Representing and querying disease networks using graph databases

Artem Lysenko, Irina A. Roznovăţ, Mansoor Saqi, Alexander Mazein, Christopher J Rawlings, Charles Auffray
2016 BioData Mining  
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
more » ... 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.
doi:10.1186/s13040-016-0102-8 pmid:27462371 pmcid:PMC4960687 fatcat:k2i6pyrtqnaafdcyxmi6xkz7pu