LCKGCN: Identifying Potential Circrna-Disease Associations Based on Large Convolutional Kernel and Graph Convolutional Network

circRNA plays an important role in multicellular organisms, and circRNA has important functions in many complex diseases. CircRNA-disease association prediction helps to find potential biomarkers of diseases. However, existing methods cannot effectively use information from multi-source data and cannot effectively extract important features of signatures. In this paper, we propose a circRNA-disease association prediction method LCKGCN (prediction circRNA-disease associations based on Large Convolutional Kernel and Graph Convolutional Network), which uses large convolution kernels to effectively redefine the information distribution in multi-source data, and uses graph convolution networks to effectively obtain potentially important features in multi-source data. The results of five-fold and ten-fold cross-validation in the data show that LCKGCN is better than existing methods. We used LCKGCN to predict 10 disease-associated circRNAs, 8 of which have been reported in relevant literature, proving that our method can effectively predict disease-associated circRNAs.