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

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dc.contributor.author Bravo Serrano, Àlex, 1984-
dc.contributor.author Piñero González, Janet, 1977-
dc.contributor.author Queralt Rosinach, Núria
dc.contributor.author Rautschka, Michael
dc.contributor.author Furlong, Laura I., 1971-
dc.date.accessioned 2015-05-26T11:59:04Z
dc.date.available 2015-05-26T11:59:04Z
dc.date.issued 2015
dc.identifier.citation Bravo À, Piñero J, Queralt-Rosinach N, Rautschka M, Furlong LI. Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research. BMC Bioinformatics. 2015 Feb 21;16(1):55. DOI: 10.1186/s12859-015-0472-9
dc.identifier.issn 1471-2105
dc.identifier.uri http://hdl.handle.net/10230/23654
dc.description.abstract 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. 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 that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. CONCLUSIONS: BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher BioMed Central
dc.relation.ispartof BMC Bioinformatics. 2015 Feb 21;16(1):55
dc.rights © 2015 Bravo et al.; licensee BioMed Central./nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain/nDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Macrodades
dc.subject.other Anàlisi del discurs
dc.subject.other Bioinformàtica
dc.title Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1186/s12859-015-0472-9
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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