Publication: Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

Background Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. Results By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources.

Laura I Furlong, Alex Bravo, Janet Piñero, Núria Queralt-Rosinach, Michael Rautschka

Full publication: BMC Bioinformatics 2015, 16:55