205 related articles for article (PubMed ID: 38177981)
1. Predicting drug-protein interactions by preserving the graph information of multi source data.
Wei J; Lu L; Shen T
BMC Bioinformatics; 2024 Jan; 25(1):10. PubMed ID: 38177981
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Drug-target interaction predication via multi-channel graph neural networks.
Li Y; Qiao G; Wang K; Wang G
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34661237
[TBL] [Abstract][Full Text] [Related]
4. SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.
Wang W; Yu M; Sun B; Li J; Liu D; Zhang H; Wang X; Zhou Y
IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(1):143-154. PubMed ID: 38051618
[TBL] [Abstract][Full Text] [Related]
5. DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network.
Muniyappan S; Rayan AXA; Varrieth GT
Math Biosci Eng; 2023 Mar; 20(5):9530-9571. PubMed ID: 37161255
[TBL] [Abstract][Full Text] [Related]
6. KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery.
Zhang C; Zang T; Zhao T
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38348746
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions.
Wang H; Huang F; Xiong Z; Zhang W
Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35641162
[TBL] [Abstract][Full Text] [Related]
10. GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.
Gao M; Zhang D; Chen Y; Zhang Y; Wang Z; Wang X; Li S; Guo Y; Webb GI; Nguyen ATN; May L; Song J
Comput Biol Med; 2024 May; 173():108339. PubMed ID: 38547658
[TBL] [Abstract][Full Text] [Related]
11. GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.
Wang W; Liang S; Yu M; Liu D; Zhang H; Wang X; Zhou Y
Methods; 2022 Oct; 206():101-107. PubMed ID: 36058415
[TBL] [Abstract][Full Text] [Related]
12. multi-type neighbors enhanced global topology and pairwise attribute learning for drug-protein interaction prediction.
Xuan P; Zhang X; Zhang Y; Hu K; Nakaguchi T; Zhang T
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35514190
[TBL] [Abstract][Full Text] [Related]
13. iGRLDTI: an improved graph representation learning method for predicting drug-target interactions over heterogeneous biological information network.
Zhao BW; Su XR; Hu PW; Huang YA; You ZH; Hu L
Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37505483
[TBL] [Abstract][Full Text] [Related]
14. Identifying drug-target interactions based on graph convolutional network and deep neural network.
Zhao T; Hu Y; Valsdottir LR; Zang T; Peng J
Brief Bioinform; 2021 Mar; 22(2):2141-2150. PubMed ID: 32367110
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.
Pan J; Lin H; Dong Y; Wang Y; Ji Y
Comput Biol Med; 2022 Sep; 148():105823. PubMed ID: 35872410
[TBL] [Abstract][Full Text] [Related]
17. A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning.
Zhao BW; You ZH; Hu L; Guo ZH; Wang L; Chen ZH; Wong L
Cancers (Basel); 2021 Apr; 13(9):. PubMed ID: 33925568
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. EDC-DTI: An end-to-end deep collaborative learning model based on multiple information for drug-target interactions prediction.
Yuan Y; Zhang Y; Meng X; Liu Z; Wang B; Miao R; Zhang R; Su W; Liu L
J Mol Graph Model; 2023 Jul; 122():108498. PubMed ID: 37126908
[TBL] [Abstract][Full Text] [Related]
20. GADTI: Graph Autoencoder Approach for DTI Prediction From Heterogeneous Network.
Liu Z; Chen Q; Lan W; Pan H; Hao X; Pan S
Front Genet; 2021; 12():650821. PubMed ID: 33912218
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]