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Open Access Publications from the University of California

KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response.

  • Author(s): Reese, Justin T
  • Unni, Deepak
  • Callahan, Tiffany J
  • Cappelletti, Luca
  • Ravanmehr, Vida
  • Carbon, Seth
  • Shefchek, Kent A
  • Good, Benjamin M
  • Balhoff, James P
  • Fontana, Tommaso
  • Blau, Hannah
  • Matentzoglu, Nicolas
  • Harris, Nomi L
  • Munoz-Torres, Monica C
  • Haendel, Melissa A
  • Robinson, Peter N
  • Joachimiak, Marcin P
  • Mungall, Christopher J
  • et al.

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time-consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community vary drastically for different tasks; the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework also can be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

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