Database technology for life sciences and medicine /

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This book presents innovative approaches from database researchers supporting the challenging process of knowledge discovery in biomedicine. Ranging from how to effectively store and organize biomedical data via data quality and case studies to sophisticated data mining methods, this book provides t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Plant, Claudia, Böhm, Christian, 1968-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; Hackensack, NJ : World Scientific, ©2010.
Colección:Science, engineering, and biology informatics ; v. 6.
Temas:
Bioinformatics.
Medical informatics.
Life sciences > Data processing.
Database management.
Databases as Topic
Computational Biology > methods
Medical Informatics > methods
Computational Biology
Bio-informatique.
Médecine > Informatique.
Sciences de la vie > Informatique.
Bases de données > Gestion.
COMPUTERS > Bioinformatics.
Bioinformatics
Database management
Life sciences > Data processing
Medical informatics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Biomedical databases and data mining. 1.1. Databases and knowledge discovery in biomedicine. 1.2. Outline of this book
  • 2. DYNASTAT : A methodology for modeling of multiagent systems. 2.1. Introduction. 2.2. Literature review. 2.3. DYNASTAT methodology. 2.4. Use of UML 2.2 in the framework of DYNASTAT methodology. 2.5. UML to model medical multi-agent systems. 2.6. Possible applications. 2.7. Conclusion
  • 3. SciPort : An extensible data management platform for biomedical research. 3.1. Introduction. 3.2. Related work. 3.3. Unified scientific data modeling. 3.4. Document authoring and searching. 3.5. Sharing distributed biomedical data. 3.6. The architecture of SciPort. 3.7. Conclusion
  • 4. An integrative framework for anonymizing clinical and genomic data. 4.1. Introduction. 4.2. Related work. 4.3. The DIANOVA framework. 4.4. Algorithms for realizing DIANOVA. 4.5. Extensions of DIANOVA. 4.6. Conclusion
  • 5. Data integration challenges : A systems biology perspective. 5.1. Introduction. 5.2. Modeling biological systems. 5.3. Biological and mathematical data sources. 5.4. Various data exchange formats in systems biology. 5.5. Building an integrative framework to combine modeling and biological data sources. 5.6. Analysis of the developed integrative environment. 5.7. Conclusion
  • 6. Ontology-based data integration : A case study in clinical trials.

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