Samuel Wieczorek
Samuel Wieczorek
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Cited by
DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics
S Wieczorek, F Combes, C Lazar, Q Giai Gianetto, L Gatto, A Dorffer, ...
Bioinformatics 33 (1), 135-136, 2017
Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata
L Gatto, LM Breckels, S Wieczorek, T Burger, KS Lilley
Bioinformatics 30 (9), 1322-1324, 2014
A peptide-level multiple imputation strategy accounting for the different natures of missing values in proteomics data
QG Gianetto, S Wieczorek, Y Couté, T Burger
BioRxiv, 2020.05. 29.122770, 2020
Protein-level statistical analysis of quantitative label-free proteomics data with ProStaR
S Wieczorek, F Combes, H Borges, T Burger
Proteomics for Biomarker Discovery: Methods and Protocols, 225-246, 2019
Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analyses
S Wieczorek, QG Gianetto, T Burger
Journal of proteomics 207, 103441, 2019
Guiding the search in the no region of the phase transition problem with a partial subsumption test
S Wieczorek, G Bisson, MB Gordon
Machine Learning: ECML 2006: 17th European Conference on Machine Learning …, 2006
Clustering of Molecules: Influence of the Similarity Measures
S Aci, G Bisson, S Roy, S Wieczorek
Selected Contributions in Data Analysis and Classification, 433-444, 2007
A new take on missing value imputation for bottom-up label-free LC-MS/MS proteomics
L Etourneau, L Fancello, S Wieczorek, N Varoquaux, T Burger
bioRxiv, 2023.11. 09.566355, 2023
Statistical analysis of quantitative peptidomics and peptide-level proteomics data with Prostar
M Tardif, E Fremy, AM Hesse, T Burger, Y Couté, S Wieczorek
Statistical Analysis of Proteomic Data: Methods and Tools, 163-196, 2021
DAPAR and ProStaR user manual
S Wieczorek, F Combes, T Burger
Bioconductor. https://www. bioconductor. org/packages/release/bioc/vignettes …, 2018
The signal: statistical aspects, normalisation, elementary analysis
S Wieczorek
Chemogenomics and Chemical Genetics: A User's Introduction for Biologists …, 2011
Criblages phénotypiques et «génétique chimique directe»: Une approche innovante pour la découverte de molécules bio-actives et/ou de candidats-médicaments
E SANS-SOLEILHAC, C Barette, S Wieczorek, S Roy, E Maréchal, ...
Spectra analyse 32 (233), 33-37, 2003
Guiding ilp search in the sparse solutions region with a partial subsumption test
S Wieczorek, G Bisson, MB Gordon
European Conference on Machine Learning 4212, 817-824, 0
Package ‘DAPARdata’
S Wieczorek, F Combes, MS Wieczorek
Package ‘pRoloc’
L Gatto, T Burger, S Wieczorek, ML Gatto, I Biobase, LT Rcpp, ...
Package ‘MSnbase’
L Gatto, G Yu, S Wieczorek, VC Lazar, V Petyuk, T Naake, S Gibb, ...
Prediction of subplastidial localization of chloroplast proteins from spectral count data-Comparison of machine learning algorithms
T Burger, S Wieczorek, C Masselon, D Salvi, N Rolland, M Ferro
RECOMB sat. conf. on proteomics 2012, 2012
Clustering Libraries of Compounds into Families: Asymmetry-Based Similarity Measure to Categorize Small Molecules
S Wieczorek, S Aci, G Bisson, MB Gordon, L Lafanechere, E Maréchal, ...
Bioinformatics-Computational Biology and Modeling, 229-244, 2011
Une mesure d'inclusion entre objets structurés. Application à la classification de molécules.
S Wieczorek
Université Joseph-Fourier-Grenoble I, 2009
Une mesure d'inclusion entre objets structurés: application à la classification de molécules| Theses. fr
S Wieczorek
Grenoble 1, 2009
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