An ontology is a set of vocabulary terms with explicitly stated meanings and relations with other terms. 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 researchers to latest developments in bio-ontologies. The book provides the theoretical foundations and examples of ontologies, as well as applications of ontologies in biomedicine, from molecular levels to clinical levels. Readers also find details on technological infrastructure for bio-ontologies. This comprehensive, one-stop volume presents a wide range of practical bio-ontology information, offering professionals detailed guidance in the clustering of biological data, protein classification, gene and pathway prediction, and text mining.
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978-1-59693-370-5 (9781596933705)
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Schweitzer Klassifikation
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. Mihail Popuscu is a 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.
Introduction in Ontologies. UMLS. Gene Ontology. Ontological Similarity Measures. Clustering Objects Described by Ontology Terms. Protein Classification Using Ontology. Go-based Gene Function Prediction Using High-Throughput Data. Mapping Genes to Gene Networks Using Ontological Fuzzy Rule Systems. Extracting Biological Knowledge by Fuzzy Association Rule Mining. Data Summarization Using Ontologies. Data Mining through Ontology Learning. Integrating Databases for Data Mining Using UMLS. Ontology Application in Text Mining.
