Model Card for BioNExt
BioNExt, is an end-to-end Biomedical Relation Extraction and Classifcation system. The work utilized three modules, a Tagger (Named Entity Recognition), Linker (Entity Linking) and an Extractor (Relation Extraction and Classification).
This repositories contains two models:
- Tagger: Named Entity Recognition module, which performs 6 class biomedical NER: Genes, Diseases, Chemicals, Variants (mutations), Species, and Cell Lines.
- Extractor: Performs Relation Extraction and classification. The classes for the relation Extraction are: Positive Correlation, Negative Correlation, Association, Binding, Drug Interaction, Cotreatment, Comparison, and Conversion.
For a full description on how to utilize our end-to-end pipeline we point you towards our GitHub repository.
- Developed by: IEETA
- Model type: BERT Base
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: BioLinkBERT-Large
Model Sources
- Repository: IEETA BioNExt GitHub
- Paper: Towards Discovery: An End-to-End System for Uncovering Novel Biomedical Relations [Awaiting Publication]
Authors:
- Tiago Almeida (ORCID: 0000-0002-4258-3350)
- Richard A A Jonker (ORCID: 0000-0002-3806-6940)
- Rui Antunes (ORCID: 0000-0003-3533-8872)
- João R Almeida (ORCID: 0000-0003-0729-2264)
- Sérgio Matos (ORCID: 0000-0003-1941-3983)
Uses
Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only.
How to Get Started with the Model
Please refer to our GitHub repository for more information on our end-to-end inference pipeline: IEETA BioNExt GitHub
Training Data
The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge.
Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, Zhiyong Lu, BioRED: a rich biomedical relation extraction dataset, Briefings in Bioinformatics, Volume 23, Issue 5, September 2022, bbac282, https://doi.org/10.1093/bib/bbac282
Results
As evaluated as an end to end system, our results are as follows:
- Tagger: 43.10
- Linker: 32.46
- Extractor: 24.59
| Configuration | Entity Pair (P/R/F%) | + Relation (P/R/F%) | + Novel (P/R/F%) |
|---|---|---|---|
| Competition best | -/-/55.84 | -/-/43.03 | -/-/32.75 |
| BioNExt (end-to-end) | 45.89/40.63/43.10 | 34.56/30.60/32.46 | 26.18/23.18/24.59 |
Citation
BibTeX:
[Awaiting Publication]