Reference : DISEASES: text mining and data integration of disease-gene associations.
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
DISEASES: text mining and data integration of disease-gene associations.
Pletscher-Frankild, Sune [> >]
Palleja, Albert [> >]
Tsafou, Kalliopi [> >]
Binder, Janos X. [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) >]
Jensen, Lars Juhl [> >]
Academic Press
Yes (verified by ORBilu)
[en] Data Mining/methods ; Databases, Genetic/statistics & numerical data ; Disease/genetics ; Genetic Predisposition to Disease/genetics ; Genome-Wide Association Study/methods ; Humans ; Data integration ; Information extraction ; Named entity recognition ; Text mining ; Web resource
[en] Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at, where the text-mining software and all associations are also freely available for download.
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
Copyright (c) 2014 The Authors. Published by Elsevier Inc. All rights reserved.

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