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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.
Abstract

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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