A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap

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.

16 pages; supplementary material (32 pages) included in this submission. Schema, annotations, and code: this https URL (archived at this https URL)

Databases (cs.DB); Artificial Intelligence (cs.AI); Genomics (q-bio.GN)

H.2.1; I.2.4; J.3

arXiv:2609.04509 [cs.DB]

(or arXiv:2609.04509v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2609.04509 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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