Explore graph representation learning and its applications in biomedicine through this insightful lecture. Delve into SubGNN, a subgraph neural network for learning disentangled subgraph embeddings that capture complex topology, including structure, neighborhood, and position within a graph. Discover how these methods have been applied to predict disease treatments, verified through laboratory experiments, and to identify safer drug combinations with fewer side effects. Learn about the development of actionable representations that allow for meaningful interpretation of model predictions. Cover topics such as machine learning on graphs, graph neural networks, subgraph challenges, attention mechanisms, and hypergraphs. Gain valuable insights into the intersection of graph theory, machine learning, and biomedical applications.