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Pletscher-Frankild S, Pallejà A, Tsafou K, et al. DISEASES: text mining and data integration of disease-gene associations. Methods. 2014;74:83-9doi: 10.1016/j.ymeth.2014.11.020.
Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X., & Jensen, L. J. (2015). DISEASES: text mining and data integration of disease-gene associations. Methods (San Diego, Calif.), 7483-9. https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild, Sune, et al. "DISEASES: text mining and data integration of disease-gene associations." Methods (San Diego, Calif.) vol. 74 (2015): 83-9. doi: https://doi.org/10.1016/j.ymeth.2014.11.020
Pletscher-Frankild S, Pallejà A, Tsafou K, Binder JX, Jensen LJ. DISEASES: text mining and data integration of disease-gene associations. Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05. PMID: 25484339.
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Methods. 2015 Mar;74:83-9. doi: 10.1016/j.ymeth.2014.11.020. Epub 2014 Dec 05.

DISEASES: text mining and data integration of disease-gene associations.

Methods (San Diego, Calif.)

Sune Pletscher-Frankild, Albert Pallejà, Kalliopi Tsafou, Janos X Binder, Lars Juhl Jensen

Affiliations

  1. Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  2. Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  3. Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany; Bioinformatics Core Facility, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg.
  4. Department of Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Electronic address: [email protected].

PMID: 25484339 DOI: 10.1016/j.ymeth.2014.11.020

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.

Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Keywords: Data integration; Information extraction; Named entity recognition; Text mining; Web resource

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