Bioinformatics and biomarker discovery: Omic data analysis for personalized medicine

Bioinformatics and Biomarker Discovery: ‘‘Omic’’ Data Analysis for Personalized Medicine......Page 5
Contents......Page 9
Author and guest contributor biographies......Page 13
Acknowledgements......Page 17
Preface......Page 19
1.1 Bioinformatics, translational research and personalized medicine......Page 21
1.2 Biomarkers: fundamental definitions and research principles......Page 22
1.3 Clinical resources for biomarker studies......Page 25
1.4 Molecular biology data sources for biomarker research......Page 26
1.5 Basic computational approaches to biomarker discovery: key applications and challenges......Page 27
1.6 Examples of biomarkers and applications......Page 30
1.7 What is next?......Page 32
2.1 Basic concepts and problems......Page 35
2.2 Hypothesis testing and group comparison......Page 39
2.3 Assessing statistical significance in multiple-hypotheses testing......Page 40
2.5 Regression and classification: basic concepts......Page 43
2.6 Survival analysis methods......Page 46
2.7 Assessing predictive quality......Page 48
2.8 Data sample size estimation......Page 52
2.9 Common pitfalls and misinterpretations......Page 54
3.1 Biomarker discovery and prediction model development......Page 57
3.2 Evaluation of biomarker-based prediction models......Page 58
3.3 Overview of data mining and key biomarker-based classification techniques......Page 60
3.4 Feature selection for biomarker discovery......Page 67
3.5 Critical design and interpretation factors......Page 72
4.1 Introduction: sources of genomic variation......Page 77
4.2 Fundamental biological and statistical concepts......Page 80
4.4 SNPs data analysis: additional concepts, approaches and applications......Page 84
4.5 CNV data analysis: additional concepts, approaches and applications......Page 88
4.6 Key problems and challenges......Page 89
Guest commentary on chapter 4: Integrative approaches to genotype-phenotype association discovery......Page 93
References......Page 96
5.1 Introduction......Page 97
5.2 Fundamental analytical steps in gene expression profiling......Page 99
5.3 Examples of advances and applications......Page 102
5.4 Examples of the roles of advanced data mining and computational intelligence......Page 104
5.5 Key limitations, common pitfalls and challenges......Page 105
Guest commentary on chapter 5: Advances in biomarker discovery with gene expression data......Page 109
Unsupervised clustering approaches......Page 110
Module-based approaches......Page 111
References......Page 112
6.1 Introduction......Page 113
6.2 Proteomics and biomarker discovery......Page 114
6.3 Metabolomics and biomarker discovery......Page 117
6.4 Experimental techniques for proteomics and metabolomics: an overview......Page 119
6.5 More on the fundamentals of spectral data analysis......Page 120
6.6 Targeted and global analyses in metabolomics......Page 121
6.7 Feature transformation, selection and classification of spectral data......Page 122
6.8 Key software and information resources for proteomics and metabolomics......Page 126
6.9 Gaps and challenges in bioinformatics......Page 127
Guest commentary on chapter 6: Data integration in proteomics and metabolomics for biomarker discovery......Page 131
Data integration and feature selection......Page 132
References......Page 134
7.1 Network-centric views of disease biomarker discovery......Page 135
7.2 Basic concepts in network analysis......Page 138
7.3 Fundamental approaches to representing and inferring networks......Page 139
7.4 Overview of key network-driven approaches to biomarker discovery......Page 140
7.5 Network-based prognostic systems: recent research highlights......Page 144
7.6 Final remarks: opportunities and obstacles in network-based biomarker research......Page 147
Guest commentary on chapter 7: Commentary on ‘disease biomarkers and biological interaction networks’......Page 151
Integrative approaches to biomarker discovery......Page 152
Pathway-based analysis of GWA data......Page 153
References......Page 154
8.1 Introduction......Page 157
8.3 Model integration based on a single-source or homogeneous data sources......Page 161
8.4 Data integration at the model level......Page 164
8.5 Multiple heterogeneous data and model integration......Page 165
8.6 Serial integration of source and models......Page 168
8.7 Component- and network-centric approaches......Page 171
8.8 Final remarks......Page 172
Guest commentary on chapter 8: Data integration: The next big hope?......Page 175
References......Page 178
9.1 Biomarker discovery frameworks: key software and information resources......Page 179
9.2 Integrating and sharing resources: databases and tools......Page 181
9.3 Data mining tools and platforms......Page 186
9.5 Integrative infrastructure initiatives and inter-institutional programmes......Page 188
9.6 Innovation outlook: challenges and progress......Page 189
10.1 Introduction......Page 193
10.2 Better software......Page 195
10.3 The clinical relevance of new biomarkers......Page 196
10.4 Collaboration......Page 197
10.5 Evaluating and validating biomarker models......Page 198
10.7 Documenting and reporting biomarker research......Page 201
10.8 Intelligent data analysis and computational models......Page 204
10.9 Integrated systems and infrastructures for biomedical computing......Page 205
10.10 Open access to research information and outcomes......Page 206
10.11 Systems-based approaches......Page 207
10.12 Training a new generation of researchers for translational bioinformatics......Page 208
10.14 Final remarks......Page 209
Guest commentary (1) on chapter 10: Towards building knowledge-based assistants for intelligent data analysis in biomarker discovery......Page 213
References......Page 216
Introduction......Page 217
Government regulations on biomarker discovery......Page 218
Open source data, intellectual property, and patient privacy......Page 219
References......Page 220
References......Page 223
Index......Page 243