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Title High-performance in-memory genome data analysis : how in-memory database technology accelerates personalized medicine / Hasso Plattner, Matthieu-P. Schapranow, editors
Published Cham : Springer, 2014
Table of Contents
 Prefacevii
1.Innovations for Personalized Medicine1
1.1.Requirements for Personalized Medicine / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger1
1.1.1.Researchers / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger3
1.1.2.Clinicians / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger5
1.1.3.Patients / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger6
1.2.Interdisciplinary Teams / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger6
1.3.Trends in Hardware / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger9
1.4.In-memory Technology Building Blocks / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger13
1.4.1.Combined Column and Row Store / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger14
1.4.2.Complete History / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger14
1.4.3.Lightweight Compression / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger14
1.4.4.Partitioning / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger15
1.4.5.Multi-core and Parallelization / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger16
1.4.6.Active and Passive Data Store / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger16
1.4.7.Reduction of Layers / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger17
1.5.High-performance In-memory Genome Platform / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger17
1.5.1.Application Layer with Micro Applications / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger19
1.5.2.Platform Layer / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger21
1.5.3.Data Layer / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger22
1.6.Structure of the Work / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger22
1.7.References / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger25
 Part I Data Processing in Personalized Medicine / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger 
2.Modeling Genome Data Processing Pipelines / Hasso Plattner, Matthieu-P. Schapranow, Franziska Häger31
2.1.Introduction / Marie Schäffer31
2.2.Related Work / Marie Schäffer32
2.3.Modeling of Genome Data Processing Pipelines / Marie Schäffer35
2.3.1.Requirements Engineering / Marie Schäffer36
2.3.2.Modeling of Execution Semantics / Marie Schäffer38
2.3.3.Machine Readable Model Representation / Marie Schäffer43
2.4.Application Example / Marie Schäffer46
2.4.1.Pipeline Configuration User Interface / Marie Schäffer46
2.4.2.Data Format for Pipelines / Marie Schäffer48
2.5.Evaluation and Discussion / Marie Schäffer50
2.6.Conclusion and Outlook / Marie Schäffer52
2.7.References / Marie Schäffer53
3.Scheduling and Execution of Genome Data Processing Pipelines / Marie Schäffer55
3.1.Introduction / Cornelius Bock55
3.2.Related Work / Cornelius Bock56
3.3.Method / Cornelius Bock59
3.3.1.Requirements of the Execution Environment / Cornelius Bock59
3.3.2.In-memory Database as Scheduler / Cornelius Bock60
3.3.3.Real-time Scheduling / Cornelius Bock62
3.4.Application Example / Cornelius Bock65
3.4.1.Architecture / Cornelius Bock65
3.4.2.Application of Design Patterns / Cornelius Bock69
3.4.3.Implementations of Scheduling Policies / Cornelius Bock70
3.5.Evaluation and Discussion / Cornelius Bock73
3.6.Conclusion and Outlook / Cornelius Bock73
3.7.References / Cornelius Bock74
4.Exchanging Medical Knowledge / Cornelius Bock75
4.1.Introduction / Franz Liedke75
