Laura Rumpf

Harnessing machine learning models for epigenome to transcriptome association studies

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.

EpiATLAS - a reference for human epigenomic research

EpiATLAS provides a comprehensive collection of human reference epigenomes to advance epigenomic research and understanding of cellular function and disease.

Predicting gene-specific regulation with transcriptomic andepigenetic 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.

Haematopoietic loss of KDM6A impairs cardiac recovery in heartfailure via epigenetic reprogramming of myeloid cells

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.

A statistical approach to identify regulatory DNA variations

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.