210 related articles for article (PubMed ID: 37415176)
1. VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder.
Zhang Y; Feng Y; Wu M; Deng Z; Wang S
BMC Bioinformatics; 2023 Jul; 24(1):278. PubMed ID: 37415176
[TBL] [Abstract][Full Text] [Related]
2. A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.
Shi Z; Zhang H; Jin C; Quan X; Yin Y
BMC Bioinformatics; 2021 Mar; 22(1):136. PubMed ID: 33745450
[TBL] [Abstract][Full Text] [Related]
3. MHADTI: predicting drug-target interactions via multiview heterogeneous information network embedding with hierarchical attention mechanisms.
Tian Z; Peng X; Fang H; Zhang W; Dai Q; Ye Y
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36242566
[TBL] [Abstract][Full Text] [Related]
4. Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving.
Shang Y; Ye X; Futamura Y; Yu L; Sakurai T
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35262678
[TBL] [Abstract][Full Text] [Related]
5. DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.
Shao K; Zhang Y; Wen Y; Zhang Z; He S; Bo X
Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35380622
[TBL] [Abstract][Full Text] [Related]
6. Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.
Sun C; Xuan P; Zhang T; Ye Y
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(1):455-464. PubMed ID: 32750854
[TBL] [Abstract][Full Text] [Related]
7. Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder.
Chen P; Zheng H
BMC Bioinformatics; 2023 Apr; 24(1):151. PubMed ID: 37069493
[TBL] [Abstract][Full Text] [Related]
8. Drug repositioning based on heterogeneous networks and variational graph autoencoders.
Lei S; Lei X; Liu L
Front Pharmacol; 2022; 13():1056605. PubMed ID: 36618933
[TBL] [Abstract][Full Text] [Related]
9. A novel method for drug-target interaction prediction based on graph transformers model.
Wang H; Guo F; Du M; Wang G; Cao C
BMC Bioinformatics; 2022 Nov; 23(1):459. PubMed ID: 36329406
[TBL] [Abstract][Full Text] [Related]
10. DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding.
Yue Y; He S
BMC Bioinformatics; 2021 Sep; 22(1):418. PubMed ID: 34479477
[TBL] [Abstract][Full Text] [Related]
11. IMCHGAN: Inductive Matrix Completion With Heterogeneous Graph Attention Networks for Drug-Target Interactions Prediction.
Li J; Wang J; Lv H; Zhang Z; Wang Z
IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(2):655-665. PubMed ID: 34115592
[TBL] [Abstract][Full Text] [Related]
12. Bridging-BPs: a novel approach to predict potential drug-target interactions based on a bridging heterogeneous graph and BPs2vec.
Li G; Zhang P; Sun W; Ren C; Wang L
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35037024
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Drug repurposing and prediction of multiple interaction types via graph embedding.
Amiri Souri E; Chenoweth A; Karagiannis SN; Tsoka S
BMC Bioinformatics; 2023 May; 24(1):202. PubMed ID: 37193964
[TBL] [Abstract][Full Text] [Related]
15. Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.
Djeddi WE; Hermi K; Ben Yahia S; Diallo G
BMC Bioinformatics; 2023 Dec; 24(1):488. PubMed ID: 38114937
[TBL] [Abstract][Full Text] [Related]
16. An effective multi-task learning framework for drug repurposing based on graph representation learning.
Ye S; Zhao W; Shen X; Jiang X; He T
Methods; 2023 Oct; 218():48-56. PubMed ID: 37516260
[TBL] [Abstract][Full Text] [Related]
17. Predicting drug-target interaction network using deep learning model.
You J; McLeod RD; Hu P
Comput Biol Chem; 2019 Jun; 80():90-101. PubMed ID: 30939415
[TBL] [Abstract][Full Text] [Related]
18. MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network.
Zhou D; Xu Z; Li W; Xie X; Peng S
Bioinformatics; 2021 Dec; 37(23):4485-4492. PubMed ID: 34180970
[TBL] [Abstract][Full Text] [Related]
19. Novel drug-target interactions via link prediction and network embedding.
Amiri Souri E; Laddach R; Karagiannis SN; Papageorgiou LG; Tsoka S
BMC Bioinformatics; 2022 Apr; 23(1):121. PubMed ID: 35379165
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]