Data Mining in Biomedicine Using Ontologies -- Contents -- Foreword -- Preface -- Chapter 1 Introduction to Ontologies -- 1.1 Introduction -- 1.2 History of Ontologies in Biomedicine -- 1.2.1 The Philosophical Connection -- 1.2.2 Recent Defi nition in Computer Science -- 1.2.3 Origins of Bio-Ontologies -- 1.2.4 Clinical and Medical Terminologies -- 1.2.5 Recent Advances in Computer Science -- 1.3 Form and Function of Ontologies -- 1.3.1 Basic Components of Ontologies -- 1.3.2 Components for Humans, Components for Computers -- 1.3.3 Ontology Engineering -- 1.4 Encoding Ontologies -- 1.4.1 The OBO Format and the OBO Consortium -- 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor -- 1.4.3 OWL and RDF/XML -- 1.4.4 Protégé-An OWL Ontology Editor -- 1.5 Spotlight on GO and UMLS -- 1.5.1 The Gene Ontology -- 1.5.2 The Unifi ed Medical Language System -- 1.6 Types and Examples of Ontologies -- 1.6.1 Upper Ontologies -- 1.6.2 Domain Ontologies -- 1.6.3 Formal Ontologies -- 1.6.4 Informal Ontologies -- 1.6.5 Reference Ontologies -- 1.6.6 Application Ontologies -- 1.6.7 Bio-Ontologies -- 1.7 Conclusion -- References -- Chapter 2 Ontological Similarity Measures -- 2.1 Introduction -- 2.1.1 History -- 2.1.2 Tversky's Parameterized Ratio Model of Similarity -- 2.1.3 Aggregation in Similarity Assessment -- 2.2 Traditional Approaches to Ontological Similarity -- 2.2.1 Path-Based Measures -- 2.2.2 Information Content Measures -- 2.2.3 A Relationship Between Path-Based and Information-Content Measures -- 2.3 New Approaches to Ontological Similarity -- 2.3.1 Entity Class Similarity in Ontologies -- 2.3.2 Cross-Ontological Similarity Measures -- 2.3.3 Exploiting Common Disjunctive Ancestors -- 2.4 Conclusion -- References -- Chapter 3 Clustering with Ontologies -- 3.1 Introduction -- 3.2 Relational Fuzzy C-Means (NERFCM) -- 3.3 Correlation Cluster Validity (CCV).

3.4 Ontological SOM (OSOM) -- 3.5 Examples of NERFCM, CCV, and OSOM Applications -- 3.5.1 Test Dataset -- 3.5.2 Clustering of the GPD194 Dataset Using NERFCM -- 3.5.3 Determining the Number of Clusters of GPD194 Dataset Using CCV -- 3.5.4 GPD194 Analysis Using OSOM -- 3.6 Conclusion -- References -- Chapter 4 Analyzing and Classifying Protein Family Data Using OWL Reasoning -- 4.1 Introduction -- 4.1.1 Analyzing Sequence Data -- 4.1.2 The Protein Phosphatase Family -- 4.2 Methods -- 4.2.1 The Phosphatase Classification Pipeline -- 4.2.2 The Datasets -- 4.2.3 The Phosphatase Ontology -- 4.3 Results -- 4.3.1 Protein Phosphatases in Humans -- 4.3.2 Results from the Analysis of A. Fumigatus -- 4.3.3 Ontology System Versus A. Fumigatus Automated Annotation Pipeline -- 4.4 Ontology Classification in the Comparative Analysis of Three Protozoan Parasites-A Case Study -- 4.4.1 TriTryps Diseases -- 4.4.2 TriTryps Protein Phosphatases -- 4.4.3 Methods for the Protozoan Parasites -- 4.4.4 Sequence Analysis Results from the TriTryps Phosphatome Study -- 4.4.5 Evaluation of the Ontology Classification Method -- 4.5 Conclusion -- References -- Chapter 5 GO-Based Gene Function and Network Characterization -- 5.1 Introduction -- 5.2 GO-Based Functional Similarity -- 5.2.1 GO Index-Based Functional Similarity -- 5.2.2 GO Semantic Similarity -- 5.3 Functional Relationship and High-Throughput Data -- 5.3.1 Gene-Gene Relationship Revealed in Microarray Data -- 5.3.2 The Relation Between Functional and Sequence Similarity -- 5.4 Theoretical Basis for Building Relationship Among Genes Through Data -- 5.4.1 Building the Relationship Among Genes Using One Dataset -- 5.4.2 Meta-Analysis of Microarray Data -- 5.4.3 Function Learning from Data -- 5.4.4 Functional-Linkage Network -- 5.5 Function-Prediction Algorithms -- 5.5.1 Local Prediction.

