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 *

351 related articles for article (PubMed ID: 34479477)

  • 1. 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]  

  • 2. 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]  

  • 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. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 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. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. A heterogeneous network embedding framework for predicting similarity-based drug-target interactions.
    An Q; Yu L
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34373895
    [TBL] [Abstract][Full Text] [Related]  

  • 14. MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction.
    Zhang B; Niu D; Zhang L; Zhang Q; Li Z
    BMC Bioinformatics; 2024 Aug; 25(1):275. PubMed ID: 39179993
    [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. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. Drug-target interaction prediction using semi-bipartite graph model and deep learning.
    Eslami Manoochehri H; Nourani M
    BMC Bioinformatics; 2020 Jul; 21(Suppl 4):248. PubMed ID: 32631230
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.