New Statistical Algorithms for the Analysis of Mass - FU Berlin, FB MI ...

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148 CHAPTER 5. COMPUTER SCIENCE GRID STRATEGIES Table 5.7.1 – Continued Project Category Description Edutain@Grid: Application EGEE Infrastructure EGRID Infrastructure EUChina Infrastructure g-Eclipse Application gLite Middleware Globus Middleware GRIDCC Application Health-e- Child: Interactive European Grid Application Infrastructure myGrid Application Continued on Next Page. . . Edutain@Grid seeks to transfer techniques from Grid computing to networks used in massive online games through development of a Grid-based framework allowing responsive and interactive applications. The Enabling Grids for E-sciencE (EGEE) project is funded by the European Commission and aims to build on recent advances in grid technology and develop a service grid infrastructure which is available to scientists 24 hours-a-day. An Italian GRID infrastructure for finance and economic research. This Grid aims to facilitate scientific data transfer and processing between Europe and China. The g-Eclipse project aims to build an integrated workbench framework to allow access to existing Grid infrastructures. It is built on top of the Eclipse framework and provides tools to customize Grid user’s applications, to manage Grid resources and to support the development cycle of new Grid applications. Lightweight middleware for Grid Computing. gLite integrates components from current middleware projects, such as Condor and the Globus Toolkit, as well as components developed for the LCG project. The Globus Project is developing fundamental technologies needed to build computational grids, which enable software applications to integrate a variety of resources. The project includes investigations of security, resource management, communication protocols, data management mechanisms, etc. GRIDCC will extend the commonly used batch access to distributed computational and storage resources by including access to and control of distributed instrumentation. is an integrated platform for European paediatrics based on a grid-enabled network of leading clinical centers. The goal of the project is to deploying and operate an inter operable production-level e-Infrastructure for demanding interactive applications. Dis- tributed parallel computing is based on MPI. The myGrid projects puts emphasis on the information Grid and provides middleware layers tailored to the need of scientists in the area of bioinformatics.
5.7. RELATED WORK 149 Table 5.7.1 – Continued Project Category Description NorduGrid Middleware Also known as Advanced Resource Connector (ARC) they provide a reliable implementation of the fundamental Grid services, such as information services, resource discovery and monitoring, job submission and management, brokering and data SEE-GRID-2 Infrastructure TeraGrid Application UniGrids Application management and resource management. The successor of the South-East Europe (SEE) GRID aims to further advance the existing infrastructure and services, to further strengthen scientific collaboration and cooperation among participating SEE communities, and ultimately achieve sustainability for regional and national e- Infrastructures. The aim of this project is to create an integrated, persistent computational resource. Currently, TeraGrid resources include more than 750 teraflops of computing capability and more than 30 petabytes of online and archival data storage. Researchers can also access more than 100 discipline-specific databases. They claim to be the world’s largest, most comprehensive distributed cyberinfrastruc- ture for open scientific research. This project develops a grid service infrastructure based on the Open Grid Services Architecture (OGSA) and on the UNICORE grid software. The goal of UniGrids is to develop translation mechanisms, such as resource ontologies, to interoperate with other OGSA compliant systems. At the same time UniGrids will target grid economics by developing a SLA framework and cross-grid brokering services. The scientific focus is set to be in the areas of biomolecular and computational biology, energy, geophysical depth imaging by oil companies and reactor safety.
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New Statistical Algorithms for the
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Contents Acknowledgments . . . . .
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New Statistical Algorithms for the
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Extended Abstract English Version M
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German Version Das Gebiet der Prote
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Chapter 1 Introduction and Survey 1
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1.2. GOALS, OBJECTIVES AND TASKS 7
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1.2. GOALS, OBJECTIVES AND TASKS 9
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Chapter 2 Preliminaries 2.1 Topic O
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2.1. TOPIC OVERVIEW 13 Figure 2.1.1
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2.1. TOPIC OVERVIEW 15 completeness
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2.2. AN EXAMPLE 17 (a) Opera A (b)
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2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
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2.2. AN EXAMPLE 21 successes. We ca
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Chapter 3 Mathematical Modeling and
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3.2. INTRODUCTION TO MALDI TOF MS 2
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3.3. PREPROCESSING 31 mix (external
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3.3. PREPROCESSING 33 Figure 3.3.5:
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3.3. PREPROCESSING 35 Figure 3.3.7:
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3.4. HIGHLY SENSITIVE PEAK DETECTIO
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3.5. PEAK DETECTION IN 2D MAPS 43
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3.6. PEAK REGISTRATION (ALIGNMENT)
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3.7. IDENTIFYING POTENTIAL FEATURES
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3.8. EXTRACTING FINGERPRINTS 57 Fig
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3.8. EXTRACTING FINGERPRINTS 59 FID
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3.8. EXTRACTING FINGERPRINTS 61 Dim
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3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Chapter 4 (Bio-)Medical Application
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4.1. DATA USED 67 4.1.2 Serum Data
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4.2. STATISTICAL REMARKS 69 1. Vali
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4.2. STATISTICAL REMARKS 71 Molar v
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4.2. STATISTICAL REMARKS 73 � Fir
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4.2. STATISTICAL REMARKS 75 Let ˆ
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4.2. STATISTICAL REMARKS 77 the boo
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4.3. STUDY RESULTS 79 Figure 4.3.3:
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4.3. STUDY RESULTS 81 Figure 4.3.4:
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4.3. STUDY RESULTS 83 � kNN (gen.
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4.3. STUDY RESULTS 85 d(x, θi) =
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4.3. STUDY RESULTS 87 pairs of obje
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4.3. STUDY RESULTS 89 � “Peptid
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4.4. IDENTIFICATION OF PROTEOMIC FI
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Page 177 and 178: 8.3. FROM BIOMARKERS TO BIOPRINTS 1
Page 179 and 180: Appendix A Implementation Details T
Page 181 and 182: Appendix B Curriculum Vitae Name Ti
Page 183 and 184: References Aebersold, R. and Mann,
Page 185 and 186: REFERENCES 179 Breiman, L. (2001).
Page 187 and 188: REFERENCES 181 Downard, K. M. and M
Page 189 and 190: REFERENCES 183 Gillette, M. A., Man
Page 191 and 192: REFERENCES 185 Huyghe, E., Muller,
Page 193 and 194: REFERENCES 187 Kuijpens, J. L. P.,
Page 195 and 196: REFERENCES 189 McLachlan, S. M. and
Page 197 and 198: REFERENCES 191 Platt, J. C. (1999).
Page 199 and 200: REFERENCES 193 Stone, M. (1974). Cr
Page 201 and 202: REFERENCES 195 Washburn, M. P., Wol
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