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Bioinformatics and biomarker discovery : "omic" data analysis for personalised medicine / Francisco Azuaje. — Chichester : Wiley-Blackwell, c2010. – (58.17115/A997) |
Contents
Contents
Author and guest contributor biographies
Acknowledgements
Preface
1 Biomarkers and bioinformatics
1.1 Bioinformatics， translational research and personalized medicine
1.2 Biomarkers: fundamental definitions and research principles
1.3 Clinical resources for biomarker studies
1.4 Molecular biology data sources for biomarker research
1.5 Basic computational approaches to biomarker discovery: key applications and challenges
1.6 Examples of biomarkers and applications
1.7 What is next?
2 Review of fundamental statistical concepts
2.1 Basic concepts and problems
2.2 Hypothesis testing and group comparison
2.3 Assessing statistical significance in multiple-hypotheses testing
2.4 Correlation
2.5 Regression and classification: basic concepts
2.6 Survival analysis methods
2.7 Assessing predictive quality
2.8 Data sample size estimation
2.9 Common pitfalls and misinterpretations
3 Biomarker-based prediction models: design and interpretation principles
3.1 Biomarker discovery and prediction model development
3.2 Evaluation of biomarker-based prediction models
3.3 Overview of data mining and key biomarker-based classification techniques
3.4 Feature selection for biomarker discovery
3.5 Critical design and interpretation factors
4 An introduction to the discovery and analysis of genotype-phenotype associations
4.1 Introduction: sources of genomic variation
4.2 Fundamental biological and statistical concepts
4.3 Multi-stage case-control analysis
4.4 SNPs data analysis: additional concepts， approaches and applications
4.5 CNV data analysis: additional concepts， approaches and applications
4.6 Key problems and challenges
Guest commentary on chapter 4: Integrative approaches to genotype-phenotype association discovery
5 Biomarkers and gene expression data analysis
5.1 Introduction
5.2 Fundamental analytical steps in gene expression profiling
5.3 Examples of advances and applications
5.4 Examples of the rotes of advanced data mining and computational intelligence
5.5 Key limitations， common pitfalls and challenges
Guest commentary on chapter 5: Advances in biomarker discovery with gene expression data
6 Proteomics and metabolomics for biomarker discovery: an introduction to spectral data analysis
6.1 Introduction
6.2 Proteomics and biomarker discovery
6.3 Metabolomics and biomarker discovery
6.4 Experimental techniques for proteomics and metabolomics: an overview
6.5 More on the fundamentals of spectral data analysis
6.6 Targeted and global analyses in metabolomics
6.7 Feature transformation， selection and classification of spectral data
6.8 Key software and information resources for proteomics and metabolomics
6.9 Gaps and challenges in bioinformatics
Guest commentary on chapter 6: Data integration in proteomics and metabolomics for biomarker discovery
7 Disease biomarkers and biological interaction networks
7.1 Network-centric views of disease biomarker discovery
7.2 Basic concepts in network analysis
7.3 Fundamental approaches to representing and inferring networks
7.4 Overview of key network-driven approaches to biomarker discovery
7.5 Network-based prognostic systems: recent research highlights
7.6 Final remarks: opportunities and obstacles in network-based biomarker research
Guest commentary on chapter 7: Commentary on 'disease biomarkers and biological interaction networks’<br> 8 Integrative data analysis for biomarker discovery
8.1 Introduction
8.2 Data aggregation at the model input Level
8.3 Model integration based on a single-source or homogeneous data sources
8.4 Data integration at the model level
8.5 Multiple heterogeneous data and model integration
8.6 Serial integration of source and models
8.7 Component- and network-centric approaches
8.8 Final remarks
Guest commentary on chapter 8: Data integration: The next big hope? 155
9 Information resources and software toots for biomarker discovery 159
9.1 Biomarker discovery frameworks: key software and information resources
9.2 Integrating and sharing resources: databases and tools
9.3 Data mining toots and platforms
9.4 Specialized information and knowledge resources
9.5 Integrative infrastructure initiatives and inter-institutional programmes
9.6 Innovation outlook: challenges and progress
10 Challenges and research directions in bioinformatics and biomarker discovery
10.1 Introduction
10.2 Better software
10.3 The clinical relevance of new biomarkers
10.4 Collaboration
10.5 Evaluating and validating biomarker models
10.6 Defining and measuring phenotypes
10.7 Documenting and reporting biomarker research
10.8 Intelligent data analysis and computational models
1O.9 Integrated systems and infrastructures for biomedical computing
10.10 Open access to research information and outcomes
10.11 Systems-based approaches
10.12 Training a new generation of researchers for translational bioinformatics
10.13 Maximizing the use of public resources
10.14 Final remarks
Guest commentary (1) on chapter 10: Towards building knowledge-based assistants for intelligent data analysis in biomarker discovery
Guest commentary (2) on chapter 10: Accompanying commentary on 'challenges and opportunities of bioinformatics in disease biomarker discovery'
References
Index
