Abstract

Proteomic profiling based on mass spectrometry is an important tool for studies at the protein and peptide level in medicine and health care. Thereby, the identification of relevant masses, which are characteristic for specific sample states e.g. a disease state is complicated. Further, the classification accuracy and safety is especially important in medicine. The determination of classification models for such high dimensional clinical data is a complex task. Specific methods, which are robust with respect to the large number of dimensions and fit to clinical needs, are required. In this contribution two such methods for the construction of nearest prototype classifiers are compared in the context of clinical proteomic studies, which are specifically suited to deal with such high-dimensional functional data. Both methods are suitable to the adaptation of the underling metric, which is useful in proteomic research to get a problem adequate representation of the clinical data. In addition they allow fuzzy classification and for one of them allows fuzzy classified training data. Both algorithms are investigated in detail with respect to their specific properties. A performance analyses is taken on real clinical proteomic cancer data in a comparative manner.

Keywords: Fuzzy classification; Learning vector quantization; Metric adaptation; Mass spectrometry; Proteomic profiling