robust locally weighted scatterplot smoothing (lowess) method Search Results


lowess plot  (SAS institute)
90
SAS institute lowess plot
Lowess Plot, 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
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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
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lowess method  (GraphPad Software Inc)
90
GraphPad Software Inc 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.
Lowess 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
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locally weighted scatterplot smoothing (lowess)  (RStudio)
90
RStudio locally weighted scatterplot smoothing (lowess)
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 Smoothing (Lowess), supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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locally weighted scatterplot smoothing lowess  (SAS institute)
90
SAS institute locally weighted scatterplot smoothing lowess
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 Smoothing Lowess, 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
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Average 90 stars, based on 1 article reviews
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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
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lowess regression function on  (RStudio)
90
RStudio lowess regression function on
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.
Lowess Regression Function On, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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scatterplot smoothing  (STATA Corporation)
99
STATA Corporation scatterplot smoothing
Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
Scatterplot Smoothing, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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locally weighted scatterplot smoothing (lowess)  (GraphPad Software Inc)
90
GraphPad Software Inc locally weighted scatterplot smoothing (lowess)
Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
Locally Weighted Scatterplot Smoothing (Lowess), 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 smoothing (lowess)/product/GraphPad Software Inc
Average 90 stars, based on 1 article reviews
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spline/locally weighted scatterplot smoothing (lowess) curves  (GraphPad Software Inc)
90
GraphPad Software Inc spline/locally weighted scatterplot smoothing (lowess) curves
Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
Spline/Locally Weighted Scatterplot Smoothing (Lowess) Curves, 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/spline/locally weighted scatterplot smoothing (lowess) curves/product/GraphPad Software Inc
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locally weighted scatterplot smoothing (lowess) fit  (TIBCO)
90
TIBCO locally weighted scatterplot smoothing (lowess) fit
Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
Locally Weighted Scatterplot Smoothing (Lowess) Fit, supplied by TIBCO, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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weighted scatterplot smoothing lowess  (STATA Corporation)
99
STATA Corporation weighted scatterplot smoothing lowess
Co‐plot of odds ratios (ORs, upper row) and Locally Weighted <t>Scatterplot</t> Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.
Weighted Scatterplot Smoothing Lowess, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 99 stars, based on 1 article reviews
weighted scatterplot smoothing lowess - by Bioz Stars, 2026-06
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Image Search Results


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

Co‐plot of odds ratios (ORs, upper row) and Locally Weighted Scatterplot Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Journal: Thoracic Cancer

Article Title: Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer

doi: 10.1111/1759-7714.13056

Figure Lengend Snippet: Co‐plot of odds ratios (ORs, upper row) and Locally Weighted Scatterplot Smoothing curves of stage migration and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an OR of a specific lymph node examined (LNE, vacant if case number < 10) from logistic regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database; and ( d ) ESCC patients from the Thoracic Surgery Department of a single institution (SI). ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Article Snippet: **Curves were generated using odds ratios (ORs, each LNE count compared to 1 LNE as a reference) in logistic regression analysis and hazard ratios (HRs) in Cox regression analysis using Locally Weighted Scatterplot Smoothing (LOWESS, Stata 12.0) with a bandwidth of 0.6.

Techniques: Migration

Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Journal: Thoracic Cancer

Article Title: Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer

doi: 10.1111/1759-7714.13056

Figure Lengend Snippet: Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Overall patients, ( b ) adenocarcinoma patients, and ( c ) esophageal squamous cell carcinoma (ESCC) patients from the Surveillance, Epidemiology, and End Results (SEER) database. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Article Snippet: **Curves were generated using odds ratios (ORs, each LNE count compared to 1 LNE as a reference) in logistic regression analysis and hazard ratios (HRs) in Cox regression analysis using Locally Weighted Scatterplot Smoothing (LOWESS, Stata 12.0) with a bandwidth of 0.6.

Techniques:

Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Node‐negative (N negative) and ( b ) node‐positive (N positive) patients. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Journal: Thoracic Cancer

Article Title: Effect of lymph node examined count on accurate staging and survival of resected esophageal cancer

doi: 10.1111/1759-7714.13056

Figure Lengend Snippet: Co‐plot of hazard ratios (HRs, upper row) and Locally Weighted Scatterplot Smoothing curves of cancer‐specific survival (CSS) and determination of structural break points with the use of the Chow test (bottom row). The fitting bandwidth was 0.6. Each dot in the co‐plot represents an HR of a specific lymph node examined (LNE, vacant if case number < 10) from Cox regression analysis. ( a ) Node‐negative (N negative) and ( b ) node‐positive (N positive) patients. ( ) T1, ( ) T2, ( ) T3, and ( ) T4.

Article Snippet: **Curves were generated using odds ratios (ORs, each LNE count compared to 1 LNE as a reference) in logistic regression analysis and hazard ratios (HRs) in Cox regression analysis using Locally Weighted Scatterplot Smoothing (LOWESS, Stata 12.0) with a bandwidth of 0.6.

Techniques: