Bio-medical Ontologies Maintenance and Change Management

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248 C.h. You, L.B. Holder, and D.J. Cook Entry to another. Reaction also has connections with two compounds (described as a substrate and a product) and an enzyme (as a catalyst). Figure 3 shows an example of our graph representation, which has five Entries: three compounds and two enzymes. There is also a Relation between two enzymes, and two Reactions sharing a compound and having relationships with two other compounds. Our research uses two kinds of graph representation: named and unnamed graph. The graph in figure 3 is the named graph, which includes the KEGG IDs. The unnamed graph is the same graph only excluding any unique IDs of each Entry and Reaction; all vertices and edges regarding “name” are removed from the graph in figure 3. Unique IDs can pose potential problems when SUBDUE searches for substructures that distinguish two groups of graphs, which contain a group of pathways as positive examples and another group of pathways as negative examples. For example, when we try to distinguish two metabolic pathways G1 and G2, an enzyme which exists only in G1, but not in G2, is sufficient to distinguish. Because our research is focused on the pattern of metabolic pathways, a specific name is not useful for finding the pattern. For this reason, our supervised (section 5.3) and unsupervised (section 5.4) learning on groups of pathways use the unnamed graphs (the first phase). The named graphs are used at the second phase to verify the biological meaning of the patterns. The following sections describe this process in more detail. 5.2 Substructure in Metabolic Pathway This section shows a brief example substructure analysis on metabolic pathways. Here we have two metabolic pathways. SUBDUE tries to find the substructures that exist in one pathway, but not in another. In this experiment, we show that the species-specificity is related to the structure of metabolic pathways. Species-specificity is one of the most important concepts in biology. Basically species-specificity is derived from protein structure and gene sequence. We arrange two glycolysis pathways from Human and E.coli: hsa00010 and eco00010. Glycolysis is a representative energy generating process in almost every cell. We seek a slightly structural difference between these pathways from two species. A named graph of hsa00010 has 1047 vertices and 1208 edges, and the one of eco00010 has 1002 vertices and 1132 edges. The former is a positive example and the latter is a negative example for supervised learning of SUBDUE. Then we run SUBUDE to find substructures that exist in hsa00010, but not in eco00010. As a result, the best substructure is found as shown in figure 4. While this substructure is discovered in eco00010 as well as hsa00010, it is still possible to show how the two pathways differ. The instances of the best substructure in figure 4 are found 10 times in hsa00010 and 6 times in eco00010. After
entry compound relation subtype compound Rel_to_E subtype E_to_Rel Substructure Analysis of Metabolic Pathways 249 relation E_to_Rel gene reversible entry type type E_to_Rct Rel_to_E reaction type relation compound Rct_to_P subtype type entry link ECrel compound Fig. 4. An instance of the best substructure found in hsa00010 inspecting locations of these instances on each pathway, we can identify structural differences between two pathways. Five cases out of 10 instances in hsa00010 are also found in eco00010. The other five instances are found only in hsa00010. These five instances show unique parts exist only in hsa00010. Figure 5 shows an example of one of these five instances. Figure 5 (A) and (B) show the part of the glycolysis pathways in human and E.coli respectively. Connected circled entities denote the instance of the best substructure. The marked instance includes a reaction (R00710) catalyzed by an enzyme (ec:1.2.1.5) and three relations related with the enzyme. The reaction R00710 exists only in hsa00010, but not in eco00010. We can confirm this concept in figure 5. Rectangles and circles denote enzymes and compounds. There is no symbol of relation in this figure. Grayed rectangles represent existing enzymes in the species and white rectangles denote only conceptual views (practically do not exist). As shown in this figure eco00010 does not include ec:1.2.1.5 and ec.1.1.1.2, which exist in hsa00010. The process of Acetaldehyde (NAD+ oxidoreductase) does not exist in eco00010. SUBDUE discovers this substructure existing in hsa00010, but not in eco00010. In this way structure analysis by SUBDUE can characterize the unique features of metabolic pathways. We execute the same method on other experiment sets: 00020 (TCA cycle), 00051 (Fructose and mannose metabolism), 00061 (Fatty acid biosynthesis) and 00272 (Cysteine metabolism) from human and E.coli. The results are shown in table 1. The first column shows the name of the pathway. The second column shows the number of instances of the best substructure found in hsa pathways, and the third column shows the number of instances of the best substructure found in eco pathways. The best substructure of the 00020 experiment is found in both pathways as in the 00010 experiment. But in the other three experiments the best substructures are found only in the hsa pathway. The best substructure existing only in one pathway precisely shows the unique features of the pathway. In
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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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100 L. Stanescu, D. Dan Burdescu, a
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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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Page 201 and 202: Classifying Patterns in Bioinformat
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304 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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