137 related articles for article (PubMed ID: 38055359)
1. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.
Jiao CN; Zhou F; Liu BM; Zheng CH; Liu JX; Gao YL
IEEE J Biomed Health Inform; 2024 Feb; 28(2):1110-1121. PubMed ID: 38055359
[TBL] [Abstract][Full Text] [Related]
2. Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning.
Zhou F; Yin MM; Jiao CN; Zhao JX; Zheng CH; Liu JX
IEEE Trans Neural Netw Learn Syst; 2023 Sep; 34(9):5570-5579. PubMed ID: 34860656
[TBL] [Abstract][Full Text] [Related]
3. Variational graph auto-encoders for miRNA-disease association prediction.
Ding Y; Tian LP; Lei X; Liao B; Wu FX
Methods; 2021 Aug; 192():25-34. PubMed ID: 32798654
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.
Chu Y; Wang X; Dai Q; Wang Y; Wang Q; Peng S; Wei X; Qiu J; Salahub DR; Xiong Y; Wei DQ
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34009265
[TBL] [Abstract][Full Text] [Related]
6. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction.
Tang X; Luo J; Shen C; Lai Z
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 33963829
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Predicting miRNA-disease associations based on PPMI and attention network.
Xie X; Wang Y; He K; Sheng N
BMC Bioinformatics; 2023 Mar; 24(1):113. PubMed ID: 36959547
[TBL] [Abstract][Full Text] [Related]
9. Predicting miRNA-disease associations based on multi-view information fusion.
Xie X; Wang Y; Sheng N; Zhang S; Cao Y; Fu Y
Front Genet; 2022; 13():979815. PubMed ID: 36238163
[TBL] [Abstract][Full Text] [Related]
10. MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction.
Li J; Liu T; Wang J; Li Q; Ning C; Yang Y
Artif Intell Med; 2021 Aug; 118():102115. PubMed ID: 34412838
[TBL] [Abstract][Full Text] [Related]
11. Predicting miRNA-disease associations based on lncRNA-miRNA interactions and graph convolution networks.
Wang W; Chen H
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36526276
[TBL] [Abstract][Full Text] [Related]
12. GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.
Li L; Wang YT; Ji CM; Zheng CH; Ni JC; Su YS
PLoS Comput Biol; 2021 Dec; 17(12):e1009655. PubMed ID: 34890410
[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. MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation.
Ma Y; He T; Ge L; Zhang C; Jiang X
BMC Med Genomics; 2019 Dec; 12(Suppl 10):185. PubMed ID: 31865912
[TBL] [Abstract][Full Text] [Related]
15. Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.
Peng L; Huang L; Tian G; Wu Y; Li G; Cao J; Wang P; Li Z; Duan L
Front Microbiol; 2023; 14():1244527. PubMed ID: 37789848
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder.
Wang S; Lin B; Zhang Y; Qiao S; Wang F; Wu W; Ren C
Cells; 2022 Dec; 11(24):. PubMed ID: 36552748
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.
Tian Z; Han C; Xu L; Teng Z; Song W
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38622356
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]