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Statistical methods for differential proteomics at peptide and protein level

Ludger Goeminne (UGent)
(2019)
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(UGent) and (UGent)
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Keywords
statistics, biostatistics, data analysis, differential protein abundance, label-free quantification, differential proteomics, peptide-based linear model, ridge regression, M estimation, Huber weights, empirical Bayes variance estimation, limma, tandem mass spectrometry, experimental design, hurdle model, missing values

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Citation

Please use this url to cite or link to this publication:

Chicago
Goeminne, Ludger. 2019. “Statistical Methods for Differential Proteomics at Peptide and Protein Level”. Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences.
APA
Goeminne, L. (2019). Statistical methods for differential proteomics at peptide and protein level. Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences.
Vancouver
1.
Goeminne L. Statistical methods for differential proteomics at peptide and protein level. Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences; 2019.
MLA
Goeminne, Ludger. “Statistical Methods for Differential Proteomics at Peptide and Protein Level.” 2019 : n. pag. Print.
@phdthesis{8615919,
  author       = {Goeminne, Ludger},
  language     = {eng},
  pages        = {XXIII, 230},
  publisher    = {Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences},
  school       = {Ghent University},
  title        = {Statistical methods for differential proteomics at peptide and protein level},
  year         = {2019},
}