| --- |
| library_name: transformers |
| tags: |
| - biomedicine |
| - classification |
| - amyloids |
| - antibodies |
| - pubmed |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract |
| --- |
| |
| # BiomedBERT-Amyloid |
|
|
| This model is a text classifier designed to screen scientific articles from the PubMed database. |
| It identifies papers that report on the experimental effects of antibodies on amyloid formation. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Sonor Kubkowski |
| - **Funded by:** National Science Center, Poland (SONATA 19 grant, Project No. DEC-2023/51/D/NZ7/02847) |
| - **Project title:** *“Taming aggregation with AmyloGraphem 2.0: database and predictive model of amyloid self-organization of modulators”* |
| - **Finetuned from model:** microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract |
| - **Task:** Binary text classification |
| - **License:** Apache 2.0 |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| This model is intended for direct use by researchers in biochemistry, neurobiology, and related fields. It can be used to rapidly filter thousands of PubMed search results to create a shortlist of relevant articles for manual review, saving significant time in literature screening. |
|
|
| ### Out-of-Scope Use |
|
|
| The model is highly specialized for the amyloid-antibody domain. It should not be used for general-purpose scientific article classification or for topics outside its training scope. It is not designed to provide medical or diagnostic advice. |
|
|
| ### Recommendations |
|
|
| Users should treat the model's output as a preliminary screening tool. It is recommended that a human expert review the articles flagged as both positive and negative to ensure comprehensiveness, especially when high recall is critical. |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| The training dataset was created by searching the PubMed database. A total of 1939 articles were **manually** assessed. |
|
|
| - **Search Queries:** |
| - `"amyloid"[Title/Abstract] AND "antibod*"[Title/Abstract]` |
| - `"amyloid"[Title/Abstract] AND "nanobod*"[Title/Abstract]` |
|
|
| - **Inclusion Criteria for Positive Label:** The study must report on the effect of antibodies on amyloid formation and include experimental data from methods such as AFM, PET, ThT, or TEM. |
|
|
| - **Dataset Split:** |
| - **Positive (useful):** 167 articles (9%) |
| - **Negative (not useful):** 1772 articles (91%) |
|
|
| ## Acknowledgments |
|
|
| We gratefully acknowledge the support for this research from: |
| - **Institution:** Bioinformatics and Multiomics Analysis Laboratory, Clinical Research Centre, Medical University of Bialystok. |
| - **Funding Source:** National Science Center, Poland, via the SONATA 19 grant. |
| - **Project No:** DEC-2023/51/D/NZ7/02847. |
| - **Project Title:** “Taming aggregation with AmyloGraphem 2.0: database and predictive model of amyloid self-organization of modulators”. |
