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

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94 CHAPTER 4. (BIO-)MEDICAL APPLICATIONS Figure 4.4.11: Classification of the training set and test sets in 10-fold-stratified cross-validation for marker-positive seminoma patients vs. healthy controls (CM10 ProteinChip � ) Figure 4.4.12: Classification of the training set and test sets in 10-fold-stratified cross-validation for marker-negative seminoma patients vs healthy controls, (CM10 ProteinChip � ) Assessment of beta-HCG-positive seminoma vs. control subjects yielded comparable but less significant results. Due to the lack of peaks with an adequate height difference, only three peaks were selected for analysis (CM10 ProteinChip � ). The highest number of significant peaks were found in a mass range of 3800-8000 Da. Marker-positive seminomas were correctly predicted in 67.6% (decision trees, 95% confidence interval of 50.2%-81.9%), 78.4% (neural networks, 95% confidence interval of 61.8%-90.2%), and 75.8% of the cases (support vector machines, 95% confidence interval of 58.8%-88.2%). The three analytical methods had adequate sensitivities ranging between 76.5% and 88.2%. Their specificities were likewise adequate at 60% (decision trees), 75% (neural networks), and 65% (support vector machines), respectively. Three different peaks (3.80 kDa; 4.98 kDa; 7.97 kDa) were chosen to create a fingerprint. Figure 4.4.11 displays the corresponding sensitivities and specificities. Decision trees achieved 80% sensitivity and 70% specificity in discriminating between beta-HCG-negative seminoma and control subjects. Figure 4.4.12 shows decision trees, neural networks, and support vector machine analyses. The rates of correctly classified subjects in all groups are 71.1%-75.7% (CM10 ProteinChip � , 95% confidence interval of 60.7%-87.1% for decision trees; 95% confidence interval of 60.7%-87.1% for neural networks; 95% confidence interval of 55.7%-83.6% for support vector machines; molecular masses: 6.94 kDa; 7.76 kDa; 8.63 kDa; 8.69 kDa). The CM10 ProteinChip � achieved excellent differentiation between seminoma and control subjects, while the IMAC-Cu+ chip yielded significantly less precise results. The combination of 6 peaks (4.19 kDa, 6.37 kDa, 7.76 kDa, 7.93 kDa, 7.97 kDa and 9.29 kDa) predicted cancer patients in 60.6%-76.6% (decision trees, neural networks and support vector machines) with a sensitivity of 27.6% to 76.6% and a specificity of 76.6% to 93.6% (Figure 4.4.13) in the 10-fold stratified cross-validation (95% confidence interval of 67.9%-85.6%, decision trees; 95% confidence interval of 64.4% -74.4%, neural networks; 95% confidence interval of 50.0% - 60.4%, support vector machines).
4.4. IDENTIFICATION OF PROTEOMIC FINGERPRINTS IN BLOOD SERUM BY HIGH-SENSITIVE B Figure 4.4.13: Classification of the training set and test sets in 10-fold-stratified cross-validation for seminoma patients vs healthy controls (ProteinChip � IMAC- Cu+). Protein profiles generated by the IMAC-Cu+ array did not differ significantly between beta-HCG-positive and beta-HCG-negative seminoma patients and healthy controls.
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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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Page 49 and 50: 3.5. PEAK DETECTION IN 2D MAPS 43
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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
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.
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Page 93 and 94: 4.3. STUDY RESULTS 87 pairs of obje
Page 95 and 96: 4.3. STUDY RESULTS 89 � “Peptid
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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
Page 119 and 120: 5.2. THE QUASI AD-HOC (QAD) GRID 11
Page 121 and 122: 5.3. QAD GRID PLATFORM SERVER 115 F
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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
Page 143 and 144: 5.4. QAD GRID WORKER 137 database b
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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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