ArticleBioinformatics (Oxford, England)2023
AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- The DSMZ Digital Diversity Annotation Hub: a pipeline for database expansion via text mining and human curation.Journal of integrative bioinformatics · 2026Article
- PubMind: literature-based genetic variant extraction and functional annotation using large language models.Nature communications · 2026Article
- Literature-derived, context-aware gene regulatory networks improve biological predictions and mathematical modeling.Bioinformatics (Oxford, England) · 2026Article
- Literature-informed gene extraction and ranking for multimodal data fusion.Briefings in bioinformatics · 2026Article
- GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition.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
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Article
- Enhancing biomedical named entity recognition with parallel boundary detection and category classification.BMC bioinformatics · 2025Article
- Integrating AI-powered text mining from PubTator into the manual curation workflow at the Comparative Toxicogenomics Database.Database : the journal of biological databases and curation · 2025Article
- ENQUIRE automatically reconstructs, expands, and drives enrichment analysis of gene and Mesh co-occurrence networks from context-specific biomedical literature.PLoS computational biology · 2025Article
- 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
- HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.Bioinformatics (Oxford, England) · 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
- The overview of the BioRED (Biomedical Relation Extraction Dataset) track at BioCreative VIII.Database : the journal of biological databases and curation · 2024Article
- BELHD: improving biomedical entity linking with homonym disambiguation.Bioinformatics (Oxford, England) · 2024Article
- Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes.Database : the journal of biological databases and curation · 2024Article
- Towards discovery: an end-to-end system for uncovering novel biomedical relations.Database : the journal of biological databases and curation · 2024Article
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6 authors.
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Abstract
motivationBiomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g. gene or disease).
resultsWe therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g. the entire PubMed data). AVAILABILITY AND IMPLEMENTATION: The source code, trained models and data for AIONER are freely available at https://github.com/ncbi/AIONER.
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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.