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Thomas Burger

Showing results (11-20 of 39) with videos related to

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Analytical Chemistry|April 16, 2026
Performance Is Not All You Need! Comment on "Unsupervised Machine Learning for Differential Analysis in Proteomics"Alicia Lionneton, Christophe Bruley, Thomas Burger
Methods in Molecular Biology (Clifton, N.J.)|October 29, 2022
Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff FiltersLucas Etourneau, Nelle Varoquaux, Thomas Burger
Studies in Health Technology and Informatics|September 7, 2017
The Role of Haptics in User Input for People with Motor and Cognitive ImpairmentsMirjam Augstein, Thomas Neumayr, Thomas Burger
Biostatistics (Oxford, England)|June 20, 2018
PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptidesLaurent Jacob, Florence Combes, Thomas Burger
Journal of Proteomics|July 14, 2019
Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analysesSamuel Wieczorek, Quentin Giai Gianetto, Thomas Burger
Journal of Proteome Research|November 6, 2018
Distinguishing between Spectral Clustering and Cluster Analysis of Mass SpectraHélène Borges, Romain Guibert, Olga Permiakova, et al.
Methods in Molecular Biology (Clifton, N.J.)|March 11, 2019
Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaRSamuel Wieczorek, Florence Combes, Hélène Borges, et al.
Proteomics|June 9, 2016
Uses and misuses of the fudge factor in quantitative discovery proteomicsQuentin Giai Gianetto, Yohann Couté, Christophe Bruley, et al.
Biostatistics (Oxford, England)|March 22, 2025
Penalized likelihood optimization for censored missing value imputation in proteomicsLucas Etourneau, Laura Fancello, Samuel Wieczorek, et al.
Journal of Proteome Research|February 25, 2016
Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation StrategiesCosmin Lazar, Laurent Gatto, Myriam Ferro, et al.
Pageof 4

Showing results (11-20 of 39) with videos related to

Sort By:
Pageof 4
Analytical Chemistry|April 16, 2026
Performance Is Not All You Need! Comment on "Unsupervised Machine Learning for Differential Analysis in Proteomics"Alicia Lionneton, Christophe Bruley, Thomas Burger
Methods in Molecular Biology (Clifton, N.J.)|October 29, 2022
Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff FiltersLucas Etourneau, Nelle Varoquaux, Thomas Burger
Studies in Health Technology and Informatics|September 7, 2017
The Role of Haptics in User Input for People with Motor and Cognitive ImpairmentsMirjam Augstein, Thomas Neumayr, Thomas Burger
Biostatistics (Oxford, England)|June 20, 2018
PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptidesLaurent Jacob, Florence Combes, Thomas Burger
Journal of Proteomics|July 14, 2019
Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analysesSamuel Wieczorek, Quentin Giai Gianetto, Thomas Burger
Journal of Proteome Research|November 6, 2018
Distinguishing between Spectral Clustering and Cluster Analysis of Mass SpectraHélène Borges, Romain Guibert, Olga Permiakova, et al.
Methods in Molecular Biology (Clifton, N.J.)|March 11, 2019
Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaRSamuel Wieczorek, Florence Combes, Hélène Borges, et al.
Proteomics|June 9, 2016
Uses and misuses of the fudge factor in quantitative discovery proteomicsQuentin Giai Gianetto, Yohann Couté, Christophe Bruley, et al.
Biostatistics (Oxford, England)|March 22, 2025
Penalized likelihood optimization for censored missing value imputation in proteomicsLucas Etourneau, Laura Fancello, Samuel Wieczorek, et al.
Journal of Proteome Research|February 25, 2016
Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation StrategiesCosmin Lazar, Laurent Gatto, Myriam Ferro, et al.
Pageof 4