Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques
Fig 7
Evaluation of PASO-TCGA-Classifier model efficiency (A) Precision-recall curve representing the performance of the PASO-TCGA-Classifier model on the TCGA test dataset.
(B) Boxplot displays the distribution of predicted drug response probabilities for different clinical drug responses on the TCGA test dataset. (C) Survival analysis of the TCGA test dataset across multiple cancer types. Patients were classified into two groups based on the median value of the predicted probability. Kaplan-Meier analysis was performed, and the log-rank test yielded a p-value of 1.59e-9. (D-E) Survival analysis of the TCGA test dataset for BRCA and BLCA. (F) Bar chart displays the predicted responses of Cisplatin and Carboplatin across different cancer types on the TCGA test dataset.