Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

BMC Bioinformatics

Research Article

Alexandros Kalousis¹  Marion Haubitz²  Mark Girolami³  Joost P Schanstra⁴  Sebastien Carpentier⁵  Mohammed Dakna⁶  Harald Mischak⁷  Antonia Vlahou⁸  Walter Kolch⁹  Keith Harris^1,10 

[1] Computer Science Department, University of Geneva, Geneva, Switzerland;Department of Nephrology, Hannover Medical School, Hannover, Germany;Department of Statistical Science, University College London, London, UK;Institut National de la Santé et de la Recherche Médicale (INSERM), U858, Toulouse, France;Université Toulouse III Paul-Sabatier, Institut de Médecine Moleculaire de Rangueil, Equipe n° 5, IFR150, Toulouse, France;Laboratory of Tropical Crop Improvement, Katholieke Universiteit, Leuven, Belgium;Mosaiques diagnostics and therapeutics, Hannover, Germany;Mosaiques diagnostics and therapeutics, Hannover, Germany;BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK;Research Foundation, Academy of Athens, Athens, Greece;The Beatson Institute for Cancer Research and Sir Henry Wellcome Functional Genomics Facility, University of Glasgow, Glasgow, UK;Systems Biology Ireland, Conway Institute, Dublin 4, Belfield, Ireland;Water and Environment Research Group, School of Engineering, University of Glasgow, Glasgow, UK;

关键词: Continuous Component;    Hannover Medical School;    Verification Bias;    Empirical Likelihood Ratio;    Training Sample Size;   

DOI  :  10.1186/1471-2105-11-594

 received in 2010-11-03, accepted in 2010-12-10,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsWe 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.ConclusionsValid 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.

【 授权许可】

Unknown   
© Dakna et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.