By You Ch.H.
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Extra info for Application of Graph Based Data Mining to Biological Networks
Example text
The number of iterations parameter is set to the same value as the number of positive examples in an experimental set. to be sure to cover all positive examples, even though Subdue usually iterates less than those times. The Limit value is set to 50 or 100, because each experimental set has around twelve initial substructures. The limit value should be greater than the number of initial substructures. But after this restriction limit value is defined base on several trials. 1 shows the results of the classification by the biological network.
Pathway element has six attributes and three child elements. Six attributes are name, org, number, title, image, and link. Name is the convention name of biological network such as eco00010, hsa00020 and map00251. As mentioned above this convention name is starting with “path:”. Org is the species name such as hsa, eco and map. Number specifies a five-digit pathway biological network number such as 00010, 00020 and 00251. Title specifies the title of this map. Image is the location of the graphic file of pathway map.
3). The former has every unique name from KGML data, and the latter does not have any unique names. In the second representation each entry is described just by type, such as an enzyme, compound, reaction, relation and so on, but not the unique name. In the Graph-based data mining (GDM) phase 1 Subdue runs to find the patterns in the unnamed-graph data. In this phase we run two kinds of experiments: supervised learning and unsupervised learning. Supervised learning experiments focus on distinguishing two groups of biological networks.
Application of Graph Based Data Mining to Biological Networks by You Ch.H.
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