Fig. 7: Proteomic classifier to predict sunitinib response.
From: Proteogenomics of clear cell renal cell carcinoma response to tyrosine kinase inhibitor
a Machine learning-based model construction pipeline for the TKI treatment response, including classification target, feature selection, model construction, model performance evaluation, and model performance validation. b, c The five repeatedly cross-validated ROC-AUC on the train cohort for proteome-based random forest (RF) and multi-omics-base RF, separately. d The confusion matrix of test cohort for multi-omics-base RF. e The comparison of ROC-AUC on the test cohort for proteome-based RF and multi-omics-base RF. f The feature importance of multi-omics-based RF model. The blue, orange, green, and red rectangles indicated proteome, transcriptome, clinical, and genomic features, respectively. g Summary of clinical and molecular characteristics in sunitinib responders and non-responders. Source data are provided as a Source Data file.
