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  • 1
    Online Resource
    Online Resource
    Bioinformatics : managing scientific data (2003)
    San Francisco, CA : Morgan Kaufmann Publishers
    Details
    ISBN: 9780080527987 , 0080527981 , 9781558608290 , 155860829X
    Language: English
    Pages: 1 online resource (xxi, 441 pages) , illustrations
    Series Statement: Morgan Kaufmann series in multimedia information and systems
    DDC: 570/.285
    Keywords: NATURE / Reference ; SCIENCE / Life Sciences / General ; SCIENCE / Life Sciences / Biology ; Bioinformatics ; Bio-informatica ; Gegevensbeheer ; Computational Biology / methods ; Information Systems ; Systems Integration ; Integrated Advanced Information Management Systems ; Bioinformatics ; Bioinformatik ; Bioinformatik
    Abstract: Life science data integration and interoperability is one of the most challenging problems facing bioinformatics today. In the current age of the life sciences, investigators have to interpret many types of information from a variety of sources: lab instruments, public databases, gene expression profiles, raw sequence traces, single nucleotide polymorphisms, chemical screening data, proteomic data, putative metabolic pathway models, and many others. Unfortunately, scientists are not currently able to easily identify and access this information because of the variety of semantics, interfaces, a
    Note: Includes bibliographical references and index
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: lizenzpflichtig
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  • 2
    Online Resource
    Online Resource
    Data-Intensive Science (2016)
    [Erscheinungsort nicht ermittelbar] : Chapman and Hall/CRC | Boston, MA : Safari
    Details
    Language: English
    Pages: 1 online resource (446 pages)
    Edition: 1st edition
    Keywords: Electronic books ; local
    Abstract: Data-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world's most challenging problems. In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations. Organizing the material based on a high-level, data-intensive science workflow, this book provides an understanding of the scientific problems that would benefit from collaborative research, the current capabilities of data-intensive science, and the solutions to enable the next round of scientific advancements.
    Note: Online resource; Title from title page (viewed April 19, 2016) , Mode of access: World Wide Web.
    URL: lizenzpflichtig
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