{
  "abstract": "Core-genome multilocus sequence typing (cgMLST) and core-genome single-nucleotide polymorphism (cgSNP) analysis have made whole-genome sequencing (WGS) the standard tool for foodborne disease surveillance. Fixed-threshold clustering is increasingly unreliable for highly clonal lineages such as Salmonella Enteritidis ST11 and Escherichia coli ST131, whose core genomes differ by only a handful of alleles even between unrelated strains. The central question is whether surveillance should keep simple, easy-to-interpret allele thresholds or move to probabilistic machine-learning models that also use accessory-genome variation. Accessory genes and mobile genetic elements, including prophages, plasmids, and genomic islands, change on timescales that match outbreak transmission and carry niche-adaptation signal absent from the core genome. Gradient-boosting and deep-learning models can combine genomic, epidemiological, and spatiotemporal data to report source probabilities instead of binary cluster assignments. A U.S. CDC random forest trained on 18,661 Salmonella isolates achieved 81% out-of-bag accuracy when every prediction was counted and 91% when limited to high-confidence isolates whose largest predicted source probability was above 0.50. Feature-attribution analysis ranked plasmid-borne and cell-surface genes among the strongest predictors, raising host-adaptation hypotheses that still require experimental testing. The review ends with the threshold-vs.-probability debate, barriers to data sharing and federated-learning alternatives, and the gap between retrospective accuracy and prospective validation in routine surveillance.",
  "abstract_shown": true,
  "affiliations": [
    "Health Monitoring Testing and Protection Institute, Hubei Provincial Center for Disease Control and Prevention"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yong Song"
    },
    {
      "affs": [
        0
      ],
      "name": "Chi Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Jing-jing Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Shi-yu Dai"
    },
    {
      "affs": [
        0
      ],
      "name": "Qin Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhen-zhen Yao"
    },
    {
      "affs": [
        0
      ],
      "name": "Ying-Ying Ouyang"
    },
    {
      "affs": [
        0
      ],
      "name": "Chen-rui Gong"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7syk7hssgmjgg.bib",
  "cited_by": 0,
  "doi": "10.3389/fpubh.2026.1968969",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13728474/?tool=EBI",
  "id": "7syk7hssgmjgg",
  "issns": [
    "2296-2565"
  ],
  "landing_url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1968969/full",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Mini Review"
    ],
    [
      "openalex",
      "1106|Salmonella and Campylobacter epidemiology"
    ]
  ],
  "openalex": "W7223001829",
  "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1968969/pdf",
  "pmcid": "PMC13728474",
  "pmid": "42978906",
  "published": "2026-10-20",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/7syk7hssgmjgg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:24Z",
      "native_id": "10.3389/fpubh.2026.1968969",
      "source": "crossref",
      "url": "https://www.frontiersin.org/articles/10.3389/fpubh.2026.1968969/full"
    },
    {
      "first_seen": "2026-11-04T15:20:45Z",
      "native_id": "PMC:PMC13728474",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13728474"
    },
    {
      "first_seen": "2026-11-07T12:21:27Z",
      "native_id": "PMC13728474",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fpubh.2026.1968969"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "issn:2296-2565",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Whole-genome sequencing-driven source tracing of foodborne diseases: from cgMLST/SNP typing toward an integrated framework of pan-genomics and machine learning",
  "title_text": "Whole-genome sequencing-driven source tracing of foodborne diseases: from cgMLST/SNP typing toward an integrated framework of pan-genomics and machine learning",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:21:27Z",
  "url": "https://research.draconitic.com/paper/7syk7hssgmjgg",
  "venue": "Frontiers in Public Health",
  "versions": []
}