Contents
Author and guest contributor biographies
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
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
2.5 Regression and classification: basic concepts
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
