简介
Summary:
Publisher Summary 1
Bioinformatics allows the analysis of huge amounts of complex data from medical and biological studies. In this introductory text, Mitra (Indian Statistical Institute, Kolkata), Datta (Texas A&M University), Perkins (McGill Center for Bioinformatics, Montreal, Canada) and Michailidis (University of Michigan) begin with a definition of the subject before moving to the mathematics of setting up an analytical program. Chapters cover learning techniques, connecting machine learning to bioinformatics, biclustering, computational intelligence, tumor classification, iTRAC experiments and methods for classifying mass spectrometry results. Each chapter concludes with exercises. Annotation 漏2008 Book News, Inc., Portland, OR (booknews.com)
目录
Table Of 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 Frequentist Statistical Inference 66
3.8 Some Computational Issues 86
3.9 Bayesian Inference 89
3.10 Exercises 97
References 100
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 Sonic 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 Proleomics 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
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 Frequentist Statistical Inference 66
3.8 Some Computational Issues 86
3.9 Bayesian Inference 89
3.10 Exercises 97
References 100
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 Sonic 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 Proleomics 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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