Jian-Nan Yang

CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations

CIGMA is proposed, a novel framework explicitly designed for out-of-distribution generalization in biological network association prediction and provides a powerful, generalizable, and interpretable framework for HSA prediction, advancing the systematic discovery of herb-based therapeutic mechanisms.