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components and clinical patient data available in a coded form from medical faculty.
Clinical data are an essential “background” for effective analyzing expression data.
Deliverables from automatic text mining tool are information about causal relations
between genes obtained from online journals of biomedical literature. AI-methods
like neural networks can provide categories of gene patterns which are typical in a
general sense for a considered disease. Results of the latter both methods are integral
parts of genetic networks which are modeled as a primary objective of the
bioinformatic system. With the help of models we have made incredible progress in
deciphering what we know today about dynamic cell. A visualization tool is utilized
for graphical presentation of genetic networks. Based on genomic data analysis and
genetic networks as models we want to give a bioinformatic approach for
understanding the dynamic of pathogenic processes and to answer questions like
“what is a disease x in the genomic sense”?
2 Micro Array Expression Data - Results from Wet Lab and Data
Analysis
2.1 Lab Phase
The information flow in processing micro array data is primarily based on samples
and micro arrays for making hybridization in laboratories. All this occurs during the
lab phase (Fig. 2).
Fig. 2. Information flow in processing micro arrays
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