Relation extraction for diet, non-communicable disease and biomarker associations (RECoDe): a CoDiet study
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Diet plays a critical role in human health, with growing evidence linking dietary habits to disease outcomes. However, extracting structured dietary knowledge from biomedical literature remains challenging due to the lack of dedicated relation extraction datasets. To address this gap, we introduce RECoDe, a novel relation extraction (RE) dataset designed specifically for diet, disease, and related biomedical entities. RECoDe captures a diverse set of relation types, including a broad spectrum of positive association patterns and explicit negative examples, with over 5000 human-annotated instances validated by up to five independent annotators. Furthermore, we benchmark various natural language processing (NLP) RE models, including BERT-based architectures and enhanced prompting techniques with locally deployed large language models (LLMs) to improve classification performance on underrepresented relation types. The best performing model was gpt-oss-20B, a locally-deployed open-weight LLM, achieving an F1-score of 61% (macro) for multi-class classification and 89% for binary classification using a hierarchical prompting strategy with a separate reflection step built in. To demonstrate the practical utility of RECoDe, we introduce the Contextual Co-occurrence Summarisation (CoCoS) framework, which aggregates sentence-level relation extractions into document-level summaries and further integrates evidence across multiple documents. CoCoS produces effect estimates consistent with established dietary knowledge, demonstrating its validity as a general framework for systematic evidence synthesis.
Date Issued
2026-08-01
Date Acceptance
2026-06-22
Citation
Bioinformatics, 2026, 42 (Supplement_2)
ISSN
1367-4803
Publisher
Oxford University Press
Journal / Book Title
Bioinformatics
Volume
42
Issue
Supplement_2
Copyright Statement
© The Author(s) 2026. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
10.1093/bioinformatics/btag439
Subjects
Biomedical Text Mining
Diet-Disease Relationships
Knowledge Graph
Large Language Models
Relation Extraction
Publication Status
Published
Article Number
btag439
Date Publish Online
2026-08-21