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For: Deng L, Zhang W, Shi Y, Tang Y. Fusion of multiple heterogeneous networks for predicting circRNA-disease associations. Sci Rep 2019;9:9605. [PMID: 31270357 DOI: 10.1038/s41598-019-45954-x] [Cited by in Crossref: 11] [Cited by in F6Publishing: 20] [Article Influence: 3.7] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Kouhsar M, Kashaninia E, Mardani B, Rabiee HR. CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning. BMC Bioinformatics 2022;23:331. [PMID: 35953785 DOI: 10.1186/s12859-022-04883-9] [Reference Citation Analysis]
3 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: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
4 Molibeli KM, Hu R, Liu Y, Xiong D, Tang L. Potential Clinical Applications of Exosomal Circular RNAs: More than Diagnosis. Front Mol Biosci 2021;8:769832. [PMID: 34901159 DOI: 10.3389/fmolb.2021.769832] [Reference Citation Analysis]
5 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] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
6 He C, Duan L, Zheng H, Li-Ling J, Song L, Li L. Graph convolutional network approach to discovering disease-related circRNA-miRNA-mRNA axes. Methods 2021:S1046-2023(21)00246-2. [PMID: 34758394 DOI: 10.1016/j.ymeth.2021.10.006] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Xiao Q, Dai J, Luo J. A survey of circular RNAs in complex diseases: databases, tools and computational methods. Brief Bioinform 2021:bbab444. [PMID: 34676391 DOI: 10.1093/bib/bbab444] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
8 Wang CC, Han CD, Zhao Q, Chen X. Circular RNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2021:bbab286. [PMID: 34329377 DOI: 10.1093/bib/bbab286] [Cited by in F6Publishing: 32] [Reference Citation Analysis]
9 Diao W, Wang Y, Zhang J, Shao H, Huang Y, Jin M. Identification and comparison of novel circular RNAs with associated co-expression and competing endogenous RNA networks in postmenopausal osteoporosis. J Orthop Surg Res 2021;16:459. [PMID: 34271965 DOI: 10.1186/s13018-021-02604-1] [Reference Citation Analysis]
10 Wei H, Xu Y, Liu B. iCircDA-LTR: identification of circRNA-disease associations based on Learning to Rank. Bioinformatics 2021:btab334. [PMID: 33963827 DOI: 10.1093/bioinformatics/btab334] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
11 He X, Xu T, Hu W, Tan Y, Wang D, Wang Y, Zhao C, Yi Y, Xiong M, Lv W, Wu M, Li X, Wu Y, Zhang Q. Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer. Front Cell Dev Biol 2021;9:647736. [PMID: 33777954 DOI: 10.3389/fcell.2021.647736] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
12 Sun M, Yang J, Jiang D, Bao G. Overexpression of hsa_circ_0094742 inhibits IL-1β-induced decline in CHON-001 cell viability by targeting microRNA-127-5p. Histol Histopathol 2021;36:207-16. [PMID: 33665792 DOI: 10.14670/HH-18-325] [Reference Citation Analysis]
13 Zhang Y, Lei X, Pan Y, Pedrycz W. Prediction of disease-associated circRNAs via circRNA–disease pair graph and weighted nuclear norm minimization. Knowledge-Based Systems 2021;214:106694. [DOI: 10.1016/j.knosys.2020.106694] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 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: 10] [Article Influence: 0.5] [Reference Citation Analysis]
15 Li G, Luo J, Wang D, Liang C, Xiao Q, Ding P, Chen H. Potential circRNA-disease association prediction using DeepWalk and network consistency projection. J Biomed Inform 2020;112:103624. [PMID: 33217543 DOI: 10.1016/j.jbi.2020.103624] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
16 Ma Y, Niu X, Yan S, Liu Y, Dong R, Li Y. Circular RNA profiling facilitates the diagnosis and prognostic monitoring of breast cancer: A pair-wise meta-analysis. J Clin Lab Anal 2021;35:e23575. [PMID: 33159705 DOI: 10.1002/jcla.23575] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
17 Fan C, Lei X, Pan Y. Prioritizing CircRNA-Disease Associations With Convolutional Neural Network Based on Multiple Similarity Feature Fusion. Front Genet 2020;11:540751. [PMID: 33193615 DOI: 10.3389/fgene.2020.540751] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
18 Lei X, Bian C. Integrating random walk with restart and k-Nearest Neighbor to identify novel circRNA-disease association. Sci Rep 2020;10:1943. [PMID: 32029856 DOI: 10.1038/s41598-020-59040-0] [Cited by in Crossref: 12] [Cited by in F6Publishing: 20] [Article Influence: 6.0] [Reference Citation Analysis]
19 Deng L, Ye D, Zhao J, Zhang J. MultiSourcDSim: an integrated approach for exploring disease similarity. BMC Med Inform Decis Mak 2019;19:269. [PMID: 31856813 DOI: 10.1186/s12911-019-0968-8] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
20 Chen X, Shi W, Deng L. Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks. CGT 2019;19:232-41. [DOI: 10.2174/1566523219666190917155959] [Cited by in Crossref: 10] [Cited by in F6Publishing: 4] [Article Influence: 3.3] [Reference Citation Analysis]