BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

371 related articles for article (PubMed ID: 36572658)

  • 1. Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network.
    Lu C; Zhang L; Zeng M; Lan W; Duan G; Wang J
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36572658
    [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. A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.
    Niu M; Wang C; Zhang Z; Zou Q
    BMC Biol; 2024 Jan; 22(1):24. PubMed ID: 38281919
    [TBL] [Abstract][Full Text] [Related]  

  • 4. HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.
    Liang S; Liu S; Song J; Lin Q; Zhao S; Li S; Li J; Liang S; Wang J
    BMC Bioinformatics; 2023 Sep; 24(1):335. PubMed ID: 37697297
    [TBL] [Abstract][Full Text] [Related]  

  • 5. MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.
    Wang L; Li ZW; You ZH; Huang DS; Wong L
    IEEE J Biomed Health Inform; 2024 Mar; 28(3):1752-1761. PubMed ID: 38145538
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.
    Wu Q; Deng Z; Zhang W; Pan X; Choi KS; Zuo Y; Shen HB; Yu DJ
    Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37561093
    [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. KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.
    Lan W; Dong Y; Chen Q; Zheng R; Liu J; Pan Y; Chen YP
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34864877
    [TBL] [Abstract][Full Text] [Related]  

  • 10. IGNSCDA: Predicting CircRNA-Disease Associations Based on Improved Graph Convolutional Network and Negative Sampling.
    Lan W; Dong Y; Chen Q; Liu J; Wang J; Chen YP; Pan S
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(6):3530-3538. PubMed ID: 34506289
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 13. iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion.
    Xiao Q; Zhong J; Tang X; Luo J
    Mol Genet Genomics; 2021 Jan; 296(1):223-233. PubMed ID: 33159254
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network.
    Wang Y; Lu P
    Comput Biol Chem; 2024 Jun; 110():108079. PubMed ID: 38704917
    [TBL] [Abstract][Full Text] [Related]  

  • 16. CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder.
    Wang H; Han J; Li H; Duan L; Liu Z; Cheng H
    Math Biosci Eng; 2023 Feb; 20(5):7957-7980. PubMed ID: 37161181
    [TBL] [Abstract][Full Text] [Related]  

  • 17. LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations.
    Wang W; Han P; Li Z; Nie R; Wang K; Wang L; Liao H
    IEEE/ACM Trans Comput Biol Bioinform; 2024; 21(2):289-300. PubMed ID: 38231821
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 19.