciences of the United States of America2016
Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed.
- Genomic signatures associated with epidemiologically defined high-risk pathogenic Escherichia coli isolates identified by interpretable machine learning.Journal of microbiology (Seoul, Korea) · 2026Article
- Epidemiology, antimicrobial resistance transmission, and One Health-based prevention of non-typhoidalFrontiers in microbiology · 2026Review
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.Communications biology · 2025Article
- Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.Nature reviews. Microbiology · 2024Review
- Predictive modeling of antibiotic eradication therapy success for new-onset Pseudomonas aeruginosa pulmonary infections in children with cystic fibrosis.PLoS computational biology · 2023Article
- Immunoinformatic prediction of the pathogenicity of bovine viral diarrhea virus genotypes: implications for viral virulence determinants, designing novel diagnostic assays and vaccines development.Frontiers in veterinary science · 2023Article
- Combination of whole genome sequencing and supervised machine learning provides unambiguous identification ofFrontiers in microbiology · 2023Article
- Comparative Genomics Applied to Systematically Assess Pathogenicity Potential in Shiga Toxin-ProducingMicroorganisms · 2022Article
- Research and Innovation Opportunities to Improve Epidemiological Knowledge and Control of Environmentally Driven Zoonoses.Annals of global health · 2022Review
- Genomic Diversity, Virulence Gene, and Prophage Arrays of Bovine and Human Shiga Toxigenic and EnteropathogenicFrontiers in microbiology · 2022Article
- Genome Sequence Analysis and Characterization of Shiga Toxin 2 Production byFrontiers in cellular and infection microbiology · 2022Article
- The future of zoonotic risk prediction.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021Article
- Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis.Sensors (Basel, Switzerland) · 2021Article
- Pathogenic potential assessment of the Shiga toxin-producingProceedings of the National Academy of Sciences of the United States of America · 2021Article
- Shiga Toxin-Producing E. coli in Animals: Detection, Characterization, and Virulence Assessment.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Genomic analysis of shiga toxin-containing Escherichia coli O157:H7 isolated from Argentinean cattle.PloS one · 2021Article
- The population genetics of pathogenic Escherichia coli.Nature reviews. Microbiology · 2021Review
- Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics.Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases · 2020Review
- The Role ofToxins · 2020Review
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Abstract
Sequence analyses of pathogen genomes facilitate the tracking of disease outbreaks and allow relationships between strains to be reconstructed and virulence factors to be identified. However, these methods are generally used after an outbreak has happened. Here, we show that support vector machine analysis of bovine E. coli O157 isolate sequences can be applied to predict their zoonotic potential, identifying cattle strains more likely to be a serious threat to human health. Notably, only a minor subset (less than 10%) of bovine E. coli O157 isolates analyzed in our datasets were predicted to have the potential to cause human disease; this is despite the fact that the majority are within previously defined pathogenic lineages I or I/II and encode key virulence factors. The predictive capacity was retained when tested across datasets. The major differences between human and bovine E. coli O157 isolates were due to the relative abundances of hundreds of predicted prophage proteins. This finding has profound implications for public health management of disease because interventions in cattle, such a vaccination, can be targeted at herds carrying strains of high zoonotic potential. Machine-learning approaches should be applied broadly to further our understanding of pathogen biology.
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