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

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88 CHAPTER 4. (BIO-)MEDICAL APPLICATIONS Figure 4.3.6: This figure shows the steps that are performed to achieve reduction of dimensionality starting with a spectrum of about 100.000 dimensions to a point in R 3 . 3. Choosing the metric (see section 3.7) involves detecting significant signals and identifying a set of the most relevant peaks that can represent a spectrum. This information is then used to build the metric. Commonly, the following two basic strategies for dimensionality reduction are used: (a) use a subset of relevant variables to construct the model (variable selection). That is, to find a subset of d ′ variables where d ′
4.3. STUDY RESULTS 89 � “Peptide Mass Fingerprinting” (Mann et al., 1993; James et al., 1993; Clauser et al., 1999) that uses the mass(es) of the (full or proteolytic) peptide as input to a database search of known proteins or � Using the Tandem MS (MS 2 , MS/MS) technique (Little et al., 1994; Mrtz et al., 1996; Wells and McLuckey, 2005; Hernandez et al., 2006) that generates collision-induced fragments of a peptide and analyzes these fragments to infer the original molecule either by searching a database for the resulting fragment pattern or by de novo sequencing the molecule. There a numerous sophisticated algorithms available for peptide identification and we have integrated some of them into our framework (see section 6.2.3. (2) Analyze biochemical pathways where this molecule plays a role: this and the next step require a sound expertise in the biological / medical area to draw meaningful conclusions. This step lays the foundation to understand the context this molecule works in. As (Villanueva, Shaffer, Philip, Chaparro, Erdjument-Bromage, Olshen, Fleisher, Lilja, Brogi, Boyd, Sanchez-Carbayo, Holland, Cordon-Cardo, Scher and Tempst, 2006) show, even small pathways such as protease activity can exhibit interesting insights. (3) Find the reason for the different occurrence frequency: This step is certainly the most complex part of the analysis and can yield insights of varying complexity. This ranges from basic findings such as men and woman have different concentrations of sex specific hormones till very complex statements such as cancer patients show a very specific activity of some proteases that result in a quite complex peptide pattern of the fragmented pieces. These findings should of course always be validated and confirmed by further biochemical tests and (eventually) clinical trials. 4.3.5 Medical Examples The above paragraphs gave a very coarse grained overview of the steps necessary to analyze the features or fingerprints that can be found with our proteomics.net pipeline. The next two sections give an example of how this can be used in clinical environment.
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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:
Page 43 and 44: 3.4. HIGHLY SENSITIVE PEAK DETECTIO
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Page 51 and 52: 3.6. PEAK REGISTRATION (ALIGNMENT)
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Page 63 and 64: 3.8. EXTRACTING FINGERPRINTS 57 Fig
Page 65 and 66: 3.8. EXTRACTING FINGERPRINTS 59 FID
Page 67 and 68: 3.8. EXTRACTING FINGERPRINTS 61 Dim
Page 69 and 70: 3.9. COMPLEXITY ANALYSIS 63 3.9 Com
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Page 73 and 74: 4.1. DATA USED 67 4.1.2 Serum Data
Page 75 and 76: 4.2. STATISTICAL REMARKS 69 1. Vali
Page 77 and 78: 4.2. STATISTICAL REMARKS 71 Molar v
Page 79 and 80: 4.2. STATISTICAL REMARKS 73 � Fir
Page 81 and 82: 4.2. STATISTICAL REMARKS 75 Let ˆ
Page 83 and 84: 4.2. STATISTICAL REMARKS 77 the boo
Page 85 and 86: 4.3. STUDY RESULTS 79 Figure 4.3.3:
Page 87 and 88: 4.3. STUDY RESULTS 81 Figure 4.3.4:
Page 89 and 90: 4.3. STUDY RESULTS 83 � kNN (gen.
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Page 107 and 108: 4.6. BIOLOGICAL APPLICATIONS 101 4.
Page 109 and 110: Chapter 5 Computer Science Grid Str
Page 111 and 112: 5.1. INTRODUCTION 105 � A node is
Page 113 and 114: 5.1. INTRODUCTION 107 of and config
Page 115 and 116: 5.1. INTRODUCTION 109 particular pr
Page 117 and 118: 5.2. THE QUASI AD-HOC (QAD) GRID 11
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Page 135 and 136: 5.4. QAD GRID WORKER 129 field. A w
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Page 139 and 140: 5.4. QAD GRID WORKER 133 Figure 5.4
Page 141 and 142: 5.4. QAD GRID WORKER 135 Checkpoint
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5.5. QAD GRID PLATFORM SERVICES 139
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5.6. QAD GRID WORKFLOWS 141 � Dat
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5.6. QAD GRID WORKFLOWS 143 Service
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5.6. QAD GRID WORKFLOWS 145 Figure
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5.7. RELATED WORK 147 to set-up sys
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5.7. RELATED WORK 149 Table 5.7.1 -
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Chapter 6 proteomics.net - Product-
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6.2. CASE STUDIES 153 6.2 Case Stud
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6.2. CASE STUDIES 155 Figure 6.2.2:
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6.2. CASE STUDIES 157 MASCOT and SE
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6.2. CASE STUDIES 159 The peak pick
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6.2. CASE STUDIES 161 first entry i
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6.2. CASE STUDIES 163 Approach Base
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6.2. CASE STUDIES 165 Figure 6.2.5:
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Chapter 7 Related Work In this chap
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Chapter 8 Conclusion and Future Dir
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8.3. FROM BIOMARKERS TO BIOPRINTS 1
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Appendix A Implementation Details T
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Appendix B Curriculum Vitae Name Ti
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References Aebersold, R. and Mann,
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REFERENCES 179 Breiman, L. (2001).
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REFERENCES 181 Downard, K. M. and M
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REFERENCES 183 Gillette, M. A., Man
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REFERENCES 185 Huyghe, E., Muller,
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REFERENCES 187 Kuijpens, J. L. P.,
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REFERENCES 189 McLachlan, S. M. and
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REFERENCES 191 Platt, J. C. (1999).
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REFERENCES 193 Stone, M. (1974). Cr
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REFERENCES 195 Washburn, M. P., Wol
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