Bioinformatics and Biomarker Discovery: "Omic" Data Analysis for Personalized Medicine

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Challenges and research directions in bioinformatics and biomarker discovery

This chapter will discuss major research challenges and directions for bioinformatics in disease biomarker discovery, with an emphasis on requirements and approaches for prediction model development based on ‘omic’ data integration and analysis. Major challenges regarding data and information sharing, computational evaluation of biomarkers and prediction models, research reporting practices, research reproducibility and validation of biomarkers and models will be introduced. This chapter also discusses strategies for training researchers in ‘translational bioinformatics’ and for supporting multi-disciplinary collaboration. Two guest commentaries accompany this chapter to summarize alternative views on problems and challenges, as well as to expand discussions on key computational methods and their applications.

10.1 Introduction

It is evident that bioinformatics, generally defined as the development and application of computational technologies for supporting the understanding of biological systems, plays a crucial role in biomedical translational research (Chapter 1). The area of translational bioinformatics focuses on the objective of bridging the gap between biological research and clinical application. This is being accomplished through the development of algorithms, methods and other information resources that bring the bench closer to the bedside. Translational bioinformatics research plays an ...