Automatic detection and extraction of key resources from tables in biomedical papers
Our pipelines allow the detection and extraction of key resources from tables with much higher accuracy, enabling the deployment of automated research resource extraction tools on BioRxiv to help authors correct unidentifiable key resources detected in their articles and improve the reproducibility of their findings. The code, table-specific language model, annotated training and evaluation data are publicly available.
Authors
- İbrahim Burak Özyurt (ORCID: https://orcid.org/0000-0003-3944-1893)
- Anita Bandrowski (ORCID: https://orcid.org/0000-0002-5497-0243)
Institutions
- University of California San Diego (US)
- Université Bourgogne Franche-Comté (FR)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-03-20
- DOI
- https://doi.org/10.1186/s13040-025-00438-9
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Silicon Valley Community Foundation
- National Institutes of Health