Erxi Wu

eSIG-Net predicts how single mutations affect protein interactions using a sequence-based interaction language model, outperforming existing methods.

MATES improves locus-specific transposable element quantification in single-cell data by accurately allocating multi-mapping reads using contextual alignment information.

This study identifies CAMKV kinase signaling as a critical driver of neuroblastoma growth and shows it as a potential therapeutic target and prognostic marker.

e-MutPath identifies edgetic mutations that perturb functional pathways, revealing cancer vulnerability genes and drug targets through network-based analysis.

The paper systematically analyzes the impact of somatic mutations on miRNA regulatory networks across 30 cancer types, identifying driver mutations that perturb these networks and function as tumor suppressors.

This study shows that misfolded α-synuclein spreads from urogenital nerves to the brain, causing MSA-like symptoms through micturition reflex pathways.