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 *

145 related articles for article (PubMed ID: 39112431)

  • 1. Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction.
    Xuan P; Wang W; Cui H; Wang S; Nakaguchi T; Zhang T
    J Chem Inf Model; 2024 Aug; 64(16):6662-6675. PubMed ID: 39112431
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.
    Xuan P; Lu S; Cui H; Wang S; Nakaguchi T; Zhang T
    J Chem Inf Model; 2024 Apr; 64(8):3569-3578. PubMed ID: 38523267
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Specific topology and topological connection sensitivity enhanced graph learning for lncRNA-disease association prediction.
    Xuan P; Bai H; Cui H; Zhang X; Nakaguchi T; Zhang T
    Comput Biol Med; 2023 Sep; 164():107265. PubMed ID: 37531860
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction.
    Sheng N; Cui H; Zhang T; Xuan P
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 32444875
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs.
    Xuan P; Gong Z; Cui H; Li B; Zhang T
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35362511
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations.
    Xuan P; Pan S; Zhang T; Liu Y; Sun H
    Cells; 2019 Aug; 8(9):. PubMed ID: 31480350
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Graph Triple-Attention Network for Disease-Related LncRNA Prediction.
    Xuan P; Zhan L; Cui H; Zhang T; Nakaguchi T; Zhang W
    IEEE J Biomed Health Inform; 2022 Jun; 26(6):2839-2849. PubMed ID: 34813484
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction.
    Xuan P; Zhao Y; Cui H; Zhan L; Jin Q; Zhang T; Nakaguchi T
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1480-1491. PubMed ID: 36173783
    [TBL] [Abstract][Full Text] [Related]  

  • 10. LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks.
    Zhang B; Wang H; Ma C; Huang H; Fang Z; Qu J
    BMC Bioinformatics; 2024 Oct; 25(1):332. PubMed ID: 39407120
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects.
    Xuan P; Xu K; Cui H; Nakaguchi T; Zhang T
    Front Pharmacol; 2023; 14():1257842. PubMed ID: 37731739
    [No Abstract]   [Full Text] [Related]  

  • 12. Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.
    Xuan P; Wang X; Cui H; Meng X; Nakaguchi T; Zhang T
    IEEE J Biomed Health Inform; 2024 Jul; 28(7):4306-4316. PubMed ID: 38709611
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Dynamic category-sensitive hypergraph inferring and homo-heterogeneous neighbor feature learning for drug-related microbe prediction.
    Xuan P; Xu Z; Cui H; Gu J; Liu C; Zhang T; Wu P
    Bioinformatics; 2024 Sep; 40(9):. PubMed ID: 39292557
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction.
    Li G; Bai P; Liang C; Luo J
    BMC Genomics; 2024 Jan; 25(1):73. PubMed ID: 38233788
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks.
    Yao D; Deng Y; Zhan X; Zhan X
    BMC Bioinformatics; 2024 Jan; 25(1):46. PubMed ID: 38287236
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration.
    Yao D; Zhang B; Zhan X; Zhang B; Li XK
    ACS Omega; 2024 Aug; 9(32):35100-35112. PubMed ID: 39157140
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.
    Zhang H; Liang Y; Peng C; Han S; Du W; Li Y
    Math Biosci; 2019 Sep; 315():108229. PubMed ID: 31323239
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction.
    Zhao X; Zhao X; Yin M
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34585231
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases.
    Sheng N; Wang Y; Huang L; Gao L; Cao Y; Xie X; Fu Y
    Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37529914
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.