ArticleBioinformatics (Oxford, England)2022
BERN2: an advanced neural biomedical named entity recognition and normalization tool.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Microbial named entity recognition and normalisation for AI-assisted literature review and meta-analysis.Bioinformatics (Oxford, England) · 2026Pooled it
- Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification.JMIR medical informatics · 2026Article
- [Structured Annotation and Information Extraction of Epilepsy Clinical Texts].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- CellExLink: End-to-end cell-type recognition and normalization in biomedical text.PLoS computational biology · 2026Article
- Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.NPJ digital medicine · 2026Article
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- TrialMatchAI: an end-to-end AI-powered clinical trial recommendation system to streamline patient-to-trial matching.Nature communications · 2026Article
- Operationalizing Large Language Models for Clinical Research Data Extraction: Methods, Quality Control, and Governance.Journal of medical systems · 2026Review
- Fine-tuned GPT-based foundation models effectively reconstruct bacterial transcriptional regulatory networks from literature.Frontiers in artificial intelligence · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- Large language model powered knowledge graph construction for mental health exploration.Nature communications · 2025Article
- PuMA: PubMed gene/cell type-relation Atlas.BMC bioinformatics · 2025Article
- PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science.Scientific data · 2025Article
- Systematic evaluation of patient-reported outcomes in clinical trials of digital health in cardiovascular diseases.NPJ digital medicine · 2025Article
- Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study.JMIR medical informatics · 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
- Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.BMC bioinformatics · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
- Systematic analysis of hepatotoxicity: combining literature mining and AI language models.Frontiers in artificial intelligence · 2025Article
- A simplified retriever to improve accuracy of phenotype normalizations by large language models.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e.g. diseases and drugs) from the ever-growing biomedical literature. In this article, we present BERN2 (Advanced Biomedical Entity Recognition and Normalization), a tool that improves the previous neural network-based NER tool by employing a multi-task NER model and neural network-based NEN models to achieve much faster and more accurate inference. We hope that our tool can help annotate large-scale biomedical texts for various tasks such as biomedical knowledge graph construction. AVAILABILITY AND IMPLEMENTATION: Web service of BERN2 is publicly available at http://bern2.korea.ac.kr. We also provide local installation of BERN2 at https://github.com/dmis-lab/BERN2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.