Exploiting Curated Databases to Train Relation Extraction Models for Gene-Disease Associations

Databases are pivotal to advancing biomedical science. Nevertheless, most of them are populated and
updated by human experts with a great deal of effort. Biomedical Relation Extraction (BioRE) aims to
shift these expensive and time-consuming processes to machines. Among its different applications, the
discovery of Gene-Disease Associations (GDAs) is one of the most pressing challenges. Despite this,
few resources have been devoted to training – and evaluating – models for GDA extraction. Besides,
such resources are 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: TBGA. TBGA is generated from more than 700K
publications and consists of over 200K instances and 100K gene-disease pairs. We have evaluated state-
of-the-art models for GDA extraction on TBGA, showing that it is a challenging dataset for the task. The
dataset and models are publicly available to foster the development of state-of-the-art BioRE models for
GDA extraction.