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A High Performance Biomarker Detection Method for Exhaled Breath Mass Spectrometry Data

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Topics in Nonparametric Statistics

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 74))

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Abstract

Selected-ion flow-tube mass spectrometry, SIFT-MS, technology seems nowadays very promising to be utilized for the discovery and profiling of biomarkers such as volatile compounds, trace gases, and proteins from biological and clinical samples. A high performance biomarker detection method for identifying biomarkers across experimental groups is proposed for the analysis of SIFT-MS mass spectrometry data. Analysis of mass spectrometry data is often complex due to experimental design. Although several methods have been proposed for the identification of biomarkers from mass spectrometry data, there has been only a handful of methods for SIFT-MS data. Our detection method entails a three-step process that facilitates a comprehensive screening of the mass spectrometry data. First, raw mass spectrometry data are pre-processed to capture true biological signal. Second, the pre-processed data are screened via a random-forest-based screening tool. Finally, a visualization tool is complementing the findings from the previous step. In this paper, we present two applications of our method; a control-asthma case study and an H1N1 Flumist time-course case study.

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Notes

  1. 1.

    SIFT-MS is a chemical ionization technique allowing calculations of analyte concentrations from the known reaction kinetics without the need for internal standard or calibration [14].

  2. 2.

    Absolute concentrations of trace gases and vapors in air are calculated based on the flow tube geometry, the ionic reaction time, flow rates and pressure and ion–molecule reaction rate coefficients [14].

  3. 3.

    The dose administered was 0.2 ml. All individuals had previously received trivalent seasonal 2009 vaccine (intramuscular). All subjects underwent nasal pharyngeal swab for influenza (Prodesse PCR) immediately prior to H1N1 vaccination to rule out the presence of sub-clinical influenza prior to vaccination and on day 1 after vaccination to determine viral load.

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Authors and Affiliations

  1. Department of Mathematics, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH, 44115, USA

    Ariadni Papana Dagiasis & Yuping Wu

  2. Department of Pulmonary, Allergy and Critical Care Medicine and Pathobiology/Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Raed A. Dweik

  3. Department of Infectious Disease, Cleveland Clinic, Cleveland, OH, USA

    David van Duin

Authors
  1. Ariadni Papana Dagiasis
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  2. Yuping Wu
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  3. Raed A. Dweik
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  4. David van Duin
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Corresponding author

Correspondence to Ariadni Papana Dagiasis .

Editor information

Editors and Affiliations

  1. Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania, USA

    Michael G. Akritas

  2. Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA

    S. N. Lahiri

  3. Department of Mathematics, University of California, San Diego, California, USA

    Dimitris N. Politis

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Dagiasis, A.P., Wu, Y., Dweik, R.A., van Duin, D. (2014). A High Performance Biomarker Detection Method for Exhaled Breath Mass Spectrometry Data. In: Akritas, M., Lahiri, S., Politis, D. (eds) Topics in Nonparametric Statistics. Springer Proceedings in Mathematics & Statistics, vol 74. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0569-0_19

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