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Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics

Ludger Goeminne (UGent) , Klaas Vandepoele (UGent) , Kris Gevaert (UGent) and Lieven Clement (UGent)
(2015)
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Keywords
statistics, biostatistics, data analysis, differential protein abundance, label-free quantification, differential proteomics, peptide-based linear model, robust ridge regression, M estimation, Huber weights, empirical Bayes variance estimation, tandem mass spectrometry, overfitting, outliers, missing values

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

Chicago
Goeminne, Ludger, Klaas Vandepoele, Kris Gevaert, and Lieven Clement. 2015. “Peptide-level Robust Ridge Regression Modeling Improves Both Sensitivity and Specificity in Quantitative Proteomics.” In .
APA
Goeminne, L., Vandepoele, K., Gevaert, K., & Clement, L. (2015). Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics. Presented at the 7th MaxQuant Summer School 2015.
Vancouver
1.
Goeminne L, Vandepoele K, Gevaert K, Clement L. Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics. 2015.
MLA
Goeminne, Ludger et al. “Peptide-level Robust Ridge Regression Modeling Improves Both Sensitivity and Specificity in Quantitative Proteomics.” 2015. Print.
@inproceedings{8616170,
  author       = {Goeminne, Ludger and Vandepoele, Klaas and Gevaert, Kris and Clement, Lieven},
  language     = {eng},
  location     = {Max Planck Institute of Biochemistry, Martinsried},
  title        = {Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics},
  year         = {2015},
}