DriverMONI: Cancer Driver Gene Prediction With Multimodal Deep Learning Integrating Multiomics Data and...

In the past few decades, several methods, ranging from statistical algorithms to deep learning models, have been proposed for driver gene identification. Although recent graph neural network-based methods have achieved progress using biological networks, the static and incomplete nature of networks can compromise the predictive power of the models. Here, we introduce a multimodal approach for driver gene prediction called DriverMONI that leverages pairs of complementary information from multiomics data and biological networks. In particular, DriverMONI utilizes condition-specific subnetworks of protein-protein interaction for generating input graphs and node attributes for graph attention network, which makes the model condition-specific and improves prediction accuracy. We empirically show the importance of multimodality and the ability of DriverMONI to mitigate the incompleteness of protein-protein interaction networks. In addition, comparative analysis showed that DriverMONI achieved the best performance among competing methods including graph neural network-based models using The Cancer Genome Atlas data, while having a strong consensus with other methods.