- Main
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.
Published Web Location
https://doi.org/10.1016/j.patter.2020.100155Abstract
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.