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192 REFERENCES Sauve, A. C. and Speed, T. P. (2004). Normalization, baseline correction and alignment of high-throughput mass spectrometry data, Procedings Gensips 2004. Scheather, S. J. (2004). Density estimation, Statistical Science 19(4): 588– 597. Schmid, H., Semjonow, A., Stieber, P. and van Poppel, H. (1999). Tumour markers in germ cell cancer: Egtm recommendations. european group on tumour markers., Anticancer Res 19(4A): 2795–2798. Serra, J. (1982). Image Analysis and Mathematical Morphology, Academic Press, London. Shao, J. and Tu, D. (1995). The Jackknife and Bootstrap, Springer. Shapiro, S. S. and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples), Biometrika 52(3): 591–611. Shaver, J. P. (1985). Chance and nonsense: A conversation about interpreting tests of statistical significance, Phi Delta Kappan 67(2): 138–141. Shepard, R. (1962). The analysis of proximities: Multidimensional scaling with an unknown distance function., I. Psychometrika . Shin, H., Mutlu, M., Koomen, J. M. and Markey, M. K. (2007). Parametric power spectral density analysis of noise from instrumentation in maldi tof mass spectrometry, Cancer Informatics 3: 317–328. Sinnott, R. W. (1984). Virtues of the haversine, Sky and Telescope 68(2): 159. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. and Siuzdak, G. (2006). Xcms: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification., Anal Chem 78(3): 779–787. Smyth, P. P. A., Wijeyaratne, C. N., Kaluarachi, W. N., Smith, D. F., Premawardhana, L. D. K. E., Parkes, A. B., Jayasinghe, A., de Silva, D. G. H. and Lazarus, J. H. (2005). Sequential studies on thyroid antibodies during pregnancy., Thyroid 15(5): 474–477. Statheropoulos, M., Pappaa, A., Karamertzanisa, P. and Meuzelaarb, H. L. C. (1999). Noise reduction of fast, repetitive gc/ms measurements using principal component analysis (pca), Analytica Chimica Acta 401(1-2): 35– 43. Stein, C. (1981). Estimation of the mean of a multivariate normal distribution., Annals of Statistics 9(6): 1135–1151. Sterling, T., Savarese, D., Becker, D. J., Dorband, J. E., Ranawake, U. A. and Packer, C. V. (1995). BEOWULF: A parallel workstation for scientific computation, Proceedings of the 24th International Conference on Parallel Processing, Oconomowoc, WI, pp. I:11–14. Stevens, R. D., Robinson, A. J. and Goble, C. A. (2003). mygrid: personalised bioinformatics on the information grid., Bioinformatics 19 Suppl 1.
REFERENCES 193 Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion), Journal of the Royal Statisticul Society 36: 111–147. SUNMicrosystems (2006). Documentation of the java platform standard edition 6, http://http://java.sun.com/javase/6/docs/api/, accessed October 11, 2007. Suriano, R., Lin, Y., Ashok, B. T., Schaefer, S. D., Schantz, S. P., Geliebter, J. and Tiwari, R. K. (2006). Pilot study using seldi-tof-ms based proteomic profile for the identification of diagnostic biomarkers of thyroid proliferative diseases., J Proteome Res 5(4): 856–861. Süssmuth, R. D. and Jung, G. (1999). Impact of mass spectrometry on combinatorial chemistry., J Chromatogr B Biomed Sci Appl 725(1): 49–65. Tammen, H., Schulte, I., Hess, R., Menzel, C., Kellmann, M., Mohring, T. and Schulz-Knappe, P. (2005). Peptidomic analysis of human blood specimens: Comparison between plasma specimens and serum by differential peptide display, Proteomics 5(13): 3414–3422. Tang, N., Tornatore, P. and Weinberger, S. (2004). Current developments in seldi affinity technology, Mass spectrometry reviews 23(1): 34–33. Taswell, C. (2000). The what, how, and why of wavelet shrinkage denoising, Computing in Science and Engineering 2(3): 12–19. Terry, S. F., Terry, P. F., Rauen, K. A., Uitto, J. and Bercovitch, L. G. (2007). Advocacy groups as research organizations: the pxe international example., Nat Rev Genet 8(2): 157–164. Thadikkaran, L., Siegenthaler, M. A., Crettaz, D., Queloz, P.-A., Schneider, P. and Tissot, J.-D. (2005). Recent advances in blood-related proteomics., Proteomics 5(12): 3019–3034. Thompson, B. (1994). The concept of statistical significance testing, Practical Assessment, Research & Evaluation 4(5). Thompson, B. (1995). Exploring the replicability of a study’s results: Bootstrap statistics for the multivariate case, Educational and Psychological Measurement 55: 84–94. Thulasidasan, S., chun Feng, W. and Gardner, M. K. (2003). Optimizing gridftp through dynamic right-sizing, hpdc 00: 14. Tibshirani, R., Hastie, T., Narasimhan, B., Soltys, S., Shi, G., Koong, A. and Le, Q.-T. (2004). Sample classification from protein mass spectrometry, by ’peak probability contrasts’., Bioinformatics 20(17): 3034–3044. Tolson, J., Bogumil, R., Brunst, E., Beck, H., Elsner, R., Humeny, A., Kratzin, H., Deeg, M., Kuczyk, M., Mueller, G. A., Mueller, C. A. and Flad, T. (2004). Serum protein profiling by seldi mass spectrometry: detection of multiple variants of serum amyloid alpha in renal cancer patients., Lab Invest 84(7): 845–856.
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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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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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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 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: REFERENCES 191 Platt, J. C. (1999).
Page 201 and 202: REFERENCES 195 Washburn, M. P., Wol
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