发表机构
Harvard University; IDEXX Laboratories; Forome Association; Deggendorf Institute of Technology（哈佛大学; IDEXX实验室; Forome协会; 德根多夫理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出适配基础与临床前研究的遗传证据语义模型，经6篇遗传学论文人机试点验证，为构建AI友好的可信变异解读基础设施提供了关键参考。
AI 中文摘要
科学与临床决策依赖于原始文献中的证据，但现有证据表示标准（FHIR Evidence、ECO、SEPIO及GA4GH基因组知识标准）面向临床试验工作流、证据代码或单变异断言，无法捕获基础与临床前研究中主张的细粒度、领域特定结构。我们提出一种科学证据的语义模型，包含三个核心类，针对遗传学进行专门化，通过以SEPIO为锚点的可信度分解将其与FHIR Evidence结构对齐，并附加紧凑的维度词汇表，其条件激活规则由SHACL模式针对已实现约束进行验证。以临床变异解读为驱动用例，我们通过对6篇遗传学论文开展人机标注试点评估该模型，得到28个证据项和95个锚定来源的断言，工作流将 curator（审核员）编写的参考标注与AI起草的标注区分开。将该试点视为可行性研究而非基准，我们认为该模型是迈向可信、适配AI的变异解读基础设施的有益进展：一种用于表示遗传证据的参考数据模型与验证模式。
英文摘要
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.
Comments16 pages; supplementary material (32 pages) included in this submission. Schema, annotations, and code: https://github.com/ForomePlatform/genetic-evidence-model (archived at https://doi.org/10.5281/zenodo.22260686)
