non-linear weighted regression Search Results


derivative-free weighted nonlinear least-squares regression analysis sas proc nlin  (SAS institute)
90
SAS institute derivative-free weighted nonlinear least-squares regression analysis sas proc nlin
Derivative Free Weighted Nonlinear Least Squares Regression Analysis Sas Proc Nlin, 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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weighted non-linear regression program  (Jandel Engineering)
90
Jandel Engineering weighted non-linear regression program
Weighted Non Linear Regression Program, supplied by Jandel Engineering, 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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iteratively weighted non-linear regression model of xr on facility-average tdi concentration  (SAS institute)
90
SAS institute iteratively weighted non-linear regression model of xr on facility-average tdi concentration
Iteratively Weighted Non Linear Regression Model Of Xr On Facility Average Tdi Concentration, 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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locally weighted scatterplot smoothing (lowess) regression  (Nonlinear Dynamics)
90
Nonlinear Dynamics locally weighted scatterplot smoothing (lowess) regression
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Locally Weighted Scatterplot Smoothing (Lowess) Regression, supplied by Nonlinear Dynamics, 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) regression - by Bioz Stars, 2026-04
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weighted least-squares nonlinear regression procedure levmarqdt  (Jandel Engineering)
90
Jandel Engineering weighted least-squares nonlinear regression procedure levmarqdt
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted Least Squares Nonlinear Regression Procedure Levmarqdt, supplied by Jandel Engineering, 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 leastsquares nonlinear regression procedure tablecurve 2d  (SYSTAT)
90
SYSTAT weighted leastsquares nonlinear regression procedure tablecurve 2d
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted Leastsquares Nonlinear Regression Procedure Tablecurve 2d, supplied by SYSTAT, 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 leastsquares nonlinear regression procedure tablecurve 2d - by Bioz Stars, 2026-04
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1/y 2 -weighted nonlinear regression  (GraphPad Software Inc)
90
GraphPad Software Inc 1/y 2 -weighted nonlinear regression
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
1/Y 2 Weighted Nonlinear Regression, 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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weighted, non-linear least squares regression  (OriginLab corp)
90
OriginLab corp weighted, non-linear least squares regression
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted, Non Linear Least Squares Regression, supplied by OriginLab corp, 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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nonlinear leastsquare regression program rstrip ii  (MicroMath Inc)
90
MicroMath Inc nonlinear leastsquare regression program rstrip ii
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Nonlinear Leastsquare Regression Program Rstrip Ii, supplied by MicroMath 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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nonlinear leastsquare regression program rstrip ii - by Bioz Stars, 2026-04
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weighted non-linear regression analysis  (RStudio)
90
RStudio weighted non-linear regression analysis
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted Non Linear Regression Analysis, 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
https://www.bioz.com/result/weighted non-linear regression analysis/product/RStudio
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weighted non-linear regression analysis - by Bioz Stars, 2026-04
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weighted linear and nonlinear regression analysis proc reg  (SAS institute)
90
SAS institute weighted linear and nonlinear regression analysis proc reg
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted Linear And Nonlinear Regression Analysis Proc Reg, 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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weighted linear and nonlinear regression analysis proc reg - by Bioz Stars, 2026-04
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weighted nonlinear regression option of prism  (GraphPad Software Inc)
90
GraphPad Software Inc weighted nonlinear regression option of prism
(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted <t>scatterplot</t> smoothing <t>(LOWESS)</t> regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.
Weighted Nonlinear Regression Option Of Prism, 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/weighted nonlinear regression option of prism/product/GraphPad Software Inc
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weighted nonlinear regression option of prism - by Bioz Stars, 2026-04
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(a) Workflow of BayesAge 2.0, a Bayesian and locally weighted scatterplot smoothing (LOWESS) regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.

