Yizhi Wang

This paper introduces scRegNet, a framework combining single-cell foundation models with graph-based learning to predict gene regulatory links, achieving state-of-the-art results on benchmark datasets.

AQuA2 quantifies molecular spatiotemporal signals from live imaging data by decomposing complex datasets into signaling events, demonstrating applications across biosensors and cell types.

This paper introduces ncGTW, a method to align LC-MS profiles by detecting and correcting misalignments using compound-specific warping functions and neighbor constraints.

SynQuant quantifies synapses from microscopy images by addressing antibody specificity, brightness heterogeneity, and noise, showing improved performance over existing tools in disease models.

The paper introduces AQuA, an event-based analysis tool for astrocyte imaging data, revealing novel single-cell and population-level physiological phenomena.

The paper analyzes the proteomic architecture of human coronary and aortic atherosclerosis using mass spectrometry data from 200 specimens, identifying key protein networks and regulatory features associated with early disease.