KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Justin T. Reese, Deepak Unni, Tiffany J. Callahan, Luca Cappelletti, Vida Ravanmehr, Seth Carbon, Kent A. Shefchek, Benjamin M. Good, James P. Balhoff, Tommaso Fontana, Hannah Blau, Nicolas Matentzoglu (+6 others)
2020 Patterns  
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 varies drastically for different tasks-the optimal data for a
more » ... e 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 can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.
doi:10.1016/j.patter.2020.100155 pmid:33196056 pmcid:PMC7649624 fatcat:bkoqhgb7mjf7roajxw4ohxuvgu