Journal: bioRxiv

Article Title: Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish

doi: 10.1101/2025.01.28.635350

Figure Lengend Snippet: (a) Workflow of BayesAge 2.0, a Bayesian and locally weighted scatterplot smoothing (LOWESS) regression model behind the aging clocks. To train a tissue clock, Leave One Sample Out Cross-Validation (LOSO-CV) was used to generate testing-training splits of the data. In each iteration of LOSO-CV, one sample was used as a test set, while the rest of the tissue samples were used for training. This was performed k times, where k is the number of tissue samples available. Each time LOSO-CV was performed, a set of top age-associated genes (the highest absolute Spearman’s rank correlation values) was selected for the feature set. Then, the probability that the sample in the test set was a given age was calculated from the probability of the observed expression value for each selected gene in the sample at that age, assuming a Poisson distribution. The product of each gene-wise probability was computed to determine the age probability. The result was an age-probability distribution from which the age prediction was the highest probability age in this distribution. (b) Bar plots of the performance metrics for the BayesAge sex-combined tissue clocks, using the coefficient of determination (R 2 ) for the relationship between chronological and predicted age and the mean absolute error (MAE). (c) Scatterplot of gut clock chronological age vs. the ‘transcriptomic age’ (tAge) for measuring the prediction accuracy of the highest performing gut sex-combined tissue clock. The ‘optimal’ BayesAge clock is defined as the model with the most concordance between chronological and predicted age among all the gene number tested. Bottom, the gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined gut samples are shown. The pink line is the locally estimated scatterplot smoothing (LOESS) regression fit across time. (d) Bar plots of R 2 and MAE values for select clocks trained on sex-combined data (left, ‘S-C’), female data (middle, ‘F’), and male data (right, ‘M’). Selected tissues include highly transcriptionally sex-dimorphic tissues (gonad, kidney, liver), moderately transcriptionally sex-dimorphic tissues (gut, skin), and one weakly sex-dimorphic tissue (brain). (e) Accuracy of tAge predictions for the optimal sex-combined (left), male-only (middle), and female-only liver clocks (right). (f) Predicted ages for liver samples from male and female killifish fed on ad libitum (AL) or dietary restricted (DR) diets using sex-dimorphic liver clocks (data from a published dataset ). Age prediction was performed using three different modeling strategies, BayesAge 2.0 (left), Elastic Net regression (middle), and Principal Component regression (right). Each dot in each box plot represents the predicted tAge for the liver transcriptome of an individual fish (4 fish per condition) and the gene set size or number of principal components used for age prediction is listed. For each model, Mann-Whitney test was used to test the significance of difference between the AL and DR conditions.

Article Snippet: This method utilizes a Bayesian framework to estimate the most likely transcriptomic age of a sample (‘tAge’) and employs locally weighted scatterplot smoothing (LOWESS) regression to model the nonlinear dynamics of gene expression, enabling age prediction between 47 to 163 days of age at day-level resolution.

Techniques: Biomarker Discovery, Expressing, MANN-WHITNEY

(a) Scatterplot of the tissue transcriptomic age (tAge) vs. chronological age for measuring the prediction accuracy of the optimal brain sex-combined tissue clock, which is the model that corresponds to the most concordance between chronological and predicted age among all the gene number tested. The coefficient of determination (R 2 ) between chronological and predicted age, as well as the mean absolute error (MAE), is listed in graphs. (b) The gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined brain samples are shown. The black line is the locally weighted scatterplot smoothing (LOWESS) regression fit across time. (c, d) The scatterplots of tAge vs. chronological age (c) and gene frequency (d) were generated as in panels a and b, but for the testis.

Journal: bioRxiv

Article Title: Multi-tissue transcriptomic aging atlas reveals predictive aging biomarkers in the killifish

doi: 10.1101/2025.01.28.635350

Figure Lengend Snippet: (a) Scatterplot of the tissue transcriptomic age (tAge) vs. chronological age for measuring the prediction accuracy of the optimal brain sex-combined tissue clock, which is the model that corresponds to the most concordance between chronological and predicted age among all the gene number tested. The coefficient of determination (R 2 ) between chronological and predicted age, as well as the mean absolute error (MAE), is listed in graphs. (b) The gene frequency scatterplots of the top 10 overall age-correlated genes trained on the sex-combined brain samples are shown. The black line is the locally weighted scatterplot smoothing (LOWESS) regression fit across time. (c, d) The scatterplots of tAge vs. chronological age (c) and gene frequency (d) were generated as in panels a and b, but for the testis.

Article Snippet: This method utilizes a Bayesian framework to estimate the most likely transcriptomic age of a sample (‘tAge’) and employs locally weighted scatterplot smoothing (LOWESS) regression to model the nonlinear dynamics of gene expression, enabling age prediction between 47 to 163 days of age at day-level resolution.

Techniques: Generated