Introduction to machine learning and bioinformatics:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
Chapman & Hall/CRC Press
2008
|
| Schriftenreihe: | Computer science and data analysis series
A Chapman & Hall book |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 366 S. Ill., graph. Darst. |
| ISBN: | 9781584886822 |
Internformat
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| 245 | 1 | 0 | |a Introduction to machine learning and bioinformatics |c Sushmita Mitra ... |
| 264 | 1 | |a Boca Raton [u.a.] |b Chapman & Hall/CRC Press |c 2008 | |
| 300 | |a 366 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Computer science and data analysis series | |
| 490 | 0 | |a A Chapman & Hall book | |
| 650 | 4 | |a Apprentissage automatique | |
| 650 | 4 | |a Bio-informatique | |
| 650 | 4 | |a Artificial Intelligence | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Computational Biology | |
| 650 | 4 | |a Machine learning | |
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Datensatz im Suchindex
| _version_ | 1804137167179481088 |
|---|---|
| adam_text | Contents
1
Introduction
1
2
The Biology of a Living Organism
5
2.1
Cells
5
2.2 DNA
and Genes
8
2.3
Proteins
12
2.4
Metabolism
15
2.5
Biological Regulation Systems: When They Go Awry
17
2.6
Measurement Technologies
19
References
24
3
Probabilistic and Model-Based Learning
25
3.1
Introduction: Probabilistic Learning
25
3.2
Basics of Probability 27
3.3
Random Variables and Probability Distributions
40
3.4
Basics of Information Theory
56
3.5
Basics of Stochastic Processes
58
3.6
Hidden Markov Models
62
3.7
Frequentisi
Statistical Inference
66
3.8
Some Computational Issues
^6
3.9
Bayesian Inference °9
3.10
Exercises 97
References 10°
4
Classification Techniques
101
4.1
Introduction and
Problem
Formulation
101
4.2
The Framework
103
4.3
Classification
Methods
108
4.4
Applications
of
Classification Techniques
to
Bioinformatics
Problems
124
4.5
Exercises
124
References
125
5
Unsupervised
Learning
Techniques
129
5.1
Introduction
129
5.2
Principal
Components Analysis
129
5.3
Multidimensional Scaling
136
5.4
Other Dimension Reduction Techniques
139
5.5
Cluster Analysis Techniques
141
5.6
Exercises
151
References
153
6
Computational Intelligence in Bioinformatics
155
6.1
Introduction
155
6.2
Fuzzy Sets (FS)
156
6.3
Artificial Neural Networks (ANN)
161
6.4
Evolutionary Computing (EC)
167
6.5
Rough Sets (RS)
171
6.6
Hybridization
173
6.7
Application to Bioinformatics
175
6.8
Conclusion
199
6.9
Exercises
200
References
201
7
Connections
between Machine Learning and
Bioinformatics
211
7.1
Sequence Analysis
211
7.2
Analysis of High-Throughput Gene Expression Data
218
7.3
Network Inference
223
7.4
Exercises
230
References
231
8
Machine Learning in Structural Biology: Interpreting
3D
Protein Images
237
8.1
Introduction
237
8.2
Background
237
8.3
arp/warp
247
8.4
resolve
252
8.5 Textal 258
8.6
acmi
264
8.7
Conclusion
273
8.8
Acknowledgments
275
References
275
9
Soft Computing in Biclustering
277
9.1
Introduction
277
9.2
Biclustering
278
9.3
Multi-Objective Biclustering
283
9.4
Fuzzy Possibilistic Biclustering
287
9.5
Experimental Results
291
9.6
Conclusions and Discussion
297
References
298
10
Bayesian Machine-Learning Methods for Tumor
Classification Using Gene Expression Data
303
10.1
Introduction
303
10.2
Classification Using RKHS
306
10.3
Hierarchical Classification Model
308
10.4
Likelihoods of RKHS Models
310
10.5
The Bayesian Analysis
312
10.6
Prediction and Model Choice
314
10.7
Some Examples
315
10.8
Concluding Remarks
321
10.9
Acknowledgments
322
References
322
11
Modeling and Analysis of Quantitative Proteomics Data
Obtained from iTRAQ Experiments
327
11.1
Introduction
327
11.2
Statistical Modeling of iTRAQ Data
328
11.3
Data Illustration
330
11.4
Discussion and Concluding Remarks
332
11.5
Acknowledgments
334
References
334
12
Statistical Methods for Classifying Mass Spectrometry
Database Search Results
339
12.1
Introduction
339
12.2
Background on Proteomics
341
12.3
Classification Methods
342
12.4
Data and Implementation
347
12.5
Results and Discussion
350
12.6
Conclusions
356
12.7
Acknowledgments
357
References
357
Index
361
|
| adam_txt |
Contents
1
Introduction
1
2
The Biology of a Living Organism
5
2.1
Cells
5
2.2 DNA
and Genes
8
2.3
Proteins
12
2.4
Metabolism
15
2.5
Biological Regulation Systems: When They Go Awry
17
2.6
Measurement Technologies
19
References
24
3
Probabilistic and Model-Based Learning
25
3.1
Introduction: Probabilistic Learning
25
3.2
Basics of Probability 27
3.3
Random Variables and Probability Distributions
40
3.4
Basics of Information Theory
56
3.5
Basics of Stochastic Processes
58
3.6
Hidden Markov Models
62
3.7
Frequentisi
Statistical Inference
66
3.8
Some Computational Issues
