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

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154CHAPTER 6. PROTEOMICS.NET - PRODUCT-ORIENTED CASE STUDIES Figure 6.2.1: Screenshot of the proteomics.net framework. Left: Login screen; right: Starting a new peak picking analysis 6.2.2 Web Services: Using the Platform Services in Local Programs A second possibility to use the algorithms provided by the proteomics.net framework is by using Web Services. Web services can be seen as APIs (application programming interface) accessible over the Internet. By using them, algorithms can be executed on a remote system hosting the requested services. This means that users can write their own software that includes calls to an algorithm hosted by the proteomics.net framework. All algorithms and services available at the proteomics.net platform are accessible via system independent web-services from outside the platform. This means (authorized) local stand-alone software (written in almost any programming language) can � query the platform (e.g. get status information), � read and send data or results, � submit new jobs, that is, use the algorithms available at the platform.
6.2. CASE STUDIES 155 Figure 6.2.2: Screenshot showing the creation of a new peak picking analysis. Left: Select dataset and parameter-set to use; right: Assign an analysis name and description. Note: in the proteomics.net framework the two screens are on top of each other. Concepts The platform provides information about available web-services in the webservice definition language (WSDL). Using tools like Apache AXIS’ “WSDL2Java” 1 from this WSDL code necessary Java classes and bindings can automatically be created to enable usage of web-services in a Java environment. To allow a local software usage of the web-services it must be authenticated and authorized (see section 5.3.1). For the following example, let us assume that there exists a web-service called “get current worker()” that simply returns a string containing type and name of the workers being online and their client machines. Let us further assume that necessary Java classes have been created automatically by the WSDL2Java tool and for this service the client does not need to be authenticated. Then, the code needed for a (very simple) Java client to connect to 1 see http://ws.apache.org/axis/java/user-guide.html\ #WSDL2JavaBuildingStubsSkeletonsAndDataTypesFromWSDL
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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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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).
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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 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
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