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Graph databases in systems biology: a systematic review
Ilya Mazein1, Adrien Rougny2, Alexander Mazein2
1Medical Informatics Laboratory, University Medicine Greifswald, Walther-Rathenau-Straße 48, Greifswald 17475, Germany.
Briefings in Bioinformatics
|November 20, 2024
Summary
Graph databases are vital for systems biology, managing complex biological data. Efficient graph database use will drive future knowledge discovery in this field.
Area of Science:
- Systems Biology
- Bioinformatics
- Data Science
Background:
- Graph databases are increasingly adopted in scientific fields for handling complex, interconnected data.
- Their application in systems biology is crucial for managing biological data repositories, ontologies, networks, and pathways.
Purpose of the Study:
- To review publications utilizing graph databases in systems biology.
- To analyze the application and advantages of specific graph databases and resources.
- To discuss standardization efforts and future prospects for knowledge generation.
Main Methods:
- Comprehensive literature search of PubMed and PubMed Central for publications mentioning graph databases.
- Categorization of publications by domain and application, focusing on pathway/network biology, ontologies, and tools.
- Analysis of top 16 graph databases and highlighted resources like UniProtKB, Disease Ontology, and Reactome.
Main Results:
- Identified widespread use of graph databases across systems biology applications.
- Highlighted the benefits of graph-based solutions from resources such as UniProtKB, Disease Ontology, and Reactome.
- Detailed various approaches and advantages of different graph databases.
Conclusions:
- Efficient design, querying, and maintenance of graph databases are essential for knowledge generation in systems biology.
- Graph databases facilitate communication between biological data repositories.
- Standardization and harmonization of knowledge graph creation are ongoing efforts in the systems biology community.
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