BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

117 related articles for article (PubMed ID: 38472636)

  • 1. DGAMDA: Predicting miRNA-disease association based on dynamic graph attention network.
    Jia C; Wang F; Xing B; Li S; Zhao Y; Li Y; Wang Q
    Int J Numer Method Biomed Eng; 2024 May; 40(5):e3809. PubMed ID: 38472636
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting miRNA-disease associations based on graph attention network with multi-source information.
    Li G; Fang T; Zhang Y; Liang C; Xiao Q; Luo J
    BMC Bioinformatics; 2022 Jun; 23(1):244. PubMed ID: 35729531
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA-disease association prediction.
    Huang D; An J; Zhang L; Liu B
    BMC Bioinformatics; 2022 Jul; 23(1):299. PubMed ID: 35879658
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A Novel Computational Model for Predicting microRNA-Disease Associations Based on Heterogeneous Graph Convolutional Networks.
    Li C; Liu H; Hu Q; Que J; Yao J
    Cells; 2019 Aug; 8(9):. PubMed ID: 31455028
    [TBL] [Abstract][Full Text] [Related]  

  • 5. NMCMDA: neural multicategory MiRNA-disease association prediction.
    Wang J; Li J; Yue K; Wang L; Ma Y; Li Q
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33778850
    [TBL] [Abstract][Full Text] [Related]  

  • 6. FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks.
    Li J; Li Z; Nie R; You Z; Bao W
    Mol Genet Genomics; 2020 Sep; 295(5):1197-1209. PubMed ID: 32500265
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction.
    Wang S; Wang F; Qiao S; Zhuang Y; Zhang K; Pang S; Nowak R; Lv Z
    IEEE J Biomed Health Inform; 2023 Oct; 27(10):4639-4648. PubMed ID: 35759606
    [TBL] [Abstract][Full Text] [Related]  

  • 8. EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network.
    Pang S; Zhuang Y; Wang X; Wang F; Qiao S
    BMC Med Inform Decis Mak; 2021 Nov; 21(1):319. PubMed ID: 34789236
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting Mirna-Disease Associations Based on Neighbor Selection Graph Attention Networks.
    Zhao H; Li Z; You ZH; Nie R; Zhong T
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1298-1307. PubMed ID: 36067101
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting miRNA-Disease Associations Based On Multi-View Variational Graph Auto-Encoder With Matrix Factorization.
    Ding Y; Lei X; Liao B; Wu FX
    IEEE J Biomed Health Inform; 2022 Jan; 26(1):446-457. PubMed ID: 34111017
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model.
    Zhang L; Liu B; Li Z; Zhu X; Liang Z; An J
    BMC Bioinformatics; 2020 Oct; 21(1):470. PubMed ID: 33087064
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion.
    Jin Z; Wang M; Tang C; Zheng X; Zhang W; Sha X; An S
    Comput Biol Med; 2024 Feb; 169():107904. PubMed ID: 38181611
    [TBL] [Abstract][Full Text] [Related]  

  • 13. MDHGI: Matrix Decomposition and Heterogeneous Graph Inference for miRNA-disease association prediction.
    Chen X; Yin J; Qu J; Huang L
    PLoS Comput Biol; 2018 Aug; 14(8):e1006418. PubMed ID: 30142158
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Adaptive deep propagation graph neural network for predicting miRNA-disease associations.
    Hu H; Zhao H; Zhong T; Dong X; Wang L; Han P; Li Z
    Brief Funct Genomics; 2023 Nov; 22(5):453-462. PubMed ID: 37078739
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism.
    Biyu H; Mengshan L; Yuxin H; Ming Z; Nan W; Lixin G
    BMC Cancer; 2024 Jun; 24(1):683. PubMed ID: 38840078
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting miRNA-disease associations based on graph random propagation network and attention network.
    Zhong T; Li Z; You ZH; Nie R; Zhao H
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35079767
    [TBL] [Abstract][Full Text] [Related]  

  • 17. PMDAGS: Predicting miRNA-Disease Associations With Graph Nonlinear Diffusion Convolution Network and Similarities.
    Yan C; Duan G
    IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(3):394-404. PubMed ID: 38358864
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An improved random forest-based computational model for predicting novel miRNA-disease associations.
    Yao D; Zhan X; Kwoh CK
    BMC Bioinformatics; 2019 Dec; 20(1):624. PubMed ID: 31795954
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Variational graph auto-encoders for miRNA-disease association prediction.
    Ding Y; Tian LP; Lei X; Liao B; Wu FX
    Methods; 2021 Aug; 192():25-34. PubMed ID: 32798654
    [TBL] [Abstract][Full Text] [Related]  

  • 20. SFGAE: a self-feature-based graph autoencoder model for miRNA-disease associations prediction.
    Ma M; Na S; Zhang X; Chen C; Xu J
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36037084
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.