Interpretable Drug Response Prediction using a Knowledge-based Neural Network

Abstract

Predicting drug response based on the genomic profile of a cancer patient is one of the hallmarks of precision oncology. Despite current methods for drug response prediction becoming more accurate, there is still a need to switch from black box predictions to methods that offer high accuracy as well as interpretable predictions. This is of particular importance in real-world applications such as drug response prediction in cancer patients. In this paper, we propose BDKANN, a novel knowledge-based method that employs the hierarchical information on how proteins form complexes and act together in pathways to form the architecture of a deep neural network. We employ BDKANN to predict cancer drug response from cell line gene expression data and our experimental results demonstrate that not only does BDKANN have a low prediction error compared to baseline models but it also allows meaningful interpretation of the network. These interpretations can both explain predictions made and discover novel connections in the biological knowledge that may lead to new hypotheses about mechanisms of drug action.