Runzhou Tang

Dual-granularity feature learning reveals static and dynamic mechanisms of ncRNA-drug interactions.

DC-MetaMG, a deep learning framework based on a causal disentanglement strategy that models association responses as the synergistic interplay between two complementary mechanisms: static binding and dynamic regulation, is proposed, demonstrating its suitability for training scenarios involving complex biological information.