Representing and querying disease networks using graph databases
Open Access
- 25 July 2016
- journal article
- review article
- Published by Springer Science and Business Media LLC in BioData Mining
- Vol. 9 (1), 1-19
- https://doi.org/10.1186/s13040-016-0102-8
Abstract
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.Keywords
Funding Information
- eTRIKS (IMI 115446)
- BBSRC, Rothamsted Research
- U-BIOPRED (IMI 115010)
- U-BIOPRED (IMI 115010)
- BBSRC, Rothamsted Research
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