186 REFERENCES Keerthi, S., Shevade, S., Bhattacharyya, C. and Murthy, K. (1999). Improvements to platt’s smo algorithm <strong>for</strong> svm classifier design. Kesselman, C. and Foster, I. (1998). The Grid: Blueprint <strong>for</strong> a <strong>New</strong> Computing Infrastructure, Morgan Kaufmann Publishers. Khan, S. M., Franke-Fayard, B., Mair, G. R., Lasonder, E., Janse, C. J., Mann, M. and Waters, A. P. (2005). Proteome analysis <strong>of</strong> separated male and female gametocytes reveals novel sex-specific plasmodium biology., Cell 121(5): 675–687. Kicman, A. T., Parkin, M. C. and Iles, R. K. (2007). An introduction to mass spectrometry based proteomics-detection and characterization <strong>of</strong> gonadotropins and related molecules., Mol Cell Endocrinol 260-262: 212– 227. Kiebel, G. R., Auberry, K. J., Jaitly, N., Clark, D. A., Monroe, M. E., Peterson, E. S., Toli, N., Anderson, G. A. and Smith, R. D. (2006). Prism: a data management system <strong>for</strong> high-throughput proteomics., Proteomics 6(6): 1783–1790. Kim, S. B., Wang, Z. and Duran, C. M. (2006). A bayesian approach <strong>for</strong> <strong>the</strong> alignment <strong>of</strong> high-resolution nmr spectra, Proceedings <strong>of</strong> <strong>the</strong> INFORMS Artificial Intelligence and Data Mining Workshop, Pittsburgh , PA. Koch, M., Mancini, L. V. and Parisi-Presicce, F. (2005). Graph-based specification <strong>of</strong> access control policies, J. Comput. Syst. Sci. 71(1): 1–33. Kohlbacher, O., Reinert, K., Grpl, C., Lange, E., Pfeifer, N., Schulz- Trieglaff, O. and Sturm, M. (2007). Topp–<strong>the</strong> openms proteomics pipeline., Bioin<strong>for</strong>matics 23(2): e191–e197. Koomen, J. M., Li, D., chun Xiao, L., Liu, T. C., Coombes, K. R., Abbruzzese, J. and Kobayashi, R. (2005). Direct tandem mass spectrometry reveals limitations in protein pr<strong>of</strong>iling experiments <strong>for</strong> plasma biomarker discovery., J Proteome Res 4(3): 972–981. Kozak, K. R., Su, F., Whitelegge, J. P., Faull, K., Reddy, S. and Farias- Eisner, R. (2005). Characterization <strong>of</strong> serum biomarkers <strong>for</strong> detection <strong>of</strong> early stage ovarian cancer., Proteomics 5(17): 4589–4596. Krantz, D., Luce, R. D., Suppes, P. and Tversky, A. (1971). Foundations <strong>of</strong> Measurement: Additive and Polynomial Representations, Vol. 1, Academic Press, <strong>New</strong> York. Kratzsch, J., Fiedler, G. M., Leichtle, A., Brügel, M., Buchbinder, S., Otto, L., Sabri, O., Mat<strong>the</strong>s, G. and Thiery, J. (2005). <strong>New</strong> reference intervals <strong>for</strong> thyrotropin and thyroid hormones based on National Academy <strong>of</strong> Clinical Biochemistry criteria and regular ultrasonography <strong>of</strong> <strong>the</strong> thyroid., Clin Chem 51(8): 1480–1486. Krause, K., Schierhorn, A., Sinz, A., Wissmann, J.-D., Beck-Sickinger, A. G., Paschke, R. and Fuhrer, D. (2006). Toward <strong>the</strong> application <strong>of</strong> proteomics to human thyroid tissue., Thyroid 16(11): 1131–1143. Kuhn, H. W. (1955). The Hungarian method <strong>for</strong> <strong>the</strong> assignment problem, Naval Research Logistic Quarterly 2: 83–97.
