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

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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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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.
APA
Goeminne, L., Clement, L., Gevaert, K., & Vandepoele, K. (2015). Peptide-level robust ridge regression modeling improves both sensitivity and specificity in quantitative proteomics. In MaxQuant summer school, 7th, Abstracts. Munich, Germany.
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. In: 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}},
}