Table 1 Peptide sample fractionation prediction using standard SVMs. This table shows the classification success rates of the different feature combinations for SVMs with the polynomial and the RBF kernel on the dataset of Oh et al. [11]. The features are (1) molecular weight, (2) sequence index, (3) length and (4) charge of the peptide calculated as in [11].

From: Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics