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Updated: Jun 7, 2025

A Web Tool for Generating High Quality Machine-readable Biological Pathways

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

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

This summary is machine-generated.

Graph databases are vital for systems biology, managing complex biological data. Efficient graph database use will drive future knowledge discovery in this field.

Keywords:

NoSQL databases RDF graph databases network biology ontology systems biology

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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.