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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

1
Rothamsted Research, Harpenden, West Common, Hertfordshire, AL5 2JQ UK.
2
European Institute for Systems Biology and Medicine (EISBM), CIRI UMR CNRS 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.
#
Contributed equally

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

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

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