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auc heatmap  (GraphPad Software Inc)


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    GraphPad Software Inc auc heatmap
    ( a ) Schematic illustrating the conversion of continuous <t>DR-AUC</t> values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) <t>Heatmap</t> showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.
    Auc Heatmap, 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/auc heatmap/product/GraphPad Software Inc
    Average 90 stars, based on 1 article reviews
    auc heatmap - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer"

    Article Title: ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer

    Journal: bioRxiv

    doi: 10.1101/2025.07.08.663664

    ( a ) Schematic illustrating the conversion of continuous DR-AUC values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) Heatmap showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.
    Figure Legend Snippet: ( a ) Schematic illustrating the conversion of continuous DR-AUC values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) Heatmap showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.

    Techniques Used: Gene Expression, Labeling



    Similar Products

    auc heatmap  (GraphPad Software Inc)
    90
    GraphPad Software Inc auc heatmap
    ( a ) Schematic illustrating the conversion of continuous <t>DR-AUC</t> values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) <t>Heatmap</t> showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.
    Auc Heatmap, 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/auc heatmap/product/GraphPad Software Inc
    Average 90 stars, based on 1 article reviews
    auc heatmap - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    ( a ) Schematic illustrating the conversion of continuous DR-AUC values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) Heatmap showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.

    Journal: bioRxiv

    Article Title: ODFormer: a Virtual Organoid for Predicting Personalized Therapeutic Responses in Pancreatic Cancer

    doi: 10.1101/2025.07.08.663664

    Figure Lengend Snippet: ( a ) Schematic illustrating the conversion of continuous DR-AUC values into binary sensitivity labels (sensitive vs. non-sensitive). ( b ) Receiver operating characteristic (ROC) and precision-recall (PR) curves comparing the performance of ODFormer, DeepCDR, and PANCANR in predicting drug sensitivity. Area under the ROC curve (AUC) and area under the PR curve (AUPR) values are shown. ( c,d ) Waterfall plot showing the individual patient performance of ODFormer, as measured by AUC. Waterfall plot showing the individual patient performance of PANCANR, as measured by AUC. ( e-f ) Kaplan-Meier survival curves comparing overall survival (OS) of patients stratified based on 5-FU ( e ) and AG ( f ) sensitivity using OS-based cut-points. P-values were calculated using the log-rank test. Density plots show the distribution of DR-AUC values and the cut-point used for stratification. ( g ) Kaplan-Meier survival curves comparing overall survival of patients stratified based on AG sensitivity in the external cohort using survival-adapted labels. P-value was calculated using the log-rank test. ( h ) Heatmap showing differential gene expression between samples labeled by the external AG survival classifier and those from the internal cohort. Known AG-resistance markers ( i )MUC2 and ( j ) SPINK4 are highlighted. Receiver operating characteristic (ROC) curves comparing the performance of ODFormer in predicting drug sensitivity in the internal cohort. Area under the ROC curve (AUC) values are shown.

    Article Snippet: The AUC heatmap for the secondary screening was generated with GraphPad Prism 8.

    Techniques: Gene Expression, Labeling