198 related articles for article (PubMed ID: 34789236)
1. EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network.
Pang S; Zhuang Y; Wang X; Wang F; Qiao S
BMC Med Inform Decis Mak; 2021 Nov; 21(1):319. PubMed ID: 34789236
[TBL] [Abstract][Full Text] [Related]
2. Adaptive deep propagation graph neural network for predicting miRNA-disease associations.
Hu H; Zhao H; Zhong T; Dong X; Wang L; Han P; Li Z
Brief Funct Genomics; 2023 Nov; 22(5):453-462. PubMed ID: 37078739
[TBL] [Abstract][Full Text] [Related]
3. Predicting miRNA-disease association from heterogeneous information network with GraRep embedding model.
Ji BY; You ZH; Cheng L; Zhou JR; Alghazzawi D; Li LP
Sci Rep; 2020 Apr; 10(1):6658. PubMed ID: 32313121
[TBL] [Abstract][Full Text] [Related]
4. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.
Zhang L; Liu B; Li Z; Zhu X; Liang Z; An J
BMC Bioinformatics; 2020 Oct; 21(1):470. PubMed ID: 33087064
[TBL] [Abstract][Full Text] [Related]
5. Combined embedding model for MiRNA-disease association prediction.
Liu B; Zhu X; Zhang L; Liang Z; Li Z
BMC Bioinformatics; 2021 Mar; 22(1):161. PubMed ID: 33765909
[TBL] [Abstract][Full Text] [Related]
6. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.
Xuan P; Sun H; Wang X; Zhang T; Pan S
Int J Mol Sci; 2019 Jul; 20(15):. PubMed ID: 31349729
[TBL] [Abstract][Full Text] [Related]
7. PMDAGS: Predicting Mirna-disease Associations with Graph Nonlinear Diffusion Convolution Network and Similarities.
Yan C; Duan G
IEEE/ACM Trans Comput Biol Bioinform; 2024 Feb; PP():. PubMed ID: 38358864
[TBL] [Abstract][Full Text] [Related]
8. Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information.
Lou Z; Cheng Z; Li H; Teng Z; Liu Y; Tian Z
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35524503
[TBL] [Abstract][Full Text] [Related]
9. NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.
Ji BY; You ZH; Chen ZH; Wong L; Yi HC
BMC Bioinformatics; 2020 Sep; 21(1):401. PubMed ID: 32912137
[TBL] [Abstract][Full Text] [Related]
10. Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks.
Zhao H; Li Z; You ZH; Nie R; Zhong T
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1298-1307. PubMed ID: 36067101
[TBL] [Abstract][Full Text] [Related]
11. Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.
Huang D; An J; Zhang L; Liu B
BMC Bioinformatics; 2022 Jul; 23(1):299. PubMed ID: 35879658
[TBL] [Abstract][Full Text] [Related]
12. Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations.
Xuan P; Pan S; Zhang T; Liu Y; Sun H
Cells; 2019 Aug; 8(9):. PubMed ID: 31480350
[TBL] [Abstract][Full Text] [Related]
13. SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.
Ma M; Na S; Zhang X; Chen C; Xu J
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36037084
[TBL] [Abstract][Full Text] [Related]
14. Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction.
Zhao C; Qiu Y; Zhou S; Liu S; Zhang W; Niu Y
BMC Genomics; 2020 Dec; 21(Suppl 13):867. PubMed ID: 33334307
[TBL] [Abstract][Full Text] [Related]
15. Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion.
Jin Z; Wang M; Tang C; Zheng X; Zhang W; Sha X; An S
Comput Biol Med; 2024 Feb; 169():107904. PubMed ID: 38181611
[TBL] [Abstract][Full Text] [Related]
16. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
Chen X; Yin J; Qu J; Huang L
PLoS Comput Biol; 2018 Aug; 14(8):e1006418. PubMed ID: 30142158
[TBL] [Abstract][Full Text] [Related]
17. FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.
Li J; Li Z; Nie R; You Z; Bao W
Mol Genet Genomics; 2020 Sep; 295(5):1197-1209. PubMed ID: 32500265
[TBL] [Abstract][Full Text] [Related]
18. A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network.
Li HY; Chen HY; Wang L; Song SJ; You ZH; Yan X; Yu JQ
Sci Rep; 2021 Jun; 11(1):12640. PubMed ID: 34135401
[TBL] [Abstract][Full Text] [Related]
19. Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases.
Sheng N; Wang Y; Huang L; Gao L; Cao Y; Xie X; Fu Y
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37529914
[TBL] [Abstract][Full Text] [Related]
20. ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.
Zhang Y; Chu Y; Lin S; Xiong Y; Wei DQ
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38517693
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
