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PLoS One. 2016 Feb 26;11(2):e0149621. doi: 10.1371/journal.pone.0149621. eCollection 2016.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

Author information

1
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
2
Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands.
3
Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, United States of America.
4
STAR Informatics / Delphinai Corporation, Port Moody, BC, Canada.
5
Dutch Techcentre for Life Sciences, Utrecht, The Netherlands.
6
Leiden Institute for Advanced Computer Science, Leiden, The Netherlands.

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.

PMID:
26919047
PMCID:
PMC4769089
DOI:
10.1371/journal.pone.0149621
[Indexed for MEDLINE]
Free PMC Article

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