4.2.Related Work / Franz Liedke76
4.2.1.In-memory Databases / Franz Liedke76
4.2.2.Sharing Medical Knowledge / Franz Liedke77
4.2.3.Requirements / Franz Liedke79
4.3.Application Example / Franz Liedke80
4.3.1.Use Case / Franz Liedke80
4.3.2.System Requirements / Franz Liedke80
4.3.3.Installing Applications / Franz Liedke83
4.3.4.Configuring Applications / Franz Liedke85
4.3.5.Cohort Analysis / Franz Liedke86
4.4.Benchmarks / Franz Liedke87
4.4.1.Method / Franz Liedke87
4.4.2.Results / Franz Liedke90
4.5.Evaluation and Discussion / Franz Liedke90
4.5.1.Predicate Scan / Franz Liedke92
4.5.2.Aggregation / Franz Liedke93
4.5.3.Join / Franz Liedke94
4.6.Conclusion and Outlook / Franz Liedke96
4.7.References / Franz Liedke99
5.Billing Processes in Personalized Medicine / Franz Liedke101
5.1.Introduction / Joseph Bethge101
5.2.Related Work / Joseph Bethge103
5.3.Requirements Engineering / Joseph Bethge104
5.3.1.Entity Definition / Joseph Bethge104
5.3.2.Free Price System / Joseph Bethge106
5.3.3.Calculation of Usage Fees / Joseph Bethge107
5.3.4.In-memory Database Technology / Joseph Bethge108
5.4.Application Example / Joseph Bethge110
5.4.1.Database Schema / Joseph Bethge110
5:4.2.Database Functionality / Joseph Bethge112
5.4.3.User Interface / Joseph Bethge113
5.4.4.Billing Process / Joseph Bethge114
5.5.Benchmarks / Joseph Bethge115
5.6.Evaluation / Joseph Bethge117
5.6.1.Impact of Transaction Log Size / Joseph Bethge118
5.6.2.Impact of Data Partitioning / Joseph Bethge119
5.7.Conclusion and Outlook / Joseph Bethge120
5.8.Appendix / Joseph Bethge121
5.9.References / Joseph Bethge122
 Part II Real-Time Data Analysis in Personalized Medicine / Joseph Bethge 
6.Real-time Analysis of Patient Cohorts / Joseph Bethge127
6.1.Introduction / Ricarda Schüler127
6.2.Related Work / Ricarda Schüler128
6.2.1.Cohort Analysis / Ricarda Schüler128
6.2.2.K-Means Clustering / Ricarda Schüler130
6.2.3.Hierarchical Clustering / Ricarda Schüler131
6.2.4.In-memory Technology Building Blocks / Ricarda Schüler132
6.3.Application Example / Ricarda Schüler133
6.3.1.Architecture / Ricarda Schüler133
6.3.2.Application / Ricarda Schüler133
6.4.Benchmarks / Ricarda Schüler137
6.4.1.Benchmark Data / Ricarda Schüler137
6.4.2.Benchmarks for In-memory Technology / Ricarda Schüler139
6.4.3.Benchmarks for R / Ricarda Schüler140
6.4.4.Impact of Selected Variables / Ricarda Schüler142
6.4.5.Test Procedure and Technical Environment / Ricarda Schüler142
6.5.Results and Discussion / Ricarda Schüler143
6.5.1.Data Size / Ricarda Schüler144
6.5.2.Number of Genes / Ricarda Schüler147
6.6.Conclusion and Outlook / Ricarda Schüler149
6.7.References / Ricarda Schüler151
7.Ad-hoc Analysis of Genetic Pathways / Ricarda Schüler153
7.1.Introduction / Dominik Müller153
7.2.Related Work / Dominik Müller154
7.2.1.Pathway Analysis / Dominik Müller156
7.2.2.Existing Analysis Possibilities / Dominik Müller157
7.2.3.Storing a Graph / Dominik Müller158
7.2.4.NoSQL / Dominik Müller159
7.2.5.In-memory Database Technology / Dominik Müller160
7.3.Creating an Integrated Pathway Database / Dominik Müller161
7.4.Application Example / Dominik Müller165
7.5.Benchmarks / Dominik Müller167
7.5.1.Occurrences of Analyzed Genes / Dominik Müller169
7.5.2.Genes in Input Set / Dominik Müller169
7.5.3.Integrated Pathways / Dominik Müller170
7.6.Results and Discussion / Dominik Müller170
7.6.1.Occurrences of Analyzed Genes / Dominik Müller170
7.6.2.Genes in Input Set / Dominik Müller171
7.6.3.Integrated Pathways / Dominik Müller172
7.7.Conclusion and Outlook / Dominik Müller174
7.8.Appendix / Dominik Müller175
7.9.References / Dominik Müller178
8.Combined Search in Structured and Unstructured Medical Data / Dominik Müller181
