BioDWH2:一个自动化的基于图形的数据仓库和映射工具。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Marcel Friedrichs
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引用次数: 4

摘要

数据集成在科学研究中发挥着至关重要的作用。在生物医学研究中,OMICS领域已经表明需要更大的数据集,如蛋白质组学、药物基因组学和食品组学等新领域。由于研究项目需要多个数据源,因此有必要在这些数据源之间进行映射。因此,使用的工作流系统和集成工具需要处理大量异构数据格式,检查数据源更新,并找到合适的映射方法来交叉引用不同数据库中的实体。本文介绍了BioDWH2,一个开源的、基于图形的数据仓库和映射工具,能够帮助研究人员解决这些问题。以工作空间为中心的方法允许选择特定于项目的数据源,Neo4j或GraphQL服务器工具允许快速访问数据库进行分析。BioDWH2工具可在https://github.com/BioDWH2.
本文章由计算机程序翻译,如有差异,请以英文原文为准。

BioDWH2: an automated graph-based data warehouse and mapping tool.

BioDWH2: an automated graph-based data warehouse and mapping tool.

BioDWH2: an automated graph-based data warehouse and mapping tool.

BioDWH2: an automated graph-based data warehouse and mapping tool.

Data integration plays a vital role in scientific research. In biomedical research, the OMICS fields have shown the need for larger datasets, like proteomics, pharmacogenomics, and newer fields like foodomics. As research projects require multiple data sources, mapping between these sources becomes necessary. Utilized workflow systems and integration tools therefore need to process large amounts of heterogeneous data formats, check for data source updates, and find suitable mapping methods to cross-reference entities from different databases. This article presents BioDWH2, an open-source, graph-based data warehouse and mapping tool, capable of helping researchers with these issues. A workspace centered approach allows project-specific data source selections and Neo4j or GraphQL server tools enable quick access to the database for analysis. The BioDWH2 tools are available to the scientific community at https://github.com/BioDWH2.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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