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Bioinformatic-driven search for metabolic biomarkers in disease

Christian Baumgartner1*, Melanie Osl1, Michael Netzer1 and Daniela Baumgartner2

Author Affiliations

1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol, Austria

2 Clinical Division of Pediatric Cardiology, Department of Pediatrics, Innsbruck Medical University, Austria

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Journal of Clinical Bioinformatics 2011, 1:2  doi:10.1186/2043-9113-1-2

Published: 20 January 2011


The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.