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Proteomes 2018, 6(2), 20; https://doi.org/10.3390/proteomes6020020

Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery

Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
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Received: 30 March 2018 / Revised: 19 April 2018 / Accepted: 25 April 2018 / Published: 26 April 2018
(This article belongs to the Special Issue Proteomic Biomarkers in Human Diseases)
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Abstract

Protein biomarkers are of great benefit for clinical research and applications, as they are powerful means for diagnosing, monitoring and treatment prediction of different diseases. Even though numerous biomarkers have been reported, the translation to clinical practice is still limited. This mainly due to: (i) incorrect biomarker selection, (ii) insufficient validation of potential biomarkers, and (iii) insufficient clinical use. In this review, we focus on the biomarker selection process and critically discuss the chemometrical and statistical decisions made in proteomics biomarker discovery to increase to selection of high value biomarkers. The characteristics of the data, the computational resources, the type of biomarker that is searched for and the validation strategy influence the decision making of the chemometrical and statistical methods and a decision made for one component directly influences the choice for another. Incorrect decisions could increase the false positive and negative rate of biomarkers which requires independent confirmation of outcome by other techniques and for comparison between different related studies. There are few guidelines for authors regarding data analysis documentation in peer reviewed journals, making it hard to reproduce successful data analysis strategies. Here we review multiple chemometrical and statistical methods for their value in proteomics-based biomarker discovery and propose to include key components in scientific documentation. View Full-Text
Keywords: biomarker; clinical proteomics; chemometrics; statistics; preprocessing; classification models; feature reduction; review biomarker; clinical proteomics; chemometrics; statistics; preprocessing; classification models; feature reduction; review
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Suppers, A.; Gool, A.J.; Wessels, H.J.C.T. Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery. Proteomes 2018, 6, 20.

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