DLGCN:Prediction of drug-lncRNA associations based on graph convolution network
To realize high-throughput identification of new drug-lncRNA associations,we propose a new method DLGCN(Drug-LncRNA graph convolution network)to identify potential drug-lncRNA associations.First,we construct drug-drug and lncRNA-lncRNA similarity networks based on drug structure information and lncRNA sequence information,and then combine them with known drug-lncRNA associations to construct drug-lncRNA heterogeneous network.Next,the attention mechanism and graph convolution operation are applied to the network to learn the low dimensional features of drugs and lncRNAs.The new drug-lncRNA associations are predicted based on the integrated low dimensional features.DLGCN identified the drug-lncRNA associations with an AUROC(Area under the receiver operator characteristic)of 0.843 1,which is superior to classical machine learning methods and common deep learning methods.In addition,DLGCN predict that curcumin could regulate MALAT1,which has been confirmed by recent studies.DLGCN can effectively predict drug-lncRNA associations,which provides an important reference for identification of new tumor therapeutic targets and development of anti-cancer drugs.
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