Bio-medical Ontologies Maintenance and Change Management

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254 C.h. You, L.B. Holder, and D.J. Cook Verification of the substructures The goal of supervised learning is to find the patterns which are not only able to distinguish between two sets of examples, but are also biologically meaningful. The pattern found by SUBDUE can differentiate well between two examples. We verify biological meaning of these patterns by using the linked database of KEGG PATHWAY [26]. Figure 7 shows the best substructure, which is found in 40 instances of 40 examples in the first iteration of the 00010(+) 00900(-):45 experiment. This substructure which covers 90.9% of the positive examples (40 out of 44) is related to two reactions. Because the edge E to Rct represents a relationship between reaction and enzyme (gene), theentryshouldbetheenzymeorthegene. reaction E_to_Rct entry E_to_Rct reaction Fig. 7. First best pattern from supervised learning on 00010(+) 00900(-):45 set rn:R01061 reversible name type entry S_to_Rct [reaction] S_to_Rct [E_to_Rct] [entry] name type [E_to_Rct] [reaction] Rct_to_P Rct_to_P name type aae:aq_1065 gene name cpd:C00118 compound cpd:C00236 entry name type type rn:R01063 reversible compound Fig. 8. Updated best pattern from supervised learning on 00010(+) 00900(-):45 set In the second phase (verification phase) SUBDUE runs on the named graph of the same example set 00010(+) 00900(-):45 with the first best pattern (figure 7) as the predefined substructure. SUBDUE can find clearly all forty instances in the named graph. The second phase adds more vertices and edges which are erased in the unnamed graph or are not found at the first phase. The substructure in figure 8, which is the updated pattern from the result of the first phase, is the final result of this experiment. The vertices and edges marked by “[ ]” are included from the original substructure learned in the first phase. With this completed substructure, we can refer to linked databases in the KEGG PATHWAY database. SUBDUE supervised learning finds the substructure representing that an enzyme catalyzing two reactions, which share the same substrate and product. Generally an enzyme catalyzes a reaction, but some enzymes can be related to two or more reactions. Figure 8 shows two reaction vertices are connected to an entry (enzyme) vertex by two E to Rct edges, which denote
Substructure Analysis of Metabolic Pathways 255 links between an enzymes and a reaction. The two reactions include a shared substrate (linked by a S to Rct edge) and product (linked by a Rct to P edge). The S to Rct edge denotes a link from a substrate to a reaction, and the Rct to P edge represents a link from a reaction to a product. SUBDUE finds that this substructure exists only in 00010 examples, not in 00090 examples. In this substructure aae:aq 1065 which is the gene name, represents the enzyme ec:1.2.1.12 (glyceraldehyde-3-phosphate dehydrogenase). This enzyme catalyzes two reactions, R01061 and R01063, which are oxidoreductase reactions of NAD + and NADP + [26]. NAD + and NADP + are coenzymes that function as carriers of hydrogen atoms and electrons in some oxidationreduction reactions, especially ATP (Adenosine TriPhosphate: energy material) related reactions. In our experiment the learned substructure is found only in the positive examples (Glycolysis), not in the negative examples (Terpenoid biosynthesis). Glycolysis is an energy generating process which degrades a molecule of glucose in a series of enzyme-catalyzed reactions to yield two molecules of the Pyruvates and ATPs. The conclusion of verification shows that the substructure found by SUBDUE can distinguish between two metabolic pathways and has an understandable biological meaning. Two different metabolic pathways have unique relations as well as unique biochemical molecules. This research is focused on the unique relations. In case of 00010(+) 00900(-):45, an enzyme has relations with two reactions at thesametime.Theenzymehasanimportantfeaturecalledsubstrate specificity, which indicates that an enzyme can be active only when binding with a specific compound. For this reason, the enzyme, ec:1.2.1.12, catalyzes two reactions which have a common relation with the compound, cpd:C00118. In addition that identification of the unique biomolecules in each biological network is a fundamental step, but discovery of the unique relations is also important to classify metabolic pathways. The pattern of relations in the metabolic pathway can be a guide to model an unrecognized metabolic pathway. 5.4 Unsupervised Learning in Metabolic Pathways Unsupervised learning tries to find common substructures in a set of different pathways of one species. The ultimate purpose of applying unsupervised learning to metabolic pathways is to provide a better understandable blueprint of metabolic pathways by using hierarchical topologies. This experiment allows us to understand what common structures the different networks have. The common patterns of relationships in metabolic pathways can contribute to biological network research accompanied with traditional bioinformatics. Like supervised learning, unsupervised learning also employs the unnamed graphs in the first phase and the named graphs in the second phase. In the first phase SUBDUE discovers the substructures and generates hierarchical
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Amandeep S. Sidhu and Tharam S. Dil
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Amandeep S. Sidhu and Tharam S. Dil
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Preface The molecular biology commu
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Preface VII biomedical systems, and
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X Contents Mining Clinical, Immunol
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2 A.S. Sidhu, M. Bellgard, and T.S.
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Towards Bioinformatics Resourceomes
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2 The New Waves Towards Bioinformat
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2.4 Semantic Web Towards Bioinforma
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A Summary of Genomic Databases: Ove
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Appendix A Summary of Genomic Datab
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Protein Data Integration Problem Am
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Protein Data Integration Problem 57
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Fig. 3. Class Hierarchy of Protein
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72 L. Stanescu, D. Dan Burdescu, an
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Bio-medical Ontologies Maintenance
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TextToOnto (Maedche and Volz 2001)
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Extraction of Constraints from Biol
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Classifying Patterns in Bioinformat
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310 M. Fernandez, M. Villasana, and
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Genetic Algorithm in Ab Initio Prot
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Author Index Apiletti, Daniele 169
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Bio-medical Ontologies Maintenance and Change Management

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