Bayesian approach to analysis of protein patterns for identification of myeloma cancer

Early detection is critical in cancer control and prevention. Proteomics is an area in discovery of biomarkers that are molecular parameters associated with presence and severity of specific disease states. Protein samples are analyzed on the basis of mass to charge ratio (m/z) of particles they are composed of. Sequences of intensities (i.e. number of particles with specific value of m/z) can be interpreted using statistical approaches or information theory and data mining tools. The data mining, statistical, and information theoretical approaches have already been successfully applied to identify several types of cancer in gene or protein samples. However, due to small size of training sets and very large dimensionality of this kind of data new approaches that incorporate theoretical information need to be developed. This paper presents an application of Bayesian approach to detection of myeloma cancer sites in a sequence of ion intensity values obtained from protein samples. The use of scoring function developed by authors for calculation of data likelihood is also proposed.