In this paper, we present a method for predictive profiling of mass spectrometry data. The method integrates a spectra preprocessing pipeline with a complete validation setup aimed at identifying the discriminating peaks and at providing an unbiased estimate of the predictive classification error, based on SVM classifiers and on Entropy-based RFE procedure. A particular emphasis is placed upon avoiding selection bias effects throughout all the analysis steps, from preprocessing to peak importance ranking.
Citation:
Annalisa Barla, Bettina Irler, Stefano Merler, Giuseppe Jurman, Silvano Paoli, Cesare Furlanello, "Proteome Profiling without Selection Bias," cbms, pp.941-946, 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06), 2006