Data set used in the publication: Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing 1H-1H TOCSY NMR. Abstract: In this work, the dynamic evolution of adipose tissue-derived human MSCs (AT-derived hMSCs) after fourteen days of cultivation, adiobocytes and osteocytes differentiation has been inspected based on 2D NMR TOCSY using machine learning techniques. Multi-class classification in addition to novelty detection of metabolites was established based on the profile of a control hMSCs sample at four days cultivation and successively detect the absence and the abundance of metabolites in differentiated MSCs following a set of 1H-1H ...
Mentors: Louis S. Kidder, Ph.D. (Radiology), Susanta Hui, Ph.D. (Therapeutic Radiology), Bruce E. Ha...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
This Article presents, for the first time to our knowledge, an untargeted nuclear magnetic resonance...
This dataset is related to the paper “Automated metabolic assignment: Semi-supervised learning in me...
PURPOSE:The purpose of this study was to evaluate the metabolomic changes in 3D-cultured human mesen...
This paper describes an untargeted NMR metabolomics study to identify potential intracellular donor-...
Purpose: The purpose of this study was to evaluate the metabolomic changes in 3D-cultured human mese...
International audienceStem cells, poised to revolutionize current medicine, stand as major workhorse...
Additional contributors: Louis S. Kidder; Bruce E. Hammer (faculty mentor).The research is relevant ...
The integration of cell metabolism with signalling pathways, transcription factor networks and epige...
Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells ...
This paper describes an untargeted NMR metabolomics study to identify potential intracellular donor-...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Mentors: Louis S. Kidder, Ph.D. (Radiology), Susanta Hui, Ph.D. (Therapeutic Radiology), Bruce E. Ha...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
This Article presents, for the first time to our knowledge, an untargeted nuclear magnetic resonance...
This dataset is related to the paper “Automated metabolic assignment: Semi-supervised learning in me...
PURPOSE:The purpose of this study was to evaluate the metabolomic changes in 3D-cultured human mesen...
This paper describes an untargeted NMR metabolomics study to identify potential intracellular donor-...
Purpose: The purpose of this study was to evaluate the metabolomic changes in 3D-cultured human mese...
International audienceStem cells, poised to revolutionize current medicine, stand as major workhorse...
Additional contributors: Louis S. Kidder; Bruce E. Hammer (faculty mentor).The research is relevant ...
The integration of cell metabolism with signalling pathways, transcription factor networks and epige...
Cell metabolism is a key determinant factor for the pluripotency and fate commitment of Stem Cells ...
This paper describes an untargeted NMR metabolomics study to identify potential intracellular donor-...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Mentors: Louis S. Kidder, Ph.D. (Radiology), Susanta Hui, Ph.D. (Therapeutic Radiology), Bruce E. Ha...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...
Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties o...