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Article

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146 Hamburg, Germany
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Academic Editors: J. Rafael Montenegro-Burke and Xavier Domingo-Almenara
Metabolites 2022, 12(1), 5; https://doi.org/10.3390/metabo12010005
Received: 17 November 2021 / Revised: 16 December 2021 / Accepted: 18 December 2021 / Published: 21 December 2021
For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data. View Full-Text
Keywords: classification; characterization; white asparagus; LC-MS; metabolomics; random forest; feature selection; feature relations; machine learning; chemometrics; surrogate minimal depth classification; characterization; white asparagus; LC-MS; metabolomics; random forest; feature selection; feature relations; machine learning; chemometrics; surrogate minimal depth
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MDPI and ACS Style

Wenck, S.; Creydt, M.; Hansen, J.; Gärber, F.; Fischer, M.; Seifert, S. Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth. Metabolites 2022, 12, 5. https://doi.org/10.3390/metabo12010005

AMA Style

Wenck S, Creydt M, Hansen J, Gärber F, Fischer M, Seifert S. Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth. Metabolites. 2022; 12(1):5. https://doi.org/10.3390/metabo12010005

Chicago/Turabian Style

Wenck, Soeren, Marina Creydt, Jule Hansen, Florian Gärber, Markus Fischer, and Stephan Seifert. 2022. "Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth" Metabolites 12, no. 1: 5. https://doi.org/10.3390/metabo12010005

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