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.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

488 related articles for article (PubMed ID: 37448360)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 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. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. HGDTI: predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network.
    Yu L; Qiu W; Lin W; Cheng X; Xiao X; Dai J
    BMC Bioinformatics; 2022 Apr; 23(1):126. PubMed ID: 35413800
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Neural networks for link prediction in realistic biomedical graphs: a multi-dimensional evaluation of graph embedding-based approaches.
    Crichton G; Guo Y; Pyysalo S; Korhonen A
    BMC Bioinformatics; 2018 May; 19(1):176. PubMed ID: 29783926
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. MGNDTI: A Drug-Target Interaction Prediction Framework Based on Multimodal Representation Learning and the Gating Mechanism.
    Peng L; Liu X; Chen M; Liao W; Mao J; Zhou L
    J Chem Inf Model; 2024 Aug; 64(16):6684-6698. PubMed ID: 39137398
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction.
    Zhang P; Wei Z; Che C; Jin B
    Comput Biol Med; 2022 Mar; 142():105214. PubMed ID: 35030496
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 25.