Christian M. Stegmann

This study maps the transcriptional landscape of the rat cardiovascular system at single-cell resolution, identifying 26 cell types and revealing tissue-specific molecular diversity and communication networks.

IgDesign designs antibody CDRs using deep learning and validates them in vitro, showing high success rates and improved affinities for multiple therapeutic antigens.

MACA is a tool for automatic cell-type annotation in single-cell transcriptomics datasets, showing improved accuracy and speed compared to existing methods.

This study uses deep learning to analyze aortic dimensions from MRI and identifies genetic loci associated with aortic diameter, highlighting SVIL as a key gene and developing a polygenic score for aneurysm risk.

The paper uses single-nucleus RNA sequencing to define the transcriptional and cellular diversity of the non-failing human heart, identifying 9 major cell types and 20 subclusters.