A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

TL;DR

This paper reviews recent advances in graph embedding techniques tailored for biomedical data, highlighting their role in managing high-dimensional, sparse graphs and enhancing biomedical research and applications.

Contribution

It provides a comprehensive overview of recent graph embedding methods, applications, and datasets in the biomedical domain, identifying current trends and challenges.

Findings

Graph embedding effectively handles high dimensionality and sparsity.

Recent methods improve preservation of biomedical graph structures.

Applications span gene, protein, drug, and disease analysis.

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, 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.