Yue Bi

OmniCleave models protease-substrate specificity using a geometric graph framework, outperforming existing methods and identifying novel substrates.

GRASP uses a graph neural network to predict RNA subcellular localization by modeling RNA structure, outperforming existing methods across RNA types and transcript lengths.

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

The paper introduces Mimosa, a Transformer-based model for miRNA target prediction that incorporates non-canonical base-pairing patterns, achieving better performance than traditional methods.

The paper introduces Allocator, a deep learning framework for predicting mRNA subcellular localization by integrating sequence and structure information, showing improved performance over existing methods.

MERITS provides a comprehensive database of PE/PPE proteins from Mycobacterium, offering structural and functional information to aid in vaccine and diagnostic development.