Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

Hdl Handle:
http://hdl.handle.net/10147/119130
Title:
Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers
Authors:
Dakna, Mohammed; Keith, Harris; Kalousis, Alexandros; Carpentier, Sebastien; Kolch, Walter; Schanstra, Joost P.; Haubitz, Marion; Vlahou, Antonia; Mischak, Harald; Girolami, Mark
Citation:
BMC Bioinformatics. 2010 Dec 10;11(1):594
Issue Date:
10-Dec-2010
URI:
http://hdl.handle.net/10147/119130
Abstract:
Abstract 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.
Item Type:
Journal Article

Full metadata record

DC FieldValue Language
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-
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