"labeling data" is a correct and common phrase in written English.
It refers to the process of assigning labels or categories to data, often in the context of organizing or classifying information. Example: "The research team spent hours labeling data from the survey responses in order to analyze the results more effectively."
Thus, its labeling state can simply be determined from other C labeling data as an additional parameter of NLSP in case it is not measured.
Metabolic flux analysis (MFA) with 13C labeling data is a high-precision technique to quantify intracellular reaction rates (fluxes).
Active learning is used for classification when labeling data are costly, while the main challenge is to identify the critical instances that should be labeled.
Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics.
Two possible errors in the models that are needed to determine the metabolic fluxes from labeling data are omitted reactions and ignored occurrence of channeling.
By properly labeling data, you are dividing information into predefined groups with shared commonalities.
In a previous study, J0 was estimated from transient C-labeling data [ 6, 7].
(D ) Example, pulse-labeling data showing the incorporation of 50% N into an r-protein peptide as a function of time.
The distribution of metabolic fluxes during methylotrophic growth was determined from C-labeling data collected during steady-state growth of M. extorquens on [C]-methanol.
The model was based on a previously published isotopomer model used for the estimation of intracellular fluxes from transient C-labeling data [ 7].
The 13C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach.
