140 related articles for article (PubMed ID: 38294927)
1. Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.
Wang Y; Song J; Dai Q; Duan X
IEEE J Biomed Health Inform; 2024 May; 28(5):3146-3157. PubMed ID: 38294927
[TBL] [Abstract][Full Text] [Related]
2. SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.
Fan Y; Zhang C; Hu X; Huang Z; Xue J; Deng L
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38754409
[TBL] [Abstract][Full Text] [Related]
3. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.
Zhao BW; Hu L; You ZH; Wang L; Su XR
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34891172
[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. Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations (GCLSDA): Predicting snoRNA-Disease Associations via Graph Convolutional Network and Contrastive Learning.
Zhang L; Chen M; Hu X; Deng L
Int J Mol Sci; 2023 Sep; 24(19):. PubMed ID: 37833876
[TBL] [Abstract][Full Text] [Related]
6. Predicting microbe-drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy.
Tian Z; Yu Y; Fang H; Xie W; Guo M
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715986
[TBL] [Abstract][Full Text] [Related]
7. Partner-Specific Drug Repositioning Approach Based on Graph Convolutional Network.
Sun X; Wang B; Zhang J; Li M
IEEE J Biomed Health Inform; 2022 Nov; 26(11):5757-5765. PubMed ID: 35921345
[TBL] [Abstract][Full Text] [Related]
8. Exploring ncRNA-Drug Sensitivity Associations Via Graph Contrastive Learning.
Hu X; Jiang Y; Deng L
IEEE/ACM Trans Comput Biol Bioinform; 2024 Apr; PP():. PubMed ID: 38578855
[TBL] [Abstract][Full Text] [Related]
9. NEDD: a network embedding based method for predicting drug-disease associations.
Zhou R; Lu Z; Luo H; Xiang J; Zeng M; Li M
BMC Bioinformatics; 2020 Sep; 21(Suppl 13):387. PubMed ID: 32938396
[TBL] [Abstract][Full Text] [Related]
10. Similarity measures-based graph co-contrastive learning for drug-disease association prediction.
Gao Z; Ma H; Zhang X; Wang Y; Wu Z
Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37261859
[TBL] [Abstract][Full Text] [Related]
11. MPHGCL-DDI: Meta-Path-Based Heterogeneous Graph Contrastive Learning for Drug-Drug Interaction Prediction.
Hu B; Yu Z; Li M
Molecules; 2024 May; 29(11):. PubMed ID: 38893359
[TBL] [Abstract][Full Text] [Related]
12. Link Prediction Only With Interaction Data and its Application on Drug Repositioning.
Liu J; Zuo Z; Wu G
IEEE Trans Nanobioscience; 2020 Jul; 19(3):547-555. PubMed ID: 32340956
[TBL] [Abstract][Full Text] [Related]
13. KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning.
An Y; Tang H; Jin B; Xu Y; Wei X
BMC Med Inform Decis Mak; 2023 Oct; 23(1):243. PubMed ID: 37904198
[TBL] [Abstract][Full Text] [Related]
14. Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.
Yao K; Wang X; Li W; Zhu H; Jiang Y; Li Y; Tian T; Yang Z; Liu Q; Liu Q
Comput Biol Med; 2023 Sep; 163():107199. PubMed ID: 37421738
[TBL] [Abstract][Full Text] [Related]
15. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.
Zhao BW; Su XR; Yang Y; Li DX; Li GD; Hu PW; Zhao YG; Hu L
Methods; 2023 Dec; 220():106-114. PubMed ID: 37972913
[TBL] [Abstract][Full Text] [Related]
16. MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.
Liu W; Tang T; Lu X; Fu X; Yang Y; Peng L
Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37328701
[TBL] [Abstract][Full Text] [Related]
17. SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks.
Jarada TN; Rokne JG; Alhajj R
BMC Bioinformatics; 2021 Jan; 22(1):28. PubMed ID: 33482713
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.
Ouyang D; Liang Y; Wang J; Li L; Ai N; Feng J; Lu S; Liao S; Liu X; Xie S
PLoS Comput Biol; 2024 Apr; 20(4):e1011927. PubMed ID: 38652712
[TBL] [Abstract][Full Text] [Related]
20. Prediction of drug-disease associations based on reinforcement symmetric metric learning and graph convolution network.
Luo H; Zhu C; Wang J; Zhang G; Luo J; Yan C
Front Pharmacol; 2024; 15():1337764. PubMed ID: 38384286
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
