BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

142 related articles for article (PubMed ID: 33003202)

  • 1. A review of computational strategies for denoising and imputation of single-cell transcriptomic data.
    Patruno L; Maspero D; Craighero F; Angaroni F; Antoniotti M; Graudenzi A
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33003202
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. scIGANs: single-cell RNA-seq imputation using generative adversarial networks.
    Xu Y; Zhang Z; You L; Liu J; Fan Z; Zhou X
    Nucleic Acids Res; 2020 Sep; 48(15):e85. PubMed ID: 32588900
    [TBL] [Abstract][Full Text] [Related]  

  • 5. ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion.
    Pan X; Li Z; Qin S; Yu M; Hu H
    BMC Genomics; 2021 Nov; 22(1):860. PubMed ID: 34844559
    [TBL] [Abstract][Full Text] [Related]  

  • 6. scHinter: imputing dropout events for single-cell RNA-seq data with limited sample size.
    Ye P; Ye W; Ye C; Li S; Ye L; Ji G; Wu X
    Bioinformatics; 2020 Feb; 36(3):789-797. PubMed ID: 31392316
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data.
    Shi Y; Wan J; Zhang X; Yin Y
    Comput Biol Med; 2023 Sep; 164():107263. PubMed ID: 37531858
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A systematic evaluation of single-cell RNA-sequencing imputation methods.
    Hou W; Ji Z; Ji H; Hicks SC
    Genome Biol; 2020 Aug; 21(1):218. PubMed ID: 32854757
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. SCDD: a novel single-cell RNA-seq imputation method with diffusion and denoising.
    Liu J; Pan Y; Ruan Z; Guo J
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 36070866
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-Seq Data.
    Chi W; Deng M
    Genes (Basel); 2020 May; 11(5):. PubMed ID: 32403260
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. FRMC: a fast and robust method for the imputation of scRNA-seq data.
    Wu H; Wang X; Chu M; Xiang R; Zhou K
    RNA Biol; 2021 Oct; 18(sup1):172-181. PubMed ID: 34459719
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data.
    Qi Y; Guo Y; Jiao H; Shang X
    BMC Bioinformatics; 2020 Jun; 21(1):240. PubMed ID: 32527285
    [TBL] [Abstract][Full Text] [Related]  

  • 18. ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.
    Malec M; Kurban H; Dalkilic M
    BMC Bioinformatics; 2022 Jul; 23(1):291. PubMed ID: 35869420
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. I-Impute: a self-consistent method to impute single cell RNA sequencing data.
    Feng X; Chen L; Wang Z; Li SC
    BMC Genomics; 2020 Nov; 21(Suppl 10):618. PubMed ID: 33208097
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.