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BioData Min. 2016 Jul 25;9:23. doi: 10.1186/s13040-016-0102-8. eCollection 2016.

Representing and querying disease networks using graph databases.

Author information

Rothamsted Research, Harpenden, West Common, Hertfordshire, AL5 2JQ UK.
European Institute for Systems Biology and Medicine (EISBM), CIRI UMR CNRS 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.
Contributed equally



Systems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.


We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.


Our study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.


Computational approach; Disease management platform; Graph database; Neo4j graph; Protein-centric framework; Systems medicine

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