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Data mining in biomedicine using ontologies /
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...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Norwood, MA :
Artech House,
©2009.
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| Colección: | Artech House bioinformatics & biomedical imaging series.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Data mining in biomedicine using ontologies / |c Mihail Popescu, Dong Xu, editors. |
| 260 | |a Norwood, MA : |b Artech House, |c ©2009. | ||
| 300 | |a 1 online resource (xv, 262 pages) : |b illustrations. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 490 | 1 | |a Artech House series bioinformatics & biomedical imaging | |
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | 3 | |a 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. |c Publisher abstract. | |
| 505 | 0 | |a 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 Definition 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 | |
| 505 | 8 | |a 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 Unified 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 | |
| 505 | 8 | |a 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) | |
| 505 | 8 | |a 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 GPD 194 Dataset Using NERFCM; 3.5.3 Determining the Number of Clusters of GPD 194 Dataset Using CCV; 3.5.4 GPD 194 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 | |
| 505 | 8 | |a 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 | |
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