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dc.contributor.authorDakna, Mohammed
dc.contributor.authorKeith, Harris
dc.contributor.authorKalousis, Alexandros
dc.contributor.authorCarpentier, Sebastien
dc.contributor.authorKolch, Walter
dc.contributor.authorSchanstra, Joost P.
dc.contributor.authorHaubitz, Marion
dc.contributor.authorVlahou, Antonia
dc.contributor.authorMischak, Harald
dc.contributor.authorGirolami, Mark
dc.date.accessioned2011-01-11T12:41:12Z
dc.date.available2011-01-11T12:41:12Z
dc.date.issued2010-12-10
dc.identifierhttp://dx.doi.org/10.1186/1471-2105-11-594
dc.identifier.citationBMC Bioinformatics. 2010 Dec 10;11(1):594
dc.identifier.urihttp://hdl.handle.net/10147/119130
dc.description.abstractAbstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.
dc.titleAddressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
dc.typeJournal Article
dc.language.rfc3066en
dc.rights.holderDakna et al.; licensee BioMed Central Ltd.
dc.description.statusPeer Reviewed
dc.date.updated2011-01-09T04:09:25Z
refterms.dateFOA2018-08-16T02:09:20Z
html.description.abstractAbstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.


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