The development of a novel two-step data analysis methodology to uncover peptide signatures of potential cancer biomarkers in large serum peptidomes is described. First, the raw MALDI-TOF data are preprocessed using smoothing, baseline correction, and normalization to identify peaks present in the spectra.  Peak identification is based on a second derivative of the data with smoothing accomplished using a maximum entropy smoothing function. A reference file is created and each spectrum is slid by n data points to the right or left along the x axis of the reference file.  At each relative position n, the Shannon entropy of the sum of the two files is computed.  Optimal alignment occurs at the shift that produces the minimum Shannon entropy.   Second, a genetic algorithm (GA) for pattern recognition analysis is applied to the peak matched data.  The pattern recognition GA selects features that optimize the separation of the sample classes in a plot of the two or three largest principal components (PC's) of the data.  Because the largest PC's capture the bulk of the variance in the data, the features chosen by the GA will convey information primarily about differences between the sample classes in the data.  In addition, the algorithm focuses on those classes and or samples that are difficult to classify as it trains by boosting the sample and class weights. Samples that consistently classify correctly are not as heavily weighted in the analysis as samples that are difficult to classify. The pattern recognition GA integrates aspects of artificial intelligence and evolutionary computations to yield a 'smart' one -pass procedure for features selection and classification.

New data analysis methodology for discovery of cancer biomarkers in MALDI-TOF spectra

246th National Meeting (2013)

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