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.

中文翻译：

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图数据库概述及其在生物医学领域的应用

在过去的几十年里，密集互连数据的爆炸式增长刺激了图数据库技术的研究、开发和采用。从早期的图模型到最近的本机图数据库，实现的格局已经发展到涵盖企业就绪的需求。由于其数据的互连性质，生物医学领域一直是图数据库的早期采用者之一，从而实现更自然的表示模型和更好的数据集成工作流程、探索和分析设施。在这项工作中，我们调查了文献，探索最新的图数据库解决方案的演变、性能以及如何在生物医学领域应用，并汇编了各种各样的用例。有了这些证据，我们得出的结论是，可用的图形数据库管理系统适合支持数据密集型、集成应用程序，针对基础研究和更接近临床的探索性任务。