Robert Luben

This study combines monogenic and polygenic risk factors to predict disease progression in Fuchs endothelial corneal dystrophy, showing improved prediction when integrating both factors.

The paper uses an autoencoder to derive retinal phenotypes from OCT images, identifies 118 genetic loci, and shows their relevance to retinal morphology and disease prediction.

This paper introduces a deep learning framework, REGLE, that improves genomic discovery and risk prediction for respiratory and circulatory diseases by learning low-dimensional embeddings from high-dimensional clinical data.

This paper introduces M-REGLE, a multimodal deep learning method that improves genetic analysis of cardiovascular traits by integrating multiple clinical data modalities and outperforms unimodal approaches in locus discovery and risk prediction.