Lysenko Artem, Roznovăţ Irina A, Saqi Mansoor, Mazein Alexander, Rawlings Christopher J, Auffray Charles
Rothamsted Research, Harpenden, West Common, Hertfordshire, AL5 2JQ UK.
European Institute for Systems Biology and Medicine (EISBM), CIRI UMR CNRS 5308, CNRS-ENS-UCBL-INSERM, Lyon, France.
BioData Min. 2016 Jul 25;9:23. doi: 10.1186/s13040-016-0102-8. eCollection 2016.
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.
系统生物学实验会生成大量多模态数据，由于复杂性与丰富语义交织，这些信息的整合面临挑战。在此，我们描述了图形数据库如何为生物数据的存储、查询和可视化提供强大框架。
我们展示了图形数据库如何非常适合表示通常高度关联、半结构化且不可预测的生物信息。我们概述了一个应用案例，该案例使用Neo4j图形数据库构建和查询一个原型网络，以提供与哮喘相关基因的生物学背景。
我们的研究表明，图形数据库为多种类型生物数据的整合提供了灵活的解决方案，并促进探索性数据挖掘以支持假设生成。
