Carl Herrmann

The paper introduces kaleidoCell, a framework for identifying transcriptional meta-programs in single-cell datasets, and applies it to glioblastoma data to uncover treatment-related signatures.

The paper benchmarks perturbation prediction tools in single-cell RNA-seq using scArchon, revealing performance disparities and highlighting the need for gene-level evaluation.

This study identifies ORF2 as a key HEV protein that suppresses antiviral responses in hepatocytes, enabling viral persistence.

The paper introduces COBRA, an interpretable deep learning model for scRNA-seq data that disentangles covariate effects, and demonstrates its application on interferon-stimulated mouse datasets.

This study identifies shared molecular mechanisms between schizophrenia and somatic disorders using a computational framework based on multi-rank non-negative matrix factorization.

This paper introduces OntoVAE, a VAE that incorporates ontologies to predict genetic and drug-induced perturbations using bulk and single-cell transcriptomic data.

The paper introduces dsMTL, a framework for privacy-preserving, distributed multi-task machine learning, and evaluates its performance in identifying biological signatures across distributed datasets.

This study profiles HIV-1 integration sites in microglia and links viral insertions to topologically associated domain organization.

The paper introduces a computational tool to identify epigenetic markers in cancer by analyzing enhancer networks across epigenomic datasets.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

The paper links cancer mutational signatures to changes in transcription factor binding site motifs, showing correlations between motif alteration and signature information content.

The paper introduces scCAT-seq, a method to simultaneously assay chromatin accessibility and transcriptome in single cells, and demonstrates its application in human pre-implantation embryos to reveal regulatory heterogeneity.

The paper analyzes 2500 cancer genomes to assess the molecular functional impact of mutations, finding that some putative passenger variants may influence gene activity and contribute to cancer phenotypes.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

The paper introduces a method to disentangle multi-omics data and identifies regulatory mechanisms in human blood cells.