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An integrative knowledge graph for rare diseases, derived from the Genetic and Rare Diseases Information Center (GARD).

Qian ZhuDac-Trung NguyenIvan GrishaginNoel SouthallEric SidAnne Pariser
Published in: Journal of biomedical semantics (2020)
By integrating well-established database resources, we developed an integrative knowledge graph containing a large volume of biomedical and research data. Demonstration of several immediate use cases and limitations of this process reveal both the potential feasibility and barriers of utilizing graph-based resources and approaches to support their use by providers of consumer health information, such as GARD, that may struggle with the needs of maintaining knowledge reliant on an evolving and growing evidence-base. Finally, the successful integration of these datasets into a freely accessible knowledge graph highlights an opportunity to take a translational science view on the field of rare diseases by enabling researchers to identify disease characteristics, which may play a role in the translation of discover across different research domains.
Keyphrases
  • health information
  • healthcare
  • convolutional neural network
  • social media
  • neural network
  • public health
  • risk assessment
  • machine learning
  • rna seq
  • deep learning
  • big data
  • adverse drug