Manuel Dileo

Learning from Dynamic Protein Interaction Networks with State-Memory Temporal Graph Neural Networks

The results demonstrate that state-based temporal modeling provides an effective and scalable inductive bias for learning from dynamic biological networks, opening new directions for temporal graph learning as an AI-driven simulation of cellular processes.