Jakob Woerner

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 identifies pleiotropic loci across 1,167 traits using GWAS meta-analysis and shows they are enriched for functional annotations and improve polygenic score predictions.

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.