ArticleProceedings of the National Academy of Sciences of the United States of America2021
Pathogenic potential assessment of the Shiga toxin-producing
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- NGS-Based Genomic Characterization of ESBL/AmpC-Producing Extraintestinal PathogenicAntibiotics (Basel, Switzerland) · 2026Article
- Interpretable learning algorithms enable pathogenic potential assessment and virulence-associated gene discovery ofFrontiers in microbiology · 2026Article
- 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
- Molecular and in silico analyses for detection of Shiga toxin-producing Escherichia coli (STEC) and highly pathogenic enterohemorrhagic Escherichia coli (EHEC) using genetic markers located on plasmid, O Island 57 and O Island 71.BMC veterinary research · 2024Article
- Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world.Nature reviews. Microbiology · 2024Review
- Use of whole genome sequencing for surveillance and control of foodborne diseases:Frontiers in microbiology · 2024Review
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
- Combination of whole genome sequencing and supervised machine learning provides unambiguous identification ofFrontiers in microbiology · 2023Article
- Genomics and pathotypes of the many faces of Escherichia coli.FEMS microbiology reviews · 2022Article
- Impact and prospect of the fourth industrial revolution in food safety: Mini-review.Food science and biotechnology · 2022Review
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
No grant is acknowledged in the PubMed record.
Abstract
Instead of conventional serotyping and virulence gene combination methods, methods have been developed to evaluate the pathogenic potential of newly emerging pathogens. Among them, the machine learning (ML)-based method using whole-genome sequencing (WGS) data are getting attention because of the recent advances in ML algorithms and sequencing technologies. Here, we developed various ML models to predict the pathogenicity of Shiga toxin-producing
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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.