Yuval Bussi

CellTune is a software tool for cell classification in spatial proteomics that outperforms existing methods with high accuracy and resolution.

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

This paper introduces a multiplexed imaging technique for lung cancer diagnosis that improves accuracy and efficiency by analyzing multiple biomarkers from a single tissue section.

The paper creates a spatial atlas of human gastrointestinal GVHD, identifying immune and epithelial changes that contribute to disease pathophysiology.

This paper introduces DeepCell Types, a language-informed vision model for spatial proteomics cell phenotyping that generalizes across diverse datasets and marker panels.

This paper introduces CombPlex, a combinatorial staining method combined with deep learning to measure more proteins at single-cell resolution without needing specialized imaging equipment.

CellSighter is a deep-learning pipeline that classifies cells in multiplexed images with high accuracy, reducing manual effort and improving consistency.