Haiqing Zhao

LiverHomo integrates human liver scRNA-seq data with PPI and cell-cell communication networks to provide an interactive resource for exploring liver health and disease.

GraPPI develops a self-supervised graph encoder for protein-protein interaction modeling, achieving high performance in binding interface prediction and affinity prediction.

LiverDCP models disease biology by integrating single-cell data and protein interactions to identify disease-risk genes and therapeutic targets.

The paper investigates electrostatic interactions in Cx36 gap junctions, showing that specific residue pairs are essential for functional GJ formation and docking specificity.

The paper integrates three computational methods to predict E. coli protein-protein interactions and functional networks, identifying over 20K high-confidence interactions.

This paper introduces two protein language models trained on dynamic biophysical data to better predict mutation effects and protein behavior.

PrePPI predicts protein-protein interactions on a proteome-wide scale using structural and non-structural clues, providing a database of ~1.3 million human PPIs.

ZIPPI evaluates protein-protein interaction models using sequence co-evolution and conservation, showing promise for genome-wide application in E. coli and potential for human interactomes.