ArticleFrontiers in microbiology2023
Combination of whole genome sequencing and supervised machine learning provides unambiguous identification of
Article in Frontiers in microbiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 6 citations in OpenAlex.
- Integrative Modular, Network-Based, and Machine Learning Framework for Predicting Accessory Genome Functions and Virulence in Escherichia coli O157:H7.MicrobiologyOpen · 2026Article
- 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
- Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.BMC genomics · 2023Article
- Precision metagenomics sequencing for food safety: hybrid assembly of Shiga toxin-producingFrontiers in microbiology · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The objective of this study was to develop, using a genome wide machine learning approach, an unambiguous model to predict the presence of highly pathogenic STEC in Methods: The Machine Learning (ML) approach was used in this study on a large curated dataset composed of 1,493 Results and discussion: It is remarkable that only using these six genes, EHEC can be clearly identified from
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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.