Xiao Fan

The paper identifies genetic suppressors of FASN dysfunction in C. elegans by using temperature-sensitive mutants and CRISPR/Cas9 to rescue embryonic lethality.

SEHI-PPI predicts human-influenza protein-protein interactions using a double-view deep learning framework with adaptive negative sampling, outperforming existing methods and demonstrating strong generalizability.

SHINE uses protein language models to predict pathogenicity of inframe insertion and deletion variants, outperforming existing methods in two test datasets.

This paper introduces gMVP, a graph attention neural network for predicting the functional effect of missense variants, showing improved performance over existing methods in identifying damaging variants and separating de novo variants in neurodevelopmental disorders.

This study links KIF1A variants to clinical severity in KAND by analyzing transport defects and protein dysfunction.