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03930nam a22006015i 4500 |
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978-3-319-03035-7 |
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DE-He213 |
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20151204143804.0 |
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cr nn 008mamaa |
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131118s2014 gw | s |||| 0|eng d |
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|a 9783319030357
|9 978-3-319-03035-7
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|a 10.1007/978-3-319-03035-7
|2 doi
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|a 570.285
|2 23
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|a High-Performance In-Memory Genome Data Analysis
|h [electronic resource] :
|b How In-Memory Database Technology Accelerates Personalized Medicine /
|c edited by Hasso Plattner, Matthieu-P. Schapranow.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2014.
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| 300 |
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|a XXI, 223 p. 78 illus.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a text file
|b PDF
|2 rda
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|a In-Memory Data Management Research,
|x 2196-8055
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|a 1. Innovations for Personalized Medicine -- 2. Modeling Genome Data Processing Pipelines -- 3. Scheduling and Execution of Genome Data processing Pipelines -- 4. Exchanging Medical Knowledge -- 5. Billing Processes in Personalized Medicine -- 6. Real-time Analysis of Patient Cohorts -- 7. Ad-hoc Analysis of Genetic Pathways -- 8. Combined Search in Structured and Unstructured Medical Data -- Real-time Collaboration in the Course of Personalized Medicine.
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|a Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.
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|a Life sciences.
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| 650 |
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|a Information technology.
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| 650 |
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|a Business
|x Data processing.
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| 650 |
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|a Health informatics.
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| 650 |
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|a Bioinformatics.
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|a Computational biology.
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|a Biomathematics.
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|a Statistics.
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|a Life Sciences.
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4 |
|a Computer Appl. in Life Sciences.
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| 650 |
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4 |
|a Computational Biology/Bioinformatics.
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| 650 |
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4 |
|a IT in Business.
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| 650 |
2 |
4 |
|a Statistics and Computing/Statistics Programs.
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| 650 |
2 |
4 |
|a Genetics and Population Dynamics.
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| 650 |
2 |
4 |
|a Health Informatics.
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| 700 |
1 |
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|a Plattner, Hasso.
|e editor.
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| 700 |
1 |
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|a Schapranow, Matthieu-P.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9783319030340
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| 830 |
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|a In-Memory Data Management Research,
|x 2196-8055
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| 856 |
4 |
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|u http://dx.doi.org/10.1007/978-3-319-03035-7
|z Full Text via HEAL-Link
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| 912 |
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|a ZDB-2-SBL
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| 950 |
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|a Biomedical and Life Sciences (Springer-11642)
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