Jonghyun Lee

The paper develops a platform integrating multisource target evidence with machine learning for Alzheimer's drug discovery, improving potency prediction and model performance.

This study decomposes scanner batch effects in pathology foundation models and shows that image corrections based on measured properties improve alignment but do not fully match model representations.

This study evaluates the limits of marker information transfer in cross-panel cytometry imputation, finding that imputation preserves broad lineage classification but not fine-phenotype details, and that patient matching improves cross-platform distribution recovery.

The paper develops MAESTRO, a self-supervised deep learning framework to transform immune profiles into embeddings capturing immune health, enabling clinical prediction and precision immunotherapy.

This study shows that eliminating nSMase2 in pancreatic cancer reduces tumor growth, promotes a less aggressive tumor subtype, and enhances immune responses by altering exosome production and macrophage polarization.