Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractUptake of the sex pheromone 3H-bombykol and related compounds by male and female Bombyx antennae    Next AbstractRole of phosphatidylserine in the localization of cell surface membrane proteins in yeast »

J Breath Res


Title:Strategies for the identification of disease-related patterns of volatile organic compounds: prediction of paratuberculosis in an animal model using random forests
Author(s):Kasbohm E; Fischer S; Kuntzel A; Oertel P; Bergmann A; Trefz P; Miekisch W; Schubert JK; Reinhold P; Ziller M; Frohlich A; Liebscher V; Kohler H;
Address:"Institute of Epidemiology, Friedrich-Loeffler-Institut (FLI), Federal Research Institute for Animal Health, Greifswald-Insel Riems, Germany. Department of Mathematics and Computer Science, University of Greifswald, Germany"
Journal Title:J Breath Res
Year:2017
Volume:20171101
Issue:4
Page Number:47105 -
DOI: 10.1088/1752-7163/aa83bb
ISSN/ISBN:1752-7163 (Electronic) 1752-7155 (Linking)
Abstract:"Modern statistical methods which were developed for pattern recognition are increasingly being used for data analysis in studies on emissions of volatile organic compounds (VOCs). With the detection of disease-related VOC profiles, novel non-invasive diagnostic tools could be developed for clinical applications. However, it is important to bear in mind that not all statistical methods are equally suitable for the investigation of VOC profiles. In particular, univariate methods are not able to discover VOC patterns as they consider each compound separately. The present study demonstrates this fact in practice. Using VOC samples from a controlled animal study on paratuberculosis, the random forest classification method was applied for pattern recognition and disease prediction. This strategy was compared with a prediction approach based on single compounds. Both methods were framed within a cross-validation procedure. A comparison of both strategies based on these VOC data reveals that random forests achieves higher sensitivities and specificities than predictions based on single compounds. Therefore, it will most likely be more fruitful to further investigate VOC patterns instead of single biomarkers for paratuberculosis. All methods used are thoroughly explained to aid the transfer to other data analyses"
Keywords:"*Algorithms Animals Biomarkers/analysis Breath Tests/*methods Decision Trees Disease Models, Animal Exhalation Feces/chemistry Goats Paratuberculosis/*diagnosis Sensitivity and Specificity Volatile Organic Compounds/*analysis;"
Notes:"MedlineKasbohm, Elisa Fischer, Sina Kuntzel, Anne Oertel, Peter Bergmann, Andreas Trefz, Phillip Miekisch, Wolfram Schubert, Jochen K Reinhold, Petra Ziller, Mario Frohlich, Andreas Liebscher, Volkmar Kohler, Heike eng England 2017/08/05 J Breath Res. 2017 Nov 1; 11(4):047105. doi: 10.1088/1752-7163/aa83bb"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 21-11-2024