Coronavirus created an unprecedented, world-wide 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. 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. 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 (Supplementary Text). 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.
Author: Katana Graph
Katana Graph is a force in shifting the paradigm. Coupling knowledge graphs with high performance computing enables organizations to not only avail themselves of sophisticated techniques to optimize AI, but also employ it at the scale and speed of contemporary data demands. (InsideBigData). Katana Graph's engines are being used in pharmaceutical research to help narrow down which molecular compounds should be tested in the lab, epidemiological tracking of COVID-19. (UT Research Showcase) Giving pharmaceutical researchers faster access to knowledge and insights saves lives.
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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, https://doi.org/10.1093/bioinformatics/btaa834