TitleKG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.
Publication TypeJournal Article
Year of Publication2020
AuthorsReese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, Shefchek KA, Good BM, Balhoff JP, Fontana T, Blau H, Matentzoglu N, Harris NL, Munoz-Torres MC, Haendel MA, Robinson PN, Joachimiak MP, Mungall CJ
JournalPatterns (N Y)
Date Published2020 Nov 09

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 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 heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG 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.

Alternate JournalPatterns (N Y)
PubMed ID33196056
PubMed Central IDPMC7649624
Grant ListR24 OD011883 / OD / NIH HHS / United States
T15 LM009451 / LM / NLM NIH HHS / United States
U01 CA239108 / CA / NCI NIH HHS / United States