Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity

Abstract

Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at https://github.com/19990915fzy/DGNNMDA.