Abstract
There is no doubt at present that computerized technologies in medicine and biological research, e.g. proteomics and genomics, need new approaches. This paper deals with "The Regression Algorithm for Statistical Identification of Markers From a Set of Spectral Courses" in cases where data error disturbances have a normal distribution.
The discovered principles are generally usable in analogical spectroscopy studies, i.e., not only for treatment of MS for the purpose of biomarker identification. They are even generally ap-plicable to the arbitrary problem of marker identification (used in miscellaneous branches of human activity) by simultaneous tests in a set of quantifying dependences.
With the help of an appropriate mass spectra database analysis, the proposed methodological approach will lead to the construction of a clinic running system which will allow statistical deci-sion making concerning suspicion of disease in patients.