Biomedical Data and Applications (eBook, PDF)

Redaktion: Sidhu, Amandeep S.

• Format: PDF

Produktbeschreibung

Compared with data from general application domains, modern biological data has many unique characteristics. Biological data are often characterized as having large volumes, complex structures, high dimensionality, evolving biological concepts, and insufficient data modelling practices. Over the past several years, bioinformatics has become an all-encompassing term for everything relating to both computer science and biology. The goal of this book is to cover data and applications identifying new issues and directions for future research in biomedical domain. The book will become a useful guide learning state-of-the-art development in biomedical data management, data-intensive bioinformatics systems, and other miscellaneous biological database applications. The book addresses various topics in bioinformatics with varying degrees of balance between biomedical data models and their real-world applications.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 344
• Erscheinungstermin: 9. Juli 2009
• Englisch
• ISBN-13: 9783642021930
• Artikelnr.: 44228696

Inhaltsangabe

Part A: Biomedical Data.- Towards Bioinformatics Resourceomes.- A summary of genomic databases: overview and discussion.- Protein Data Integration Problem.- Multimedia Medical Databases.- Bio-Medical Ontologies Maintenance and Change Management.- Extraction of Constraints from Biological Data.- Classifying Patterns in Bioinformatics Databases by using Alpha-Beta Associative Memories.- Mining Clinical, Immunological, and Genetic Data of Solid Organ Tansplantation.- Substructure Analysis of Metabolic Pathways by Graph-based Relational Learning.- Part B: Biomedical Applications.- Design of an Online Physician-Mediated Personal Health Record System.- Completing the Total Wellbeing Puzzle using a Multi-agent System.- The Minimal Model of Glucose Disappearance in Type I Diabetes.- Genetic Algorithm in Ab Initio Protein Structure Prediction using Low Resolution Model: A Review.