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Automatic Detection and Extraction of Key Resources from Tables in Biomedical Papers.
Ibrahim Burak Ozyurt1, Anita Bandrowski1
1FDI Lab, University of California, San Diego, 9500 Gilman Drive, M/C 0608, La Jolla, CA 92093-0608, USA.
Biorxiv : the Preprint Server for Biology
|October 28, 2024
Summary
Researchers developed automated tools to extract key resource tables from BioRxiv preprints. This improves the findability of essential research materials like antibodies and cell lines across the biomedical literature.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Scientific Publishing
Background:
- Tables are crucial for identifying research resources and reagents.
- STAR*Methods tables enhance resource findability but are limited in scope.
- Automating table extraction from preprints can broaden resource discoverability.
Purpose of the Study:
- To develop automated methods for extracting and structuring key resource tables from BioRxiv preprints.
- To improve the findability of research resources in the broader biomedical literature.
Main Methods:
- Utilized multimodal pipelines with machine learning for page identification.
- Employed Table Transformer models for table detection and structure recognition.
- Developed a novel language model for row over-segmentation to enhance text extraction.
Main Results:
- Achieved a Grid Table Similarity (GriTS) score of approximately 0.90, a significant improvement from 0.12.
- Successfully automated the extraction of key resource tables from PDF preprints.
Conclusions:
- The developed automated tools enable effective extraction of research resource tables from BioRxiv.
- This advancement facilitates improved findability of critical scientific inputs across a larger corpus of literature.
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