REFERENCES 187 Kuijpens, J. L. P., Nyklctek, I., Louwman, M. W. J., Weetman, T. A. P., Pop, V. J. M. and Coebergh, J.-W. W. (2005). Hypothyroidism might be related to breast cancer in post-menopausal women., Thyroid 15(11): 1253–1259. Langley, P. (1994). Selection <strong>of</strong> relevant feature in machine learning, Proceedings <strong>of</strong> <strong>the</strong> AAAI Fall Symposium on Relevance, AAAI Press, <strong>New</strong> Orleans, p. 140144. Lee, M. S. and Kerns, E. H. (1999). Lc/ms applications in drug development., <strong>Mass</strong> Spectrom Rev 18(3-4): 187–279. Lehmann, E. L. (1993). The fisher, neyman-pearson <strong>the</strong>ories <strong>of</strong> testing hypo<strong>the</strong>ses: One <strong>the</strong>ory or two?, Journal <strong>of</strong> <strong>the</strong> American <strong>Statistical</strong> Association 88(424): 1242–1249. Lewis, G. (2004). Tissue collection and <strong>the</strong> pharmaceutical industriy: corporate biobanks. In: Genetic Databases: Socio-ethical Issues in <strong>the</strong> Collection and Use <strong>of</strong> Dna, R Tutton and O Corrigan (Eds.), Routlege, London, UK. Li, L., Tang, H., Wu, Z., Gong, J., Gruidl, M., Zou, J., Tockman, M. and Clark, R. A. (2004). Data mining techniques <strong>for</strong> cancer detection using serum proteomic pr<strong>of</strong>iling., Artif Intell Med 32(2): 71–83. Li, X., Li, J. and Yao, X. (2007). A wavelet-based data pre-processing analysis approach in mass spectrometry., Comput Biol Med 37(4): 509–516. Licklider, J. C. R. and Taylor, R. W. (1968). The computer as a communication device, Sci. Technol. . Lillie<strong>for</strong>s, H. (1967). On <strong>the</strong> kolmogorov-smirnov test <strong>for</strong> normality with mean and variance unknown, Journal <strong>of</strong> <strong>the</strong> American <strong>Statistical</strong> Association . Lin, J. (1991). Divergence measures based on <strong>the</strong> shannon entropy, IEEE Trans. on In<strong>for</strong>mation Theory, 37(1): 145–151. Lipworth, W. (2005). Navigating tissue banking regulation: conceptual frameworks <strong>for</strong> researchers, administrators, regulators and policy-makers., J Law Med 13(2): 245–255. Little, D. P., Speir, J. P., Senko, M. W., O’Connor, P. B. and McLafferty, F. W. (1994). Infrared multiphoton dissociation <strong>of</strong> large multiply charged ions <strong>for</strong> biomolecule sequencing., Anal Chem 66(18): 2809–2815. Liu, B.-F., Sera, Y., Matsubara, N., Otsuka, K. and Terabe, S. (2003). Signal denoising and baseline correction by discrete wavelet trans<strong>for</strong>m <strong>for</strong> microchip capillary electrophoresis., Electrophoresis 24(18): 3260–3265. Liu, Q., Krishnapuram, B., Pratapa, P., Liao, X., Hartemink, A. and Carin, L. (2003). Identification <strong>of</strong> differentially expressed proteins using maldi-t<strong>of</strong> mass spectra, Conference Record <strong>of</strong> <strong>the</strong> Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, Vol. 2, pp. 1323– 1327.
- Page 1 and 2:
New Statistical Algorithms for the
- Page 3 and 4:
Contents Acknowledgments . . . . .
- Page 5 and 6:
New Statistical Algorithms for the
- Page 7 and 8:
Extended Abstract English Version M
- Page 9 and 10:
German Version Das Gebiet der Prote
- Page 11 and 12:
Chapter 1 Introduction and Survey 1
- Page 13 and 14:
1.2. GOALS, OBJECTIVES AND TASKS 7
- Page 15 and 16:
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 33 and 34:
3.2. INTRODUCTION TO MALDI TOF MS 2
- Page 35 and 36:
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 45 and 46:
3.4. HIGHLY SENSITIVE PEAK DETECTIO
- Page 47 and 48:
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 53 and 54:
3.6. PEAK REGISTRATION (ALIGNMENT)
- Page 55 and 56:
3.6. PEAK REGISTRATION (ALIGNMENT)
- Page 57 and 58:
3.7. IDENTIFYING POTENTIAL FEATURES
- Page 59 and 60:
3.7. IDENTIFYING POTENTIAL FEATURES
- Page 61 and 62:
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 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.
- 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 99 and 100:
4.4. IDENTIFICATION OF PROTEOMIC FI
- Page 101 and 102:
4.4. IDENTIFICATION OF PROTEOMIC FI
- Page 103 and 104:
4.5. IDENTIFICATION OF PROTEOMIC FI
- Page 105 and 106:
4.5. 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
- 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
- Page 123 and 124:
5.3. QAD GRID PLATFORM SERVER 117 j
- Page 125 and 126:
5.3. QAD GRID PLATFORM SERVER 119 (
- Page 127 and 128:
5.3. QAD GRID PLATFORM SERVER 121 p
- Page 129 and 130:
5.3. QAD GRID PLATFORM SERVER 123 D
- Page 131 and 132:
5.3. QAD GRID PLATFORM SERVER 125 F
- Page 133 and 134:
5.3. QAD GRID PLATFORM SERVER 127 t
- Page 135 and 136:
5.4. QAD GRID WORKER 129 field. A w
- Page 137 and 138:
5.4. QAD GRID WORKER 131 2. This re
- 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
- Page 145 and 146: 5.5. QAD GRID PLATFORM SERVICES 139
- Page 147 and 148: 5.6. QAD GRID WORKFLOWS 141 � Dat
- Page 149 and 150: 5.6. QAD GRID WORKFLOWS 143 Service
- Page 151 and 152: 5.6. QAD GRID WORKFLOWS 145 Figure
- Page 153 and 154: 5.7. RELATED WORK 147 to set-up sys
- Page 155 and 156: 5.7. RELATED WORK 149 Table 5.7.1 -
- Page 157 and 158: Chapter 6 proteomics.net - Product-
- Page 159 and 160: 6.2. CASE STUDIES 153 6.2 Case Stud
- Page 161 and 162: 6.2. CASE STUDIES 155 Figure 6.2.2:
- Page 163 and 164: 6.2. CASE STUDIES 157 MASCOT and SE
- Page 165 and 166: 6.2. CASE STUDIES 159 The peak pick
- Page 167 and 168: 6.2. CASE STUDIES 161 first entry i
- Page 169 and 170: 6.2. CASE STUDIES 163 Approach Base
- Page 171 and 172: 6.2. CASE STUDIES 165 Figure 6.2.5:
- Page 173 and 174: Chapter 7 Related Work In this chap
- Page 175 and 176: Chapter 8 Conclusion and Future Dir
- 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: REFERENCES 185 Huyghe, E., Muller,
- 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