Features extracted by SHP algorithm in different stages.

<p>Features extracted by SHP algorithm in different stages.</p

F1-measure of different methods.

<p>F1-measure of different methods.</p

Identification precision of different categories.

<p>Identification precision of different categories.</p

ROC performance of different methods.

<p>ROC performance of different methods.</p

Additional file 8 of Pan-cancer analysis of cuproptosis regulation patterns and identification of mTOR-target responder in clear cell renal cell carcinoma

Additional file 8: Fig. S8. Construction and evaluation of nomograms for PFS. (A-B) The univariate (A) and multivariate (B) Cox analyses of clinicopathologic data and CRP score for PFS outcomes. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns: no significance. (C) The predictive nomogram of PFS at 1-, 3-, and 5-years in TCGA-KIRC. (D) Calibration plots of 1-, 3-, and 5-years were utilized to evaluate the predictive accuracy of PFS in the CRP model. (E–G) Decision curve analysis to assess the clinical utility of 1- (E), 3- (F), and 5-years (G) nomogram

Features extracted by CFS algorithm.

<p>Features extracted by CFS algorithm.</p

Quantity of different categories in the three databases.

<p>Quantity of different categories in the three databases.</p

Precision of different categories in the three databases.

<p>Precision of different categories in the three databases.</p

Performance of F1-measure on FTP-DATA category.

<p>Performance of F1-measure on FTP-DATA category.</p

Performance of F1-measure on GAMES category.

<p>Performance of F1-measure on GAMES category.</p