8.1.Introduction / David Heller181
8.2.Related Work / David Heller182
8.3.In-memory Database Text Analysis Features / David Heller185
8.4.Application Example / David Heller186
8.4.1.Customized Biomedical Dictionaries / David Heller187
8.4.2.Customized Extraction Rules / David Heller188
8.4.3.Post-processing of Text Analysis Results / David Heller190
8.4.4.Trial Filtering / David Heller191
8.4.5.User Interface / David Heller192
8.5.Benchmarks / David Heller193
8.5.1.Genes / David Heller195
8.5.2.Partitioning the Text Analysis Result Table / David Heller196
8.5.3.Indexed Trials / David Heller196
8.6.Discussion / David Heller197
8.7.Conclusion and Outlook / David Heller201
8.8.Appendix / David Heller202
8.9.References / David Heller204
9.Real-time Collaboration in the Course of Personalized Medicine / David Heller207
9.1.Real-time Combination of Oncology Data / Hasso Plattner, Matthieu-P. Schapranow207
9.1.1.Holistic Patient View / Hasso Plattner, Matthieu-P. Schapranow209
9.1.2.Search in Structured and Unstructured Data / Hasso Plattner, Matthieu-P. Schapranow210
9.1.3.Real-time Analysis of Patient Cohorts / Hasso Plattner, Matthieu-P. Schapranow211
9.2.Building Research Hypotheses / Hasso Plattner, Matthieu-P. Schapranow211
9.3.Pharmaceutical Feedback Loop / Hasso Plattner, Matthieu-P. Schapranow212
9.4.Federal Bureau of Statistics / Hasso Plattner, Matthieu-P. Schapranow213
9.5.Health Insurance Companies / Hasso Plattner, Matthieu-P. Schapranow214
9.6.Tumor Board of the Future / Hasso Plattner, Matthieu-P. Schapranow214
9.7.References / Hasso Plattner, Matthieu-P. Schapranow216
 List of Abbreviations / Hasso Plattner, Matthieu-P. Schapranow217
 Index / Hasso Plattner, Matthieu-P. Schapranow221
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Description 1 online resource (xxi, 223 pages) : illustrations
Series In-Memory Data Management Research, 2196-8055
In-memory data management research. 2196-8055
Contents Innovations for Personalized Medicine / Hasso Plattner, Matthieu-P. Schapranow and Franziska Häger -- Data Processing in Personalized Medicine -- Modeling Genome Data Processing Pipelines / Marie Schäffer -- Scheduling and Execution of Genome Data Processing Pipelines / Cornelius Bock -- Exchanging Medical Knowledge / Franz Liedke -- Billing Processes in Personalized Medicine / Joseph Bethge -- Real-Time Data Analysis in Personalized Medicine -- Real-time Analysis of Patient Cohorts / Ricarda Schüler -- Ad-hoc Analysis of Genetic Pathways / Dominik Müller -- Combined Search in Structured and Unstructured Medical Data / David Heller -- Real-time Collaboration in the Course of Personalized Medicine / Hasso Plattner, Matthieu-P. Schapranow
Innovations for Personalized Medicine -- Modeling Genome Data Processing Pipelines -- Scheduling and Execution of Genome Data processing Pipelines -- Exchanging Medical Knowledge -- Billing Processes in Personalized Medicine -- Real-time Analysis of Patient Cohorts -- Ad-hoc Analysis of Genetic Pathways -- Combined Search in Structured and Unstructured Medical Data -- Real-time Collaboration in the Course of Personalized Medicine
Summary 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
Bibliography Includes bibliographical references and index
Notes Online resource; title from PDF title page (SpringerLink, viewed November 25, 2013)
Subject Bioinformatics.
Nucleotide sequence.
Human gene mapping -- Data processing.
Computing Methodologies.
Precision Medicine.
Sequence Analysis, DNA.
Form Electronic book
Author Plattner, Hasso, 1944- editor
Schapranow, Matthieu-P., editor
ISBN 9783319030357 (electronic bk.)
3319030353 (electronic bk.)
3319030345
9783319030340