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very inactive genes by underlying them dark (grey markers) in the same presentation.
An interactive mode for graphical representing of genetic networks is advised in
future to give users more efficient implements for understanding regulatory network.
Given that to grasp the dynamic behaviour of cells under disease condition it is
evident to understand the principles of regulatory genes networks organisation. And
wouldn't be “learning by doing” with intuitive visualization the simplest way to
succeed in understanding complex behaviour of cells?
6 Future Work - Integrating Methods of Case-Based-Reasoning
(CBR)
In addition to neural network component ART1 we apply AI-methods of case-based-
reasoning in our software system. As the technique of case-based-reasoning has been
practised successfully in several domains like diagnostics, prediction, control and
planning [40], [41] we want to utilize this technique for incremental modelling genetic
networks. Each genetic network is considered as a case within the human genome.
Similar cases represent similar genetic networks. Each stored identified case in the
case base facilitates the retrieval of furthermore cases, i.e. genetic networks. The
single cases have to be induced qualified for retrieving similar cases very fast and for
integrating new cases into the case base, respectively. Inconsistence and
incompleteness are characteristic features of genetic networks in consequence of
incremental steady increase of knowledge about the human proteome. As a result the
revise-phase is particularly important within the retrieval-reuse-revise-retain-loop of
case-based-reasoning systems to control and revise the case base permanently. For
this task a set of practicable techniques of our previous work [42] and according to the
international level of research are available (e.g. contrast model by Tversky) [43], [44] .
We will obtain a similarity tree of prototypes of genetic networks of different
diseases. These prototypes will be represented by nodes of the similarity tree.
A similarity tree of experimental expression data is available from our previous work.
The experimental data come from labs from Universities of Rostock, Bochum and
Greifswald and from research institutes like DKFZ Heidelberg and Stanford. Nodes
are representing autoimmune diseases like chronic pancreatitis, multiple sclerosis,
rheumatic arthritis and further ones, but the focus is on actual research themes like
autoimmune diseases. First genetic networks as nodes of similarity tree ( Drosophila ,
Sea Urchin, intestinal inflammation or NFκB interactions as immune response in MS
and RA) are generated with single software components developed at our institute,
further ones like a genetic network of CP will follow soon. Available networks are
nodes of similarity tree which have only one leaf up to now. In other words the node
is in the same state as the leaf. These networks are to be considered as a start up and
may demonstrate a prototype version of a software system for genomic data analysis.
7 Conclusions
Large-scale gene expression analysis is opening new perspectives in therapeutic
research by providing objective global views of biological behaviour inside cells.
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