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.Funding: This work was supported by grants from the Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy [to J.R., D.U., S.C., N.L.H., M.J., C.J.M], the Laboratory Directed Research and Development (LDRD) Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231, the NIH (Monarch R24 OD011883, Illuminating the Druggable Genome U01 CA239108-01), a Training Grant from the NLM, NIH to the University of Colorado Anschutz Medical Campus Computational Bioscience Training Program [T15LM009451 to T.J.C.], the National Virtual Biotechnology Laboratory (NVBL), and the Google Cloud COVID-19 Research Grants program. Conflict of Interest: The authors declare no competing interests.
Highlights
IntroductionMost coronaviruses typically cause common-cold symptoms in humans, three betacoronaviruses have emerged in the past few decades that can cause a range of serious manifestations, including pneumonia and death: the severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV-1), the Middle East respiratory syndrome coronavirus (MERS-CoV), and the novel betacoronavirus that emerged in late 2019, subsequently named SARS-CoV-2, the agent of the disease COVID19.1 The rapid spread of SARS-CoV-2 has led to a global pandemic
The knowledge graphs (KGs)-COVID-19 Framework We created KG-COVID-19 to address the challenge of integrating data for COVID-19 response
KG-COVID-19 is a framework that enables the creation of customized KGs containing COVID-19 knowledge for different applications
Summary
Most coronaviruses typically cause common-cold symptoms in humans, three betacoronaviruses have emerged in the past few decades that can cause a range of serious manifestations, including pneumonia and death: the severe acute respiratory syndrome (SARS) coronavirus (SARS-CoV-1), the Middle East respiratory syndrome coronavirus (MERS-CoV), and the novel betacoronavirus that emerged in late 2019, subsequently named SARS-CoV-2, the agent of the disease COVID19.1 The rapid spread of SARS-CoV-2 has led to a global pandemic. Initial symptoms of COVID-19 typically include fever, cough, fatigue, anorexia, anosmia, myalgia, and diarrhea. Severe illness ensues roughly 1 week after the initial onset of symptoms, and can present with rapidly progressive respiratory failure.[2] In addition to the symptoms highlighted, COVID-19 infections can lead to secondary health problems, such as blood clots,[3] tissue necrosis, organ damage, and, in some cases, cardiac failure. Given that the research community is still learning about COVID-19, understanding its symptoms and their underlying pathological mechanisms, which are still being uncovered, is of vital importance
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