ArticleDatabase : the journal of biological databases and curation2024
The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.
Article in Database : the journal of biological databases and curation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- eGoT: enhanced graph-of-thoughts for multi-hop knowledge retrieval and hypothesis generation in biomedicine.Bioinformatics (Oxford, England) · 2026Article
- Article
- BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.Bioinformatics (Oxford, England) · 2026Article
- 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 comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- JTIS: enhancing biomedical document-level relation extraction through joint training with intermediate steps.Database : the journal of biological databases and curation · 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
- The biomedical relationship corpus of the BioRED track at the BioCreative VIII challenge and workshop.Database : the journal of biological databases and curation · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
23 authors.
Funding
Abstract
The BioRED track at BioCreative VIII calls for a community effort to identify, semantically categorize, and highlight the novelty factor of the relationships between biomedical entities in unstructured text. Relation extraction is crucial for many biomedical natural language processing (NLP) applications, from drug discovery to custom medical solutions. The BioRED track simulates a real-world application of biomedical relationship extraction, and as such, considers multiple biomedical entity types, normalized to their specific corresponding database identifiers, as well as defines relationships between them in the documents. The challenge consisted of two subtasks: (i) in Subtask 1, participants were given the article text and human expert annotated entities, and were asked to extract the relation pairs, identify their semantic type and the novelty factor, and (ii) in Subtask 2, participants were given only the article text, and were asked to build an end-to-end system that could identify and categorize the relationships and their novelty. We received a total of 94 submissions from 14 teams worldwide. The highest F-score performances achieved for the Subtask 1 were: 77.17% for relation pair identification, 58.95% for relation type identification, 59.22% for novelty identification, and 44.55% when evaluating all of the above aspects of the comprehensive relation extraction. The highest F-score performances achieved for the Subtask 2 were: 55.84% for relation pair, 43.03% for relation type, 42.74% for novelty, and 32.75% for comprehensive relation extraction. The entire BioRED track dataset and other challenge materials are available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/ and https://codalab.lisn.upsaclay.fr/competitions/13377 and https://codalab.lisn.upsaclay.fr/competitions/13378. Database URL: https://ftp.ncbi.nlm.nih.gov/pub/lu/BC8-BioRED-track/https://codalab.lisn.upsaclay.fr/competitions/13377https://codalab.lisn.upsaclay.fr/competitions/13378.
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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.