KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

202014 citationsPreprintgreen Open Access

Authors

Justin Reese · Lawrence Berkeley National Laboratory

Deepak Unni · Lawrence Berkeley National Laboratory

Tiffany J. Callahan

Luca Cappelletti · University of Milan

Vida Ravanmehr · Jackson Laboratory

Seth Carbon · Lawrence Berkeley National Laboratory

Tommaso Fontana · Politecnico di Milano

Abstract

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.

Topics & Keywords

Biomedical Text Mining and Ontologies Bioinformatics and Genomic Networks Tuberculosis Research and Epidemiology

UN Sustainable Development Goals

Good health and well-being

Publication Details

Published in: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/2020.08.17.254839