A Semantic Model of Genetic Evidence

A conceptual framework for representing scientific and genetic evidence from the biomedical literature in a form suitable for variant interpretation, automated reasoning, and AI-ready clinical infrastructure.

This repository accompanies a paper submitted to the 17th International Conference on Biological and Biomedical Ontology (ICBO 2026, Washington D.C., July 15–17 2026).

What this repository contains

.
├── paper/          LaTeX source for the manuscript (main.tex, references.bib)
├── schema/         SHACL shapes + supporting definitions
├── annotations/    One YAML file per annotated publication + raw PDF extractions
├── extraction/     Scripts that extract highlights and callouts from annotated PDFs
├── figures/        Source files for figures used in the paper
└── .github/        CI configuration validating annotations against the schema

The annotation corpus

Six publications, chosen to span the major epistemic shapes of genetic evidence encountered in literature-based variant interpretation:

Annotation	Role in the paper	Source
jossin2017.yaml (Llgl1)	molecular mechanism	manual (ground truth)
davis2011.yaml (TTC21B)	breadth exemplar	manual (ground truth)
nelson1992.yaml (CD18)	classical molecular genetics	manual (ground truth)
gupta2015.yaml (ATP6AP2)	low-credibility edge case	manual (ground truth)
duerr2006.yaml (IL23R)	clean GWAS exemplar	AI-drafted, expert-reviewed
inouye2018.yaml (metaGRS)	polygenic-score model-extension case	AI-drafted, expert-reviewed

For manually annotated papers, PDF highlights and sticky-note callouts are the authoritative ground truth. The YAML is a structured transformation of those artefacts, with every assertion carrying a source_span pointing back to the specific page and quoted passage. Disagreements between the annotator and the AI reviewer are not silently resolved — they are captured as reviewer_query, reviewer_suggestion, or reviewer_disagreement fields so they remain auditable.

For AI-drafted annotations, the same source_span anchoring is used, and curator review is the evaluation signal.

Extraction pipeline

The scripts in extraction/ read a PDF with highlights and callouts and emit a structured JSON record of every annotation, including the text covered by each highlight (extracted via coordinate lookup) and the free-text notes attached to callouts.

python3 extraction/extract_annotations.py paper.pdf out.json

Two extractors are provided. extract_annotations.py uses PyMuPDF and is the recommended one — it reliably recovers the text under each highlight. extract_annotations_pypdf.py uses pypdf and is provided as a fallback.

Dependencies:

pip install pymupdf pypdf pyyaml

Schema

Two complementary representations:

• schema/genetic_evidence.shacl.ttl — SHACL shapes encoding the class hierarchy, dimension types, cardinalities, and conditional activation rules. This is the machine-checkable validation layer.
• schema/dimensions.md — the human-readable enumeration reference for all categorical value types (knowledge domain, method, target type, etc.).

Annotations in annotations/ are validated against the SHACL shapes by the CI workflow in .github/workflows/validate.yml. A failing validation blocks merges and indicates either a schema bug or an annotation error.

Citing this work

See CITATION.cff. Once the paper is accepted, a DOI will be registered via Zenodo and the CITATION file updated.

License

See LICENSE (content) and LICENSE-CODE (scripts and schema). Both are permissive and compatible with CEUR-WS publication terms.

Contact

See the corresponding-author block on the paper's title page.