BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

278 related articles for article (PubMed ID: 36070619)

  • 1. GraphCDA: a hybrid graph representation learning framework based on GCN and GAT for predicting disease-associated circRNAs.
    Dai Q; Liu Z; Wang Z; Duan X; Guo M
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070619
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network.
    Cao R; He C; Wei P; Su Y; Xia J; Zheng C
    Biomolecules; 2022 Jul; 12(7):. PubMed ID: 35883487
    [TBL] [Abstract][Full Text] [Related]  

  • 3. GGAECDA: Predicting circRNA-disease associations using graph autoencoder based on graph representation learning.
    Li G; Lin Y; Luo J; Xiao Q; Liang C
    Comput Biol Chem; 2022 Aug; 99():107722. PubMed ID: 35810557
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Predicting CircRNA disease associations using novel node classification and link prediction models on Graph Convolutional Networks.
    Bamunu Mudiyanselage T; Lei X; Senanayake N; Zhang Y; Pan Y
    Methods; 2022 Feb; 198():32-44. PubMed ID: 34748953
    [TBL] [Abstract][Full Text] [Related]  

  • 6. GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm.
    Wang L; You ZH; Li YM; Zheng K; Huang YA
    PLoS Comput Biol; 2020 May; 16(5):e1007568. PubMed ID: 32433655
    [TBL] [Abstract][Full Text] [Related]  

  • 7. GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.
    Ji C; Liu Z; Wang Y; Ni J; Zheng C
    Int J Mol Sci; 2021 Aug; 22(16):. PubMed ID: 34445212
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting CircRNA-Disease Associations via Feature Convolution Learning With Heterogeneous Graph Attention Network.
    Peng L; Yang C; Chen Y; Liu W
    IEEE J Biomed Health Inform; 2023 Jun; 27(6):3072-3082. PubMed ID: 37030839
    [TBL] [Abstract][Full Text] [Related]  

  • 9. NSL2CD: identifying potential circRNA-disease associations based on network embedding and subspace learning.
    Xiao Q; Fu Y; Yang Y; Dai J; Luo J
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 33954582
    [TBL] [Abstract][Full Text] [Related]  

  • 10. MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction.
    Wu Q; Deng Z; Pan X; Shen HB; Choi KS; Wang S; Wu J; Yu DJ
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35907779
    [TBL] [Abstract][Full Text] [Related]  

  • 11. GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network.
    Bian C; Lei XJ; Wu FX
    Cancers (Basel); 2021 May; 13(11):. PubMed ID: 34070678
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.
    Wang Y; Liu X; Shen Y; Song X; Wang T; Shang X; Peng J
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36847701
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting circRNA-drug resistance associations based on a multimodal graph representation learning framework.
    Liu Z; Dai Q; Yu X; Duan X; Wang C
    IEEE J Biomed Health Inform; 2023 Jul; PP():. PubMed ID: 37498762
    [TBL] [Abstract][Full Text] [Related]  

  • 14. DRGCNCDA: Predicting circRNA-disease interactions based on knowledge graph and disentangled relational graph convolutional network.
    Lan W; Zhang H; Dong Y; Chen Q; Cao J; Peng W; Liu J; Li M
    Methods; 2022 Dec; 208():35-41. PubMed ID: 36280134
    [TBL] [Abstract][Full Text] [Related]  

  • 15. DPMGCDA: Deciphering circRNA-Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder.
    Luo Y; Deng L
    J Chem Inf Model; 2024 May; 64(10):4359-4372. PubMed ID: 38745420
    [TBL] [Abstract][Full Text] [Related]  

  • 16. CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network.
    Ma Z; Kuang Z; Deng L
    BMC Bioinformatics; 2021 Nov; 22(1):551. PubMed ID: 34772332
    [TBL] [Abstract][Full Text] [Related]  

  • 17. MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.
    Li Y; Hu XG; Wang L; Li PP; You ZH
    Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36384071
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of circRNA-MiRNA Association Using Singular Value Decomposition and Graph Neural Networks.
    Qian Y; Zheng J; Jiang Y; Li S; Deng L
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3461-3468. PubMed ID: 36395130
    [TBL] [Abstract][Full Text] [Related]  

  • 19. MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning.
    Li G; Zeng F; Luo J; Liang C; Xiao Q
    BMC Med Inform Decis Mak; 2023 Dec; 23(1):291. PubMed ID: 38110886
    [TBL] [Abstract][Full Text] [Related]  

  • 20. THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.
    Guo Y; Yi M
    Brief Funct Genomics; 2023 Sep; ():. PubMed ID: 37738503
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.