@article{473bcce816644bd7b968ab4ae674c7b1,
title = "KG-COVID-19: A Framework to Produce Customized Knowledge Graphs for COVID-19 Response",
abstract = "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. 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.",
keywords = "COVID-19, DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems, MERS-CoV, SARS-CoV, SARS-CoV-2, coronavirus, data integration, knowledge graph, machine learning, ontology",
author = "Reese, {Justin T.} and Deepak Unni and Callahan, {Tiffany J.} and Luca Cappelletti and Vida Ravanmehr and Seth Carbon and Shefchek, {Kent A.} and Good, {Benjamin M.} and Balhoff, {James P.} and Tommaso Fontana and Hannah Blau and Nicolas Matentzoglu and Harris, {Nomi L.} and Munoz-Torres, {Monica C.} and Haendel, {Melissa A.} and Robinson, {Peter N.} and Joachimiak, {Marcin P.} and Mungall, {Christopher J.}",
note = "Funding Information: 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. Funding Information: 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. The KG-COVID-19 framework was conceived and designed by J.R. D.U. M.P.J. C.J.M. T.J.C. N.M. S.C. V.R. and P.N.R.; software was written by J.R. D.U. L.C. T.F. B.M.G. J.P.B. M.P.J. and K.A.S.; and the manuscript was prepared by J.R. D.U. M.P.J. C.J.M. H.B. N.H. M.M.T. and M.A.H. The authors declare no competing interests. Publisher Copyright: {\textcopyright} 2020 The Authors",
year = "2021",
month = jan,
day = "8",
doi = "10.1016/j.patter.2020.100155",
language = "English (US)",
volume = "2",
journal = "Patterns",
issn = "2666-3899",
publisher = "Cell Press",
number = "1",
}