Xiaoming He

This paper develops a computational method using graph neural networks and Metropolis Monte Carlo sampling to design high-affinity nanobodies, achieving a Pearson correlation of 0.62 on benchmark.

This paper presents a collagen-based biomimetic material that successfully regenerates functional tissues in animal models, including sciatic nerve, tendon, and muscle, with high degradation synchronization and regenerative outcomes.

This paper introduces AiDTA, a deep reinforcement learning method for de novo design of protein-binding aptamers, achieving high-affinity DNA aptamers for disease-related proteins.

This study identifies miR-146a as a key regulator of the foreign body response to biomaterial implants, showing its role in macrophage function, fibrosis, and FBGC formation.