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 *

615 related articles for article (PubMed ID: 34850828)

  • 1. GNN-based embedding for clustering scRNA-seq data.
    Ciortan M; Defrance M
    Bioinformatics; 2022 Jan; 38(4):1037-1044. PubMed ID: 34850828
    [TBL] [Abstract][Full Text] [Related]  

  • 2. scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering.
    Wang Y; Yu Z; Li S; Bian C; Liang Y; Wong KC; Li X
    Bioinformatics; 2023 Feb; 39(2):. PubMed ID: 36734596
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Contrastive self-supervised clustering of scRNA-seq data.
    Ciortan M; Defrance M
    BMC Bioinformatics; 2021 May; 22(1):280. PubMed ID: 34044773
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Attention-based deep clustering method for scRNA-seq cell type identification.
    Li S; Guo H; Zhang S; Li Y; Li M
    PLoS Comput Biol; 2023 Nov; 19(11):e1011641. PubMed ID: 37948464
    [TBL] [Abstract][Full Text] [Related]  

  • 5. scGAC: a graph attentional architecture for clustering single-cell RNA-seq data.
    Cheng Y; Ma X
    Bioinformatics; 2022 Apr; 38(8):2187-2193. PubMed ID: 35176138
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep structural clustering for single-cell RNA-seq data jointly through autoencoder and graph neural network.
    Gan Y; Huang X; Zou G; Zhou S; Guan J
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35172334
    [TBL] [Abstract][Full Text] [Related]  

  • 7. scGCL: an imputation method for scRNA-seq data based on graph contrastive learning.
    Xiong Z; Luo J; Shi W; Liu Y; Xu Z; Wang B
    Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36825817
    [TBL] [Abstract][Full Text] [Related]  

  • 8. CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks.
    Buterez D; Bica I; Tariq I; Andrés-Terré H; Liò P
    Bioinformatics; 2022 Feb; 38(5):1277-1286. PubMed ID: 34864884
    [TBL] [Abstract][Full Text] [Related]  

  • 9. scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.
    Tian SW; Ni JC; Wang YT; Zheng CH; Ji CM
    IEEE J Biomed Health Inform; 2023 Dec; 27(12):6133-6143. PubMed ID: 37751336
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.
    Wang YM; Sun Y; Wang B; Wu Z; He XY; Zhao Y
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 37991248
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
    Gan Y; Chen Y; Xu G; Guo W; Zou G
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.
    Lee J; Kim S; Hyun D; Lee N; Kim Y; Park C
    Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37233193
    [TBL] [Abstract][Full Text] [Related]  

  • 13. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.
    Wu X; Zhou Y
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901457
    [TBL] [Abstract][Full Text] [Related]  

  • 14. scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data.
    Zhang L; Xiang H; Wang F; Chen Z; Shen M; Ma J; Liu H; Zheng H
    Methods; 2024 Sep; 229():115-124. PubMed ID: 38950719
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks.
    Mao G; Pang Z; Zuo K; Wang Q; Pei X; Chen X; Liu J
    Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37985457
    [TBL] [Abstract][Full Text] [Related]  

  • 16. JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.
    Lan W; Liu M; Chen J; Ye J; Zheng R; Zhu X; Peng W
    Methods; 2024 Feb; 222():1-9. PubMed ID: 38128706
    [TBL] [Abstract][Full Text] [Related]  

  • 17. scNAME: neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data.
    Wan H; Chen L; Deng M
    Bioinformatics; 2022 Mar; 38(6):1575-1583. PubMed ID: 34999761
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multi-View Clustering With Graph Learning for scRNA-Seq Data.
    Wu W; Zhang W; Hou W; Ma X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3535-3546. PubMed ID: 37486829
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An optimized graph-based structure for single-cell RNA-seq cell-type classification based on non-linear dimension reduction.
    Abadi SAR; Laghaee SP; Koohi S
    BMC Genomics; 2023 May; 24(1):227. PubMed ID: 37127578
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data.
    Xu Y; Zhang W; Zheng X; Cai X
    Interdiscip Sci; 2024 Mar; 16(1):1-15. PubMed ID: 37815679
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 31.