Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation is designed to preserve a knowledge graph’s local and/or global structure. Additional machine learning methods can be applied to this representation to make predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around knowledge graph construction and then around unifying representational learning techniques and unifying applications. Advances in machine learning for biomedicine are creating new opportunities across many domains, and we note potential avenues for future work with knowledge graphs that appear particularly promising.

知识图可以支持许多生物医学应用。这些图以节点和边的形式表示生物医学概念和关系。在这篇评论中，我们讨论了如何构建和应用这些图，特别关注机器学习方法如何改变这些过程。生物医学知识图谱通常是通过集成专家通过手动管理填充的数据库来构建的，但我们现在看到自动化系统的使用更加强大。许多技术用于表示知识图，但通常使用机器学习方法来构建可以支持许多不同应用的低维表示。这种表示旨在保留知识图的局部和/或全局结构。可以将其他机器学习方法应用于此表示，以在基因组、制药和临床领域进行预测。我们首先围绕知识图构建进行讨论，然后围绕统一表征学习技术和统一应用程序进行讨论。生物医学机器学习的进步正在许多领域创造新的机会，我们注意到知识图谱未来工作的潜在途径似乎特别有前途。