Data pre-processing. The quantitative peptide data entered by the user is rescaled to be between 0 and 1 before being fed to the neural network. The user is also given the option to apply a logarithmic transformation to the raw data, if its distribution appears to be too squashed towards low values. Outliers deviating more than 3 standard deviations from the average, which after rescaling would produce sparse regions in the spectrum with no data, are set at a value of exactly 3 standard deviations. This procedure produces ideal data for artificial neural network (ANN) training, with all values in the range [0:1] and the bulk of the data in the central region of the spectrum. The parameters for the rescaling function are defined separately on each of the training sets used in cross-validation, and then also applied to rescale their relative test sets.