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Methods in Molecular Biology (Clifton, N.J.)|September 16, 2024
Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHITLaura Rumpf, Marcel H SchulzIscience|May 13, 2024
A statistical approach for identifying single nucleotide variants that affect transcription factor bindingNina Baumgarten, Laura Rumpf, Thorsten Kessler, et al.Bioinformatics (Oxford, England)|May 17, 2026
Predicting gene-specific regulation with transcriptomic and epigenetic single-cell dataLaura Rumpf, Fatemeh Behjati Ardakani, Dennis Hecker, et al.Genome Biology|July 14, 2026
Comparing machine learning methods predicting transcriptome from epigenome with applications to association studiesFatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf, et al.Genome Biology|April 2, 2015
Letting the data speak for themselves: a fully Bayesian approach to transcriptome assemblyMarcel H SchulzBioinformatics (Oxford, England)|August 8, 2018
On the problem of confounders in modeling gene expressionFlorian Schmidt, Marcel H SchulzMethods in Molecular Biology (Clifton, N.J.)|September 16, 2024
Prediction of Enhancer-Gene Interactions Using Chromatin-Conformation Capture and Epigenome Data Using STAREDennis Hecker, Marcel H SchulzBioinformatics (Oxford, England)|November 24, 2020
Fast detection of differential chromatin domains with SCIDDOPeter Ebert, Marcel H SchulzBioinformatics (Oxford, England)|June 16, 2023
Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite dataJonas Fischer, Marcel H SchulzMethods in Molecular Biology (Clifton, N.J.)|December 20, 2024
Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAsRanjan Kumar Maji, Marcel H SchulzPageof 13