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 *

164 related articles for article (PubMed ID: 33671799)

  • 1. Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model.
    Liu Z
    Genes (Basel); 2021 Feb; 12(2):. PubMed ID: 33671799
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.
    Liu JX; Wang CY; Gao YL; Zhang Y; Wang J; Li SJ
    Interdiscip Sci; 2021 Sep; 13(3):476-489. PubMed ID: 34076860
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data.
    Qi J; Zhou Y; Zhao Z; Jin S
    PLoS Comput Biol; 2021 Jun; 17(6):e1009118. PubMed ID: 34138847
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis.
    Qiao TJ; Liu JX; Shang J; Yuan S; Zheng CH; Wang J
    IEEE J Biomed Health Inform; 2023 May; 27(5):2575-2584. PubMed ID: 37027680
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation.
    Chen L; Zhai Y; He Q; Wang W; Deng M
    Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32674393
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering.
    Shu Z; Long Q; Zhang L; Yu Z; Wu XJ
    J Chem Inf Model; 2022 Dec; 62(23):6271-6286. PubMed ID: 36459053
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A parameter-free deep embedded clustering method for single-cell RNA-seq data.
    Zeng Y; Wei Z; Zhong F; Pan Z; Lu Y; Yang Y
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35524494
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data.
    Qi R; Wu J; Guo F; Xu L; Zou Q
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33003206
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics.
    Hu Q; Greene CS
    Pac Symp Biocomput; 2019; 24():362-373. PubMed ID: 30963075
    [TBL] [Abstract][Full Text] [Related]  

  • 14. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
    Elyanow R; Dumitrascu B; Engelhardt BE; Raphael BJ
    Genome Res; 2020 Feb; 30(2):195-204. PubMed ID: 31992614
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Visualization and Analysis of Single Cell RNA-Seq Data by Maximizing Correntropy Based Non-Negative Low Rank Representation.
    Jiao CN; Liu JX; Wang J; Shang J; Zheng CH
    IEEE J Biomed Health Inform; 2022 Apr; 26(4):1872-1882. PubMed ID: 34495855
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis.
    Zhu M; Lai Y
    J Comput Biol; 2022 Jul; 29(7):634-649. PubMed ID: 35575729
    [TBL] [Abstract][Full Text] [Related]  

  • 18. An active learning approach for clustering single-cell RNA-seq data.
    Lin X; Liu H; Wei Z; Roy SB; Gao N
    Lab Invest; 2022 Mar; 102(3):227-235. PubMed ID: 34244616
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 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 9.