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For: 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: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Lan W, Dong Y, Zhang H, Li C, Chen Q, Liu J, Wang J, Chen YP. Benchmarking of computational methods for predicting circRNA-disease associations. Brief Bioinform 2023;24:bbac613. [PMID: 36611256 DOI: 10.1093/bib/bbac613] [Reference Citation Analysis]
2 Zhang Y, Wang Y, Li X, Liu Y, Chen M. Identifying lncRNA–disease association based on GAT multiple-operator aggregation and inductive matrix completion. Front Genet 2022;13:1029300. [DOI: 10.3389/fgene.2022.1029300] [Reference Citation Analysis]
3 Dai Q, Liu Z, Wang Z, Duan X, Guo M. GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs. Brief Bioinform 2022:bbac379. [PMID: 36070619 DOI: 10.1093/bib/bbac379] [Reference Citation Analysis]
4 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]
5 Li G, Fang T, Zhang Y, Liang C, Xiao Q, Luo J. Predicting miRNA-disease associations based on graph attention network with multi-source information. BMC Bioinformatics 2022;23:244. [PMID: 35729531 DOI: 10.1186/s12859-022-04796-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Chen Y, Lei X. Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction. Front Microbiol 2022;13:919380. [PMID: 35711758 DOI: 10.3389/fmicb.2022.919380] [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 Ji C, Liu Z, Qiao L, Wang Y, Zheng C. A Unified Graph Attention Network Based Framework for Inferring circRNA-Disease Associations. Intelligent Computing Methodologies 2022. [DOI: 10.1007/978-3-031-13832-4_52] [Reference Citation Analysis]
9 Ji C, Liu Z, Wang Y, Ni J, Zheng C. GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations. Int J Mol Sci 2021;22:8505. [PMID: 34445212 DOI: 10.3390/ijms22168505] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
10 Buratin A, Gaffo E, Dal Molin A, Bortoluzzi S. CircIMPACT: An R Package to Explore Circular RNA Impact on Gene Expression and Pathways. Genes (Basel) 2021;12:1044. [PMID: 34356060 DOI: 10.3390/genes12071044] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]