Predicting circRNA-drug resistance associations based on a multimodal graph representation learning framework

Circular RNA (circRNA) is a class of noncoding RNA that is highly conserved and exhibit exceptional stability. Due to its function as a microRNA sponge, circRNA has gained significant attention as an essential biomarker and potential drug target in the pathogenesis of several cancers. Although many circRNAs have been identified to play a role in cancer resistance, traditional methods are time-consuming and expensive. In this context, computational methods offer a promising way to facilitate the discovery process. However, most existing prediction models focus on the association between circRNAs and drug resistance, without considering the corresponding disease-related information in the circRNA-drug resistance association. Incorporating disease-related information into the prediction of circRNA-drug resistance associations could potentially improve the efficiency and speed of discovering and developing circRNA-targeting drugs. We propose a computational framework, named GraphCDD, for predicting the association between circRNA and drug resistance. Our model utilizes data from three sources, namely circRNA, disease, and drug, to construct three similarity networks that represent the features of circRNA, disease, and drug, respectively. We utilize a multimodal graph neural network to acquire efficient representations of circRNAs, diseases, and drugs by integrating various types of information, and establish a predictive model. The experimental results have validated the effectiveness of our model and provided a promising method in predicting potential associations between circRNA and drug resistance. The source code and dataset of GraphCDD can be found at
https://github.com/Ziqiang-Liu/GraphCDD
.