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Marcel H Schulz

Showing results (1-10 of 125) with videos related to

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Genome Biology|April 2, 2015
Letting the data speak for themselves: a fully Bayesian approach to transcriptome assemblyMarcel H Schulz
Bioinformatics (Oxford, England)|August 8, 2018
On the problem of confounders in modeling gene expressionFlorian Schmidt, Marcel H Schulz
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 Schulz
Methods 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 Schulz
Bioinformatics (Oxford, England)|November 24, 2020
Fast detection of differential chromatin domains with SCIDDOPeter Ebert, Marcel H Schulz
Bioinformatics (Oxford, England)|June 16, 2023
Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite dataJonas Fischer, Marcel H Schulz
Methods in Molecular Biology (Clifton, N.J.)|December 20, 2024
Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAsRanjan Kumar Maji, Marcel H Schulz
Bioinformatics (Oxford, England)|May 7, 2016
Informed kmer selection for de novo transcriptome assemblyDilip A Durai, Marcel H Schulz
Bioinformatics (Oxford, England)|June 19, 2018
In silico read normalization using set multi-cover optimizationDilip A Durai, Marcel H Schulz
Scientific Reports|March 28, 2019
Improving in-silico normalization using read weightsDilip A Durai, Marcel H Schulz
Pageof 13

Showing results (1-10 of 125) with videos related to

Sort By:
Pageof 13
Genome Biology|April 2, 2015
Letting the data speak for themselves: a fully Bayesian approach to transcriptome assemblyMarcel H Schulz
Bioinformatics (Oxford, England)|August 8, 2018
On the problem of confounders in modeling gene expressionFlorian Schmidt, Marcel H Schulz
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 Schulz
Methods 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 Schulz
Bioinformatics (Oxford, England)|November 24, 2020
Fast detection of differential chromatin domains with SCIDDOPeter Ebert, Marcel H Schulz
Bioinformatics (Oxford, England)|June 16, 2023
Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite dataJonas Fischer, Marcel H Schulz
Methods in Molecular Biology (Clifton, N.J.)|December 20, 2024
Temporal Expression Analysis to Unravel Gene Regulatory Dynamics by microRNAsRanjan Kumar Maji, Marcel H Schulz
Bioinformatics (Oxford, England)|May 7, 2016
Informed kmer selection for de novo transcriptome assemblyDilip A Durai, Marcel H Schulz
Bioinformatics (Oxford, England)|June 19, 2018
In silico read normalization using set multi-cover optimizationDilip A Durai, Marcel H Schulz
Scientific Reports|March 28, 2019
Improving in-silico normalization using read weightsDilip A Durai, Marcel H Schulz
Pageof 13