The document discusses improving the sensitivity and specificity of quantitative proteomics through peptide-level robust ridge regression modeling. It proposes using (1) shrinkage estimation to produce more stable fold change estimates, (2) borrowing information across proteins through empirical Bayes variance estimation, and (3) weighing down outlier peptides through M-estimation with Huber weights. This leads to better fold change estimates and improved sensitivity and specificity in quantitative proteomics.