ROBOKOP

reasoning over biomedical objects linked in knowledge oriented pathways

ROBOKOP is an open-source biomedical knowledge graph that integrates and semantically harmonizes important knowledge sources. ROBOKOP contains tens of millions of nodes representing entities such as genes, chemicals, and diseases, and hundreds of millions of edges representing the relationships between them. Here, users can query this integrated knowledge to explain connections between biomedical entities, and use the underlying integrated knowledge to rapidly deploy their own informatics tools.

Ask a question

Use the ROBOKOP Question Builder to construct a new query, then use the visualization tool to explore relevant publications.

Explore the data

Learn about the data in ROBOKOP and how to access it programmatically using the Automat API.

Additional Tools

ROBOKOP KG

ROBOKOP KG is an open-source biomedical knowledge graph containing ~10M nodes and ~250M edges drawn from ~30 biological data sources and bio-ontologies. Query the ROBOKOP KG directly in the Neo4j browser.

ExEmPLAR

ExEmplar is a generic Neo4j query-and-answer exploration tool that supports queries of the ROBOKOP KG and other Neo4j databases.

ExEmplar is under development and should be considered an experimental alternative to the ROBOKOP UI.

Citations

If you use ROBOKOP in your work, please cite the following papers:

Bizon C, Cox S, Balhoff J, Kebede Y, Wang P, Morton K, Fecho K, Tropsha A. ROBOKOP KG and KGB: integrated knowledge graphs from federated sources. J Chem Inf Model 2019 Dec 23;59(12):4968–4973. doi: 10.1021/acs.jcim.9b00683. https://pubmed.ncbi.nlm.nih.gov/31769676/.
Morton K, Wang P, Bizon C, Cox S, Balhoff J, Kebede Y, Fecho K, Tropsha A. ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering. Bioinformatics 2019;pii:btz604. doi: 10.1093/bioinformatics/btz604. https://pubmed.ncbi.nlm.nih.gov/31410449/.

Funding

ROBOKOP is a joint creation of the Renaissance Computing Institute (RENCI) at the University of North Carolina at Chapel Hill and CoVar LLC. The prototype was developed with funding from the National Center for Advancing Translational Science, National Institutes of Health (award #OT2TR002514). ROBOKOP's continued development is supported with joint funding from the National Institute of Environmental Health Sciences and the Office of Data Science Strategy within the Institutes of Health (award #U24ES035214). ExEmPLAR is funded by XXX

License

ROBOKOP is available under the MIT license.

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