Yingying Zhang

PBCNet2.0 predicts protein-ligand binding affinity using a cartesian tensor-based Siamese Neural Network, achieving high accuracy and efficiency compared to physics-based simulations.

This paper introduces NetFlow3D, a framework that integrates protein structure and interaction networks to identify significant mutation clusters and modules in cancer, revealing biological processes not found by traditional methods.

The paper develops a computational framework to analyze mutational hotspots in cancer genomes using a human 3D interactome network, identifying clinically relevant interactions and proteins.

The paper introduces IMCC, a method to quantify inter-module connectivity in biological networks using mouse microarray data, revealing core-periphery structures and functional coordination in stroke networks.