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 *

312 related articles for article (PubMed ID: 36892155)

  • 1. MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction.
    Zhang R; Wang Z; Wang X; Meng Z; Cui W
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36892155
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Meta-HGT: Metapath-aware HyperGraph Transformer for heterogeneous information network embedding.
    Liu J; Song L; Wang G; Shang X
    Neural Netw; 2023 Jan; 157():65-76. PubMed ID: 36334540
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction.
    Li M; Cai X; Xu S; Ji H
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36592060
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Metapath Aggregated Graph Neural Network and Tripartite Heterogeneous Networks for Microbe-Disease Prediction.
    Chen Y; Lei X
    Front Microbiol; 2022; 13():919380. PubMed ID: 35711758
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction.
    Xuan P; Li P; Cui H; Wang M; Nakaguchi T; Zhang T
    Molecules; 2023 Sep; 28(18):. PubMed ID: 37764319
    [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. CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.
    Zeng X; Chen W; Lei B
    BMC Bioinformatics; 2024 Apr; 25(1):141. PubMed ID: 38566002
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 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. Effective drug-target interaction prediction with mutual interaction neural network.
    Li F; Zhang Z; Guan J; Zhou S
    Bioinformatics; 2022 Jul; 38(14):3582-3589. PubMed ID: 35652721
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs.
    Xuan P; Wang S; Cui H; Zhao Y; Zhang T; Wu P
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36088549
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Predicting Drug-Target Interactions Over Heterogeneous Information Network.
    Su X; Hu P; Yi H; You Z; Hu L
    IEEE J Biomed Health Inform; 2023 Jan; 27(1):562-572. PubMed ID: 36327172
    [TBL] [Abstract][Full Text] [Related]  

  • 18. MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks.
    Fu X; King I
    Neural Netw; 2024 Feb; 170():266-275. PubMed ID: 38000310
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 16.