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Henrik Failmezger

Showing results (1-10 of 16) with videos related to

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BMC Bioinformatics|October 5, 2013
Unsupervised automated high throughput phenotyping of RNAi time-lapse moviesHenrik Failmezger, Holger Fröhlich, Achim Tresch
Bioinformatics (Oxford, England)|April 19, 2013
Learning gene network structure from time laps cell imaging in RNAi Knock downsHenrik Failmezger, Paurush Praveen, Achim Tresch, et al.
Plos Computational Biology|May 3, 2013
Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic imagesHenrik Failmezger, Benjamin Jaegle, Andrea Schrader, et al.
Frontiers in Oncology|January 5, 2023
Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarkerHenrik Failmezger, Harald Hessel, Ansh Kapil, et al.
Frontiers in Oncology|April 1, 2021
Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal CancerHenrik Failmezger, Natalie Zwing, Achim Tresch, et al.
Plant Methods|March 31, 2018
MowJoe: a method for automated-high throughput dissected leaf phenotypingHenrik Failmezger, Janne Lempe, Nasim Khadem, et al.
Cancer Research|December 26, 2019
Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma HistologyHenrik Failmezger, Sathya Muralidhar, Antonio Rullan, et al.
Frontiers in Oncology|November 5, 2019
Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma HistopathologyKonstantinos Zormpas-Petridis, Henrik Failmezger, Shan E Ahmed Raza, et al.
Bioinformatics (Oxford, England)|November 20, 2018
Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imagingHenrik Failmezger, Ezgi Dursun, Sebastian Dümcke, et al.
Molecular Cell|October 15, 2013
Global analysis of eukaryotic mRNA degradation reveals Xrn1-dependent buffering of transcript levelsMai Sun, Björn Schwalb, Nicole Pirkl, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
BMC Bioinformatics|October 5, 2013
Unsupervised automated high throughput phenotyping of RNAi time-lapse moviesHenrik Failmezger, Holger Fröhlich, Achim Tresch
Bioinformatics (Oxford, England)|April 19, 2013
Learning gene network structure from time laps cell imaging in RNAi Knock downsHenrik Failmezger, Paurush Praveen, Achim Tresch, et al.
Plos Computational Biology|May 3, 2013
Semi-automated 3D leaf reconstruction and analysis of trichome patterning from light microscopic imagesHenrik Failmezger, Benjamin Jaegle, Andrea Schrader, et al.
Frontiers in Oncology|January 5, 2023
Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarkerHenrik Failmezger, Harald Hessel, Ansh Kapil, et al.
Frontiers in Oncology|April 1, 2021
Computational Tumor Infiltration Phenotypes Enable the Spatial and Genomic Analysis of Immune Infiltration in Colorectal CancerHenrik Failmezger, Natalie Zwing, Achim Tresch, et al.
Plant Methods|March 31, 2018
MowJoe: a method for automated-high throughput dissected leaf phenotypingHenrik Failmezger, Janne Lempe, Nasim Khadem, et al.
Cancer Research|December 26, 2019
Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma HistologyHenrik Failmezger, Sathya Muralidhar, Antonio Rullan, et al.
Frontiers in Oncology|November 5, 2019
Superpixel-Based Conditional Random Fields (SuperCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma HistopathologyKonstantinos Zormpas-Petridis, Henrik Failmezger, Shan E Ahmed Raza, et al.
Bioinformatics (Oxford, England)|November 20, 2018
Clustering of samples with a tree-shaped dependence structure, with an application to microscopic time lapse imagingHenrik Failmezger, Ezgi Dursun, Sebastian Dümcke, et al.
Molecular Cell|October 15, 2013
Global analysis of eukaryotic mRNA degradation reveals Xrn1-dependent buffering of transcript levelsMai Sun, Björn Schwalb, Nicole Pirkl, et al.
Pageof 2