REFINED-CNN framework for survival prediction with high-dimensional features

ABSTRACT: Summary 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

SUBMITTER: Bazgir O 

PROVIDER: S-EPMC10474067 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature