Yuval Tamir

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.

The paper introduces CISM, a method to identify spatial multicellular motifs associated with disease states in single-cell spatial data, showing they outperform existing methods in predicting disease states in TNBC and melanoma.

This study uses RNA secondary structure to predict A-to-I editing sites, achieving moderate accuracy with a PPV of 0.68 and AUC of 0.71.