BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

184 related articles for article (PubMed ID: 37187536)

  • 1. Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders.
    Liao Q; Ye Y; Li Z; Chen H; Zhuo L
    Front Microbiol; 2023; 14():1170559. PubMed ID: 37187536
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. DAmiRLocGNet: miRNA subcellular localization prediction by combining miRNA-disease associations and graph convolutional networks.
    Bai T; Yan K; Liu B
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37332057
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction.
    Jiao CN; Zhou F; Liu BM; Zheng CH; Liu JX; Gao YL
    IEEE J Biomed Health Inform; 2024 Feb; 28(2):1110-1121. PubMed ID: 38055359
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.
    Chu Y; Wang X; Dai Q; Wang Y; Wang Q; Peng S; Wei X; Qiu J; Salahub DR; Xiong Y; Wei DQ
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34009265
    [TBL] [Abstract][Full Text] [Related]  

  • 6. CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.
    Xuan P; Sheng N; Zhang T; Liu Y; Guo Y
    Int J Mol Sci; 2019 Aug; 20(17):. PubMed ID: 31480319
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multi-channel graph attention autoencoders for disease-related lncRNAs prediction.
    Sheng N; Huang L; Wang Y; Zhao J; Xuan P; Gao L; Cao Y
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35108355
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.
    Liang X; Guo M; Jiang L; Fu Y; Zhang P; Chen Y
    Interdiscip Sci; 2024 Jan; ():. PubMed ID: 38286905
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction.
    Tang X; Luo J; Shen C; Lai Z
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 33963829
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks.
    Xuan P; Sun H; Wang X; Zhang T; Pan S
    Int J Mol Sci; 2019 Jul; 20(15):. PubMed ID: 31349729
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Predicting miRNA-disease associations via learning multimodal networks and fusing mixed neighborhood information.
    Lou Z; Cheng Z; Li H; Teng Z; Liu Y; Tian Z
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35524503
    [TBL] [Abstract][Full Text] [Related]  

  • 12. GCNPCA: miRNA-Disease Associations Prediction Algorithm Based on Graph Convolutional Neural Networks.
    Liu J; Kuang Z; Deng L
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1041-1052. PubMed ID: 36049014
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features.
    Guo Y; Zhou D; Ruan X; Cao J
    Neural Netw; 2023 Aug; 165():491-505. PubMed ID: 37336034
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder.
    Xie GB; Yu JR; Lin ZY; Gu GS; Chen RB; Xu HJ; Liu ZG
    Comput Biol Chem; 2024 Feb; 108():107992. PubMed ID: 38056378
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. MHDMF: Prediction of miRNA-disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network.
    Ai N; Liang Y; Yuan HL; Ou-Yang D; Liu XY; Xie SL; Ji YH
    Comput Biol Med; 2022 Oct; 149():106069. PubMed ID: 36115300
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting miRNA-disease associations based on multi-view information fusion.
    Xie X; Wang Y; Sheng N; Zhang S; Cao Y; Fu Y
    Front Genet; 2022; 13():979815. PubMed ID: 36238163
    [TBL] [Abstract][Full Text] [Related]  

  • 18. GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.
    Li L; Wang YT; Ji CM; Zheng CH; Ni JC; Su YS
    PLoS Comput Biol; 2021 Dec; 17(12):e1009655. PubMed ID: 34890410
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder.
    Wang S; Lin B; Zhang Y; Qiao S; Wang F; Wu W; Ren C
    Cells; 2022 Dec; 11(24):. PubMed ID: 36552748
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 10.