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

Ludger Goeminne (UGent) , Lieven Clement (UGent) , Kris Gevaert (UGent) and Klaas Vandepoele (UGent)
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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:

MLA
Goeminne, Ludger et al. “Peptide-level Robust Ridge Regression Modeling Improves Both Sensitivity and Specificity in Quantitative Proteomics.” MaxQuant Summer School, 7th, Abstracts. 2015. Print.
APA
Goeminne, L., Clement, L., Gevaert, K., & Vandepoele, K. (2015). Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics. MaxQuant summer school, 7th, Abstracts. Presented at the 7th MaxQuant summer school on Computational mass spectrometry-based proteomics.
Chicago author-date
Goeminne, Ludger, Lieven Clement, Kris Gevaert, and Klaas Vandepoele. 2015. “Peptide-level Robust Ridge Regression Modeling Improves Both Sensitivity and Specificity in Quantitative Proteomics.” In MaxQuant Summer School, 7th, Abstracts.
Chicago author-date (all authors)
Goeminne, Ludger, Lieven Clement, Kris Gevaert, and Klaas Vandepoele. 2015. “Peptide-level Robust Ridge Regression Modeling Improves Both Sensitivity and Specificity in Quantitative Proteomics.” In MaxQuant Summer School, 7th, Abstracts.
Vancouver
1.
Goeminne L, Clement L, Gevaert K, Vandepoele K. Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics. MaxQuant summer school, 7th, Abstracts. 2015.
IEEE
[1]
L. Goeminne, L. Clement, K. Gevaert, and K. Vandepoele, “Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics,” in MaxQuant summer school, 7th, Abstracts, Munich, Germany, 2015.
@inproceedings{8616170,
  author       = {Goeminne, Ludger and Clement, Lieven and Gevaert, Kris and Vandepoele, Klaas},
  booktitle    = {MaxQuant summer school, 7th, Abstracts},
  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},
  language     = {eng},
  location     = {Munich, Germany},
  title        = {Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics},
  year         = {2015},
}