KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response
Summary: 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 difficul...
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doaj-9e19b29da8ec431ca617fc2a36eacbf02021-01-10T04:11:10ZengElsevierPatterns2666-38992021-01-0121100155KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 ResponseJustin T. Reese0Deepak Unni1Tiffany J. Callahan2Luca Cappelletti3Vida Ravanmehr4Seth Carbon5Kent A. Shefchek6Benjamin M. Good7James P. Balhoff8Tommaso Fontana9Hannah Blau10Nicolas Matentzoglu11Nomi L. Harris12Monica C. Munoz-Torres13Melissa A. Haendel14Peter N. Robinson15Marcin P. Joachimiak16Christopher J. Mungall17Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Corresponding authorDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USAComputational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz School of Medicine, Aurora, CO 80045, USADepartment of Computer Science, University of Milano, 20122 Milan, ItalyThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USADivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USALinus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USADivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USARenaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USADipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, ItalyThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USAIndependent Semantic Technology Contractor, London, UKDivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Linus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USALinus Pauling Institute, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USAThe Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USADivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADivision of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USASummary: 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. The Bigger Picture: An 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.http://www.sciencedirect.com/science/article/pii/S2666389920302038COVID-19SARS-CoV-2SARS-CoVMERS-CoVcoronavirusknowledge graph |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 Nomi L. Harris Monica C. Munoz-Torres Melissa A. Haendel Peter N. Robinson Marcin P. Joachimiak Christopher J. Mungall |
spellingShingle |
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 Nomi L. Harris Monica C. Munoz-Torres Melissa A. Haendel Peter N. Robinson Marcin P. Joachimiak Christopher J. Mungall KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response Patterns COVID-19 SARS-CoV-2 SARS-CoV MERS-CoV coronavirus knowledge graph |
author_facet |
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 Nomi L. Harris Monica C. Munoz-Torres Melissa A. Haendel Peter N. Robinson Marcin P. Joachimiak Christopher J. Mungall |
author_sort |
Justin T. Reese |
title |
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response |
title_short |
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response |
title_full |
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response |
title_fullStr |
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response |
title_full_unstemmed |
KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response |
title_sort |
kg-covid-19: a framework to produce customized knowledge graphs for covid-19 response |
publisher |
Elsevier |
series |
Patterns |
issn |
2666-3899 |
publishDate |
2021-01-01 |
description |
Summary: 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. The Bigger Picture: An 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. |
topic |
COVID-19 SARS-CoV-2 SARS-CoV MERS-CoV coronavirus knowledge graph |
url |
http://www.sciencedirect.com/science/article/pii/S2666389920302038 |
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