linear locally weighted scatterplot smoother (lowess) method Search Results


lowess locally-weighted scatterplot smoother 0.3 degrees 2 steps  (Minitab Inc)
90
Minitab Inc lowess locally-weighted scatterplot smoother 0.3 degrees 2 steps
Lowess Locally Weighted Scatterplot Smoother 0.3 Degrees 2 Steps, supplied by Minitab Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/lowess locally-weighted scatterplot smoother 0.3 degrees 2 steps/product/Minitab Inc
Average 90 stars, based on 1 article reviews
lowess locally-weighted scatterplot smoother 0.3 degrees 2 steps - by Bioz Stars, 2026-03
90/100 stars
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linear locally weighted scatterplot smoother (lowess) method  (SAS institute)
90
SAS institute linear locally weighted scatterplot smoother (lowess) method
Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed <t>(LOWESS)</t> reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.
Linear Locally Weighted Scatterplot Smoother (Lowess) Method, supplied by SAS institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/linear locally weighted scatterplot smoother (lowess) method/product/SAS institute
Average 90 stars, based on 1 article reviews
linear locally weighted scatterplot smoother (lowess) method - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
locally weighted scatterplot (lowess) smoothing method  (GraphPad Software Inc)
90
GraphPad Software Inc locally weighted scatterplot (lowess) smoothing method
Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed <t>(LOWESS)</t> reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.
Locally Weighted Scatterplot (Lowess) Smoothing Method, supplied by GraphPad Software Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/locally weighted scatterplot (lowess) smoothing method/product/GraphPad Software Inc
Average 90 stars, based on 1 article reviews
locally weighted scatterplot (lowess) smoothing method - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

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Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

Journal: Biotechnology and Bioengineering

Article Title: Segmented linear modeling of CHO fed‐batch culture and its application to large scale production

doi: 10.1002/bit.26214

Figure Lengend Snippet: Developed methodology to identify and characterize metabolic phases. Experimental data are first cleaned using the methodology presented in Figure 2 and additionally by removing data with a viability below 50% or a depletion of metabolites during a measurement interval. The number of metabolic phases during the cell culture process are determined by differentiating the smoothed (LOWESS) reaction rates of all metabolites with respect to the growth rate (dR/dµ). Recursive partitioning is then applied on those derivatives to get a vector of possible metabolic phase breakpoints. Hierarchical clustering is then applied on this vector of possible breakpoints to define the number of final metabolic phases (clusters). Knowing the number of metabolic phases, the segmented regression can then be calibrated on the calibration dataset for each metabolite and validated on the cross validation dataset of the 2 L bioreactor and also of the 2000 L bioreactor.

Article Snippet: As the derivative can amplify possible biological and analytical errors, the specific production rates were, preliminarily to deriving, smoothed as a function of the specific growth rate with the linear Locally Weighted Scatterplot Smoother (LOWESS) method (Cleveland, ) by using SAS software JMP 11 ©.

Techniques: Cell Culture, Plasmid Preparation, Biomarker Discovery