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TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction.

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  • 1Department of Information Engineering, University of Padova, Padova, Italy. stefano.marchesin@unipd.it.

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|April 1, 2022
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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.

Keywords:
Biomedical Relation ExtractionGene-Disease AssociationWeak supervision

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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.