Lucas Arnoldt

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

The paper constructs a healthy human pancreas reference atlas to contextualize disease states and benchmark models.

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