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--- |
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license: mit |
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datasets: |
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- bigbio/biored |
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language: |
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- en |
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metrics: |
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- f1 |
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--- |
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# Model Card for BioNExt |
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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). |
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This repositories contains two models: |
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1. **Tagger:** Named Entity Recognition module, which performs 6 class biomedical NER: **Genes, Diseases, Chemicals, Variants (mutations), Species, and Cell Lines**. |
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2. **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.** |
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For a full description on how to utilize our end-to-end pipeline we point you towards our [GitHub](https://github.com/ieeta-pt/BioNExt) repository. |
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- **Developed by:** IEETA |
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- **Model type:** BERT Base |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** BioLinkBERT-Large |
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### Model Sources |
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- **Repository:** [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt) |
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- **Paper:** Towards Discovery: An End-to-End System for Uncovering Novel Biomedical Relations [Awaiting Publication] |
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**Authors:** |
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- Tiago Almeida ([ORCID: 0000-0002-4258-3350](https://orcid.org/0000-0002-4258-3350)) |
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- Richard A A Jonker ([ORCID: 0000-0002-3806-6940](https://orcid.org/0000-0002-3806-6940)) |
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- Rui Antunes ([ORCID: 0000-0003-3533-8872](https://orcid.org/0000-0003-3533-8872)) |
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- João R Almeida ([ORCID: 0000-0003-0729-2264](https://orcid.org/0000-0003-0729-2264)) |
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- Sérgio Matos ([ORCID: 0000-0003-1941-3983](https://orcid.org/0000-0003-1941-3983)) |
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## Uses |
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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. |
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## How to Get Started with the Model |
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Please refer to our GitHub repository for more information on our end-to-end inference pipeline: [IEETA BioNExt GitHub](https://github.com/ieeta-pt/BioNExt) |
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## Training Data |
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The training data utilized was the BioRED corpus, wihtin the scope of the BioCreative-VIII challenge. |
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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 |
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## Results |
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As evaluated as an end to end system, our results are as follows: |
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- **Tagger**: 43.10 |
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- **Linker**: 32.46 |
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- **Extractor**: 24.59 |
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| Configuration | Entity Pair (P/R/F%) | + Relation (P/R/F%) | + Novel (P/R/F%) | |
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|---------------------------------------|-----------------------|----------------------|------------------| |
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| Competition best | -/-/55.84 | -/-/43.03 | -/-/32.75 | |
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| BioNExt (end-to-end) | 45.89/40.63/43.10 | 34.56/30.60/32.46 | 26.18/23.18/24.59 | |
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## Citation |
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**BibTeX:** |
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[Awaiting Publication] |
