Stefan Wuchty

scCont is an unsupervised contrastive learning framework that extracts interpretable functional transition units from scRNA-seq data, outperforming baseline methods in capturing biologically meaningful dynamics.

The paper uses long-read sequencing data to analyze STR variability across 2,645 individuals, identifying disease-associated STR loci with elevated allelic variability and introducing the PLVI metric.

RExPRT predicts pathogenicity of tandem repeat expansions with high precision, identifying ~30,000 likely pathogenic TRs in the reference genome.

The paper identifies microRNA-mediated interference with RNA stem-loop structures as a novel mechanism regulating alternative splicing across the transcriptome.

The paper identifies MIG-encoded proteins as potential pan-viral drug targets, showing that repurposed anti-cancer drugs target these proteins.

The paper predicts human-virus protein-protein interactions using transfer learning with CNN and MLP, outperforming existing methods and applying it to SARS-CoV-2.

The paper maps EMT transitions through multi-omic profiling, identifies key regulatory factors, and reveals discordance between molecular layers in human mammary epithelial cells.

This study identifies dme-miR-1002 as a microRNA that enhances stress resistance by promoting alternative splicing of NMNAT pre-mRNA to the protective RB variant in Drosophila.

The paper identifies proteins that control multiple molecular pathways and shows their enrichment in regulatory genes, disease genes, and drug targets.