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 *

745 related articles for article (PubMed ID: 37529914)

  • 1. 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]  

  • 2. Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.
    Huang L; Sheng N; Gao L; Wang L; Hou W; Hong J; Wang Y
    IEEE J Biomed Health Inform; 2024 Sep; PP():. PubMed ID: 39316476
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.
    Si Y; Huang Z; Fang Z; Yuan Z; Huang Z; Li Y; Wei Y; Wu F; Yao YF
    Brief Bioinform; 2024 Jul; 25(5):. PubMed ID: 39256197
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 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. HOPEXGB: A Consensual Model for Predicting miRNA/lncRNA-Disease Associations Using a Heterogeneous Disease-miRNA-lncRNA Information Network.
    He J; Li M; Qiu J; Pu X; Guo Y
    J Chem Inf Model; 2024 Apr; 64(7):2863-2877. PubMed ID: 37604142
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction.
    Zhao X; Wu J; Zhao X; Yin M
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36528809
    [TBL] [Abstract][Full Text] [Related]  

  • 10. LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.
    Xuan P; Jia L; Zhang T; Sheng N; Li X; Li J
    Int J Mol Sci; 2019 Sep; 20(18):. PubMed ID: 31510011
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 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. DeepMNE: Deep Multi-Network Embedding for lncRNA-Disease Association Prediction.
    Ma Y
    IEEE J Biomed Health Inform; 2022 Jul; 26(7):3539-3549. PubMed ID: 35180094
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 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. 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]  

    [Next]    [New Search]
    of 38.