Cell Biochemistry Martinsried
Predictive Medicine by cytomics represents a new concept for the prediction of future disease development in individual patients.
Predictions by cytomics are dynamic because they are therapy dependant. Patients with prediction for "disease aggravation" may convert under therapy within some time into "no complication" patients such as e.g. in intensive care medicine. The early detection of disease aggravation or amelioration provides in principle a lead time for earlier therapy onset or offset.
This lead time should be useful for an increase of overall therapeutic efficiency by reducing irreversible tissue damage as well as unwanted therapeutic side effects. Sufficient feedback is obtained by sequential monitoring of the molecular status of disease associated cellular systems during patient treatment like e.g. granulo- and monocytes in sepsis or leukemic and remission cells in leukemias.
With the use of disease associated molecular alterations in cellular systems, the approach is to a substantial degree independent of the exact knowledge on the ultimate molecular causes of disease. This facilitates disease course predictions in complex malignant, infectious, inflammatory, metabolic or degenerative diseases. New hypotheses on disease generation may be developed by the interpretation of the predictive molecular data patterns. They may prove useful for the better recognition of the molecular causes of complex diseases. The reverse engineering like analysis of the molecular phenotype of cytomes utilizes deductive experimental hypothesis, inductive evaluation of all collected multiparameter data followed by deductive interpretation of the algorithmically selected prediction parameters (CLASSIF1 data mining).
The potential of the concept consists in its general
applicability in various areas of clinical or ambulant
medicine. This is illustrated below by a number of
collaborative projects with individual hospitals and
institutions as well as within the framework of the
European Working Group for Clinical Cell Analysis
The evident challenge is to advance this effort fully to the patient level in a multistep effort of scientists, clinicians and industry.
3. Non Medical Data Classification
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