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Statistical Aspects in Proteomic Biomarker Discovery

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Statistical Analysis in Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1362))

Abstract

In the pursuit of a personalized medicine, i.e., the individual treatment of a patient, many medical decision problems are desired to be supported by biomarkers that can help to make a diagnosis, prediction, or prognosis. Proteomic biomarkers are of special interest since they can not only be detected in tissue samples but can also often be easily detected in diverse body fluids. Statistical methods play an important role in the discovery and validation of proteomic biomarkers. They are necessary in the planning of experiments, in the processing of raw signals, and in the final data analysis. This review provides an overview on the most frequent experimental settings including sample size considerations, and focuses on exploratory data analysis and classifier development.

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Acknowledgements

The author would like to thank Prof Olga Vitek (Northeastern University, Boston) for very helpful comments on a previous version of the manuscript.

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Authors and Affiliations

  1. Department of Medical Statistics, Georg-August-University Göttingen, Humboldtallee 32, 37073, Göttingen, Germany

    Klaus Jung Ph.D.

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  1. Klaus Jung Ph.D.
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Correspondence to Klaus Jung Ph.D. .

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  1. Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany

    Klaus Jung

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Jung, K. (2016). Statistical Aspects in Proteomic Biomarker Discovery. In: Jung, K. (eds) Statistical Analysis in Proteomics. Methods in Molecular Biology, vol 1362. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3106-4_19

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  • DOI: https://doi.org/10.1007/978-1-4939-3106-4_19

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3105-7

  • Online ISBN: 978-1-4939-3106-4

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