Review





Similar Products

smooth function with a lowess local linear regression method  (MathWorks Inc)
90
MathWorks Inc smooth function with a lowess local linear regression method
Smooth Function With A Lowess Local Linear Regression Method, supplied by MathWorks 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/smooth function with a lowess local linear regression method/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
smooth function with a lowess local linear regression method - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
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
https://www.bioz.com/result/locally weighted scatterplot smoothing (lowess) regression/product/Nonlinear Dynamics
Average 90 stars, based on 1 article reviews
locally weighted scatterplot smoothing (lowess) regression - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
lowess regression matlab function 'malowess  (MathWorks Inc)
90
MathWorks Inc lowess regression matlab function 'malowess
(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.
Lowess Regression Matlab Function 'malowess, supplied by MathWorks 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 regression matlab function 'malowess/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lowess regression matlab function 'malowess - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
locally weighted linear regression smoothing (lowess)  (MathWorks Inc)
90
MathWorks Inc locally weighted linear regression smoothing (lowess)
(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 Linear Regression Smoothing (Lowess), supplied by MathWorks 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 linear regression smoothing (lowess)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
locally weighted linear regression smoothing (lowess) - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
scatter plots and local weighted regression curves (lowess curves)  (STATA Corporation)
90
STATA Corporation scatter plots and local weighted regression curves (lowess curves)
(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.
Scatter Plots And Local Weighted Regression Curves (Lowess Curves), supplied by STATA Corporation, 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/scatter plots and local weighted regression curves (lowess curves)/product/STATA Corporation
Average 90 stars, based on 1 article reviews
scatter plots and local weighted regression curves (lowess curves) - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
locally weighted regression stata lowess  (STATA Corporation)
90
STATA Corporation locally weighted regression stata lowess
(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 Regression Stata Lowess, supplied by STATA Corporation, 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 regression stata lowess/product/STATA Corporation
Average 90 stars, based on 1 article reviews
locally weighted regression stata lowess - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
lowess regression  (MathWorks Inc)
90
MathWorks Inc 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.
Lowess Regression, supplied by MathWorks 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 regression/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lowess regression - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
fit spline/lowess regression analysis  (GraphPad Software Inc)
90
GraphPad Software Inc fit spline/lowess 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.
Fit Spline/Lowess Regression Analysis, 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/fit spline/lowess regression analysis/product/GraphPad Software Inc
Average 90 stars, based on 1 article reviews
fit spline/lowess regression analysis - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
lowess regression line  (MathWorks Inc)
90
MathWorks Inc lowess regression line
(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.
Lowess Regression Line, supplied by MathWorks 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 regression line/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lowess regression line - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


(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