Prediction of gene expression from epigenome data

Thu, 26 Oct 2023 00:00:00 +0000

Prediction of gene expression from epigenome data is an important task to link epigenome variation to changes in gene expression. Over the years we have designed a number of machine learning methods that present approaches for learning those links. We have made a number of models available for the community in the EpiExpress repository that allows to conduct histone-acetylation based epigenome association studies.

Linked publications

Fatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf, Dennis Hecker, Marcel H Schulz (2026). Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies. Genome Biology.

URL DOI

Florian Schmidt, Alexander Marx, Nina Baumgarten, Marie Hebel, Martin Wegner, Manuel Kaulich, Matthias S. Leisegang, Ralf P. Brandes, Jonathan Göke, Jilles Vreeken, Marcel H. Schulz (2021). Integrative analysis of epigenetics data identifies gene-specific regulatory elements. Nucleic Acids Research.

DOI

Florian Schmidt, Fabian Kern, Marcel H. Schulz (2020). Integrative prediction of gene expression with chromatin accessibility and conformation data. Epigenetics & Chromatin.

DOI

EpiExpress |

Prediction of gene expression from epigenome data

Linked publications