Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeeds in vast bioinformatics scenarios with data represented in Euclidean domain. However, rich relational information between biological elements is retained in the non-Euclidean biomedical graphs, which is not learning friendly to classic machine learning methods. Graph representation learning aims to embed graph into a low-dimensional space while preserving graph topology and node properties. It bridges biomedical graphs and modern machine learning methods and has recently raised widespread interest in both machine learning and bioinformatics communities. In this work, we summarize the advances of graph representation learning and its representative applications in bioinformatics. To provide a comprehensive and structured analysis and perspective, we first categorize and analyze both graph embedding methods (homogeneous graph embedding, heterogeneous graph embedding, attribute graph embedding) and graph neural networks. Furthermore, we summarize their representative applications from molecular level to genomics, pharmaceutical and healthcare systems level. Moreover, we provide open resource platforms and libraries for implementing these graph representation learning methods and discuss the challenges and opportunities of graph representation learning in bioinformatics. This work provides a comprehensive survey of emerging graph representation learning algorithms and their applications in bioinformatics. It is anticipated that it could bring valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.

中文翻译：

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生物信息学中的图表示学习：趋势、方法和应用

图是描述复杂系统的自然数据结构，它包含一组对象和关系。无处不在的现实生活中的生物医学问题可以建模为图形分析任务。机器学习，尤其是深度学习，在欧几里得域中表示数据的大量生物信息学场景中取得了成功。然而，在非欧几里得生物医学图中保留了丰富的生物元素之间的关系信息，这对经典的机器学习方法不友好。图表示学习旨在将图嵌入到低维空间中，同时保留图拓扑和节点属性。它连接了生物医学图和现代机器学习方法，最近引起了人们对机器学习和生物信息学社区的广泛兴趣。在这项工作中，我们总结了图表示学习的进展及其在生物信息学中的代表性应用。为了提供全面和结构化的分析和视角，我们首先对图嵌入方法（同构图嵌入、异构图嵌入、属性图嵌入）和图神经网络进行分类和分析。此外，我们总结了它们从分子水平到基因组学、制药和医疗保健系统水平的代表性应用。此外，我们为实现这些图表示学习方法提供了开放的资源平台和库，并讨论了图表示学习在生物信息学中的挑战和机遇。这项工作对新兴的图表示学习算法及其在生物信息学中的应用进行了全面调查。