{
  "abstract": "Scientific and clinical decision-making depends on evidence from the primary literature, but existing standards for representing that evidence (FHIR Evidence, ECO, SEPIO, and the GA4GH Genomic Knowledge Standards) are oriented toward clinical-trial workflows, evidence codes, or single-variant assertions, and do not capture the fine-grained, domain-specific structure of claims in basic and pre-clinical research. We introduce a semantic model for scientific evidence with three core classes, specialize it for genetics, align it structurally to FHIR Evidence with a SEPIO-anchored credibility decomposition, and attach a compact dimensional vocabulary whose conditional-activation rules are validated by a SHACL schema for the implemented constraints. Using clinical variant interpretation as the driving use case, we evaluate the model through a human-AI annotation pilot over six genetics papers, yielding 28 evidence items and 95 source-anchored assertions, with a workflow that keeps curator-authored reference annotations distinct from AI-drafted annotations. Treating the pilot as a feasibility study rather than a benchmark, we argue that the model is a useful increment toward trustworthy, AI-ready infrastructure for variant interpretation: a reference data model and validation schema for representing genetic evidence.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.04509",
  "author_total": 2,
  "authors": [
    {
      "name": "Michael Bouzinier"
    },
    {
      "name": "Dmitry Etin"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/csjjk3bgfsw4k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.04509",
  "fulltext_url": null,
  "id": "csjjk3bgfsw4k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.04509",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.DB"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1312|Biomedical Text Mining and Ontologies"
    ]
  ],
  "openalex": "W7211906294",
  "pdf_url": "https://arxiv.org/pdf/2609.04509",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/csjjk3bgfsw4k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:19Z",
      "native_id": "2609.04509",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.04509"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap",
  "title_text": "A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:19Z",
  "url": "https://research.draconitic.com/paper/csjjk3bgfsw4k",
  "venue": "arXiv",
  "versions": []
}