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Representing and querying disease networks using graph databases
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
doi:10.1186/s13040-016-0102-8
pmid:27462371
pmcid:PMC4960687
fatcat:k2i6pyrtqnaafdcyxmi6xkz7pu