BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

471 related articles for article (PubMed ID: 35108355)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.
    Wang L; Zhong C
    BMC Bioinformatics; 2022 Jan; 23(1):11. PubMed ID: 34983363
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations.
    Shi Z; Zhang H; Jin C; Quan X; Yin Y
    BMC Bioinformatics; 2021 Mar; 22(1):136. PubMed ID: 33745450
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model.
    Liang Y; Zhang ZQ; Liu NN; Wu YN; Gu CL; Wang YL
    BMC Bioinformatics; 2022 May; 23(1):189. PubMed ID: 35590258
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Heterogeneous graph neural network for lncRNA-disease association prediction.
    Shi H; Zhang X; Tang L; Liu L
    Sci Rep; 2022 Oct; 12(1):17519. PubMed ID: 36266433
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. A random forest based computational model for predicting novel lncRNA-disease associations.
    Yao D; Zhan X; Zhan X; Kwoh CK; Li P; Wang J
    BMC Bioinformatics; 2020 Mar; 21(1):126. PubMed ID: 32216744
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.
    Wu QW; Xia JF; Ni JC; Zheng CH
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33415333
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field.
    Wang W; Zhang L; Sun J; Zhao Q; Shuai J
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36305458
    [TBL] [Abstract][Full Text] [Related]  

  • 20. GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations.
    Gao M; Liu S; Qi Y; Guo X; Shang X
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36305456
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 24.