Marcel H. Schulz

The paper improves the Split-ORF pipeline to analyze Ribo-seq data, identifies over 14,000 candidate Split-ORF transcripts, and finds many show significant Ribo-seq coverage across cell types and diseases.

The paper benchmarks machine learning models for predicting gene expression from epigenomic data using the IHEC EpiATLAS dataset and identifies genes linked to B-cell leukemia.

AtlasLens enables interactive exploration and analysis of large scRNA-seq datasets using metadata-driven filtering and downstream analysis tools.

The paper introduces k4neo, a tool that computes tumor specificity of transcript variants by k-mer indexing of healthy tissue RNA-seq data, identifying tumor-specific antigens across multiple cancer cohorts.

RANKOR ranks candidate drugs from transcriptomic signatures using a machine learning framework, outperforming existing methods in bulk and single-cell data.

The paper introduces MetaFR, a method that links scATAC-seq open-chromatin variation to scRNA-seq gene expression, showing improved performance over existing methods.

The paper identifies Parkinson's disease-associated regulatory variants in human dopaminergic neurons and shows they modulate SCARB2 and BAG3 expression.

The paper identifies 1,580 cell type-specific genes and non-coding RNAs associated with coronary artery disease by integrating genetic and epigenetic data from multiple cell types.

miRXplain predicts miRNA-mRNA interactions using CLIP-L data and hybrid attention transformers, outperforming existing models and revealing isomiR-specific targeting mechanisms.

This paper introduces a hierarchical representation learning framework for drug mechanism-of-action prediction from gene expression data, achieving improved F1 performance and interpretable latent space capturing MoA-level separation and compound-level substructure.

The paper identifies the transaminase-omega-amidase pathway as a redox-sensitive metabolic switch in endothelial glutamine metabolism, showing that NIT2 oxidation inhibits its activity and affects angiogenesis.

This study shows ischemic stroke causes long-term changes in brain stromal progenitor cells, leading to vascular dysfunction and potential recurrent stroke.

The paper reconstructs metabolic networks across 58 human tissues and cell types using RNA-seq and epigenomic data, identifying core and cell-type-specific metabolic functions and their regulatory elements.

This study shows that loss of KDM6A in hematopoietic cells impairs cardiac recovery after heart failure by causing epigenetic changes in myeloid cells that increase inflammation and worsen cardiac outcomes.

This study identifies differentially explained genes in heart failure subtypes using neural networks and XAI tools to improve understanding of molecular mechanisms.

This study identifies vascular niches as key hotspots for age-related inflammation and fibrosis in cardiac tissue using single-nucleus RNA sequencing and spatial transcriptomics.

The paper benchmarks DNA methylation sequencing workflows against an experimental gold-standard, identifying superior workflows and developing a sustainable benchmarking platform.

The paper identifies reduced STAT3 expression in cardiac pericytes in HFpEF and shows its loss leads to senescence and impaired adhesion.

GeneCOCOA identifies context-specific functions of individual genes using co-expression data and curated functional gene sets, outperforming existing methods in gene-disease association recall.

This study identifies long non-coding RNAs (lncRNAs) as guides for the SWI/SNF complex to cell-specific enhancers, revealing a mechanism for chromatin accessibility and gene expression control.

The paper introduces RADIAnT, a computational pipeline for detecting RNA-DNA interactions from ligation data, showing improved accuracy in identifying Malat1-DNA interactions and dynamic chromatin-associated RNAs.

This study shows that DNMT3A mutations in CHIP lead to metabolic changes in macrophages that drive inflammation, and targeting these changes can improve heart failure outcomes.

The paper introduces a statistical method to assess the impact of DNA variations on transcription factor binding, showing improved speed and accuracy over existing approaches.

The paper identifies regulatory SNPs linked to CVDs and shows their impact on non-coding RNA expression and cardiac function in disease models.

This study identifies circPLOD2 as a hypoxia-regulated circRNA that modulates pericyte function by regulating KLF4, affecting proliferation and vascular interactions.

FAME quantifies DNA methylation from bulk and single-cell WGBS data efficiently without intermediate files, achieving speed comparable to standard methods without loss of accuracy.

The paper introduces a cross-species integration pipeline for scRNA-seq data, validated on heart failure datasets from mice and humans, improving ortholog assignment and enabling pathway comparison.

This study identifies LINC00607 as an endothelial-specific lncRNA that supports angiogenic function by interacting with BRG1 to regulate chromatin accessibility and ERG target gene transcription.

The paper adapts the ABC model for enhancer-gene assignment, improves accuracy by incorporating TF binding and single-cell data, and introduces the STARE pipeline.