Shantong Hu

ACENet predicts enzyme pHmin using a graph neural network integrating structural and evolutionary features, achieving high accuracy and stability across conditions.

This paper develops a genetically engineered whole-cell biosensor using E. coli to detect multiple PCB congeners with high sensitivity, and integrates it with a smartphone for real-time monitoring.

KcatNet predicts enzyme catalytic efficiency using geometric deep learning, outperforming existing methods and providing insights into enzyme-substrate interactions.

The paper introduces YST-PPI, a method to study ER resident protein interactions in situ, identifies 74 protein pairs, and highlights Lhs1p's role in regulating these interactions.

HPClas is a machine learning classifier using catBoost to identify halophilic proteins with high accuracy, achieving AUROC scores of 0.877 and 0.845 on training and test sets respectively.