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.
| Release | Date | Articles |
|---|---|---|
| 0.0.1 | 12.04.2020 | 145 |
| 0.0.2 | 15.05.2020 | 160 |
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.