title: Addressing the challenge of defining valid proteomic biomarkers and classifiers.
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.
authors.affiliation: Mosaiques diagnostics and therapeutics, Hannover, Germany.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Dakna
authors.forename: Keith
authors.initials: K
authors.lastname: Harris
authors.forename: Alexandros
authors.initials: A
authors.lastname: Kalousis
authors.forename: Sebastien
authors.initials: S
authors.lastname: Carpentier
authors.forename: Walter
authors.initials: W
authors.lastname: Kolch
authors.forename: Joost P
authors.initials: JP
authors.lastname: Schanstra
authors.forename: Marion
authors.initials: M
authors.lastname: Haubitz
authors.forename: Antonia
authors.initials: A
authors.lastname: Vlahou
authors.forename: Harald
authors.initials: H
authors.lastname: Mischak
authors.forename: Mark
authors.initials: M
authors.lastname: Girolami
mesh_terms: D000328:Adult; D000465:Algorithms; D015415:Biomarkers / Q000652:urine*; D019075:Electrophoresis, Capillary; D005260:Female; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D040901:Proteomics / Q000379:methods*; D012016:Reference Values; D018401:Sample Size; D055815:Young Adult
chemical_list: D015415:Biomarkers