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生物信息學(xué) 分子生物學(xué) Bioinformatics and Biomarker Discovery
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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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