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Cited by in F6Publishing
For: Lei X, Mudiyanselage TB, Zhang Y, Bian C, Lan W, Yu N, Pan Y. A comprehensive survey on computational methods of non-coding RNA and disease association prediction. Brief Bioinform 2021;22:bbaa350. [PMID: 33341893 DOI: 10.1093/bib/bbaa350] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Wang Z, Lei X. A web server for identifying circRNA-RBP variable-length binding sites based on stacked generalization ensemble deep learning network. Methods 2022;205:179-90. [PMID: 35810958 DOI: 10.1016/j.ymeth.2022.06.014] [Reference Citation Analysis]
3 Fan C, Lei X, Tie J, Zhang Y, Wu F, Pan Y. CircR2Disease v2.0: An Updated Web Server for Experimentally Validated circRNA-disease Associations and Its Application. Genomics Proteomics Bioinformatics 2021:S1672-0229(21)00246-1. [PMID: 34856391 DOI: 10.1016/j.gpb.2021.10.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Bian C, Lei XJ, Wu FX. GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network. Cancers (Basel) 2021;13:2595. [PMID: 34070678 DOI: 10.3390/cancers13112595] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
5 Wu B, Li L, Cui Y, Zheng K. Cross-Adversarial Learning for Molecular Generation in Drug Design. Front Pharmacol 2022;12:827606. [DOI: 10.3389/fphar.2021.827606] [Reference Citation Analysis]
6 Niu M, Zou Q, Wang C. GMNN2CD: Identification of circRNA-disease associations based on variational inference and graph markov neural networks. Bioinformatics 2022:btac079. [PMID: 35157027 DOI: 10.1093/bioinformatics/btac079] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
7 Li G, Wang D, Zhang Y, Liang C, Xiao Q, Luo J. Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data. Front Genet 2022;13:829937. [PMID: 35198012 DOI: 10.3389/fgene.2022.829937] [Reference Citation Analysis]
8 Xu L, Li X, Yang Q, Tan L, Liu Q, Liu Y. Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases. Front Genet 2022;13:936823. [DOI: 10.3389/fgene.2022.936823] [Reference Citation Analysis]
9 Lan W, Dong Y, Chen Q, Zheng R, Liu J, Pan Y, Chen YP. KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network. Brief Bioinform 2021:bbab494. [PMID: 34864877 DOI: 10.1093/bib/bbab494] [Reference Citation Analysis]