LinkedImm: a linked data graph database for integrating immunological data | BMC Bioinformatics | Full Text

Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

LinkedImm: a linked data graph database for integrating immunological data Research Open access Published: 25 August 2021 Volume 22 , article number 105 ( 2021 ) Cite this article You have full access to this open access article Download PDF Save article View saved research BMC Bioinformatics Aims and scope Submit manuscript LinkedImm: a linked data graph database for integrating immunological data Download PDF Abstract Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological sys

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