在药物基因组学领域，利用高通量基因组学数据进行临床试验的健壮且准确的生存预测是一个基本挑战。当前的机器学习工具在这些场景下通常提供有限的预测性能和模型解释能力。在本研究中，我们将REFINED-CNN的应用范围从回归任务扩展到生存预测，通过将高维RNA测序数据映射为有利于CNN建模的REFINED图像。我们展示了REFINED-CNN生存模型能够通过少量患者的迁移学习轻松适应相似类型的新任务（例如，在新的癌症类型上进行预测）。此外，该模型还可以通过风险得分反向传播在局部和全局上进行解释，从而量化每个特征（例如，基因）在患者或感兴趣的癌症类型的生存预测任务中的重要性。© 2023 作者。

Robust and accurate survival prediction of clinical trials using high-throughput genomics data is a fundamental challenge in pharmacogenomics. Current machine learning tools often provide limited predictive performance and model interpretation in these settings. In the present study, we extend the application of REFINED-CNN from regression tasks to making survival predictions, by mapping high-dimensional RNA sequencing data into REFINED images which are conducive to CNN modeling. We show that the REFINED-CNN survival model can be easily adapted to new tasks of a similar nature (e.g., predicting on new cancer types) using transfer learning with a low number of patients. Furthermore, the model can also be interpreted both locally and globally through risk score back propagation that quantifies each feature (e.g., gene) importance in survival prediction task for the patient or cancer type of interest.© 2023 The Author(s).