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Silke Szymczak

Showing results (1-10 of 84) with videos related to

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Biodata Mining|April 16, 2024
Evaluation of network-guided random forest for disease gene discoveryJianchang Hu, Silke Szymczak
Briefings in Bioinformatics|January 18, 2023
A review on longitudinal data analysis with random forestJianchang Hu, Silke Szymczak
Briefings in Bioinformatics|October 19, 2017
Evaluation of variable selection methods for random forests and omics data setsFrauke Degenhardt, Stephan Seifert, Silke Szymczak
Bioinformatics (Oxford, England)|March 3, 2019
Surrogate minimal depth as an importance measure for variables in random forestsStephan Seifert, Sven Gundlach, Silke Szymczak
BMC Bioinformatics|August 26, 2025
fuseMLR: an R package for integrative prediction modeling of multi-omics dataCésaire J K Fouodo, Marina Bleskina, Silke Szymczak
Bioinformatics (Oxford, England)|May 14, 2020
Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking studyStephan Seifert, Sven Gundlach, Olaf Junge, et al.
BMC Bioinformatics|May 4, 2026
ShadowVIMP: permutation-based multiple testing-controlled variable selectionTim Müller, Roman Hornung, Silke Szymczak, et al.
Molecular Plant-Microbe Interactions : MPMI|January 11, 2008
Identification of genes relevant to symbiosis and competitiveness in Sinorhizobium meliloti using signature-tagged mutantsNataliya Pobigaylo, Silke Szymczak, Tim W Nattkemper, et al.
BMC Proceedings|December 19, 2009
Evaluation of single-nucleotide polymorphism imputation using random forestsDaniel F Schwarz, Silke Szymczak, Andreas Ziegler, et al.
BMC Proceedings|May 10, 2008
Picking single-nucleotide polymorphisms in forestsDaniel F Schwarz, Silke Szymczak, Andreas Ziegler, et al.
Pageof 9

Showing results (1-10 of 84) with videos related to

Sort By:
Pageof 9
Biodata Mining|April 16, 2024
Evaluation of network-guided random forest for disease gene discoveryJianchang Hu, Silke Szymczak
Briefings in Bioinformatics|January 18, 2023
A review on longitudinal data analysis with random forestJianchang Hu, Silke Szymczak
Briefings in Bioinformatics|October 19, 2017
Evaluation of variable selection methods for random forests and omics data setsFrauke Degenhardt, Stephan Seifert, Silke Szymczak
Bioinformatics (Oxford, England)|March 3, 2019
Surrogate minimal depth as an importance measure for variables in random forestsStephan Seifert, Sven Gundlach, Silke Szymczak
BMC Bioinformatics|August 26, 2025
fuseMLR: an R package for integrative prediction modeling of multi-omics dataCésaire J K Fouodo, Marina Bleskina, Silke Szymczak
Bioinformatics (Oxford, England)|May 14, 2020
Integrating biological knowledge and gene expression data using pathway-guided random forests: a benchmarking studyStephan Seifert, Sven Gundlach, Olaf Junge, et al.
BMC Bioinformatics|May 4, 2026
ShadowVIMP: permutation-based multiple testing-controlled variable selectionTim Müller, Roman Hornung, Silke Szymczak, et al.
Molecular Plant-Microbe Interactions : MPMI|January 11, 2008
Identification of genes relevant to symbiosis and competitiveness in Sinorhizobium meliloti using signature-tagged mutantsNataliya Pobigaylo, Silke Szymczak, Tim W Nattkemper, et al.
BMC Proceedings|December 19, 2009
Evaluation of single-nucleotide polymorphism imputation using random forestsDaniel F Schwarz, Silke Szymczak, Andreas Ziegler, et al.
BMC Proceedings|May 10, 2008
Picking single-nucleotide polymorphisms in forestsDaniel F Schwarz, Silke Szymczak, Andreas Ziegler, et al.
Pageof 9