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

• URL: http://arxiv.org/abs/2609.04509v1
• Date: Thu, 03 Sep 2026 21:57:23 GMT
• Title: A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap
• Abstract summary: We introduce a semantic model for scientific evidence with three core classes, specialize it for genetics, and align it structurally to existing standards.<n>We evaluate the model through a human-AI annotation pilot over six genetics papers, yielding 28 evidence items and 95 source-anchored assertions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Explanation-Guided Metamorphic Testing of Specialized Language Models: An Empirical Study [0.5368630420272898]
  Task-specialized language models are increasingly integrated into software engineering to support vertical-domain activities such as issue triaging, document classification, and automated analysis.<n>This paper investigates whether explainability-guided metamorphic testing can improve the effectiveness and validity of robustness testing for specialized language models.
  arXiv  Detail & Related papers  (2026-08-07T10:28:50Z)
• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• The Calibration Turn in AI-Assisted Research: A Conceptual and Methodological Framework for Evidence-Licensed Claims [0.9805949492148788]
  This paper develops a conceptual framework for evidence-licensed claims in AI-assisted research.<n>It represents AI-assisted research as five operators: hypothesis generation, model-mediated consequence derivation, external validation, belief update, and claim calibration.
  arXiv  Detail & Related papers  (2026-06-30T07:46:54Z)
• Position: Anthropomorphic Misalignment Research Needs Stronger Evidence [52.76826423649968]
  We show how conceptual ambiguity, non-robust datasets, experimental design, and insufficient causal interventions can lead to overinterpretation of model behaviors.<n>This position paper aims to offer guidance on evidentiary considerations that can help improve methodological rigor in AMR.
  arXiv  Detail & Related papers  (2026-05-29T16:38:53Z)
• Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods [8.920725431658777]
  We argue for a validation framework analogous to clinical trials.<n>Just as trials require rigorous design and adverse-event reporting, genomic interpretability must move beyond cherry-picked plausibility.
  arXiv  Detail & Related papers  (2026-05-29T12:20:12Z)
• I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models [5.562984399879219]
  We introduce the textscI-SAFE (Interventional Secure, Accurate, Fair and Explainable) framework for scientific AI models.<n>textscI-SAFE evaluates raw model outputs under structurally guided perturbations of the input.<n>We instantiate textscI-SAFE on drug--target interaction (DTI) prediction using the Davis kinase benchmark.
  arXiv  Detail & Related papers  (2026-05-20T20:44:30Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity [66.94391219005291]
  This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic.<n>Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling.<n>ACP achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines.
  arXiv  Detail & Related papers  (2026-03-18T06:15:35Z)
• Refine Medical Diagnosis Using Generation Augmented Retrieval and Clinical Practice Guidelines [16.56254046507092]
  We introduce GARMLE-G, a Generation-Augmented Retrieval framework that grounds medical language model outputs in authoritative guidelines.<n>Unlike conventional Retrieval-Augmented Generation based approaches, GARMLE-G enables hallucination-free outputs by directly retrieving authoritative guideline content.<n>A prototype system for hypertension diagnosis was developed and evaluated on multiple metrics, demonstrating superior retrieval precision, semantic relevance, and clinical guideline adherence.
  arXiv  Detail & Related papers  (2025-06-22T11:31:13Z)
• Lean-STaR: Learning to Interleave Thinking and Proving [53.923617816215774]
  We present Lean-STaR, a framework for training language models to produce informal thoughts prior to each step of a proof.<n>Lean-STaR achieves state-of-the-art results on the miniF2F-test benchmark within the Lean theorem proving environment.
  arXiv  Detail & Related papers  (2024-07-14T01:43:07Z)
• GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models [56.63218531256961]
  We introduce GenBench, a benchmarking suite specifically tailored for evaluating the efficacy of Genomic Foundation Models. GenBench offers a modular and expandable framework that encapsulates a variety of state-of-the-art methodologies. We provide a nuanced analysis of the interplay between model architecture and dataset characteristics on task-specific performance.
  arXiv  Detail & Related papers  (2024-06-01T08:01:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.