- Author
- Ludger Goeminne
- Promoter
- Lieven Clement (UGent) and Kris Gevaert (UGent)
- Organization
- 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
Downloads
-
doctoraatsthesis Ludger Goeminne.pdf
- full text (Published version)
- |
- open access
- |
- |
- 10.36 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8615919
- MLA
- Goeminne, Ludger. Statistical Methods for Differential Proteomics at Peptide and Protein Level. Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences, 2019.
- 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.
- Chicago author-date
- Goeminne, Ludger. 2019. “Statistical Methods for Differential Proteomics at Peptide and Protein Level.” Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences.
- Chicago author-date (all authors)
- Goeminne, Ludger. 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.
- IEEE
- [1]L. Goeminne, “Statistical methods for differential proteomics at peptide and protein level,” Ghent University. Faculty of Science ; Faculty of Medicine and Health Sciences, 2019.
@phdthesis{8615919, author = {{Goeminne, Ludger}}, 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}}, 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}}, }