^6
3.9
Bayesian Inference °9
3.10
Exercises 97
References 10°
4
Classification Techniques
101
4.1
Introduction and
Problem
Formulation
101
4.2
The Framework
103
4.3
Classification
Methods
108
4.4
Applications
of
Classification Techniques
to
Bioinformatics
Problems
124
4.5
Exercises
124
References
125
5
Unsupervised
Learning
Techniques
129
5.1
Introduction
129
5.2
Principal
Components Analysis
129
5.3
Multidimensional Scaling
136
5.4
Other Dimension Reduction Techniques
139
5.5
Cluster Analysis Techniques
141
5.6
Exercises
151
References
153
6
Computational Intelligence in Bioinformatics
155
6.1
Introduction
155
6.2
Fuzzy Sets (FS)
156
6.3
Artificial Neural Networks (ANN)
161
6.4
Evolutionary Computing (EC)
167
6.5
Rough Sets (RS)
171
6.6
Hybridization
173
6.7
Application to Bioinformatics
175
6.8
Conclusion
199
6.9
Exercises
200
References
201
7
Connections
between Machine Learning and
Bioinformatics
211
7.1
Sequence Analysis
211
7.2
Analysis of High-Throughput Gene Expression Data
218
7.3
Network Inference
223
7.4
Exercises
230
References
231
8
Machine Learning in Structural Biology: Interpreting
3D
Protein Images
237
8.1
Introduction
237
8.2
Background
237
8.3
arp/warp
247
8.4
resolve
252
8.5 Textal 258
8.6
acmi
264
8.7
Conclusion
273
8.8
Acknowledgments
275
References
275
9
Soft Computing in Biclustering
277
9.1
Introduction
277
9.2
Biclustering
278
9.3
Multi-Objective Biclustering
283
9.4
Fuzzy Possibilistic Biclustering
287
9.5
Experimental Results
291
9.6
Conclusions and Discussion
297
References
298
10
Bayesian Machine-Learning Methods for Tumor
Classification Using Gene Expression Data
303
10.1
Introduction
303
10.2
Classification Using RKHS
306
10.3
Hierarchical Classification Model
308
10.4
Likelihoods of RKHS Models
310
10.5
The Bayesian Analysis
312
10.6
Prediction and Model Choice
314
10.7
Some Examples
315
10.8
Concluding Remarks
321
10.9
Acknowledgments
322
References
322
11
Modeling and Analysis of Quantitative Proteomics Data
Obtained from iTRAQ Experiments
327
11.1
Introduction
327
11.2
Statistical Modeling of iTRAQ Data
328
11.3
Data Illustration
330
11.4
Discussion and Concluding Remarks
332
11.5
Acknowledgments
334
References
334
12
Statistical Methods for Classifying Mass Spectrometry
Database Search Results
339
12.1
Introduction
339
12.2
Background on Proteomics
341
12.3
Classification Methods
342
12.4
Data and Implementation
347
12.5
Results and Discussion
350
12.6
Conclusions
356
12.7
Acknowledgments
357
References
357
Index
361 |
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| language | English |
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| spelling | Introduction to machine learning and bioinformatics Sushmita Mitra ... Boca Raton [u.a.] Chapman & Hall/CRC Press 2008 366 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Computer science and data analysis series A Chapman & Hall book Apprentissage automatique Bio-informatique Artificial Intelligence Bioinformatics Computational Biology Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Bioinformatik (DE-588)4611085-9 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Bioinformatik (DE-588)4611085-9 s DE-604 Mitra, Sushmita Sonstige oth Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016138794&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Introduction to machine learning and bioinformatics Apprentissage automatique Bio-informatique Artificial Intelligence Bioinformatics Computational Biology Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Bioinformatik (DE-588)4611085-9 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)4611085-9 |
| title | Introduction to machine learning and bioinformatics |
| title_auth | Introduction to machine learning and bioinformatics |
| title_exact_search | Introduction to machine learning and bioinformatics |
| title_exact_search_txtP | Introduction to machine learning and bioinformatics |
| title_full | Introduction to machine learning and bioinformatics Sushmita Mitra ... |
| title_fullStr | Introduction to machine learning and bioinformatics Sushmita Mitra ... |
| title_full_unstemmed | Introduction to machine learning and bioinformatics Sushmita Mitra ... |
| title_short | Introduction to machine learning and bioinformatics |
| title_sort | introduction to machine learning and bioinformatics |
| topic | Apprentissage automatique Bio-informatique Artificial Intelligence Bioinformatics Computational Biology Machine learning Maschinelles Lernen (DE-588)4193754-5 gnd Bioinformatik (DE-588)4611085-9 gnd |
| topic_facet | Apprentissage automatique Bio-informatique Artificial Intelligence Bioinformatics Computational Biology Machine learning Maschinelles Lernen Bioinformatik |
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