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

Kristina M Hettne1, Mark Thompson1, Herman H H B M van Haagen1

  • 1Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.

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Computational methods reveal hidden gene-disease links in biomedical literature. This "implicitome" expands knowledge, aiding in the discovery and interpretation of genetic associations with diseases, including those from genome-wide association studies (GWAS).

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput methods like medical sequencing and genome-wide association studies (GWAS) generate vast data on genetic variant-disease relationships.
  • Biological complexity and rapid data growth necessitate computational tools for prioritizing gene-disease associations.

Purpose of the Study:

  • To develop and apply concept profile technology to extract explicit and implicit gene-disease relations from biomedical literature.
  • To demonstrate how the 'implicitome' of gene-disease associations can rationalize known and novel findings, including those from GWAS.

Main Methods:

  • Utilized concept profile technology to identify explicit gene-disease relations (explicitome).
  • Extracted a larger set of implied gene-disease associations (implicitome) from biomedical literature.
  • Published extracted data using FAIR Data Publishing recommendations and nanopublications.

Main Results:

  • Identified a substantial 'implicitome' of gene-disease associations, extending beyond explicitly stated relations.
  • Demonstrated the utility of the implicitome in rationalizing known and novel gene-disease associations, including GWAS findings.
  • Made the data accessible via an online tool (http://knowledge.bio) for exploring gene-disease associations.

Conclusions:

  • The implicitome significantly expands the scope of known biomedical knowledge regarding gene-disease associations.
  • Computational extraction of implicit relations is crucial for interpreting complex genetic data and accelerating disease research.
  • FAIR-compliant data publication and accessible tools enhance the re-use and impact of biomedical knowledge.