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 *

177 related articles for article (PubMed ID: 35016602)

  • 1. Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks.
    Song F; Tan S; Dou Z; Liu X; Ma X
    BMC Bioinformatics; 2022 Jan; 23(Suppl 1):34. PubMed ID: 35016602
    [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-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]  

  • 4. Topological feature generation for link prediction in biological networks.
    Temiz M; Bakir-Gungor B; Güner Şahan P; Coskun M
    PeerJ; 2023; 11():e15313. PubMed ID: 37187525
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding.
    Nasiri E; Berahmand K; Rostami M; Dabiri M
    Comput Biol Med; 2021 Oct; 137():104772. PubMed ID: 34450380
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A model-agnostic framework to enhance knowledge graph-based drug combination prediction with drug-drug interaction data and supervised contrastive learning.
    Gu J; Bang D; Yi J; Lee S; Kim DK; Kim S
    Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37544660
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep semi-supervised learning via dynamic anchor graph embedding in latent space.
    Tu E; Wang Z; Yang J; Kasabov N
    Neural Netw; 2022 Feb; 146():350-360. PubMed ID: 34929418
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.
    Djeddi WE; Hermi K; Ben Yahia S; Diallo G
    BMC Bioinformatics; 2023 Dec; 24(1):488. PubMed ID: 38114937
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DACPGTN: Drug ATC Code Prediction Method Based on Graph Transformer Network for Drug Discovery.
    Yan C; Suo Z; Wang J; Zhang G; Luo H
    Front Pharmacol; 2022; 13():907676. PubMed ID: 35721178
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MathEagle: Accurate prediction of drug-drug interaction events via multi-head attention and heterogeneous attribute graph learning.
    Hou LX; Yi HC; You ZH; Chen SH; Zheng J; Kwoh CK
    Comput Biol Med; 2024 Jul; 177():108642. PubMed ID: 38820777
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network.
    Su Y; Hu Z; Wang F; Bin Y; Zheng C; Li H; Chen H; Zeng X
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38145949
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction.
    Zhao C; Qiu Y; Zhou S; Liu S; Zhang W; Niu Y
    BMC Genomics; 2020 Dec; 21(Suppl 13):867. PubMed ID: 33334307
    [TBL] [Abstract][Full Text] [Related]  

  • 16. HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.
    Zhao BW; Hu L; You ZH; Wang L; Su XR
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34891172
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Network-principled deep generative models for designing drug combinations as graph sets.
    Karimi M; Hasanzadeh A; Shen Y
    Bioinformatics; 2020 Jul; 36(Suppl_1):i445-i454. PubMed ID: 32657357
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.
    Thafar MA; Olayan RS; Ashoor H; Albaradei S; Bajic VB; Gao X; Gojobori T; Essack M
    J Cheminform; 2020 Jun; 12(1):44. PubMed ID: 33431036
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Drug response prediction using graph representation learning and Laplacian feature selection.
    Xie M; Lei X; Zhong J; Ouyang J; Li G
    BMC Bioinformatics; 2022 Dec; 23(Suppl 8):532. PubMed ID: 36494630
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.