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

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KEGG PATHWAY is a central database which has the information of five categories of biological networks. KEGG GENES is a collection of gene catalogs for complete genomes and some partial genomes. It consists of GENES (high-quality genomes), DGENES (Draft genomes), EGENES (EST consensus contigs), VGENES (complete viral 32 genomes), and OGENES (complete mitochondrial genomes, plastid ge nomes and nucleomorph genomes). KEGG LIGAND is a composite database consisting of COMPOUND (chemical compounds), GLYCAN (carbohydrate structure), DRUG (drug data), REACTION (chemical reactions), RPAIR (reactant pairs) and EZNYME (enzyme nomenclatur).

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. 3) #PosEgsNotCovered is the number of positive examples not covered by the substructure, and #NegEgsCovered is the number of negative examples covered by the substructure. #PosEgs is the number of positive examples remaining in the experimental set, of which the positive examples that have already been covered in a previous iteration were removed, and #NegEgs is the total number of negative examples, which is constant, because negative examples are not removed [50].

The ultimate purpose of applying clustering to biological networks is to gain a better understanding of the networks by using hierarchical topologies. In the unsupervised learning two kinds of experiments are processed. First one is to find common patterns in one kind of network across a group of species. It is for finding common substructures across all species to describe the biological network. Second trial is to find a common substructures in a group of networks in one species. This experiment allows us to understand what common structures the different networks have.

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Application of Graph Based Data Mining to Biological Networks by You Ch.H.

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