ArticleJournal of biomedical informatics2023
BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- A unified framework and benchmark for generalizable biomedical knowledge extraction and applications with large language models.Cell reports. Medicine · 2026Article
- Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.Bioinformatics (Oxford, England) · 2026Article
- Relation extraction for diet, non-communicable disease and biomarker associations (RECoDe): a CoDiet study.Bioinformatics (Oxford, England) · 2026Article
- BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.Bioinformatics (Oxford, England) · 2026Article
- Integrating text mining and knowledge graph to enhance biopharmaceutical process optimization.PloS one · 2026Article
- The Role of N-ary Relations in Representing Biomedical Complexity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Knowledge-augmented pre-trained language models for biomedical relation extraction.BMC bioinformatics · 2025Article
- Enhancing biomedical relation extraction with directionality.Bioinformatics (Oxford, England) · 2025Article
- DiMB-RE: mining the scientific literature for diet-microbiome associations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Enhancing biomedical relation extraction through data-centric and preprocessing-robust ensemble learning approach.Database : the journal of biological databases and curation · 2025Article
- A large language model framework for literature-based disease-gene association prediction.Briefings in bioinformatics · 2024Article
- Optimized biomedical entity relation extraction method with data augmentation and classification using GPT-4 and Gemini.Database : the journal of biological databases and curation · 2024Article
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Integrating deep learning architectures for enhanced biomedical relation extraction: a pipeline approach.Database : the journal of biological databases and curation · 2024Article
- Functional implications of glycans and their curation: insights from the workshop held at the 16th Annual International Biocuration Conference in Padua, Italy.Database : the journal of biological databases and curation · 2024Article
- The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.Database : the journal of biological databases and curation · 2024Article
- The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.Database : the journal of biological databases and curation · 2024Article
- PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.Nucleic acids research · 2024Article
- Enhancing the coverage of SemRep using a relation classification approach.Journal of biomedical informatics · 2024Article
- Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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5 authors.
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Abstract
Biomedical relation extraction (RE) is the task of automatically identifying and characterizing relations between biomedical concepts from free text. RE is a central task in biomedical natural language processing (NLP) research and plays a critical role in many downstream applications, such as literature-based discovery and knowledge graph construction. State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation. Manual dataset annotation, however, is highly expensive and time-consuming, as it requires domain knowledge. Existing RE datasets are usually domain-specific or small, which limits the development of generalized and high-performing RE models. In this work, we present a novel framework for systematically addressing the data heterogeneity of individual datasets and combining them into a large dataset. Based on the framework and dataset, we report on BioREx, a data-centric approach for extracting relations. Our evaluation shows that BioREx achieves significantly higher performance than the benchmark system trained on the individual dataset, setting a new SOTA from 74.4% to 79.6% in F-1 measure on the recently released BioRED corpus. We further demonstrate that the combined dataset can improve performance for five different RE tasks. In addition, we show that on average BioREx compares favorably to current best-performing methods such as transfer learning and multi-task learning. Finally, we demonstrate BioREx's robustness and generalizability in two independent RE tasks not previously seen in training data: drug-drug N-ary combination and document-level gene-disease RE. The integrated dataset and optimized method have been packaged as a stand-alone tool available at https://github.com/ncbi/BioREx.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.