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Data Mining in Biomedicine Using Ontologies

Data Mining in Biomedicine Using Ontologies

By (author)s: Mihail Popescu, Dong Xu
Copyright: 2009
Pages: 350
ISBN: 9781596933705

Hardback $129.00
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Presently, a growing number of ontologies are being built and used for annotating data in biomedical research. Thanks to the tremendous amount of data being generated, ontologies are now being used in numerous ways, including connecting different databases, refining search capabilities, interpreting experimental/clinical data, and inferring knowledge. This cutting-edge resource introduces you to latest developments in bio-ontologies. The book provides you with the theoretical foundations and examples of ontologies, as well as applications of ontologies in biomedicine, from molecular levels to clinical levels. You also find details on technological infrastructure for bio-ontologies. This comprehensive, one-stop volume presents a wide range of practical bio-ontology information, offering you detailed guidance in the clustering of biological data, protein classification, gene and pathway prediction, and text mining. More than 160 illustrations support key topics throughout the book.
Foreword ; Preface ; Introduction to Ontologies -Introduction. History of Ontologies in Biomedicine. Form and Function of Ontologies. Encoding Ontologies. Spotlight on GO and UMLS. Types and Examples of Ontologies. Conclusion. ; Ontological Similarity Measures -Introduction. Traditional Approaches to Ontological Similarity. New Approaches to Ontological Similarity. Conclusion. ; Clustering with Ontologies -Introduction. Relational Fuzzy C-Means (NERFCM). Correlation Cluster Validity (CCV). Ontological SOM (OSOM). Examples of NERFCM, CCV, and OSOM Applications. Conclusion. ; Analyzing and Classifying Protein Family Data Using OWL Reasoning -Introduction. Methods. Results. Ontology Classification in the Comparative Analysis of Three Protozoan ParasitesA Case Study. Conclusion. ; GO-Based Gene Function and Network Characterization -Introduction. GO-Based Functional Similarity. Functional Relationship and High-Throughput Data. Theoretical Basis for Building Relationship Among Genes Through Data. Function-Prediction Algorithms. Gene Function-Prediction Experiments. Transcription Network Feature Analysis. Software Implementation. Conclusion. ; Mapping Genes to Biological Pathways Using Ontological Fuzzy Rule Systems - Rule-Based Representation in Biomedical Applications. Ontological Similarity as a Fuzzy Membership. Ontological Fuzzy Rule System (OFRS). Application of OFRSs: Mapping Genes to Biological Pathways. Conclusion. ; Extracting Biological Knowledge by Association Rule Mining - Association Rule Mining and Fuzzy Association Rule Mining Overview. Using GO in Association Rule Mining. Applications for Extracting Knowledge from Microarray Data. ; Text Summarization Using Ontologies -Introduction. Representing Background KnowledgeOntology. Referencing the Background KnowledgeProviding Descriptions. Data Summarization Through Background Knowledge. Conclusion. ; Reasoning over Anatomical Ontologies - Why Reasoning Matters. Data, Reasoning, and a New Frontier. Biological Ontologies Today. Facilitating Reasoning About Anatomy. Some Visions for the Future. ; Ontology Applications in Text Mining -Introduction. The Importance of Ontology to Text Mining. Semantic Document Clustering and Summarization: Ontology Applications in Text Mining. Swanson 's Undiscovered Public Knowledge (UDPK). Conclusion. ; About the Editors. List of Contributors. Index ;
  • Mihail Popescu Mihail Popescu is an adjunct professor in the Department of Computer Science and an assistant professor in the Department of Health Management and Informatics at the University of Missouri, Columbia. He holds an M.S. in electrical and computer engineering, an M.S. medical physics, and a Ph.D. in computer engineering from the University of Missouri, Columbia.
  • Dong Xu Dong Xu is a professor and chair of the Department of Computer Science at the University of Missouri, Columbia. He holds an M.S. in solid state physics from Peking University and a Ph.D. in computational biology from the University of Illinois.
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