Apr 19, 2015

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

BMC Bioinformatics
Àlex BravoLaura I Furlong


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. 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. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information...Continue Reading

  • References37
  • Citations21


  • References37
  • Citations21

Mentioned in this Paper

Morbidity Aspects
Drug Use Disorders
Gene Type
Aphasia, Syntactical
Pharmacologic Substance
Translational Research
Knowledge Translation

Related Feeds

Bioinformatics in Biomedicine

Bioinformatics in biomedicine incorporates computer science, biology, chemistry, medicine, mathematics and statistics. Discover the latest research on bioinformatics in biomedicine here.