Capturing functional context of genetic pathways through hyperedge disentanglement

The hypergraph data structure has been used to represent the multiway interactions of a set of genes of a genetic pathway. Since genes within each genetic pathway collaboratively perform a biological function, the functional context of a pathway (i.e., the interaction context of a hyperedge), which is often unannotated, needs to be captured. However, most existing hypergraph neural networks fail to reflect the interaction context of each hyperedge due to their limited ability to capture important or relevant factors. In this paper, we propose a simple yet effective hyperedge disentangling method, Natural-HNN, which captures the interaction context of a hyperedge. We introduce a novel guidance mechanism for hyperedge disentanglement based on the naturality condition in category theory. In our experiments, we applied our model to hypergraphs of genetic pathways for the cancer subtype classification task and demonstrated that our model outperforms baseline approaches by capturing the functional semantic similarity of genetic pathways.