Leon Eyrich Jessen

This paper introduces SCENE, a probabilistic model for embedding cells and genes in scRNA-seq data, achieving state-of-the-art performance and interpretable visualization.

This study benchmarks three deconvolution methods in spatial transcriptomics for cardiovascular and kidney diseases, finding comparable performance with method-specific trade-offs.

This study benchmarks two denoising methods for TCR-pMHC single-cell data, finding ITRAP outperforms ICON in signal-to-noise ratio and data consistency.

A sequence-only convolutional network trained on T cell receptor (TCR)-peptide pairs for HLA-A*02:01 can single out TCRs that bind a target peptide among many non-binding ones.

This paper conducts a consortium-wide GWAS to identify genetic variants associated with childhood dental caries, finding two significant loci but low heritability estimates.