ReviewFrontiers in cell and developmental biology2020
Named Entity Recognition and Relation Detection for Biomedical Information Extraction.
Review in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 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
52 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Artificial Intelligence in Pharmaceutical Regulatory Science: Opportunities, Challenges, and Emerging Frameworks.The AAPS journal · 2026Review
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Literature-informed gene extraction and ranking for multimodal data fusion.Briefings in bioinformatics · 2026Article
- DA-BioNER: data augmentation based on few-shot learning and distant supervision for biomedical named entity recognition.Bioinformatics (Oxford, England) · 2026Article
- Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.Biomedicines · 2026Article
- Integrating curation into scientific publishing to train AI models.Bioinformatics (Oxford, England) · 2026Article
- A FAIR gold-standard benchmark for geographic entity recognition in cancer genomics literature for biomedical NLP.Bioinformatics advances · 2026Article
- Bangla MedER: Multi-BERT ensemble approach for the recognition of Bangla medical entity.PloS one · 2026Article
- Assessing patient perceptions of off-label cannabidiol use for insomnia through sentiment analysis.Journal of cannabis research · 2025Article
- Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs).BMC medical informatics and decision making · 2025Article
- Ontology enrichment using a large language model: Applying lexical, semantic, and knowledge network-based similarity for concept placement.Journal of biomedical informatics · 2025Article
- PuMA: PubMed gene/cell type-relation Atlas.BMC bioinformatics · 2025Article
- Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform.Science advances · 2025Article
- Precision in Parsing: Evaluation of an Open-Source Named Entity Recognizer (NER) in Veterinary Oncology.Veterinary and comparative oncology · 2025Article
- Biomedical named entity recognition using improved green anaconda-assisted Bi-GRU-based hierarchical ResNet model.BMC bioinformatics · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Chinese medical named entity recognition based on multimodal information fusion and hybrid attention mechanism.PloS one · 2025Article
- Natural language processing-based analysis of the level of adoption by expert radiologists of the ASSR, ASNR and NASS version 2.0 of lumbar disc nomenclature: an eight-year survey.Quantitative imaging in medicine and surgery · 2024Article
- Lifestyle factors in the biomedical literature: an ontology and comprehensive resources for named entity recognition.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of scientific publications in the literature is steadily growing, containing our knowledge in the biomedical, health, and clinical sciences. Since there is currently no automatic archiving of the obtained results, much of this information remains buried in textual details not readily available for further usage or analysis. For this reason, natural language processing (NLP) and text mining methods are used for information extraction from such publications. In this paper, we review practices for Named Entity Recognition (NER) and Relation Detection (RD), allowing, e.g., to identify interactions between proteins and drugs or genes and diseases. This information can be integrated into networks to summarize large-scale details on a particular biomedical or clinical problem, which is then amenable for easy data management and further analysis. Furthermore, we survey novel deep learning methods that have recently been introduced for such tasks.
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.