An overview of graph databases and their applications in the biomedical domain

Abstract

Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

“…These tools allow the acceleration of the visualization of large PPI networks ( Yu and Zhang, 2008 ; Gerasch et al, 2014 ; Zaki and Tennakoon, 2017 ). It is also possible to improve the speed of visualizations by connecting directly to graph databases such as Neo4j ( Gong et al, 2018 , p. 4) and ArangoDB ( Touré et al, 2016 ; Timón-Reina, Rincón and Martínez-Tomás, 2021 ; ArangoDB NoSQL Multi-Model Database: Graph, Document, Key/Value , 2022 ). Since graph databases store data directly in a graph form, they are becoming a preferred resource for storing complex relationships of heterogeneous biological data ( Yoon, Kim and Kim, 2017 ; Jupe et al, 2018 ; Castillo-Arnemann et al, 2021 ).…”

A cancer graph: a lung cancer property graph database in Neo4j

“…The complex interrelationships among all of these factors determine disease origin, trajectory, and outcomes of interventions [ 2 ]. Strategies that allow operation directly on the topology of the graph structures defined by these relationships are enabled by the development and growing maturity of native graph databases such as TigerGraph and Neo4j [ 3 – 5 ].…”

Bluster or Lustre: Can AI Improve Crops and Plant Health?

“…The adoption of multi-omic data in plant science research has resulted in availability of a large volume of curated and non-curated datasets. As such, we are witnessing a rise in the development and usage of Knowledge Base technologies, in particular Graph Databases [67,68], for organizing the knowledge captured by the scientific community. Graph databases capture the interconnected nature of the biology and are proving to be a valuable technology for organizing omic data and applying graph-theoretic and NLP based AI techniques to answer questions such as "what-if", to identify causal relationships, and to perform gap analysis.…”