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TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction
Stefano Marchesin1, Gianmaria Silvello2
1Department of Information Engineering, University of Padova, Padova, Italy. stefano.marchesin@unipd.it.
BMC Bioinformatics
|April 1, 2022
Summary
We created TBGA, a large dataset for extracting gene-disease associations (GDAs) from scientific literature. This resource aids in training models for biomedical relation extraction, advancing GDA discovery.
Area of Science:
- Biomedical informatics
- Computational biology
- Bioinformatics
Background:
- Biomedical databases require extensive manual updates.
- Biomedical Relation Extraction (BioRE) automates data population.
- Gene-Disease Association (GDA) extraction is a key BioRE task with limited training data.
Purpose of the Study:
- To develop a large-scale dataset for training GDA extraction models.
- To address the scarcity of resources for GDA extraction model development.
Main Methods:
- Leveraged the DisGeNET database for data curation.
- Semi-automatically annotated a dataset from over 700K publications.
- Constructed the TBGA dataset with over 200K instances and 100K gene-disease pairs.
Main Results:
- Developed TBGA, a comprehensive dataset for GDA extraction.
- TBGA comprises sentences, extracted GDAs, and gene-disease pair information.
- The dataset is one of the largest available for GDA extraction.
Conclusions:
- TBGA is a large and challenging dataset suitable for GDA extraction.
- Evaluated state-of-the-art models on TBGA, demonstrating its utility.
- The publicly available TBGA dataset aims to advance BioRE model development for GDA extraction.
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