These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
268 related articles for article (PubMed ID: 33729947)
1. Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network. Xu X; Xuan P; Zhang T; Chen B; Sheng N IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(4):2294-2304. PubMed ID: 33729947 [TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. 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]
5. Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities. Jiang L; Sun J; Wang Y; Ning Q; Luo N; Yin M Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35224614 [TBL] [Abstract][Full Text] [Related]
6. CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations. Xuan P; Sheng N; Zhang T; Liu Y; Guo Y Int J Mol Sci; 2019 Aug; 20(17):. PubMed ID: 31480319 [TBL] [Abstract][Full Text] [Related]
7. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks. Xuan P; Sun H; Wang X; Zhang T; Pan S Int J Mol Sci; 2019 Jul; 20(15):. PubMed ID: 31349729 [TBL] [Abstract][Full Text] [Related]
8. Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations. Xuan P; Gao L; Sheng N; Zhang T; Nakaguchi T IEEE J Biomed Health Inform; 2021 May; 25(5):1793-1804. PubMed ID: 33216722 [TBL] [Abstract][Full Text] [Related]
9. 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]
10. GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph. Zhu Y; Ning C; Zhang N; Wang M; Zhang Y BMC Biol; 2024 Jul; 22(1):156. PubMed ID: 39020316 [TBL] [Abstract][Full Text] [Related]
11. Integration of Neighbor Topologies Based on Meta-Paths and Node Attributes for Predicting Drug-Related Diseases. Xuan P; Lu Z; Zhang T; Liu Y; Nakaguchi T Int J Mol Sci; 2022 Mar; 23(7):. PubMed ID: 35409235 [TBL] [Abstract][Full Text] [Related]
12. Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures. Song Y; Cui H; Zhang T; Yang T; Li X; Xuan P IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(5):2963-2974. PubMed ID: 34133286 [TBL] [Abstract][Full Text] [Related]
13. Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit. Xuan P; Zhao L; Zhang T; Ye Y; Zhang Y Molecules; 2019 Jul; 24(15):. PubMed ID: 31349692 [TBL] [Abstract][Full Text] [Related]
14. 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]
15. Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction. Zhang H; Cui H; Zhang T; Cao Y; Xuan P Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136910 [TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction. Xuan P; Wang M; Liu Y; Wang D; Zhang T; Nakaguchi T Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35470853 [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. 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; 2024; 20(6):1013-1024. PubMed ID: 37448360 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]