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Fractal functional regression for classification of gene expression data by wavelets

Functional classification of fractal temporal gene expression data is performed in terms of logistic regression based on the wavelet-vaguelette decomposition of the temporal gene expression curves. The fractality features of the gene expression profiles comes from the stochastic evolutionary forces acting on genomes. The noise level introduced by such forces increases local singularity, that must be removed to make robust the classification procedure. Specifically, thresholding rules are applied to the wavelet-like decomposition of the gene expression profiles to eliminate the noise. Leave-one-out cross-validation is then performed to choose the threshold minimizing the classification error.