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

Databases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs...

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Bibliographic Details
Published in:BMC bioinformatics 2022-03, Vol.23 (1), p.111-16, Article 111
Main Authors: Marchesin, Stefano, Silvello, Gianmaria
Format: Article
Language:English
Subjects:
Analysis
Annotations
Archives & records
Biomedical Relation Extraction
Data Mining
Databases, Factual
Datasets
Disease
Gene-Disease Association
Genes
Genetic susceptibility
Genome-wide association studies
Humans
Knowledge discovery
Methods
Noise
Precision medicine
Relational databases
Research Design
State-of-the-art reviews
Supervision
Testing
Weak supervision
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Summary:Databases are fundamental to advance biomedical science. However, most of them are populated and updated with a great deal of human effort. Biomedical Relation Extraction (BioRE) aims to shift this burden to machines. Among its different applications, the discovery of Gene-Disease Associations (GDAs) is one of BioRE most relevant tasks. Nevertheless, few resources have been developed to train models for GDA extraction. Besides, these resources are all limited in size-preventing models from scaling effectively to large amounts of data. To overcome this limitation, we have exploited the DisGeNET database to build a large-scale, semi-automatically annotated dataset for GDA extraction. DisGeNET stores one of the largest available collections of genes and variants involved in human diseases. Relying on DisGeNET, we developed TBGA: a GDA extraction dataset generated from more than 700K publications that consists of over 200K instances and 100K gene-disease pairs. Each instance consists of the sentence from which the GDA was extracted, the corresponding GDA, and the information about the gene-disease pair. TBGA is amongst the largest datasets for GDA extraction. We have evaluated state-of-the-art models for GDA extraction on TBGA, showing that it is a challenging and well-suited dataset for the task. We made the dataset publicly available to foster the development of state-of-the-art BioRE models for GDA extraction.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04646-6