A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

Yankai Chen, Yaozu Wu, Shicheng Ma, Irwin King

Abstract: 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. However, the main difficulty is how to handle high dimensionality and sparsity of the data. 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 paper, we conduct a literature review of recent graph embedding techniques for biomedical data. We also introduce important applications and tasks in the biomedical domain as well as associated public biomedical datasets.