Finds genes, proteins, and cells in English biomedical research text
Zero-shot NER (your own labels)Genes, proteins and cellsEnglishMedium · 220Mapache-2.0
What it does
This zero-shot model detects DNA, RNA, cell lines, cell types, and proteins in English biomedical research text, with user-supplied entity labels at inference. It reports an F1 of 0.791 on the JNLPBA benchmark test set.
Best for
- Pathway curation from research literature
- Molecular interaction mining in articles
- Custom entity detection in biomedical text
What it detects (5 labels)
Trained on the label below; you can pass any other label names at inference time.
- DNA
- RNA
- Cell line
- Cell tyle
- Protein
Quality
| Benchmark | JNLPBA · all models on this benchmark |
|---|---|
| F1 | 0.791 |
| Precision | 0.743 |
| Recall | 0.845 |
| Accuracy | 0.942 |
| Source | Reported by the authors (test_results.json in the repository) |
Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-DNA-Base-220M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))All model information shown here is public and belongs to its authors: use and download it freely under each model’s license. The selection, organization, search design and presentation of this catalog are © 2026 Clinical Corpus. Please link to these pages rather than copy them.