Chapter 8. Integrative Data Analysis for Biomarker Discovery

  1. Dr Francisco Azuaje Senior Member Associate Editor

Published Online: 14 JAN 2010

DOI: 10.1002/9780470686423.ch8

Bioinformatics and Biomarker Discovery: “Omic” Data Analysis for Personalized Medicine

Bioinformatics and Biomarker Discovery: “Omic” Data Analysis for Personalized Medicine

How to Cite

Azuaje, F. (2010) Integrative Data Analysis for Biomarker Discovery, in Bioinformatics and Biomarker Discovery: “Omic” Data Analysis for Personalized Medicine, John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470686423.ch8

Author Information

  1. Public Research Centre for Health (CRP-Santé), Luxembourg

Publication History

  1. Published Online: 14 JAN 2010
  2. Published Print: 19 FEB 2010

ISBN Information

Print ISBN: 9780470744604

Online ISBN: 9780470686423

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Keywords:

  • translational bioinformatics;
  • biomarkers;
  • network-based biology;
  • systems biology;
  • systems medicine;
  • integrative bioinformatics;
  • personalized medicine

Summary

This chapter introduces fundamental principles for the design, implementation and interpretation of integrative data analysis approaches to biomarker discovery. A variety of biomarker discovery problems and applications in diverse clinical areas are illustrated and compared. To facilitate the discussion of key techniques and applications the following major types of integrative data analysis strategies are analyzed: data aggregation at the model input level, model integration based on single or homogeneous data sources, integration at the model level, multiple heterogeneous data and model integration, and serial integration of sources and models. The chapter concludes with a discussion of current limitations, critical application issues and emerging research directions