New PDF release: Application of Graph Based Data Mining to Biological

By You Ch.H.

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Extra info for Application of Graph Based Data Mining to Biological Networks

Example text

Map, reference network has 167 biological networks and other species has various number of networks based on research results of each species. But each network in different species is basically same even though it has a few species-specific 34 feature. Glycolysis in hsa is the metabolite to degrade sugar for energy generation, and Glycolysis in eco is the almost same function, wherever this metabolite is identified and stored in KEGG database. Each network is named using five digits number. For example 00010 means Glycolysis and 00251 represents Glutamate metabolism.

The first section will introduce our graph representation to depict KGML data. The second section will explain the application of the supervised learning of the Subdue to biological networks. The last section will apply the unsupervised learning of the Subdue to biological network. 1 Graph Representation As mentioned above a graph representation is widely used for the biological network as well as other biological domains. 3. We use a directed graph, an ordered pair G = (V, E), where V is a set of vertices and E is a set of directed edges.

The first form is closer to the standard supervised learning problem, because we have a set of clearly defined examples. The main approach of supervised learning is to find a substructure that appears often in the positive examples, but not in the negative examples. The substructure value is increased when positive examples are covered by the substructure, but is decreased where negative examples are covered. Positive examples not covered by the substructure and negative examples covered by the substructure are considered error.

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