195 related articles for article (PubMed ID: 33217543)
1. Potential circRNA-disease association prediction using DeepWalk and network consistency projection.
Li G; Luo J; Wang D; Liang C; Xiao Q; Ding P; Chen H
J Biomed Inform; 2020 Dec; 112():103624. PubMed ID: 33217543
[TBL] [Abstract][Full Text] [Related]
2. NCPCDA: network consistency projection for circRNA-disease association prediction.
Li G; Yue Y; Liang C; Xiao Q; Ding P; Luo J
RSC Adv; 2019 Oct; 9(57):33222-33228. PubMed ID: 35529153
[TBL] [Abstract][Full Text] [Related]
3. Potential circRNA-Disease Association Prediction Using DeepWalk and Nonnegative Matrix Factorization.
Qiao LJ; Gao Z; Ji CM; Liu ZH; Zheng CH; Wang YT
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(5):3154-3162. PubMed ID: 37018084
[TBL] [Abstract][Full Text] [Related]
4. GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.
Wang L; You ZH; Li YM; Zheng K; Huang YA
PLoS Comput Biol; 2020 May; 16(5):e1007568. PubMed ID: 32433655
[TBL] [Abstract][Full Text] [Related]
5. DeepWalk-aware graph attention networks with CNN for circRNA-drug sensitivity association identification.
Li G; Li Y; Liang C; Luo J
Brief Funct Genomics; 2023 Dec; ():. PubMed ID: 38061910
[TBL] [Abstract][Full Text] [Related]
6. Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations.
Lu C; Zeng M; Zhang F; Wu FX; Li M; Wang J
IEEE J Biomed Health Inform; 2021 Mar; 25(3):891-899. PubMed ID: 32750925
[TBL] [Abstract][Full Text] [Related]
7. iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.
Xiao Q; Zhong J; Tang X; Luo J
Mol Genet Genomics; 2021 Jan; 296(1):223-233. PubMed ID: 33159254
[TBL] [Abstract][Full Text] [Related]
8. Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.
Wang Y; Liu X; Shen Y; Song X; Wang T; Shang X; Peng J
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36847701
[TBL] [Abstract][Full Text] [Related]
9. An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network.
Deepthi K; Jereesh AS
Gene; 2020 Dec; 762():145040. PubMed ID: 32777520
[TBL] [Abstract][Full Text] [Related]
10. Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.
Cao R; He C; Wei P; Su Y; Xia J; Zheng C
Biomolecules; 2022 Jul; 12(7):. PubMed ID: 35883487
[TBL] [Abstract][Full Text] [Related]
11. Prediction of CircRNA-Disease Associations Using KATZ Model Based on Heterogeneous Networks.
Fan C; Lei X; Wu FX
Int J Biol Sci; 2018; 14(14):1950-1959. PubMed ID: 30585259
[TBL] [Abstract][Full Text] [Related]
12. GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning.
Li G; Lin Y; Luo J; Xiao Q; Liang C
Comput Biol Chem; 2022 Aug; 99():107722. PubMed ID: 35810557
[TBL] [Abstract][Full Text] [Related]
13. Integrating Bipartite Network Projection and KATZ Measure to Identify Novel CircRNA-Disease Associations.
Zhao Q; Yang Y; Ren G; Ge E; Fan C
IEEE Trans Nanobioscience; 2019 Oct; 18(4):578-584. PubMed ID: 31199265
[TBL] [Abstract][Full Text] [Related]
14. Predicting human disease-associated circRNAs based on locality-constrained linear coding.
Ge E; Yang Y; Gang M; Fan C; Zhao Q
Genomics; 2020 Mar; 112(2):1335-1342. PubMed ID: 31394170
[TBL] [Abstract][Full Text] [Related]
15. Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.
Zhang H; Liang Y; Peng C; Han S; Du W; Li Y
Math Biosci; 2019 Sep; 315():108229. PubMed ID: 31323239
[TBL] [Abstract][Full Text] [Related]
16. Prioritizing CircRNA-Disease Associations With Convolutional Neural Network Based on Multiple Similarity Feature Fusion.
Fan C; Lei X; Pan Y
Front Genet; 2020; 11():540751. PubMed ID: 33193615
[TBL] [Abstract][Full Text] [Related]
17. AMPCDA: Prediction of circRNA-disease associations by utilizing attention mechanisms on metapaths.
Lu P; Zhang W; Wu J
Comput Biol Chem; 2024 Feb; 108():107989. PubMed ID: 38016366
[TBL] [Abstract][Full Text] [Related]
18. circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.
Li H; Deng Z; Yang H; Pan X; Wei Z; Shen HB; Choi KS; Wang L; Wang S; Wu J
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34571539
[TBL] [Abstract][Full Text] [Related]
19. DWNN-RLS: regularized least squares method for predicting circRNA-disease associations.
Yan C; Wang J; Wu FX
BMC Bioinformatics; 2018 Dec; 19(Suppl 19):520. PubMed ID: 30598076
[TBL] [Abstract][Full Text] [Related]
20. PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations.
Lei X; Fang Z; Chen L; Wu FX
Int J Mol Sci; 2018 Oct; 19(11):. PubMed ID: 30384427
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
