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 *

604 related articles for article (PubMed ID: 37991248)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. Clustering scRNA-seq data with the cross-view collaborative information fusion strategy.
    Lou Z; Wei X; Hu Y; Hu S; Wu Y; Tian Z
    Brief Bioinform; 2024 Sep; 25(6):. PubMed ID: 39402696
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.
    Zhang T; Ren J; Li L; Wu Z; Zhang Z; Dong G; Wang G
    Int J Mol Sci; 2024 May; 25(11):. PubMed ID: 38892162
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
    Petegrosso R; Li Z; Kuang R
    Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Learning deep features and topological structure of cells for clustering of scRNA-sequencing data.
    Wang H; Ma X
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35302164
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
    Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
    PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.
    Gao Q; Ai Q
    Sci Rep; 2024 Jun; 14(1):13541. PubMed ID: 38866896
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations.
    Mondal AK; Joshi I; Singh P; Ap P
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):1983-1994. PubMed ID: 37015582
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network.
    Wang J; Xia J; Wang H; Su Y; Zheng CH
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36631401
    [TBL] [Abstract][Full Text] [Related]  

  • 19. ScCAEs: deep clustering of single-cell RNA-seq via convolutional autoencoder embedding and soft K-means.
    Hu H; Li Z; Li X; Yu M; Pan X
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34472585
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.
    Lakkis J; Wang D; Zhang Y; Hu G; Wang K; Pan H; Ungar L; Reilly MP; Li X; Li M
    Genome Res; 2021 Oct; 31(10):1753-1766. PubMed ID: 34035047
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 31.