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 *

173 related articles for article (PubMed ID: 38715444)

  • 1. DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.
    Wei H; Gao L; Wu S; Jiang Y; Liu B
    Bioinformatics; 2024 May; 40(5):. PubMed ID: 38715444
    [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. HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.
    Ouyang D; Liang Y; Wang J; Li L; Ai N; Feng J; Lu S; Liao S; Liu X; Xie S
    PLoS Comput Biol; 2024 Apr; 20(4):e1011927. PubMed ID: 38652712
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs.
    Xiao Q; Zhang N; Luo J; Dai J; Tang X
    Brief Bioinform; 2021 Mar; 22(2):2043-2057. PubMed ID: 32186712
    [TBL] [Abstract][Full Text] [Related]  

  • 5. CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure.
    Chen Y; Hu Y; Hu X; Feng C; Chen M
    Bioinformatics; 2022 Sep; 38(18):4380-4386. PubMed ID: 35900147
    [TBL] [Abstract][Full Text] [Related]  

  • 6. GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning.
    Wei J; Zhuo L; Zhou Z; Lian X; Fu X; Yao X
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37427977
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.
    Tian Z; Han C; Xu L; Teng Z; Song W
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38622356
    [TBL] [Abstract][Full Text] [Related]  

  • 9. SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning.
    Fan Y; Zhang C; Hu X; Huang Z; Xue J; Deng L
    Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38754409
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.
    Wang Y; Song J; Dai Q; Duan X
    IEEE J Biomed Health Inform; 2024 May; 28(5):3146-3157. PubMed ID: 38294927
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Heterogeneous biomedical entity representation learning for gene-disease association prediction.
    Meng Z; Liu S; Liang S; Jani B; Meng Z
    Brief Bioinform; 2024 Jul; 25(5):. PubMed ID: 39154194
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning.
    Ruan X; Jiang C; Lin P; Lin Y; Liu J; Huang S; Liu X
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36790856
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. AEMDA: inferring miRNA-disease associations based on deep autoencoder.
    Ji C; Gao Z; Ma X; Wu Q; Ni J; Zheng C
    Bioinformatics; 2021 Apr; 37(1):66-72. PubMed ID: 32726399
    [TBL] [Abstract][Full Text] [Related]  

  • 15. GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization.
    Ai N; Liang Y; Yuan H; Ouyang D; Xie S; Liu X
    BMC Genomics; 2023 Jul; 24(1):424. PubMed ID: 37501127
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features.
    Yan C; Duan G; Li N; Zhang L; Wu FX; Wang J
    Bioinformatics; 2022 Apr; 38(8):2226-2234. PubMed ID: 35150255
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning.
    Zhang Y; Wang Z; Wei H; Chen M
    BMC Med Inform Decis Mak; 2024 Jun; 24(1):159. PubMed ID: 38844961
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.