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Predictive medicine by cytomics: potential and challenges

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons78821

Valet,  G.
Former Research Groups, Max Planck Institute of Biochemistry, Max Planck Society;

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Zitation

Valet, G. (2002). Predictive medicine by cytomics: potential and challenges. Journal of Biological Regulators and Homeostatic Agents, 16(2), 164-167.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-6F80-6
Zusammenfassung
Predictive medicine by cytomics represents a new concept which provides disease course predictions for individual patients. The predictive information is derived from the molecular cell phenotypes as they are determined by patient's genotype and exposure to external or internal influences. The predictions are dynamic because they are therapy dependent. They may provide a therapeutic lead time for preventive therapy or for the diminution of disease associated irreversible tissue damage. Multiparametric data from cytometry, multiple clinical chemistry assays, chip or bead arrays serve as input for an algorithmic data sieving procedure (http://www.biochem.mpg.de/valet/classif1.html). Data sieving enriches the discriminatory parameters in form of standardized data masks for predictive or diagnostic disease classification in the individual patient (http://www.biochem.mpg.de/valet/cellclas.html). Besides predictive and diagnostic utility, the data patterns can be used in a bottom-tip approach for the development of scientific hypotheses on disease inducing mechanisms in complex inflammatory, infectious, allergic, malignant or degenerative diseases.