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 *

190 related articles for article (PubMed ID: 33575614)

  • 1. Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization.
    Zhang S; Yang L; Yang J; Lin Z; Ng MK
    NAR Genom Bioinform; 2020 Sep; 2(3):lqaa064. PubMed ID: 33575614
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. NISC: Neural Network-Imputation for Single-Cell RNA Sequencing and Cell Type Clustering.
    Zhang X; Chen Z; Bhadani R; Cao S; Lu M; Lytal N; Chen Y; An L
    Front Genet; 2022; 13():847112. PubMed ID: 35591853
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning.
    Zhu J; Yang Y
    J Bioinform Comput Biol; 2023 Dec; 21(6):2350029. PubMed ID: 38248911
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. scRMD: imputation for single cell RNA-seq data via robust matrix decomposition.
    Chen C; Wu C; Wu L; Wang X; Deng M; Xi R
    Bioinformatics; 2020 May; 36(10):3156-3161. PubMed ID: 32119079
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.
    Chen S; Yan X; Zheng R; Li M
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36567258
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A robust semi-supervised NMF model for single cell RNA-seq data.
    Wu P; An M; Zou HR; Zhong CY; Wang W; Wu CP
    PeerJ; 2020; 8():e10091. PubMed ID: 33088619
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey.
    Ou-Yang L; Lu F; Zhang ZC; Wu M
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34864871
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Propensity score matching enables batch-effect-corrected imputation in single-cell RNA-seq analysis.
    Xu X; Yu X; Hu G; Wang K; Zhang J; Li X
    Brief Bioinform; 2022 Jul; 23(4):. PubMed ID: 35821114
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Graph-Regularized Non-Negative Matrix Factorization for Single-Cell Clustering in scRNA-Seq Data.
    Jiang H; Wang MN; Huang YA; Huang Y
    IEEE J Biomed Health Inform; 2024 May; PP():. PubMed ID: 38787664
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.
    Azim R; Wang S; Dipu SA
    Comput Biol Med; 2022 Jul; 146():105658. PubMed ID: 35751187
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Single Cell Self-Paced Clustering with Transcriptome Sequencing Data.
    Zhao P; Xu Z; Chen J; Ren Y; King I
    Int J Mol Sci; 2022 Mar; 23(7):. PubMed ID: 35409258
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.
    Hozumi Y; Wei GW
    J Comput Appl Math; 2024 Aug; 445():. PubMed ID: 38464901
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Comparison of scRNA-seq data analysis method combinations.
    Xu L; Xue T; Ding W; Shen L
    Brief Funct Genomics; 2022 Nov; 21(6):433-440. PubMed ID: 36124658
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Deep Imputation Bi-stochastic Graph Regularized Matrix Factorization for Clustering Single-cell RNA-sequencing Data.
    Lan W; Chen J; Liu M; Chen Q; Liu J; Wang J; Chen YP
    IEEE/ACM Trans Comput Biol Bioinform; 2024 Apr; PP():. PubMed ID: 38607719
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
    Lin P; Troup M; Ho JW
    Genome Biol; 2017 Mar; 18(1):59. PubMed ID: 28351406
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.