Julius Upmeier zu Belzen

The paper introduces an interpretable neural network architecture for genomic prediction, outperforming existing methods in disease risk prediction and capturing non-linear genetic interactions.

NetworkVI integrates multi-omics data using a biologically informed variational autoencoder, enabling interpretable cell phenotyping and discovery of lineage-specific immune states.

This paper introduces an optogenetic tool to control NmeCas9 activity using a light-switchable anti-CRISPR protein, enabling precise genome editing in mammalian cells.

The paper designs improved anti-CRISPR proteins with enhanced inhibition potency and expanded specificity through structure-based engineering.