Yixuan Wu

This paper develops a computational pipeline to predict sex-specific antiarrhythmic drug strategies for atrial fibrillation by integrating regression and mechanistic modeling of sex-specific cardiac electrophysiology.

This paper develops and validates sex-specific computational models of human atrial cardiomyocytes to investigate sex differences in atrial fibrillation mechanisms and therapeutic responses.

This study uses computational models to show female atrial cardiomyocytes in AF have higher Ca2+-driven arrhythmia risk than males, with specific interventions identified to address these differences.

This paper introduces ProtET, a method for CLIP-informed protein editing using multi-modality learning, achieving significant improvements in protein stability and functionality.