Hoyeon Jeong

GraphTME predicts immune checkpoint inhibitor response by analyzing spatial cell-cell interactions in tumor microenvironments using spatial transcriptomics data.

SHEST predicts cell types and reconstructs spatial gene expression from histopathology images, achieving high accuracy in lung adenocarcinoma datasets.

This study shows that CRISPR suppression of NUTM1 promotes differentiation and increases TROP2 in NUT carcinoma cells, enhancing immunocytokine response and improving clinical outcomes.

GraphMHC predicts neoantigen binding using graph neural networks on molecular structures, achieving high accuracy and showing survival differences in melanoma patients.