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• W4221128994 abstract "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." @default.
• W4221128994 created "2022-04-03" @default.
• W4221128994 creator A5003820981 @default.
• W4221128994 creator A5078254809 @default.
• W4221128994 date "2022-03-31" @default.
• W4221128994 modified "2023-10-14" @default.
• W4221128994 title "TBGA: a large-scale Gene-Disease Association dataset for Biomedical Relation Extraction" @default.
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• W4221128994 doi "https://doi.org/10.1186/s12859-022-04646-6" @default.
• W4221128994 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35361129" @default.
• W4221128994 hasPublicationYear "2022" @default.
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