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[资源] 生物信息学分子生物学 Bioinformatics and Biomarker Discovery

Author and guest contributor biographies xi
Acknowledgements xv
Preface xvii
1 Biomarkers and bioinformatics 1
1.1 Bioinformatics, translational research and personalized
medicine 1
1.2 Biomarkers: fundamental definitions and research principles 2
1.3 Clinical resources for biomarker studies 5
1.4 Molecular biology data sources for biomarker research 6
1.5 Basic computational approaches to biomarker discovery:
key applications and challenges 7
1.6 Examples of biomarkers and applications 10
1.7 What is next? 12
2 Review of fundamental statistical concepts 15
2.1 Basic concepts and problems 15
2.2 Hypothesis testing and group comparison 19
2.3 Assessing statistical significance in multiple-hypotheses testing 20
2.4 Correlation 23
2.5 Regression and classification: basic concepts 23
2.6 Survival analysis methods 26
2.7 Assessing predictive quality 28
2.8 Data sample size estimation 32
2.9 Common pitfalls and misinterpretations 34
3 Biomarker-based prediction models: design and interpretation
principles 37
3.1 Biomarker discovery and prediction model development 37
3.2 Evaluation of biomarker-based prediction models 38
3.3 Overview of data mining and key biomarker-based
classification techniques 40
3.4 Feature selection for biomarker discovery 47
3.5 Critical design and interpretation factors 52
4 An introduction to the discovery and analysis
of genotype-phenotype associations 57
4.1 Introduction: sources of genomic variation 57
4.2 Fundamental biological and statistical concepts 60
4.3 Multi-stage case-control analysis 64
4.4 SNPs data analysis: additional concepts, approaches and applications 64
4.5 CNV data analysis: additional concepts, approaches and applications 68
4.6 Key problems and challenges 69
Guest commentary on chapter 4: Integrative approaches
to genotype-phenotype association discovery 73
Ana Dopazo
References 76
5 Biomarkers and gene expression data analysis 77
5.1 Introduction 77
5.2 Fundamental analytical steps in gene expression profiling 79
5.3 Examples of advances and applications 82
5.4 Examples of the roles of advanced data mining and
computational intelligence 84
5.5 Key limitations, common pitfalls and challenges 85
Guest commentary on chapter 5: Advances in biomarker discovery
with gene expression data 89
Haiying Wang, Huiru Zheng
Unsupervised clustering approaches 90
Module-based approaches 91
Final remarks 92
References 92
6 Proteomics and metabolomics for biomarker discovery:
an introduction to spectral data analysis 93
6.1 Introduction 93
6.2 Proteomics and biomarker discovery 94
6.3 Metabolomics and biomarker discovery 97
6.4 Experimental techniques for proteomics and metabolomics:
an overview 99
6.5 More on the fundamentals of spectral data analysis 100
viii CONTENTS
6.6 Targeted and global analyses in metabolomics 101
6.7 Feature transformation, selection and classification of spectral data 102
6.8 Key software and information resources for proteomics and
metabolomics 106
6.9 Gaps and challenges in bioinformatics 107
Guest commentary on chapter 6: Data integration in proteomics
and metabolomics for biomarker discovery 111
Kenneth Bryan
Data integration and feature selection 112
References 114
7 Disease biomarkers and biological interaction networks 115
7.1 Network-centric views of disease biomarker discovery 115
7.2 Basic concepts in network analysis 118
7.3 Fundamental approaches to representing and inferring networks 119
7.4 Overview of key network-driven approaches to biomarker discovery 120
7.5 Network-based prognostic systems: recent research highlights 124
7.6 Final remarks: opportunities and obstacles in network-based
biomarker research 127
Guest commentary on chapter 7: Commentary on
‘disease biomarkers and biological interaction networks’ 131
Zhongming Zhao
Integrative approaches to biomarker discovery 132
Pathway-based analysis of GWA data 133
Integrative analysis of networks and pathways 134
References 134
8 Integrative data analysis for biomarker discovery 137
8.1 Introduction 137
8.2 Data aggregation at the model input level 141
8.3 Model integration based on a single-source or homogeneous
data sources 141
8.4 Data integration at the model level 144
8.5 Multiple heterogeneous data and model integration 145
8.6 Serial integration of source and models 148
8.7 Component- and network-centric approaches 151
8.8 Final remarks 152
Guest commentary on chapter 8: Data integration: The next big hope? 155
Yves Moreau
References 158
9 Information resources and software tools for biomarker discovery 159
9.1 Biomarker discovery frameworks: key software and information
resources 159
CONTENTS ix
9.2 Integrating and sharing resources: databases and tools 161
9.3 Data mining tools and platforms 166
9.4 Specialized information and knowledge resources 168
9.5 Integrative infrastructure initiatives and inter-institutional
programmes 168
9.6 Innovation outlook: challenges and progress 169
10 Challenges and research directions in bioinformatics
and biomarker discovery 173
10.1 Introduction 173
10.2 Better software 175
10.3 The clinical relevance of new biomarkers 176
10.4 Collaboration 177
10.5 Evaluating and validating biomarker models 178
10.6 Defining and measuring phenotypes 181
10.7 Documenting and reporting biomarker research 181
10.8 Intelligent data analysis and computational models 184
10.9 Integrated systems and infrastructures for biomedical
computing 185
10.10 Open access to research information and outcomes 186
10.11 Systems-based approaches 187
10.12 Training a new generation of researchers for translational
bioinformatics 188
10.13 Maximizing the use of public resources 189
10.14 Final remarks 189
Guest commentary (1) on chapter 10: Towards building knowledge-based
assistants for intelligent data analysis in biomarker discovery 193
Riccardo Bellazzi
References 196
Guest commentary (2) on chapter 10: Accompanying commentary
on ‘challenges and opportunities of bioinformatics in disease
biomarker discovery’ 197
Gary B. Fogel
Introduction 197
Biocyberinfrastructure 198
Government Regulations on biomarker discovery 198
Computational intelligence approaches for biomarker discovery 199
Open source data, intellectual property, and patient privacy 199
Conclusions 200
References 200
References 203
Index 223
x CONTENTS

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