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

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168 CHAPTER 7. RELATED WORK There are also platforms such as GenePattern 5 (Reich et al., 2006) that extend other systems (PEPPeR (Jaffe et al., 2006)) by data management functionality and further analysis algorithms. Besides the academic efforts there are of course also commercial solutions either by instrument vendors (e.g. ClinProt Tools by Bruker Daltronics, Protein Expression Systems by Waters, ProteinChip by BioRad, ProteinPilot by Applied Biosystems) or other proteomics companies (e.g. CellCarta by caprion proteomics, ProteomeIQ by proteome systems, or Progenesis by Nonlinear Dynamics). Although a comprehensive comparison of these products is not possible, in conclusion it can be said that all of them share the common goal of biomarker detection from MS data. 5 http://www.broad.mit.edu/cancer/software/genepattern/desc/proteomics.html
Chapter 8 Conclusion and Future Directions 8.1 Conclusion In this thesis we have described a new pipeline for preprocessing, processing and analysis of Time-Of-Flight Mass Spectrometry proteomics data. We have shown the application of this web-based framework in the area of clinical diagnostics to detect molecular patterns (fingerprints) of diseases and used these to classify unknown datasets (spectra). Newly developed algorithms allow the detection of very small signals that presumably represent very low abundant molecules, such as hormones. Fingerprints including these very small signals can be much more sensitive compared to standard approaches that do not detect them. The datasets are typically very large (several Gigabytes per patient). To enable handling of these large datasets we have also introduced a new distributed computing platform that allows creation of heterogeneous ad-hoc Grids. To demonstrate the universality of this framework we have shown in a case study the integration of a Playstation 3 to perform data preprocessing tasks. In the current stage of this work it is possible to reliably detect multicomponent fingerprints for a given disease. The components of a fingerprint represent particular molecular species (peptides or proteins) contained in some body fluid (e.g. blood serum) that significantly differentiate in concentration between two patient groups, such as healthy vs. diseased. These molecules - once identified - can be used as so called biomarkers. A biomarker is defined as a molecular, biological or physical characteristic (e.g. DNA sequences, antibodies or - as in our case - proteins) that indicates a specific physiologic state which can be directly linked to the clinical manifestations and outcome of a particular disease. After identification of a disease specific set of biomarkers subsequent studies can be designed to eventually find drugs that can target these biomarkers and hopefully open up new steps for actually curing this disease. However, until this vision becomes reality there are some open problems that need further investigation to allow for a deeper understanding and thus reliable and comprehensible mass-spectrometry based clinical diagnostics. These open problems include: 169
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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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4.6. BIOLOGICAL APPLICATIONS 101 4.
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Chapter 5 Computer Science Grid Str
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5.1. INTRODUCTION 105 � A node is
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5.1. INTRODUCTION 107 of and config
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5.1. INTRODUCTION 109 particular pr
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5.2. THE QUASI AD-HOC (QAD) GRID 11
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5.2. THE QUASI AD-HOC (QAD) GRID 11
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5.3. QAD GRID PLATFORM SERVER 115 F
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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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