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bioRxiv · 10.1101/2020.08.17.254839

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

Reese, J. T.·Unni, D. R.·Callahan, T. J.·Cappelletti, L.·Ravanmehr, V.·Carbon, S.·Fontana, T.·Blau, H.·Matentzoglu, N.·Harris, N. L.·Munoz-Torres, M. C.·Robinson, P. N.·Joachimiak, M. P.·Mungall, C. J.
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Abstract

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (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 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 biomedical data to produce knowledge graphs (KGs) 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. BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

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Reese, J. T., Unni, D. R., Callahan, T. J., Cappelletti, L., Ravanmehr, V., Carbon, S., Fontana, T., Blau, H., Matentzoglu, N., Harris, N. L., Munoz-Torres, M. C., Robinson, P. N., Joachimiak, M. P., Mungall, C. J.. 2020-08-18. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. https://doi.org/10.1101/2020.08.17.254839

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