BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

132 related articles for article (PubMed ID: 35636720)

  • 1. idse-HE: Hybrid embedding graph neural network for drug side effects prediction.
    Yu L; Cheng M; Qiu W; Xiao X; Lin W
    J Biomed Inform; 2022 Jul; 131():104098. PubMed ID: 35636720
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Multi-type feature fusion based on graph neural network for drug-drug interaction prediction.
    He C; Liu Y; Li H; Zhang H; Mao Y; Qin X; Liu L; Zhang X
    BMC Bioinformatics; 2022 Jun; 23(1):224. PubMed ID: 35689200
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. A biomedical knowledge graph-based method for drug-drug interactions prediction through combining local and global features with deep neural networks.
    Ren ZH; You ZH; Yu CQ; Li LP; Guan YJ; Guo LX; Pan J
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070624
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.
    He H; Chen G; Yu-Chian Chen C
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35511112
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Drug-drug interaction prediction with learnable size-adaptive molecular substructures.
    Nyamabo AK; Yu H; Liu Z; Shi JY
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34695842
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. SGFNNs: Signed Graph Filtering-based Neural Networks for Predicting Drug-Drug Interactions.
    Chen M; Jiang W; Pan Y; Dai J; Lei Y; Ji C
    J Comput Biol; 2022 Oct; 29(10):1104-1116. PubMed ID: 35723646
    [No Abstract]   [Full Text] [Related]  

  • 17. 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]  

  • 18. MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.
    Geng G; Wang L; Xu Y; Wang T; Ma W; Duan H; Zhang J; Mao A
    Methods; 2024 Aug; 228():22-29. PubMed ID: 38754712
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Macromolecular crowding: chemistry and physics meet biology (Ascona, Switzerland, 10-14 June 2012).
    Foffi G; Pastore A; Piazza F; Temussi PA
    Phys Biol; 2013 Aug; 10(4):040301. PubMed ID: 23912807
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding.
    Ma J; Li D; Chen Y; Qiao Y; Zhu H; Zhang X
    Comput Intell Neurosci; 2021; 2021():2878189. PubMed ID: 34603428
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.