530 related articles for article (PubMed ID: 35883487)
1. 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]
2. 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]
3. GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.
Ji C; Liu Z; Wang Y; Ni J; Zheng C
Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445212
[TBL] [Abstract][Full Text] [Related]
4. Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network.
Peng L; Yang C; Chen Y; Liu W
IEEE J Biomed Health Inform; 2023 Jun; 27(6):3072-3082. PubMed ID: 37030839
[TBL] [Abstract][Full Text] [Related]
5. IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling.
Lan W; Dong Y; Chen Q; Liu J; Wang J; Chen YP; Pan S
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(6):3530-3538. PubMed ID: 34506289
[TBL] [Abstract][Full Text] [Related]
6. Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.
Lu C; Zhang L; Zeng M; Lan W; Duan G; Wang J
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36572658
[TBL] [Abstract][Full Text] [Related]
7. GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs.
Dai Q; Liu Z; Wang Z; Duan X; Guo M
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070619
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.
Ma Z; Kuang Z; Deng L
BMC Bioinformatics; 2021 Nov; 22(1):551. PubMed ID: 34772332
[TBL] [Abstract][Full Text] [Related]
10. GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network.
Wang Y; Lu P
Comput Biol Chem; 2024 Jun; 110():108079. PubMed ID: 38704917
[TBL] [Abstract][Full Text] [Related]
11. Predicting CircRNA disease associations using novel node classification and link prediction models on Graph Convolutional Networks.
Bamunu Mudiyanselage T; Lei X; Senanayake N; Zhang Y; Pan Y
Methods; 2022 Feb; 198():32-44. PubMed ID: 34748953
[TBL] [Abstract][Full Text] [Related]
12. GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
Bian C; Lei XJ; Wu FX
Cancers (Basel); 2021 May; 13(11):. PubMed ID: 34070678
[TBL] [Abstract][Full Text] [Related]
13. Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.
Zhang Y; Wang Z; Wei H; Chen M
BMC Med Inform Decis Mak; 2024 Jun; 24(1):159. PubMed ID: 38844961
[TBL] [Abstract][Full Text] [Related]
14. MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning.
Li G; Zeng F; Luo J; Liang C; Xiao Q
BMC Med Inform Decis Mak; 2023 Dec; 23(1):291. PubMed ID: 38110886
[TBL] [Abstract][Full Text] [Related]
15. MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.
Li Y; Hu XG; Wang L; Li PP; You ZH
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36384071
[TBL] [Abstract][Full Text] [Related]
16. RGCNCDA: Relational graph convolutional network improves circRNA-disease association prediction by incorporating microRNAs.
Chen Y; Wang Y; Ding Y; Su X; Wang C
Comput Biol Med; 2022 Apr; 143():105322. PubMed ID: 35217342
[TBL] [Abstract][Full Text] [Related]
17. CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder.
Wang H; Han J; Li H; Duan L; Liu Z; Cheng H
Math Biosci Eng; 2023 Feb; 20(5):7957-7980. PubMed ID: 37161181
[TBL] [Abstract][Full Text] [Related]
18. Association prediction of CircRNAs and diseases using multi-homogeneous graphs and variational graph auto-encoder.
Fu Y; Yang R; Zhang L
Comput Biol Med; 2022 Dec; 151(Pt A):106289. PubMed ID: 36401973
[TBL] [Abstract][Full Text] [Related]
19. MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes.
Hua M; Yu S; Liu T; Yang X; Wang H
Interdiscip Sci; 2022 Sep; 14(3):669-682. PubMed ID: 35428964
[TBL] [Abstract][Full Text] [Related]
20. DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph convolutional network.
Lan W; Zhang H; Dong Y; Chen Q; Cao J; Peng W; Liu J; Li M
Methods; 2022 Dec; 208():35-41. PubMed ID: 36280134
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]