831 related articles for article (PubMed ID: 34661237)
1. 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]
2. 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]
3. 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]
4. 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]
5. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.
E Z; Qiao G; Wang G; Li Y
Methods; 2024 Mar; 223():136-145. PubMed ID: 38360082
[TBL] [Abstract][Full Text] [Related]
6. EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.
Jin Y; Lu J; Shi R; Yang Y
Biomolecules; 2021 Nov; 11(12):. PubMed ID: 34944427
[TBL] [Abstract][Full Text] [Related]
7. Supervised graph co-contrastive learning for drug-target interaction prediction.
Li Y; Qiao G; Gao X; Wang G
Bioinformatics; 2022 May; 38(10):2847-2854. PubMed ID: 35561181
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug-Target Interaction Prediction.
Liu L; Zhang Q; Wei Y; Zhao Q; Liao B
Molecules; 2023 Sep; 28(18):. PubMed ID: 37764321
[TBL] [Abstract][Full Text] [Related]
12. Graph-DTI: A New Model for Drug-Target Interaction Prediction Based on Heterogenous Network Graph Embedding.
Qu X; Du G; Hu J; Cai Y
Curr Comput Aided Drug Des; 2023 Jul; ():. PubMed ID: 37448360
[TBL] [Abstract][Full Text] [Related]
13. ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.
Hu K; Cui H; Zhang T; Sun C; Xuan P
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35108362
[TBL] [Abstract][Full Text] [Related]
14. An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.
Peng J; Wang Y; Guan J; Li J; Han R; Hao J; Wei Z; Shang X
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33517357
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction.
Xuan P; Fan M; Cui H; Zhang T; Nakaguchi T
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34718408
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.
Peng J; Li J; Shang X
BMC Bioinformatics; 2020 Sep; 21(Suppl 13):394. PubMed ID: 32938374
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
