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arXiv AI By Michael Bouzinier, Dmitry Etin

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

Read the original on arXiv AI →

The article presents a new semantic model for representing scientific evidence, specifically tailored to genetics, that extends existing standards by adding fine‑grained, domain‑specific structure. It aligns with FHIR Evidence and SEPIO, incorporates a compact vocabulary validated by SHACL, and was tested in a human‑AI annotation pilot on six genetics papers, producing 28 evidence items and 95 source‑anchored assertions. The authors argue that this model advances trustworthy, AI‑ready infrastructure for variant interpretation by providing a reference data model and validation schema for genetic evidence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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