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Article

Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)

1
DataBionics Research Group, University of Marburg, Hans-Meerwein-Straße, Marburg 35032, Germany
2
Institute of Clinical Pharmacology, Goethe-University, Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany
3
Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Project Group Translational Medicine and Pharmacology TMP, Theodor-Stern-Kai 7, Frankfurt am Main 60590, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Irmgard Tegeder
Int. J. Mol. Sci. 2015, 16(10), 25897-25911; https://doi.org/10.3390/ijms161025897
Received: 19 August 2015 / Revised: 28 September 2015 / Accepted: 21 October 2015 / Published: 28 October 2015
(This article belongs to the Special Issue Molecular and Cellular Mechanisms of Pain)
Biomedical data obtained during cell experiments, laboratory animal research, or human studies often display a complex distribution. Statistical identification of subgroups in research data poses an analytical challenge. Here were introduce an interactive R-based bioinformatics tool, called “AdaptGauss”. It enables a valid identification of a biologically-meaningful multimodal structure in the data by fitting a Gaussian mixture model (GMM) to the data. The interface allows a supervised selection of the number of subgroups. This enables the expectation maximization (EM) algorithm to adapt more complex GMM than usually observed with a noninteractive approach. Interactively fitting a GMM to heat pain threshold data acquired from human volunteers revealed a distribution pattern with four Gaussian modes located at temperatures of 32.3, 37.2, 41.4, and 45.4 °C. Noninteractive fitting was unable to identify a meaningful data structure. Obtained results are compatible with known activity temperatures of different TRP ion channels suggesting the mechanistic contribution of different heat sensors to the perception of thermal pain. Thus, sophisticated analysis of the modal structure of biomedical data provides a basis for the mechanistic interpretation of the observations. As it may reflect the involvement of different TRP thermosensory ion channels, the analysis provides a starting point for hypothesis-driven laboratory experiments. View Full-Text
Keywords: pain; R software; bioinformatics; data modeling; molecular mechanisms pain; R software; bioinformatics; data modeling; molecular mechanisms
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MDPI and ACS Style

Ultsch, A.; Thrun, M.C.; Hansen-Goos, O.; Lötsch, J. Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss). Int. J. Mol. Sci. 2015, 16, 25897-25911. https://doi.org/10.3390/ijms161025897

AMA Style

Ultsch A, Thrun MC, Hansen-Goos O, Lötsch J. Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss). International Journal of Molecular Sciences. 2015; 16(10):25897-25911. https://doi.org/10.3390/ijms161025897

Chicago/Turabian Style

Ultsch, Alfred, Michael C. Thrun, Onno Hansen-Goos, and Jörn Lötsch. 2015. "Identification of Molecular Fingerprints in Human Heat Pain Thresholds by Use of an Interactive Mixture Model R Toolbox (AdaptGauss)" International Journal of Molecular Sciences 16, no. 10: 25897-25911. https://doi.org/10.3390/ijms161025897

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