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For: Lei X, Fang Z. GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion. Int J Biol Sci 2019;15:2911-24. [PMID: 31853227 DOI: 10.7150/ijbs.33806] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
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2 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: 4] [Article Influence: 2.0] [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] [Reference Citation Analysis]
4 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: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Phyo P, Byun Y, Park N. Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression. Symmetry 2022;14:160. [DOI: 10.3390/sym14010160] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
6 Zheng Q, Zhang J, Zhang T, Liu Y, Du X, Dai X, Gu D. Hsa_circ_0000520 overexpression increases CDK2 expression via miR-1296 to facilitate cervical cancer cell proliferation. J Transl Med 2021;19. [DOI: 10.1186/s12967-021-02953-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Xiao Q, Yu H, Zhong J, Liang C, Li G, Ding P, Luo J. An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations. Genomics 2020;112:3407-15. [DOI: 10.1016/j.ygeno.2020.06.017] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
8 Pan Y, Lei X, Zhang Y. Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach. Med Res Rev 2021. [PMID: 34346083 DOI: 10.1002/med.21847] [Reference Citation Analysis]
9 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: 2] [Reference Citation Analysis]
10 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]
11 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]
12 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] [Reference Citation Analysis]
13 Zuo ZL, Cao RF, Wei PJ, Xia JF, Zheng CH. Double matrix completion for circRNA-disease association prediction. BMC Bioinformatics 2021;22:307. [PMID: 34103016 DOI: 10.1186/s12859-021-04231-3] [Reference Citation Analysis]
14 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] [Reference Citation Analysis]
15 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]
16 Lu C, Zeng M, Wu FX, Li M, Wang J. Improving circRNA-disease association prediction by sequence and ontology representations with convolutional and recurrent neural networks. Bioinformatics 2020:btaa1077. [PMID: 33367690 DOI: 10.1093/bioinformatics/btaa1077] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 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] [Reference Citation Analysis]