Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
It addresses the need for comprehensive insights into GRL's role in biomedicine, but is incremental as it synthesizes existing knowledge rather than presenting new findings.
This survey reviews recent advancements in graph representation learning methods and their applications in biomedicine, while highlighting key challenges and future research directions.
Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.