By You Ch.H.
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Additional resources for Application of Graph Based Data Mining to Biological Networks
Instead of using the compression-based evaluation measure with error measure, Subdue can use the a set-cover approach. At each iteration Subdue adds a new substructure to the disjunctive hypothesis and removes covered positive examples. 5 Summary This section described the Subdue graph-based data mining algorithm. Graph- based data mining is defined as finding the relational patterns in a graph representation of data. Frequent Subgraph Mining Approach and Graph-Based Relational Learning are introduced as two approaches of graph-based data mining in the first section.
For example 00061 00010:Eukaryote set has two groups: The positive example (XP) group has the 00061 biological network in the Eukaryote species group. The negative example (XN) group has the 00010 biological network in the same group. 1 shows every set used in this experiment. The first six consist of pairs to identify difference of accuracy when positive examples and negatives examples are exchanged to each other. 1. Subdue runs with a graph file containing all positive examples and negative examples in the one set to find some substructures that are in the positive examples, but not in the negative examples.
Values of two attributes as vertices are connected to Reaction vertex by Type and Name edges respectively. Reaction element has two or more child elements are categorized as a Substrate and a Product, which are already represented as Entry as above. A Substrate vertex is connected to Reaction vertex by a S to Rct edge, and as a Product vertex is connected by a Rct to P edge. As mentioned above, Reaction entry is connected to the Entry which is catalyzing this Reaction by E to Rct edge. The directions of all edges are headed for all attribute vertices and child vertices from Reaction vertex.
Application of Graph Based Data Mining to Biological Networks by You Ch.H.