Representing and querying disease networks using graph databases.
Representing and querying disease networks using graph databases.
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DOI：
10.1186/s13040-016-0102-8
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发表时间：
2016
期刊：
影响因子：
4.5
通讯作者：
Auffray C
中科院分区：
文献类型：
--
作者：
Lysenko A;Roznovăţ IA;Saqi M;Mazein A;Rawlings CJ;Auffray C
系统生物学实验会产生大量多种模式的数据，由于复杂性与丰富的语义相结合，这些信息对整合提出了挑战。在这里，我们描述图数据库如何为生物数据的存储、查询和设想提供强大的框架。我们展示了图数据库如何非常适合生物信息的表示，这些信息通常是高度连接、半结构化和不可预测的。我们概述了一个应用案例，该案例使用 Neo4j 图数据库构建和查询原型网络，为哮喘相关基因提供生物背景。我们的研究表明，图数据库为多种类型的生物数据的集成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的生成。本文的在线版本 (doi:10.1186/s13040-016-0102-8) 包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/s13040-016-0102-8) contains supplementary material, which is available to authorized users.