wmjpillow / biokg

A Knowledge Graph for Relational Learning On Biological Data

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A knowledge graph for relational learning on biological data.


Compiling BioKG from source

BioKG requires python3.7 or greater to run

Clone the repo

git clone https://github.com/dsi-bdi/biokg.git
cd biokg

Install requirements

pip install -r requirements.txt

Compile the BioKG

python run_all.py '<drugbank_username>' '<drugbank_password>'

After the script completes there should be a data folder in the biokg folder This data folder will have 4 folders

  • sources which contains the sources used to compile BioKG
  • preprocessed which contains the extracted data in preprocessed form
  • output which contains the process data and benchmarks
  • biokg which contains the final biokg data

There will also be 2 zip files similar to the files contained in the release

  • biokg.zip which contains the compressed contents of the biokg folder
  • benchmarks.zip which contains the compressed contents of the output/benchmarks folder

Compiling BioKG with docker

Building docker image

sudo docker build . -t dsi-bdi/biokg

Running the image

sudo docker run --rm -v <data_path>:/biokg/data -e DB_USER='<drugbank_username>' -e DB_PASS='<drugbank_password>' dsi-bdi/biokg:latest
  • where <data_path> is the fully qualified path to your data folder

Data Sources

The biokg is built using the following data sources.

Source Database License Type URL
UniProt CC BY 4.0 https://www.uniprot.org/help/license
Drugbank CC BY NC 4.0 https://www.drugbank.ca/legal/terms_of_use
KEGG Custom https://www.kegg.jp/kegg/legal.html
Sider CC BY-NC-SA http://sideeffects.embl.de/about/
HPA CC BY SA 3.0 https://www.proteinatlas.org/about/licence
Cellosaurus CC BY 4.0 https://web.expasy.org/cgi-bin/cellosaurus/faq#Q22
Reactome CC0 https://reactome.org/license
CTD Custom http://ctdbase.org/about/legal.jsp
Intact Apache 2.0 https://www.ebi.ac.uk/intact/downloads
MedGen Custom https://www.nlm.nih.gov/databases/download/terms_and_conditions.html
MESH Custom https://www.nlm.nih.gov/databases/download/terms_and_conditions.html
InterPro Custom ftp://ftp.ebi.ac.uk/pub/databases/interpro/release_notes.txt
SMPDB Custom https://smpdb.ca/about
Hajazi20

Funding

The development of this module has been fully supported by the CLARIFY project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875160.

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A Knowledge Graph for Relational Learning On Biological Data

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Language:Python 100.0%