Representing and querying disease networks using graph databases.
Ontology highlight
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.
SUBMITTER: Lysenko A
PROVIDER: S-EPMC4960687 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
Representing and querying disease networks using graph databases.
Lysenko Artem A Roznovăţ Irina A IA Saqi Mansoor M Mazein Alexander A Rawlings Christopher J CJ Auffray Charles C
BioData mining 20160725
<h4>Background</h4>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.<h4>Results</h4>We show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structur ...[more]
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