4 citations · 5 across the 4 of their papers we have counts for
4 papers
Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos +2
Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals tha…
Pseudodata-guided Invariant Representation Learning Boosts the Out-of-Distribution Generalization in Enzymatic Kinetic Parameter Prediction
Haomin Wu, Zhiwei Nie, Hongyu Zhang +1
Accurate prediction of enzyme kinetic parameters is essential for understanding catalytic mechanisms and guiding enzyme engineering.However, existing deep learning-based enzyme-sub…
OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Zhiwei Nie, Hongyu Zhang, Hao Jiang +8
Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering. Although a large number of pred…
ProtFAD: Introducing function-aware domains as implicit modality towards protein function prediction
Mingqing Wang, Zhiwei Nie, Yonghong He +2
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead…