Bio-medical Ontologies Maintenance and Change Management

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250 C.h. You, L.B. Holder, and D.J. Cook Fig. 5. Part of Glycolysis pathways in human (A) and E.Coli (B) [26]. Connected three circled entities represents an instance of the best substructure in figure 4. Table 1. Results of structure analysis on pathways Pathway Number of instance in hsa Number of instances in eco 00010 10 6 00020 66 47 00051 6 0 00061 171 0 00272 3 0 the case of the best substructure existing in both pathways, the number or the location of the instances can also indicate distinguishing features of the pathways: how many instances are in the pathway or what process is included
Substructure Analysis of Metabolic Pathways 251 into the pathway. In this way substructure analysis allows us to reveal the system-level features of metabolic pathways. 5.3 Supervised Learning in Metabolic Pathways The main goal of supervised learning is to distinguish between a biological network in a species group and a different network in the same group. This task provides us the unique substructures in the specific group of pathways to understand better how pathways differ. The distinguishing pattern of relationships between molecules, with limited consideration of the features of each molecule, can also play an important role in system-level understanding of organisms. As described previously supervised learning uses the unnamed graphs for substructure discovery (the first phase) and the named graphs for verification of the substructure (the second phase). Graphs of metabolic pathways are divided into two groups: positive and negative examples. SUBDUE searches for patterns which exist in positive examples, but not in negative examples. We then use SUBDUE to find the erased unique IDs or unfound (in the first phase) vertices and edges in a group of named graphs using the best substructure from the first phase as predefined substructures. The second phase takes aim at verifying the biological meaning of the discovered substructures. Linked databases of the KEGG PATHWAY are also used to identify biological meaning of the final substructures. The discovery algorithm uses the set-cover approach and it is iterated for the number of positive examples. We use the heuristic constraint for polynomial running time as described in section 4.2. Our heuristic Limit, L, is calculated by L = V + B(Eγ − 1) (1) where V is the number of initial vertices, B is Beam length, E is the number of unique labeled edges, and γ is a heuristic constant. V and E are determined from the input graph (positive examples). B is set as 4 because this value is generally used in the successful application of SUBDUE to various domains. When we assume that the substructure learned by SUBDUE has the same number of edges as the number of unique labeled edges in the metabolic pathway, γ is 1.0. We try different γ values, and determine which value gives the best substructure in the shortest time. After several experiments, we found 1.5 supervised learning) and 1.0 (unsupervised learning) as the best choice for γ in this domain. Results of supervised learning Supervised learning in SUBDUE tries to find the substructures that exist in the positive examples, but not in the negative examples. The choice of which set of pathways is the positive example might affect the classification result. Since our goal is the better classification between two groups, we run two
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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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306 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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