Yufang Zhang

RNASTOP predicts and optimizes mRNA chemical stability using deep learning, improving accuracy by 13% and reducing free energy in a vaccine sequence.

ORIGAMI reconstructs cis-regulatory networks using multi-omics data and deep learning, revealing synergistic transcription control and disease-associated rewiring.

ProStab predicts protein stability changes upon mutations using a deep learning framework combining sequence and structure information, showing strong performance and experimental validation.

This paper introduces Hot2Mol, a deep learning framework for designing drug-like PPI inhibitors by generating molecules that mimic hot-spot residues, showing improved performance over baselines.

DTI-CDF improves drug-target interaction prediction by integrating hybrid features into a cascade deep forest model, achieving higher performance than existing methods.