roc calculation function glmval Search Results


glmval function  (MathWorks Inc)
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MathWorks Inc glmval function
Glmval Function, 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
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glmval command  (MathWorks Inc)
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MathWorks Inc glmval command
Glmval Command, 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
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matlab glmval function  (MathWorks Inc)
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MathWorks Inc matlab glmval function
Matlab Glmval Function, 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
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glmfit and glmval functions  (MathWorks Inc)
90
MathWorks Inc glmfit and glmval functions
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Glmfit And Glmval Functions, 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
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glmval.m  (MathWorks Inc)
90
MathWorks Inc glmval.m
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Glmval.M, 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
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glmfit.m function  (MathWorks Inc)
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MathWorks Inc glmfit.m function
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Glmfit.M Function, 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
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matlab glmval  (MathWorks Inc)
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MathWorks Inc matlab glmval
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Matlab Glmval, 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
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log-linear regression matlab glmval function  (MathWorks Inc)
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MathWorks Inc log-linear regression matlab glmval function
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Log Linear Regression Matlab Glmval Function, 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
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logistic regression classification glmfit.m  (MathWorks Inc)
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MathWorks Inc logistic regression classification glmfit.m
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Logistic Regression Classification Glmfit.M, 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
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matlab version 9.4.0.813654  (MathWorks Inc)
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MathWorks Inc matlab version 9.4.0.813654
Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link <t>using</t> <t>MATLAB’s</t> glmfit and <t>glmval</t> functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Matlab Version 9.4.0.813654, 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
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Image Search Results


Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Journal: Biomaterials

Article Title: Deep learning identification of stiffness markers in breast cancer

doi: 10.1016/j.biomaterials.2022.121540

Figure Lengend Snippet: Young’s modulus (global stiffness) characterization and composition analysis of breast tissue. a, Heatmap including (columns) 15 tissue samples from 6 patients (P#) clustered using Euclidean distance with complete linkage by (rows) related features. Each parameter is normalized using a z score. The values within each feature are color coded by low to high. The heatmap key on the left denotes the following color-coded parameters of each feature: cell component, extracellular matrix (ECM) component, pathologic feature, or mechanical measurement. b, Univariate analysis comparing Young’s modulus (global stiffness; kPa) to the percent composition of cell component class: (i) blood vessels, (ii) tumor cells; c, extracellular matrix combined; d, (i) straight collagen and (ii) fibrotic tissue; e, straight collagen from patients who received neoadjuvant chemotherapy; and f, percent breast density. g, Highest correlated pair of tissue composition classes with Young’s Modulus. The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions. Rank ordered by correlation. The error is the fit-error. i, Plot of Spearman Correlation (ρs) versus the p-value for all cellular and extracellular classes versus straight collagen. Values below the dashed line where p = 0.05 are significant. j, Plots showing the monotonic relationship between straight collagen and (i) blood vessels, (ii) tumor cells, (iii) wavy collagen, and (iv) fibrotic tissue. Plots in j show the r2 value and root mean squared error (RMSE) at the top of the plot. Plots with square data points represent luminal A patients who have not received chemotherapy. Plots with circles represent patients who received neoadjuvant chemotherapy. Each data point is color coded by patient. The lines denote the best fit trend line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Article Snippet: The Pearson Correlation (r), p-value, r2 value, and error is listed at the top of plots b-e and g. One-way ANOVA was used to perform statistics in f. h, Table of top five correlated tissue composition pairs from bivariate analysis using normal distribution and identity link using MATLAB’s glmfit and glmval functions.

Techniques: