117 related articles for article (PubMed ID: 38846568)
1. LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble.
Yang Z; Wang L; Zhang X; Zeng B; Zhang Z; Liu X
Front Microbiol; 2024; 15():1366272. PubMed ID: 38846568
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. GSAMDA: a computational model for predicting potential microbe-drug associations based on graph attention network and sparse autoencoder.
Tan Y; Zou J; Kuang L; Wang X; Zeng B; Zhang Z; Wang L
BMC Bioinformatics; 2022 Nov; 23(1):492. PubMed ID: 36401174
[TBL] [Abstract][Full Text] [Related]
4. GACNNMDA: a computational model for predicting potential human microbe-drug associations based on graph attention network and CNN-based classifier.
Ma Q; Tan Y; Wang L
BMC Bioinformatics; 2023 Feb; 24(1):35. PubMed ID: 36732704
[TBL] [Abstract][Full Text] [Related]
5. Prediction of microbe-drug associations based on a modified graph attention variational autoencoder and random forest.
Wang B; Ma F; Du X; Zhang G; Li J
Front Microbiol; 2024; 15():1394302. PubMed ID: 38881658
[TBL] [Abstract][Full Text] [Related]
6. A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.
Kuang H; Zhang Z; Zeng B; Liu X; Zuo H; Xu X; Wang L
BMC Bioinformatics; 2024 Feb; 25(1):78. PubMed ID: 38378437
[TBL] [Abstract][Full Text] [Related]
7. Ensembling graph attention networks for human microbe-drug association prediction.
Long Y; Wu M; Liu Y; Kwoh CK; Luo J; Li X
Bioinformatics; 2020 Dec; 36(Suppl_2):i779-i786. PubMed ID: 33381844
[TBL] [Abstract][Full Text] [Related]
8. Predicting human microbe-drug associations via graph convolutional network with conditional random field.
Long Y; Wu M; Kwoh CK; Luo J; Li X
Bioinformatics; 2020 Dec; 36(19):4918-4927. PubMed ID: 32597948
[TBL] [Abstract][Full Text] [Related]
9. HKFGCN: A novel multiple kernel fusion framework on graph convolutional network to predict microbe-drug associations.
Wu Z; Li S; Luo L; Ding P
Comput Biol Chem; 2024 Jun; 110():108041. PubMed ID: 38471354
[TBL] [Abstract][Full Text] [Related]
10. GNAEMDA: Microbe-Drug Associations Prediction on Graph Normalized Convolutional Network.
Huang H; Sun Y; Lan M; Zhang H; Xie G
IEEE J Biomed Health Inform; 2023 Jan; PP():. PubMed ID: 37022036
[TBL] [Abstract][Full Text] [Related]
11. Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations.
Deng L; Huang Y; Liu X; Liu H
Bioinformatics; 2022 Jan; 38(4):1118-1125. PubMed ID: 34864873
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Association Mining to Identify Microbe Drug Interactions Based on Heterogeneous Network Embedding Representation.
Long Y; Luo J
IEEE J Biomed Health Inform; 2021 Jan; 25(1):266-275. PubMed ID: 32750918
[TBL] [Abstract][Full Text] [Related]
14. NMGMDA: a computational model for predicting potential microbe-drug associations based on minimize matrix nuclear norm and graph attention network.
Liang M; Liu X; Chen Q; Zeng B; Wang L
Sci Rep; 2024 Jan; 14(1):650. PubMed ID: 38182635
[TBL] [Abstract][Full Text] [Related]
15. Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.
Chen Y; Lei X
Front Microbiol; 2022; 13():919380. PubMed ID: 35711758
[TBL] [Abstract][Full Text] [Related]
16. Neighborhood-based inference and restricted Boltzmann machine for microbe and drug associations prediction.
Cheng X; Qu J; Song S; Bian Z
PeerJ; 2022; 10():e13848. PubMed ID: 35990901
[TBL] [Abstract][Full Text] [Related]
17. Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy.
Shi K; Li L; Wang Z; Chen H; Chen Z; Fang S
Front Neurosci; 2022; 16():1124315. PubMed ID: 36741060
[TBL] [Abstract][Full Text] [Related]
18. MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.
Liang Y; Zhang ZQ; Liu NN; Wu YN; Gu CL; Wang YL
BMC Bioinformatics; 2022 May; 23(1):189. PubMed ID: 35590258
[TBL] [Abstract][Full Text] [Related]
19. OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.
Zhao J; Kuang L; Hu A; Zhang Q; Yang D; Wang C
Front Genet; 2024; 15():1370013. PubMed ID: 38689654
[TBL] [Abstract][Full Text] [Related]
20. Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.
Xuan P; Gu J; Cui H; Wang S; Toshiya N; Liu C; Zhang T
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38269610
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]