Figure 2
Building Classifier Training Data. This diagram describes the construction of training data. A set of protein sequences undergo a trypsin in silico digestion to form a collection of tryptic peptides. A number (m) of peptide physicochemical properties are computed for each peptide (for peptide i, properties 1-m are denoted in the figure as: Vi,1, Vi,2, Vi,3,... Vi,m,) and prior MS results are searched to determine if the peptide has been observed or not (for peptide i, the detection call is denoted in the figure as D i ). The resulting training data forms a matrix of values where each row represents the values related to a particular peptide. This output training data associates peptide properties with the MS detection call and will later train a classifier to produce peptide detection probabilities based on peptide physicochemical properties.