RAMP: response -aware multi -task learning with contrastive regularization for cancer drug response prediction

The accurate prediction of cancer drug sensitivity according to the multiomics profiles of individual patients is crucial for precision cancer medicine. However, the development of prediction models has been challenged by the complex crosstalk of input features and the resistance -dominant drug response information contained in public databases. In this study, we propose a novel multidrug response prediction framework, response -aware multitask prediction (RAMP), via a Bayesian neural network and restrict it by soft -supervised contrastive regularization. To utilize network embedding vectors as representation learning features for heterogeneous networks, we harness response -aware negative sampling, which applies cell line-drug response information to the training of network embeddings. RAMP overcomes the prediction accuracy limitation induced by the imbalance of trained response data based on the comprehensive selection and utilization of drug response features. When trained on the Genomics of Drug Sensitivity in Cancer dataset, RAMP achieved an area under the receiver operating characteristic curve > 89%, an area under the precision -recall curve > 59% and an Fi score > 52% and outperformed previously developed methods on both balanced and imbalanced datasets. Furthermore, RAMP predicted many missing drug responses that were not included in the public databases. Our results showed that RAMP will be suitable for the high -throughput prediction of cancer drug sensitivity and will be useful for guiding cancer drug selection processes. The Python implementation for RAMP is available at https://github.com/hvc1/RAMP.

Automated summary

In this study, we propose a novel multidrug response prediction framework, which utilizes a Bayesian neural network and soft-supervised contrastive regularization. The framework overcomes the prediction accuracy limitation induced by imbalanced data and achieves better performance than previous methods. It also predicts missing drug responses that were not included in public databases.