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

Patterns
Justin ReeseChristopher J Mungall

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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Citations

Jan 24, 2021·BMC Biology·Andra WaagmeesterJasper J Koehorst
Apr 4, 2021·International Journal of Environmental Research and Public Health·Heyoung Yang, Eunsoo Sohn
Apr 13, 2021·Expert Opinion on Drug Discovery·Finlay MacLean
May 1, 2021·Journal of Personalized Medicine·Avishek ChatterjeePhilippe Lambin
May 4, 2021·Information Systems Frontiers : a Journal of Research and Innovation·Francesco PiccialliSalvatore Cuomo
Jun 22, 2021·Journal of the American Medical Informatics Association : JAMIA·Yi GuoJiang Bian

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