Jul 24, 2014

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

BioRxiv : the Preprint Server for Biology
Àlex BravoLaura I Furlong

Abstract

Background Current biomedical research needs to leverage and exploit the large amount of information reported in 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. 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 inf...Continue Reading

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Mentioned in this Paper

Morbidity Aspects
Drug Use Disorders
Genes
Gene Type
Aphasia, Syntactical
Pharmacologic Substance
Extraction
Translational Research
Literature
Analysis

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