Abstract Motivation Automatic information extraction from biomedical texts requires machine learning methodology that can recognise biomedical entities, characterise inter-entity relationships, and relate extracted information to specific research topics. Large language models (LLMs) excel in general tasks but perform less reliably in the biomedical domain, where texts are characterised by extensive technical terminology and semantic variations from general literature. There is an unmet need for annotated full-text datasets that can be used to fine-tune language models for significant biomedical applications. Here, we focus on extraction of the complex relationships between genes and diseases. Results We present BioTriplex, a corpus of 100 full-length biomedical research articles (comprising 604 subsection texts) manually annotated with disease names, genes, and 21 subtypes of disease-gene relationships. We employ BioTriplex to train the LLaMA 3.1 8B language model in gene-disease relation extraction. Our fine-tuned model outperforms zero-shot and few-shot approaches, both within the LLaMA 3.1 architecture and across the larger state-of-the-art LLMs GPT-4 and Claude Sonnet 3.7, and classifies gene-disease relation types with broader scope and greater granularity than previously described. These results validate BioTriplex as a useful full-text data resource and underscore the value of specialised datasets in fine-tuning language models for important biomedical tasks. Availability https://github.com/PanagiotisFytas/BioTriplex Supplementary information Supplementary data are available at Bioinformatics online.
Collins et al. (2026) studied this question.