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Cell Biochemistry Martinsried |
Molecular cell phenotypes in the naturally existing cellular and cell population heterogeneity of disease affected body cytomes contain information on the future development (prediction) as well as on the present status (diagnosis) of diseases, since diseases are caused by molecular changes in cell systems or organs. With the concept, from cells to patient, the analysis of the full heterogeneity of cellular data opens the way for therapy dependent individualised disease course predictions for the general practice of medicine. Predictions provide a therapeutic lead time. Early preventive therapies can for example try to prevent irreversible tissue damage. It may also be possible to achieve disease retardation or prevention in certain situations like for potential asthma patients by the early recognition of a beginning sensibilisation phase. The immediate sanitation of patients environment may then postpone or prevent disease declaration.
Data classifications are presently considered predictive for individual patients at predictive values >95% for each classified disease category of the learning set while they are prognostic at values <95%. The effort will be to elevate this level to >99% through the search for more efficiently discriminating molecular data patterns.
General concept
for predictive medicine by cytomics:
a.) multiparametric cytometric
determination of cell constituents or cell
functions
in disease associated cytomes
b.) analysis
(1,
2)
of all measured numeric parameters
for all cell populations that is in practice for >95% of the
collected cells
c.) data pattern classification
of this entire information against patient's future disease course
during the learning phase by exhaustive knowledge extraction
d.) classification of the embedded test set of patient
data, measured under the same conditions as the learning set but
remaining unknown to the learning process. Typically, every
5th or 10th patient is assigned to the test set prior to the
learning phase to exclude classification biases.
e.) prospective classification of data collected from
subsequent new patients during the clinical evaluation phase
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