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The implicitome: a resource for rationalizing gene-disease associations

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Overview

related to degree

  • Li, Tong Shu, Ph.D. in Biology, Scripps Research 2014 -

authors

  • Hettne, K. M.
  • Thompson, M.
  • van Haagen, H. H. B. M.
  • van der Horst, E.
  • Kaliyaperumal, R.
  • Mina, E.
  • Tatum, Z.
  • Laros, J. F. J.
  • van Mulligen, E. M.
  • Schuemie, M.
  • Aten, E.
  • Li, Tong Shu
  • Bruskiewich, R.
  • Good, Benjamin M.
  • Su, Andrew
  • Kors, J. A.
  • den Dunnen, J.
  • van Ommen, G. J. B.
  • Roos, M.
  • ‘t Hoen, P. A. C.
  • Mons, B.
  • Schultes, E. A.

publication date

  • 2016

journal

  • PLoS One  Journal

abstract

  • High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.

subject areas

  • Computational Biology
  • Databases, Genetic
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Humans
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Identity

PubMed Central ID

  • PMC4769089

International Standard Serial Number (ISSN)

  • 1932-6203

Digital Object Identifier (DOI)

  • 10.1371/journal.pone.0149621

PubMed ID

  • 26919047
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Additional Document Info

volume

  • 11

issue

  • 2

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