COVID Case for Knowledge Graph

By: Katana Graph

September 08, 2021

COVID Case for Knowledge Graph

Coronavirus created an unprecedented, worldwide health crisis, killing millions and causing widespread economic and social disruption.

Scientific and medical communities including virologists, medical researchers, and pharmacists collaborated to develop a vaccine in record time. It was an incredible situation that needed an incredible response by the scientific and medical community.

We’ve never progressed so fast with any other infectious agent,” said virologist Theodora Hatziioannou at Rockefeller University.

There are many steps to bringing a safe and effective vaccine to the public, including vaccine development, clinical trials, US Food and Drug Administration (FDA) authorization or approval, manufacturing, and distribution. Then there is the challenge of getting the different public organizations and private companies involved and working together to make COVID-19 publicly available.

Immediately after SARS-CoV-2 was discovered, researchers worldwide began investigations aimed at understanding and combating the virus. This research generated a tremendous number of publications containing immense amounts of unstructured data.

One approach to handling the surge of COVID knowledge was developed by a group of bio-informationists and mathematicians headed by computational biologist Daniel Domingo-Fernández. They created COVID-19 Knowledge Graph: a computable, multimodal, cause-and-effect knowledge model of COVID-19 pathophysiology.

Domingo-Fernández et al introduced a knowledge graph that comprises mechanistic information on COVID-19 published in 160 original research articles. In its current state, the COVID-19 KG incorporates 4,016 nodes, covering 10 entity types (e.g. proteins, genes, chemicals, and biological processes) and 10,232 relationships (e.g. increases, decreases, and association), forming a seamless interaction network. Given the selected collection, these cause-and-effect relations primarily denote host-pathogen interactions as well as comorbidities and symptoms associated with COVID-19. Furthermore, the KG contains molecular interactions related to host invasion (e.g. spike glycoprotein and its interaction with the host via receptor ACE2) and the effects of the downstream inflammatory, cell survival, and apoptosis signaling pathways. This is an enormous effort and prodigious volume of computational work. The COVID-19 KG is accessible as a web application and several other formats for researchers. This work was supported by the MAVO and ICON programs of the Fraunhofer Society.

As aforementioned, bringing many forms of information and knowledge together requires an incredible amount of computing power. Graph intelligence is changing the nature of the race to insights in cases like COVID-19.


Daniel Domingo-Fernández, Shounak Baksi, Bruce Schultz, Yojana Gadiya, Reagon Karki, Tamara Raschka, Christian Ebeling, Martin Hofmann-Apitius, Alpha Tom Kodamullil, COVID-19 Knowledge Graph: a computable, multimodal, cause-and-effect knowledge model of COVID-19 pathophysiology, Bioinformatics, Volume 37, Issue 9, 1 May 2021, Pages 1332–1334,

Additional Links:

Developing COVID-19 Vaccine
Tech Startup Powered by Two Computer Science Professors Teams Up with Intel


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