Yonghyun Nam

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 identifies genetic risk factors for meniscus degeneration and shows they are linked to various systemic conditions through a phenome-wide association study.

This study develops a protein risk score model to predict cardiovascular events in CKD patients, showing better performance than clinical and polygenic risk models.

The paper evaluates proteomic and polygenic risk scores for disease prediction, finding proteomic scores outperform polygenic scores in most traits and show temporal responsiveness.

The paper introduces cyMAE, a masked autoencoder for automated immunophenotyping, showing it improves accuracy and interpretability over manual methods in cytometry data analysis.

This study constructs an augmented disease network incorporating genetic correlations with endophenotypes to uncover genetic associations across diseases and biomarkers.

This paper introduces netCRS, a network-based comorbidity risk score that improves myocardial infarction prediction using PheWAS data, achieving better AUC than traditional PRS methods.