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• W4200313900 abstract "Complex and diverse microbial communities have certain impacts on human health, and specific drugs are needed to treat diseases caused by microbes. However, most of the discovery of associations between microbes and drugs is through biological experiments, which are time-consuming and expensive. Therefore, it is crucial to develop an effective and computational model to detect novel microbe–drug associations. In this study, we propose a model based on Multiple Kernel fusion on Graph Convolutional Network, called MKGCN, for inferring novel microbe–drug associations. Our model is built on the heterogeneous network of microbes and drugs to extract multi-layer features, through Graph Convolutional Network (GCN). Then, we respectively calculate the kernel matrix by embedding features on each layer, and fuse multiple kernel matrices based on the average weighting method. Finally, Dual Laplacian Regularized Least Squares is used to infer new microbe–drug associations by the combined kernel in microbe and drug spaces. Compared with the existing tools for detecting biological bipartite networks, our model has excellent prediction effect on three datasets via three types of cross-validation. Furthermore, we also conduct a case study of the SARS-Cov-2 virus and make a deduction about drugs that may be able to associate with COVID-19. We have proved the accuracy of the prediction results through the existing literature." @default.
• W4200313900 created "2021-12-31" @default.
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• W4200313900 date "2022-02-01" @default.
• W4200313900 modified "2023-10-14" @default.
• W4200313900 title "Inferring human microbe–drug associations via multiple kernel fusion on graph neural network" @default.
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• W4200313900 doi "https://doi.org/10.1016/j.knosys.2021.107888" @default.
• W4200313900 hasPublicationYear "2022" @default.
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