Lavoisier S.A.S.
14 rue de Provigny
94236 Cachan cedex
FRANCE

Heures d'ouverture 08h30-12h30/13h30-17h30
Tél.: +33 (0)1 47 40 67 00
Fax: +33 (0)1 47 40 67 02


Url canonique : www.lavoisier.fr/livre/mathematiques/statistical-diagnostics-for-cancer/dehmer/descriptif_2709828
Url courte ou permalien : www.lavoisier.fr/livre/notice.asp?ouvrage=2709828

Statistical Diagnostics for Cancer Analyzing High-Dimensional Data Quantitative and Network Biology (VCH) Series

Langue : Anglais

Coordonnateur : Dehmer Matthias

Directeur de Collection : Emmert-Streib Frank

Couverture de l’ouvrage Statistical Diagnostics for Cancer
This ready reference discusses different methods for statistically analyzing and validating data created with high-throughput methods. As opposed to other titles, this book focusses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network. From a methodological point of view, the well balanced contributions describe a variety of modern supervised and unsupervised statistical methods applied to various large-scale datasets from genomics and genetics experiments. Furthermore, since the availability of sufficient computer power in recent years has shifted attention from parametric to nonparametric methods, the methods presented here make use of such computer-intensive approaches as Bootstrap, Markov Chain Monte Carlo or general resampling methods. Finally, due to the large amount of information available in public databases, a chapter on Bayesian methods is included, which also provides a systematic means to integrate this information. A welcome guide for mathematicians and the medical and basic research communities.

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

Frank Emmert-Streib studied physics at the University of Siegen (Germany) and received his Ph.D. in Theoretical Physics from the University of Bremen (Germany). He was a postdoctoral research associate at the Stowers Institute for Medical Research (Kansas City, USA) in the Department for Bioinformatics and a Senior Fellow at the University of Washington (Seattle, USA) in the Department of Biostatistics and the Department of Genome Sciences. Currently, he is Lecturer/Assistant Professor at the Queen's University Belfast at the Center for Cancer Research and Cell Biology (CCRCB) leading the Computational Biology and Machine Learning Lab. His research interests are in the field of computational biology, machine learning and biostatistics in the development and application of methods from statistics and machine learning for the analysis of high-throughput data from genomics and genetics experiments.

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 :

Ouvrage de 312 p.

17.8x24.6 cm

Disponible chez l'éditeur (délai d'approvisionnement : 12 jours).

Prix indicatif 131,29 €

Ajouter au panier