Statistical Diagnostics for Cancer Analyzing High-Dimensional Data Quantitative and Network Biology (VCH) Series
Coordonnateur : Dehmer Matthias
Directeur de Collection : Emmert-Streib Frank
Part One. General Overview.
1 Control of Type I Error Rates for Oncology Biomarker Discovery with
High-Throughput Platforms.
Jeffrey Miecznikowski, Dan Wang,
and Song Liu
1.1 Brief Summary 3
1.2 Introduction 3
1.3 High-Throughput Platforms 4
1.4 Analysis of Experiments 8
1.5 Multiple Testing Type I Errors 15
1.6 Discussion 19
1.7 Perspective 20
2. Overview of Public Cancer Databases, Resources, and Visualization
Tools.
Frank Emmert-Streib, Ricardo de Matos Simoes, Shailesh
Tripathi, and Matthias Dehmer
2.1 Brief Overview 27
2.2 Introduction 27
2.3 Different Cancer Types are Genetically Related 28
2.4 Incidence and Mortality Rates of Cancer 29
2.5 Cancer and Disorder Databases 30
2.6 Visualization and Network-Based Analysis Tools 34
2.7 Conclusions 35
2.8 Perspective 37
Part Two. Bayesian Methods.
3. Discovery of Expression Signatures in Chronic Myeloid Leukemia by
Bayesian Model Averaging.
Ka Yee Yeung
3.1 Brief Introduction 43
3.2 Chronic Myeloid Leukemia (CML) 44
3.3 Variable Selection on Gene Expression Data 44
3.4 Bayesian Model Averaging (BMA) 46
3.5 Case Study: CML Progression Data 49
3.6 The Power of iBMA 50
3.7 Laboratory Validation 51
3.8 Conclusions 52
3.9 Perspective 53
3.10 Publicly Available Resources 54
4. Bayesian Ranking and Selection Methods in Microarray Studies.
Hisashi
Noma and Shigeyuki Matsui
4.1 Brief Summary 57
4.2 Introduction 57
4.3 Hierarchical Mixture Modeling and Empirical Bayes Estimation 59
4.4 Ranking and Selection Methods 60
4.5 Simulations 65
4.6 Application 67
4.7 Concluding Remarks 71
4.8 Perspective 72
4.9 Appendix: The EM Algorithm 72
5. Multiclass Classification via Bayesian Variable Selection with
Gene Expression Data.
Yang Aijun, Song Xinyuan, and Li Yunxian
5.1 Brief Summary 75
5.2 Introduction 75
5.3 Matrix Variate Distribution 77
5.4 Method 77
5.6 Discussion 89
5.7 Perspective 89
6. Semisupervised Methods for Analyzing High-dimensional Genomic Data.
Devin
C. Koestler
6.1 Brief Summary 93
6.2 Motivation 93
6.3 Existing Approaches 95
6.4 Data Application: Mesothelioma Cancer Data Set 102
6.4.1 Results: Mesothelioma Cancer Data Set 104
6.5 Perspective 105
Part Three. Network-Based Approaches.
7. Colorectal Cancer and Its Molecular Subsystems: Construction,
Interpretation, and Validation.
Vishal N. Patel and Mark R.
Chance
7.1 Brief Summary 109
7.2 Colon Cancer: Etiology 109
7.3 Colon Cancer: Development 110
7.4 The Pathway Paradigm 111
7.5 Cancer Subtypes and Therapies 112
7.6 Molecular Subsystems: Introduction 113
7.7 Molecular Subsystems: Construction 113
7.8 Molecular Subsystems: Interpretation 117
7.9 Molecular Subsystems: Validation 119
7.10 Worked Example: Label-Free Proteomics 120
7.11 Conclusions 127
7.12 Perspective 128
8. Network Medicine: Disease Genes in Molecular Networks.
Sreenivas
Chavali and Kartiek Kanduri
8.1 Brief Summary 133
8.2 Introduction 133
8.3 Genetic Architecture of Human Diseases 134
8.4 Systems Properties of Disease Genes 136
8.5 Disease Gene Prioritization 145
8.6 Conclusion 147
8.7 Perspectives 148
9. Inference of Gene Regulatory Networks in Breast and Ovarian Cancer
by Integrating Different Genomic Data.
Binhua Tang, Fei Gu,
and Victor X. Jin
9.1 Brief Summary 153
9.2 Introduction 153
9.3 Theory and Contents of Gene Regulatory Network 154
9.4 Inference of Gene Regulatory Networks in Human Cancer 156
9.5 Conclusions 167
9.6 Perspective 168
10. Network-Module-Based Approaches in Cancer Data Analysis.
Guanming
Wu and Lincoln Stein
10.1 Brief Summary 173
10.2 Introduction 173
10.3 Notation and Terminology 174
10.4 Network Modules Containing Functionally Similar Genes or Proteins 174
10.5 Network Module Searching Methods 175
10.6 Applications of Network-Module-Based Approaches in Cancer Studies 179
10.7 The Reactome FI Cytoscape Plug-in 180
10.8 Conclusions 189
10.9 Perspective 189
11. Discriminant and Network Analysis to Study Origin of Cancer.
Li
Chen, Ye Tian, Guoqiang Yu, David J. Miller, Ie-Ming Shih, and Yue Wang
11.1 Brief Summary 193
11.2 Introduction 193
11.3 Overview of Relevant Machine Learning Techniques 194
11.4 Methods 198
11.5 Experiments and Results 204
11.6 Conclusion 211
11.7 Perspective 212
12. Intervention and Control of Gene Regulatory Networks: Theoretical
Framework and Application to Human Melanoma Gene Regulation.
Nidhal
Bouaynaya, Roman Shterenberg, Dan Schonfeld, and Hassan M.
Fathallah-Shaykh
12.1 Brief Summary 215
12.2 Gene Regulatory Network Models 216
12.3 Intervention in Gene Regulatory Networks 218
12.4 Optimal Perturbation Control of Gene Regulatory Networks 223
12.5 Human Melanoma Gene Regulatory Network 231
12.6 Perspective 235
Part Four. Phenotype Influence of DNA Copy Number Aberrations.
13. Identification of Recurrent DNA Copy Number Aberrations in Tumors.
Vonn
Walter, Andrew B. Nobel, D. Neil Hayes, and Fred A. Wright
13.1 Introduction 241
13.2 Genetic Background 242
13.3 Analyzing DNA Copy Number: Single Sample Methods 246
13.4 Analyzing DNA Copy Number Data: Multiple Sample Methods to Detect Recurrent CNAs 249
13.5 Analyzing DNA Copy Number Data with DiNAMIC 251
13.6 Open Questions 258
14. The Cancer Cell, Its Entropy, and High-Dimensional Molecular Data.
Wessel
N. van Wieringen and Aad W. van der Vaart
14.1 Brief Summary 261
14.2 Introduction 261
14.3 Background 262
14.4 Entropy Increase 264
14.5 Statistical Arguments 266
14.6 Statistical Methodology 268
14.7 Simulation 275
14.8 Application to Cancer Data 275
14.9 Conclusion 283
14.10 Perspective 283
14.11 Software 284
Index 287
Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology and University of Coimbra (Portugal). Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry.
Date de parution : 02-2013
Ouvrage de 312 p.
17.8x24.6 cm
Disponible chez l'éditeur (délai d'approvisionnement : 12 jours).
Prix indicatif 131,29 €
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