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Biological database modeling / Jake Chen, Amandeep S. Sidhu, editors.

Contributor(s): Chen, Jake | Sidhu, Amandeep SMaterial type: TextTextSeries: Artech House bioinformatics & biomedical imagingPublication details: Norwood, MA : Artech House, ©2008. Description: 1 online resource (xvii, 224 pages) : illustrationsContent type: text Media type: computer Carrier type: online resourceISBN: 9781596932593; 1596932597Subject(s): Bioinformatics | Database management | Computational biology | Bio-informatique | Bases de données -- Gestion | COMPUTERS -- Bioinformatics | Bioinformatics | Computational biology | Database management | Database Management Systems | Models, BiologicalGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Biological database modeling.DDC classification: 572.80285 LOC classification: QH324.2 | .B559 2008ebNLM classification: 2009 L-392 | QU 26.5Online resources: Click here to access online
Contents:
Introduction to data modeling / Amandeep S. Sidhu, Jake Chen -- Public biological database for -omics studies in medicine / Viroj Wiwanitkit -- Modeling biomedical data / Ramez Elmasri, Feng Ji, and Jack Fu -- Fundamentals of gene ontology / Viroj Wiwanitkit -- Protein Ontology / Amandeep S. Sidhu, Tharam S. Dillon, and Elizabeth Chang -- Information quality management challenges for high-throughput data / Cornelia Hedeler and Paolo Missier -- Data management for fungal genomics: an experience report / Greg Butler [and others] -- Microarray data management: an enterprise information approach / Wily A. Valdvia-Granda, Christopher Dwan -- Data mangament in expression-based proteomics / Zhong Yan [and others] -- Model-driven drug discovery: principles and practices / Karthik Raman, Yeturu Kalidas, and Nagasuma Chandra -- Information mangament and interaction in high-throughput screening for drug discovery / Preeti Malik [and others].
Abstract: Modern biological research in areas like drug discovery produces a staggering volume of data, and the right modeling tools can help scientists apply it in ways never before imaginable. This collection of next-generation biodata modeling techniques combines innovative concepts, methods, and applications with case studies in genome, microarray, proteomics, and drug discovery projects that helps bioinformatics professionals develop ever-more powerful data management systems in any domain. Publisher abstract.
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Includes bibliographical references and index.

Introduction to data modeling / Amandeep S. Sidhu, Jake Chen -- Public biological database for -omics studies in medicine / Viroj Wiwanitkit -- Modeling biomedical data / Ramez Elmasri, Feng Ji, and Jack Fu -- Fundamentals of gene ontology / Viroj Wiwanitkit -- Protein Ontology / Amandeep S. Sidhu, Tharam S. Dillon, and Elizabeth Chang -- Information quality management challenges for high-throughput data / Cornelia Hedeler and Paolo Missier -- Data management for fungal genomics: an experience report / Greg Butler [and others] -- Microarray data management: an enterprise information approach / Wily A. Valdvia-Granda, Christopher Dwan -- Data mangament in expression-based proteomics / Zhong Yan [and others] -- Model-driven drug discovery: principles and practices / Karthik Raman, Yeturu Kalidas, and Nagasuma Chandra -- Information mangament and interaction in high-throughput screening for drug discovery / Preeti Malik [and others].

Print version record.

Modern biological research in areas like drug discovery produces a staggering volume of data, and the right modeling tools can help scientists apply it in ways never before imaginable. This collection of next-generation biodata modeling techniques combines innovative concepts, methods, and applications with case studies in genome, microarray, proteomics, and drug discovery projects that helps bioinformatics professionals develop ever-more powerful data management systems in any domain. Publisher abstract.

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