BioGrakn — New Version Released!

A knowledge graph of biomedical data for precision medicine, text mining and disease networks.

Building on the previous work done in BioGrakn — DiseaseNetworks, I’m really excited to announce the next release of BioGrakn, which expands the use cases to include precision medicine, text mining and BLAST.

Github: github.com/graknlabs/biograkn

Example query with the Text Mining data.

We want to inspire anyone working in life sciences how they can leverage Grakn to better organise their complex networks of data and accelerate their knowledge discovery. We want BioGrakn to be used by anyone, whether you’re in academia, a startup or as part of a team in a large pharmaceutical. We encourage you to extend, modify and play around with this data!

In this release, you will find four knowledge graphs:

1. Precision Medicine (ClinicalTrials.gov, ClinVar, CTDBase, DisGeNet, Drugs@FDA, HGNC, and PharmGKB)
2. Text Mining (PubMed)
3. BLAST
4. Disease Networks (Uniprot, Reactome, DGIdb, DisGeNET, HPA-Tissue, EBI IntAct, Kaneko, Gene Expression Omnibus and TissueNet)

When you download the Grakn distribution, you will find them as four keyspaces with their schemas and data pre-populated, so you can immediately begin to query it. If you want access to the schema, source data and migrator files, you will find these in the Github repo.

To learn more, make sure to read the readme HERE!

Further Reading

• Text Mined Knowledge Graphs — Beyond Text Mining
• How to Use a Knowledge Graph for Precision Medicine
• BioGrakn: Accelerating Biomedical Knowledge Discovery with a Grakn Knowledge Graph
• BioGrakn: A Knowledge Graph-based Semantic Database for Biomedical Sciences
• SQL vs. Graql: Modelling and Querying of Biomedical Data