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 *

759 related articles for article (PubMed ID: 33535230)

  • 1. jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.
    Wu W; Liu Z; Ma X
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33535230
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
    Wu W; Ma X
    Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.
    Wu W; Ma X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):566-575. PubMed ID: 35316190
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Learning discriminative and structural samples for rare cell types with deep generative model.
    Wang H; Ma X
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35914950
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
    Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.
    Wang H; Zhao J; Zheng C; Su Y
    PLoS Comput Biol; 2022 Dec; 18(12):e1010772. PubMed ID: 36534702
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
    Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
    Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.
    Wu W; Zhang W; Ma X
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35043143
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
    Srinivasan S; Leshchyk A; Johnson NT; Korkin D
    RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.
    Qiu Y; Yan C; Zhao P; Zou Q
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37122068
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
    Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
    BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 38.