Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing <sup>1</sup>H-<sup>1</sup>H TOCSY NMR

Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose-Tissue-Derived Human Mesenchymal Stem Cells Employing <sup>1</sup>H-<sup>1</sup>H TOCSY NMR

The ability to monitor the dynamics of stem cell differentiation is a major goal for understanding biochemical evolution pathways. Automating the process of metabolic profiling using 2D NMR helps us to understand the various differentiation behaviors of stem cells, and therefore sheds light on the c...

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Bibliographic Details
Main Authors: Lubaba Migdadi, Nour Sharar, Hanan Jafar, Ahmad Telfah, Roland Hergenröder, Christian Wöhler
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Metabolites
Subjects:
machine learning
novelty detection
2D NMR
mesenchymal stem cells (MSCs)
adipogenic differentiation
osteogenic differentiation
Online Access:https://www.mdpi.com/2218-1989/13/3/352

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https://www.mdpi.com/2218-1989/13/3/352

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