Figure 6From: Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomicsVisualization of filter performance. This plot shows the improvement in classification rate one can get by using our retention time filter for a) varying fractions of the significance threshold value, b) all predictions of spectra having a score equal or greater than 95% of the significance threshold value, c) all predictions of spectra having a score equal or greater than 60% of the significance threshold value. The model was trained using the vds3 dataset and the performance was measured on ds1 and ds2. If there was more than one spectrum with the same identification we plotted the mean values of the observed NRTs against the predicted NRT.Back to article page