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

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

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 h...

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Bibliographic Details
Main Authors: Soeren Wenck, Marina Creydt, Jule Hansen, Florian Gärber, Markus Fischer, Stephan Seifert
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Metabolites
Subjects:
classification
characterization
white asparagus
LC-MS
metabolomics
random forest
Online Access:https://www.mdpi.com/2218-1989/12/1/5

Internet

https://www.mdpi.com/2218-1989/12/1/5

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