CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

[1] Jarada TN(2021)SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks BMC Bioinform 22 28-5664

[2] Rokne JG(2021)A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network BMC Bioinform 22 169-298

[3] Alhajj R(2021)IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier BMC Bioinform 22 175-2546

[4] Wang L(2021)PMDFI: predicting miRNA-disease associations based on high-order feature interaction Front Genet 12 318-19

[5] Zhong X(2020)SDN2GO: an integrated deep learning model for protein function prediction Front Bioeng Biotechnol 8 391-9

[6] Wang S(2021)Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks Bioinformatics 36 5656-54041

[7] Zhang H(2021)Prediction of disease-associated circRNAs via circRNA-disease pair graph and weighted nuclear norm minimization Knowl -Based Syst 214 106694-151812

[8] Liu Y(2021)Predicting CircRNA-disease associations based on improved weighted biased meta-structure J Comput Sci Technol 36 288-1367

[9] Zhu R(1933)Analysis of a complex of statistical variables into principal components J Educ Psychol 24 417-1670

[10] Wang Y(2020)Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction Bioinformatics 36 2538-291

[1] Jarada TN(2021)SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks BMC Bioinform 22 28-5664

[2] Rokne JG(2021)A novel end-to-end method to predict RNA secondary structure profile based on bidirectional LSTM and residual neural network BMC Bioinform 22 169-298

[3] Alhajj R(2021)IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier BMC Bioinform 22 175-2546

[4] Wang L(2021)PMDFI: predicting miRNA-disease associations based on high-order feature interaction Front Genet 12 318-19

[5] Zhong X(2020)SDN2GO: an integrated deep learning model for protein function prediction Front Bioeng Biotechnol 8 391-9

[6] Wang S(2021)Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks Bioinformatics 36 5656-54041

[7] Zhang H(2021)Prediction of disease-associated circRNAs via circRNA-disease pair graph and weighted nuclear norm minimization Knowl -Based Syst 214 106694-151812

[8] Liu Y(2021)Predicting CircRNA-disease associations based on improved weighted biased meta-structure J Comput Sci Technol 36 288-1367

[9] Zhu R(1933)Analysis of a complex of statistical variables into principal components J Educ Psychol 24 417-1670

[10] Wang Y(2020)Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction Bioinformatics 36 2538-291