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For: Zhao X, Zhao X, Yin M. Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction. Brief Bioinform 2021:bbab407. [PMID: 34585231 DOI: 10.1093/bib/bbab407] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Yan J, Wang R, Tan J. Recent advances in predicting lncRNA-disease associations based on computational methods. Drug Discov Today 2023;28:103432. [PMID: 36370992 DOI: 10.1016/j.drudis.2022.103432] [Reference Citation Analysis]
2 Sheng N, Huang L, Lu Y, Wang H, Yang L, Gao L, Xie X, Fu Y, Wang Y. Data resources and computational methods for lncRNA-disease association prediction. Comput Biol Med 2023;153:106527. [PMID: 36610216 DOI: 10.1016/j.compbiomed.2022.106527] [Reference Citation Analysis]
3 Zhao X, Wu J, Zhao X, Yin M. Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction. Brief Bioinform 2023;24:bbac548. [PMID: 36528809 DOI: 10.1093/bib/bbac548] [Reference Citation Analysis]
4 Liang Q, Zhang W, Wu H, Liu B. LncRNA-disease association identification using graph auto-encoder and learning to rank. Brief Bioinform 2023;24:bbac539. [PMID: 36545805 DOI: 10.1093/bib/bbac539] [Reference Citation Analysis]
5 Jin Y, Ji W, Shi Y, Wang X, Yang X. Meta-path guided graph attention network for explainable herb recommendation. Health Inf Sci Syst 2023;11:5. [PMID: 36660407 DOI: 10.1007/s13755-022-00207-6] [Reference Citation Analysis]
6 Tan J, Li X, Zhang L, Du Z. Recent advances in machine learning methods for predicting LncRNA and disease associations. Front Cell Infect Microbiol 2022;12:1071972. [PMID: 36530425 DOI: 10.3389/fcimb.2022.1071972] [Reference Citation Analysis]
7 Peng L, Yang J, Wang M, Zhou L. Editorial: Machine learning-based methods for RNA data analysis—Volume II. Front Genet 2022;13. [DOI: 10.3389/fgene.2022.1010089] [Reference Citation Analysis]
8 Zhou Y, Wang X, Yao L, Zhu M. LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder. Brief Bioinform 2022;23:bbac370. [PMID: 36094081 DOI: 10.1093/bib/bbac370] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Shi H, Zhang X, Tang L, Liu L. Heterogeneous graph neural network for lncRNA-disease association prediction. Sci Rep 2022;12:17519. [PMID: 36266433 DOI: 10.1038/s41598-022-22447-y] [Reference Citation Analysis]
10 Guo Z, Hui Y, Kong F, Lin X. Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk. Front Genet 2022;13:933009. [DOI: 10.3389/fgene.2022.933009] [Reference Citation Analysis]
11 Zheng J, Qian Y, He J, Kang Z, Deng L. Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction. J Chem Inf Model 2022. [PMID: 35838124 DOI: 10.1021/acs.jcim.2c00367] [Reference Citation Analysis]
12 Li H, Sun Y, Hong H, Huang X, Tao H, Huang Q, Wang L, Xu K, Gan J, Chen H, Bo X. Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks. Nat Mach Intell. [DOI: 10.1038/s42256-022-00469-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]