COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology.

This Knowledge Graphs comprises information encoded in Biological Expression Language (BEL) for a selected corpus around COVID-19. A summary of the corpus is listed here. Additional information about customized terms used is available here.

If you use COVID-19 KG for your research please cite our paper:

Daniel Domingo-Fernández, Shounak Baksi, Bruce T Schultz, Yojana Gadiya, Reagon Karki, Tamara Raschka, Christian Ebeling, Martin Hofmann-Apitius, and Alpha Tom Kodamullil (2020). COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology. Bioinformatics, 37(9), 1332-1334.

Although the COVID-19 KG was generated using BEL, it can also be exported to multiple standard formats:

• Edgelist (.lst)
• Node-Link (.json)
• GML (.gml or .xml)
• GraphML (.graphml or .xml)
• SIF (.csv, .tsv, or .txt)
• Pickle
• CX (.cx)
• JGF (.jgif)

The table below contains information of the different releases of the COVID-19 KG. Each release contains the original BEL files are aforementioned formats before.

The COVID-19 Knowledge Graph can be programmatically used as a Python package.

To install the covid19kg Python package for programmatic access to the BEL files in this repository, use the following code in your shell:

git clone https://github.com/covid19kg/covid19kg.git
cd covid19kg
pip install -e .

To see all the commands, simply run:

covid19kg

To get the BEL graph, use the following code in Python:

>>> import covid19kg
>>> graph = covid19kg.get_graph()
>>> graph.summarize()

The COVID-19 Knowledge Graph is a resource developed in an academic capacity funded by Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e. V., and thus comes with no warranty or guarantee of maintenance or support.