5.5.2 Global Prediction Using a Boltzmann Machine -- 5.6 Gene Function-Prediction Experiments -- 5.6.1 Data Processing -- 5.6.2 Sequence-Based Prediction -- 5.6.3 Meta-Analysis of Yeast Microarray Data -- 5.6.4 Case Study: Sin1 and PCBP2 Interactions -- 5.7 Transcription Network Feature Analysis -- 5.7.1 Time Delay in Transcriptional Regulation -- 5.7.2 Kinetic Model for Time Series Microarray -- 5.7.3 Regulatory Network Reconstruction -- 5.7.4 GO-Enrichment Analysis -- 5.8 Software Implementation -- 5.8.1 GENEFAS -- 5.8.2 Tools for Meta-Analysis -- 5.9 Conclusion -- Acknowledgements -- References -- Chapter 6 Mapping Genes to Biological Pathways Using Ontological Fuzzy Rule Systems -- 6.1 Rule-Based Representation in Biomedical Applications -- 6.2 Ontological Similarity as a Fuzzy Membership -- 6.3 Ontological Fuzzy Rule System (OFRS) -- 6.4 Application of OFRSs: Mapping Genes to Biological Pathways -- 6.4.1 Mapping Gene to Pathways Using a Disjunctive OFRS -- 6.4.2 Mapping Genes to Pathways Using an OFRS in an Evolutionary Framework -- 6.5 Conclusion -- Acknowledgments -- References -- Chapter 7 Extracting Biological Knowledge by Association Rule Mining -- 7.1 Association Rule Mining and Fuzzy Association Rule Mining Overview -- 7.1.1 Association Rules: Formal Defi nition -- 7.1.2 Association Rule Mining Algorithms -- 7.1.3 Apriori Algorithm -- 7.1.4 Fuzzy Association Rules -- 7.2 Using GO in Association Rule Mining -- 7.2.1 Unveiling Biological Associations by Extracting Rules Involving GO Terms -- 7.2.2 Giving Biological Signifi cance to Rule Sets by Using GO -- 7.2.3 Other Joint Applications of Association Rules and GO -- 7.3 Applications for Extracting Knowledge from Microarray Data -- 7.3.1 Association Rules That Relate Gene Expression Patterns with Other Features.

7.3.2 Association Rules to Obtain Relations Between Genes and TheirExpression Values -- Acknowledgements -- References -- Chapter 8 Text Summarization Using Ontologies -- 8.1 Introduction -- 8.2 Representing Background Knowledge-Ontology -- 8.2.1 An Algebraic Approach to Ontologies -- 8.2.2 Modeling Ontologies -- 8.2.3 Deriving Similarity -- 8.3 Referencing the Background Knowledge-Providing Descriptions -- 8.3.1 Instantiated Ontology -- 8.4 Data Summarization Through Background Knowledge -- 8.4.1 Connectivity Clustering -- 8.4.2 Similarity Clustering -- 8.5 Conclusion -- References -- Chapter 9 Reasoning over Anatomical Ontologies -- 9.1 Why Reasoning Matters -- 9.2 Data, Reasoning, and a New Frontier -- 9.2.1 A Taxonomy of Data and Reasoning -- 9.2.2 Contemporary Reasoners -- 9.2.3 Anatomy as a New Frontier for Biological Reasoners -- 9.3 Biological Ontologies Today -- 9.3.1 Current Practices -- 9.3.2 Structural Issues That Limit Reasoning -- 9.3.3 A Biological Example: The Maize Tassel -- 9.3.4 Representational Issues -- 9.4 Facilitating Reasoning About Anatomy -- 9.4.1 Link Different Kinds of Knowledge -- 9.4.2 Layer on Top of the Ontology -- 9.4.3 Change the Representation -- 9.5 Some Visions for the Future -- Acknowledgments -- References -- Chapter 10 Ontology Applications in Text Mining -- 10.1 Introduction -- 10.1.1 What Is Text Mining? -- 10.1.2 Ontologies -- 10.2 The Importance of Ontology to Text Mining -- 10.3 Semantic Document Clustering and Summarization: Ontology Applications in Text Mining -- 10.3.1 Introduction to Document Clustering -- 10.3.2 The Graphical Representation Model -- 10.3.3 Graph Clustering for Graphical Representations -- 10.3.4 Text Summarization -- 10.3.5 Document Clustering and Summarization with Graphical Representation -- 10.4 Swanson's Undiscovered Public Knowledge (UDPK) -- 10.4.1 How Does UDPK Work?.

10.4.2 A Semantic Version of Swanson's UDPK Model -- 10.4.3 The Bio-SbKDS Algorithm -- 10.5 Conclusion -- References -- About the Editors -- List of Contributors -- Index.