Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8340710 | Methods | 2015 | 7 Pages |
Abstract
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 http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.
Keywords
HGNCHUGO Gene Nomenclature CommitteeGHRWeb resourceUMLSSNOMED CTNERInformation extractionICDHuman genome organizationUnified Medical Language SystemNamed entity recognitionInternational Classification of DiseasesLinkage disequilibriumText miningMeshgenome wide association studyGWASMedical Subject HeadingsHUGOCOSMICData integration
Related Topics
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Biochemistry, Genetics and Molecular Biology
Biochemistry
Authors
Sune Pletscher-Frankild, Albert Pallejà , Kalliopi Tsafou, Janos X. Binder, Lars Juhl Jensen,