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

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62 CHAPTER 3. MATHEMATICAL MODELING AND ALGORITHMS whether it contains peaks similar to those the fingerprint consists of (see chapter 4). � Second, the components (peaks) of a fingerprint can be analyzed further, that is, the underlying molecules can be determined and advanced studies can be made to investigate the role of these molecules in the human body (see section 6.2.3). � Third, they can be used to cluster spectra to detect sub-groups even within a group, e.g. a particular type of cancer (see section 4.3.3.
3.9. COMPLEXITY ANALYSIS 63 3.9 Complexity Analysis Using the single complexity analyses from the previous chapters the full complexity analysis results in: Preprocessing: O(n log n), n being the number of sample points. Peak Picking: O(n), n being the number of sample points. Peak Registration: O(m log m), m being the number of peaks. Feature Extraction: O(p 3 ), p being the number of masterpeaks. From empirical studies we know the approximate value ranges of these variables: n: 10 4 m: n 10 = 103 p: m n 10 = 100 = 102
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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
Page 17 and 18: Chapter 2 Preliminaries 2.1 Topic O
Page 19 and 20: 2.1. TOPIC OVERVIEW 13 Figure 2.1.1
Page 21 and 22: 2.1. TOPIC OVERVIEW 15 completeness
Page 23 and 24: 2.2. AN EXAMPLE 17 (a) Opera A (b)
Page 25 and 26: 2.2. AN EXAMPLE 19 Figure 2.2.6: Tw
Page 27 and 28: 2.2. AN EXAMPLE 21 successes. We ca
Page 29 and 30: Chapter 3 Mathematical Modeling and
Page 31 and 32: 3.2. INTRODUCTION TO MALDI TOF MS 2
Page 37 and 38: 3.3. PREPROCESSING 31 mix (external
Page 39 and 40: 3.3. PREPROCESSING 33 Figure 3.3.5:
Page 41 and 42: 3.3. PREPROCESSING 35 Figure 3.3.7:
Page 43 and 44: 3.4. HIGHLY SENSITIVE PEAK DETECTIO
Page 49 and 50: 3.5. PEAK DETECTION IN 2D MAPS 43
Page 51 and 52: 3.6. PEAK REGISTRATION (ALIGNMENT)
Page 57 and 58: 3.7. IDENTIFYING POTENTIAL FEATURES
Page 63 and 64: 3.8. EXTRACTING FINGERPRINTS 57 Fig
Page 65 and 66: 3.8. EXTRACTING FINGERPRINTS 59 FID
Page 67: 3.8. EXTRACTING FINGERPRINTS 61 Dim
Page 71 and 72: Chapter 4 (Bio-)Medical Application
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.
Page 91 and 92: 4.3. STUDY RESULTS 85 d(x, θi) =
Page 93 and 94: 4.3. STUDY RESULTS 87 pairs of obje
Page 95 and 96: 4.3. STUDY RESULTS 89 � “Peptid
Page 97 and 98: 4.4. IDENTIFICATION OF PROTEOMIC FI
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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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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5.3. QAD GRID PLATFORM SERVER 117 j
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5.3. QAD GRID PLATFORM SERVER 119 (
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5.3. QAD GRID PLATFORM SERVER 121 p
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5.3. QAD GRID PLATFORM SERVER 123 D
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5.3. QAD GRID PLATFORM SERVER 125 F
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5.3. QAD GRID PLATFORM SERVER 127 t
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5.4. QAD GRID WORKER 129 field. A w
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5.4. QAD GRID WORKER 131 2. This re
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5.4. QAD GRID WORKER 133 Figure 5.4
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5.4. QAD GRID WORKER 135 Checkpoint
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5.4. QAD GRID WORKER 137 database b
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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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