Contents

Author and guest contributor biographies

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 ...

Get Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.