Cancer classification based on mass spectrometry

Abstract

In this paper wavelet analysis and Genetic Algorithm (GA) are performed to extract features and reduce dimensionality of mass spectrometry data. A set of wavelet features, which include detail coefficients and approximation coefficients, are extracted from mass spectrometry data. Detail coefficients are used to characterize the localized change of mass spectrometry data and approximation coefficients are used to compress mass spectrometry data, reducing the dimensionality. GA performs the further dimensionality reduction and optimizes the wavelet features. Experiments prove that this hybrid method of feature extraction is efficient way to characterize mass spectrometry data. © Springer-Verlag Berlin Heidelberg 2007.