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Jean-Fred Fontaine

Showing results (1-10 of 42) with videos related to

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BMC Bioinformatics|May 17, 2005
Computing expectation values for RNA motifs using discrete convolutionsAndré Lambert, Matthieu Legendre, Jean-Fred Fontaine, et al.
BMC Bioinformatics|July 14, 2022
Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of qualityMaximilian Sprang, Miguel A Andrade-Navarro, Jean-Fred Fontaine
Genome Biology|August 16, 2024
Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasetsMaximilian Sprang, Jannik Möllmann, Miguel A Andrade-Navarro, et al.
Nucleic Acids Research|March 19, 2020
Computational identification of cell-specific variable regions in ChIP-seq dataTommaso Andreani, Steffen Albrecht, Jean-Fred Fontaine, et al.
Methods (San Diego, Calif.)|July 19, 2017
Evaluation of in vivo and in vitro models of toxicity by comparison of toxicogenomics data with the literatureKaterina Taškova, Jean-Fred Fontaine, Ralf Mrowka, et al.
Genome Biology|March 6, 2021
seqQscorer: automated quality control of next-generation sequencing data using machine learningSteffen Albrecht, Maximilian Sprang, Miguel A Andrade-Navarro, et al.
Plos One|January 15, 2019
Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasetsKaterina Taškova, Jean-Fred Fontaine, Ralf Mrowka, et al.
Plos One|July 1, 2022
Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputationSteffen Albrecht, Tommaso Andreani, Miguel A Andrade-Navarro, et al.
Life Science Alliance|August 31, 2021
Statistical guidelines for quality control of next-generation sequencing techniquesMaximilian Sprang, Matteo Krüger, Miguel A Andrade-Navarro, et al.
Bioinformatics (Oxford, England)|August 6, 2021
LipiDisease: associate lipids to diseases using literature miningPiyush More, Laura Bindila, Philipp Wild, et al.
Pageof 5

Showing results (1-10 of 42) with videos related to

Sort By:
Pageof 5
BMC Bioinformatics|May 17, 2005
Computing expectation values for RNA motifs using discrete convolutionsAndré Lambert, Matthieu Legendre, Jean-Fred Fontaine, et al.
BMC Bioinformatics|July 14, 2022
Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of qualityMaximilian Sprang, Miguel A Andrade-Navarro, Jean-Fred Fontaine
Genome Biology|August 16, 2024
Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasetsMaximilian Sprang, Jannik Möllmann, Miguel A Andrade-Navarro, et al.
Nucleic Acids Research|March 19, 2020
Computational identification of cell-specific variable regions in ChIP-seq dataTommaso Andreani, Steffen Albrecht, Jean-Fred Fontaine, et al.
Methods (San Diego, Calif.)|July 19, 2017
Evaluation of in vivo and in vitro models of toxicity by comparison of toxicogenomics data with the literatureKaterina Taškova, Jean-Fred Fontaine, Ralf Mrowka, et al.
Genome Biology|March 6, 2021
seqQscorer: automated quality control of next-generation sequencing data using machine learningSteffen Albrecht, Maximilian Sprang, Miguel A Andrade-Navarro, et al.
Plos One|January 15, 2019
Literature optimized integration of gene expression for organ-specific evaluation of toxicogenomics datasetsKaterina Taškova, Jean-Fred Fontaine, Ralf Mrowka, et al.
Plos One|July 1, 2022
Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputationSteffen Albrecht, Tommaso Andreani, Miguel A Andrade-Navarro, et al.
Life Science Alliance|August 31, 2021
Statistical guidelines for quality control of next-generation sequencing techniquesMaximilian Sprang, Matteo Krüger, Miguel A Andrade-Navarro, et al.
Bioinformatics (Oxford, England)|August 6, 2021
LipiDisease: associate lipids to diseases using literature miningPiyush More, Laura Bindila, Philipp Wild, et al.
Pageof 5