Bio-medical Ontologies Maintenance and Change Management

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242 C.h. You, L.B. Holder, and D.J. Cook 3.2 Logic-Based Data Mining Logic-based data mining is a multi-relational data mining technique using first order logic to represent data. Inductive Logic Programming (ILP), a typical logic-based data mining technique, is an approach to induce hypotheses (rules, concepts, or knowledge) from observations (examples, instances, or experiences) by using logic to represent hypotheses and observations [7]. ILP has been applied in several ways to biological domains such as genomics and proteomics. ILP in company with other approaches has been applied to metabolic pathways. Support Vector Inductive Logic Programming (SVILP) is the intersection approach between Support Vector Machines (SVM) and ILP. By using logic to represent background knowledge, the SVILP technique has been applied to the prediction of toxicity of given materials [23]. Stochastic methods are also applied to logic programming to model metabolic pathways. This approach models rates of biochemical reactions using probabilities in addition to representation of metabolic pathways by logic [21]. A last approach is a cooperation between induction and abduction. This approach generates hypotheses from not only abductive reasoning from experimental data (concentrations of metabolites), but also inductive reasoning from general rules (from the KEGG database) to model inhibition in metabolic pathways [25]. An inhibitor is a chemical compound to control biochemical reactions. Several ILP-related techniques have been successfully applied to metabolic pathways. Logic programming can efficiently represent relational data, but prior rules and examples may be necessary to represent entire pathways. 3.3 Frequent Subgraph Mining The graph is an abstract data structure consisting of vertices and edges which are relationships between vertices. Graph-baseddataminingdenotesacollection of algorithms for mining the relational aspects of data represented as a graph. Graph-based data mining has two major approaches: frequent subgraph mining and graph-based relational learning. Frequent subgraph mining is focused on finding frequent subgraphs in a graph. There are two well-known approaches to bioinformatics domains. Frequent SubGraph discovery, FSG, finds all connected subgraphs that appear frequently in a set of graphs. FSG starts by finding all frequent single and double edge graphs. During each iteration FSG expands the size of frequent subgraphs by adding one edge to generate candidate subgraphs. Then, it evaluates and prunes discovered subgraphs with user-defined constraints [19]. Graph-based Substructure Pattern Mining, gSpan, uses the depth-first search and lexicographic ordering. First gSpan sorts the labels, removes infrequent vertices and edges, and it relabels the remaining vertices and edges. Next it starts to find all frequent one-edge subgraphs. The labels on these edges and vertices define a code for each graph. Larger subgraphs map themselves to longer codes. If the code of B is longer than A, the B code is a
Substructure Analysis of Metabolic Pathways 243 child of the A code in a code tree. If there are two not-unique codes in the tree, one of them is removed during the depth-first search traversal to reduce the cost of matching frequent subgraphs [28]. There are a few approaches of frequent subgraph mining applied to metabolic pathways. Pathway Miner, a simplified graph-mining approach on metabolic pathways, proposes a simplified graph representation consisting of just enzyme relationships. In this way, the approach may avoid the NP-hard subgraph isomorphism problem and find frequent patterns quickly [17]. However, the over-simplified representation makes this approach focus on just enzyme relationships, missing some important features of metabolic pathways. Mining coherent dense subgraphs uses the correlation of graphs which represent gene regulatory networks [11]. This approach compresses a group of graphs into two meta-graphs using correlated occurrences of edges for efficient clustering. This approach also loses some biological characteristics of gene regulatory networks, because its representation of a graph is derived only from the similarity of the gene expression patterns between two genes, not representing how the practical biological interactions exist. 4 Graph-Based Relational Learning Graph-based relational learning is focused on novel and meaningful, but not necessarily most frequent, substructures in a graph representation of data. We use the SUBDUE graph-based relational learning approach to discover patterns which not only abstract instances of the patterns by compression, but also provide better understanding of the data [5]. SUBDUE can perform unsupervised learning and supervised learning by substructure discovery based on Minimum Description Length (MDL). Using background knowledge given as predefined substructures can guide graph-based relational learning to find more meaningful substructures. SUBDUE has been applied to a variety of areas such as Chemical Toxicity [4], Molecular Biology [24], Security [9] and Web Search [6]. SUBDUE accepts input data which is represented as a graph with labeled vertices and labeled, directed or undirected edges between vertices. The objects and attribute values of the data are usually represented as vertices, while attributes and relationships between objects are represented as edges. Figure 3 shows a graph representation of a KEGG biological network. There are five ‘Entry’ vertices which represents Enzyme or Compound. Each Entry has two attributes: name and type. Relationships are given as directed and labeled edges from Entry to its attributes. More detail on this graph representation will be provided later. 4.1 Discovery Algorithm The SUBDUE discovery algorithm is based on a beam search as shown in figure 2. The algorithm starts with three parameters: input graph, beam length
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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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Classifying Patterns in Bioinformat
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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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