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For: Chen Y, Wang Y, Ding Y, Su X, Wang C. RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs. Computers in Biology and Medicine 2022. [DOI: 10.1016/j.compbiomed.2022.105322] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Li P, Tiwari P, Xu J, Qian Y, Ai C, Ding Y, Guo F. Sparse regularized joint projection model for identifying associations of non-coding RNAs and human diseases. Knowledge-Based Systems 2022;258:110044. [DOI: 10.1016/j.knosys.2022.110044] [Reference Citation Analysis]
2 Guo X, Tiwari P, Zhang Y, Han S, Wang Y, Ding Y. Random Fourier features-based sparse representation classifier for identifying DNA-binding proteins. Comput Biol Med 2022;151:106268. [PMID: 36370585 DOI: 10.1016/j.compbiomed.2022.106268] [Reference Citation Analysis]
3 Chen Y, Wang J, Wang C, Liu M, Zou Q. Deep learning models for disease-associated circRNA prediction: a review. Brief Bioinform 2022;23:bbac364. [PMID: 36130259 DOI: 10.1093/bib/bbac364] [Reference Citation Analysis]
4 Wu E, Fan X, Tang T, Li J, Wang J, Liu X, Zungar Z, Ren J, Wu C, Shen B. Biomarkers discovery for endometrial cancer: A graph convolutional sample network method. Computers in Biology and Medicine 2022;150:106200. [DOI: 10.1016/j.compbiomed.2022.106200] [Reference Citation Analysis]
5 Wu Q, Deng Z, Pan X, Shen HB, Choi KS, Wang S, Wu J, Yu DJ. MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction. Brief Bioinform 2022:bbac289. [PMID: 35907779 DOI: 10.1093/bib/bbac289] [Reference Citation Analysis]
6 Li G, Lin Y, Luo J, Xiao Q, Liang C. GGAECDA: predicting circRNA-disease associations using graph autoencoder based on graph representation learning. Computational Biology and Chemistry 2022. [DOI: 10.1016/j.compbiolchem.2022.107722] [Reference Citation Analysis]
7 Niu M, Hesham AE, Zou Q. GATSDCD: Prediction of circRNA-Disease Associations Based on Singular Value Decomposition and Graph Attention Network. Intelligent Computing Theories and Application 2022. [DOI: 10.1007/978-3-031-13829-4_2] [Reference Citation Analysis]
8 Wang D, Ji C, Wang Y, Li L, Ni J, Li B. GCNMFCDA: A Method Based on Graph Convolutional Network and Matrix Factorization for Predicting circRNA-Disease Associations. Intelligent Computing Theories and Application 2022. [DOI: 10.1007/978-3-031-13829-4_14] [Reference Citation Analysis]