Robust temporal knowledge inference via pathway snapshots with liquid neural network

Abstract

Static graphs play a pivotal role in modeling and analyzing biological and biomedical data. However, many real-world scenarios—such as disease progression and drug pharmacokinetic processes—exhibit dynamic behaviors. Consequently, static graph methods often struggle to robustly address new environments characterized by complex and previously unseen relationship changes. Here, we propose a method for constructing temporal knowledge inference agents tailored to disease pathways, enabling effective relation reasoning beyond their training environment under complex shifts. To achieve this, we developed an imitation learning framework using liquid neural networks, a class of continuous-time neural models inspired by the brain function that are causal and adaptable to changing conditions. Our findings indicate that liquid agents can distill the essential tasks from knowledge graph inputs while accounting temporal evolution, thereby enabling the transfer of temporal skills to novel time nodes. Compared to state-of-the-art deep reinforcement learning agents, experiments demonstrate that temporal robustness in decision-making emerges uniquely in liquid networks.