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Author

Yu-an Huang

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Jul 2026

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.

Runzhou Tang, Xi Zhou, Yujie Qi et al. · 0 citations
Aug 2026

A Novel Graph Transformer Framework for Predicting Drug-Disease Associations with Structural Awareness.

SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.

Bowei Zhao, Hui Zhao, Yu-an Huang et al. · 0 citations