Cancer has become one of the critical diseases threatening human life and health. The sensitivity difference of cancer drugs has always been a critical cause of the treatment come to nothing. Once drug resistance occurs, it will make the anticancer treatment or even various drugs ineffective. With the deepening of cancer research, a growing number of evidence shows that microRNA has a particular regulatory effect on the sensitivity of cancer drugs, which provides new research ideas. However, using traditional biological experiments to verify and discover the relations of microRNA-drug sensitivity is cumbersome and time-consuming, significantly slowing down cancer drug sensitivity's research progress. Therefore, this paper proposes a computational method (PDSM-LGCN) that spreads information through the high-order connection between cancer drug sensitivity and microRNA. At the same time, the model constructs an optimized-GCN as an embedding propagation layer to obtain the practical embeddings of microRNA and medicines. Finally, based on a collaborative filtering algorithm, the model brings the prediction score between microRNA and drug sensitivity. The results of fivefold cross-validation show that the AUC of PDSM-LGCN is 0.8872, and the AUPR is as high as 0.9026. At the same time, we also reproduced the five latest models of similar problems and compared the results. Our model has the best comprehensive effect among them. In addition, the reliability of PDSM-LGCN was further confirmed through the case study of Cisplatin and Doxorubicin, which can be used as a powerful tool for clinical and biological research. The source code and datasets can be obtained from https://github.com/19990915fzy/PDSM-LGCN/.
Keywords: Graph Convolutional Network; association prediction; embedding representations; microRNA-drug sensitivity.
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