Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks

ArXi:2211.13231v3 Announce Type: replace-cross Heterogeneous molecular entities and their interactions, commonly depicted as a network, are crucial for advancing our systems-level understanding of biology. With recent advancements in high-throughput data generation and a significant improvement in computational power, graph neural networks (GNNs) have nstrated their effectiveness in predicting biomedical interactions.