Feature extraction from mass spectra for classification of pathological states

Abstract

Mass spectrometry is becoming an important tool in pro teomics. The representation of mass spectra is characterized by very high dimensionality and a high level of redundancy. Here we present a feature extraction method for mass spectra that directly models for domain knowledge, reduces the dimensionality and redundancy of the initial representation and controls for the level of granularity of feature extraction by seeking to optimize classification accuracy. A number of experiments are performed which show that the feature extraction preserves the initial discriminatory content of the learning examples. © Springer-Verlag Berlin Heidelberg 2005.