Bioinformatics & biomarker discovery: Omic data analysis for personalized medicine "Omic" Data Analysis for Personalized Medicine

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 (Ana Dopazo)

References

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 roles 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 (Haiying Wang and Huiru Zheng)

Unsupervised clustering approaches

Module-based approaches

Final remarks

References

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 (Kenneth Bryan)

Data...