Document Type

Article

Publication Date

1-8-2021

Keywords

JGM

JAX Source

Patterns (NY) 2021 Jan 8; 2(1):100155

Volume

2

Issue

1

First Page

100155

Last Page

100155

ISSN

2666-3899

PMID

33196056

DOI

https://doi.org/10.1016/j.patter.2020.100155

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.

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