ArticleBMC bioinformatics2022
TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.Bioinformatics (Oxford, England) · 2026Article
- Article
- Therapeutic gene target prediction using novel deep hypergraph representation learning.Briefings in bioinformatics · 2024Article
- An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology.Journal of biomedical semantics · 2024Article
- MetaTron: advancing biomedical annotation empowering relation annotation and collaboration.BMC bioinformatics · 2024Article
- Multi-ontology embeddings approach on human-aligned multi-ontologies representation for gene-disease associations prediction.Heliyon · 2023Article
- Building a large gene expression-cancer knowledge base with limited human annotations.Database : the journal of biological databases and curation · 2023Article
- SCREENER: Streamlined collaborative learning of NER and RE model for discovering gene-disease relations.PloS one · 2023Article
- TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction.BMC bioinformatics · 2022Article
- Empowering digital pathology applications through explainable knowledge extraction tools.Journal of pathology informatics · 2022Article
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2 authors.
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Abstract
backgroundDatabases 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.
resultsTo 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.
conclusionsTBGA 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.
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