  With the rapid development of biomedical software and hardware, a large
amount of relational data interlinking genes, proteins, chemical components,
drugs, diseases, and symptoms has been collected for modern biomedical
research. Many graph-based learning methods have been proposed to analyze such
type of data, giving a deeper insight into the topology and knowledge behind
the biomedical data, which greatly benefit to both academic research and
industrial application for human healthcare. However, the main difficulty is
how to handle high dimensionality and sparsity of the biomedical graphs.
Recently, graph embedding methods provide an effective and efficient way to
address the above issues. It converts graph-based data into a low dimensional
vector space where the graph structural properties and knowledge information
are well preserved. In this survey, we conduct a literature review of recent
developments and trends in applying graph embedding methods for biomedical
data. We also introduce important applications and tasks in the biomedical
domain as well as associated public biomedical datasets.
