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Data Mining Applications Using Ontologies in Biomedicine.
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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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Norwood :
Artech House,
2009.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Popescu, Mihail. | |
| 245 | 1 | 0 | |a Data Mining Applications Using Ontologies in Biomedicine. |
| 260 | |a Norwood : |b Artech House, |c 2009. | ||
| 300 | |a 1 online resource (278 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 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 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. | |
| 505 | 8 | |a 1.4.2 OBO-Edit-The Open Biomedical Ontologies Editor1.4.3 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. | |
| 505 | 8 | |a 2.1.3 Aggregation in Similarity Assessment2.2 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 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. | |
| 505 | 8 | |a 4.3.1 Protein Phosphatases in Humans4.3.2 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. | |
| 520 | |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. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Biology |x Data processing. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Medicine |x Data processing. | |
| 650 | 0 | |a Ontologies (Information retrieval) | |
| 650 | 0 | |a Medical informatics. | |
| 650 | 2 | |a Data Mining | |
| 650 | 2 | |a Medical Informatics | |
| 650 | 2 | |a Medical Informatics Applications | |
| 650 | 6 | |a Biologie |x Informatique. | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Médecine |x Informatique. | |
| 650 | 6 | |a Ontologies (Recherche de l'information) | |
| 650 | 7 | |a Medical informatics |2 fast | |
| 650 | 7 | |a Biology |x Data processing |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Medicine |x Data processing |2 fast | |
| 650 | 7 | |a Ontologies (Information retrieval) |2 fast | |
| 700 | 1 | |a Xu, Dong. | |
| 758 | |i has work: |a Data Mining Applications Using Ontologies in Biomedicine (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYqbq68Jghv3GCK36CDXMK |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Popescu, Mihail. |t Data Mining Applications Using Ontologies in Biomedicine. |d Norwood : Artech House, ©2009 |z 9781596933705 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=946525 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL946525 | ||
| 994 | |a 92 |b IZTAP | ||