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 *

1192 related articles for article (PubMed ID: 35901457)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.
    Huang Z; Wang J; Lu X; Mohd Zain A; Yu G
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36733262
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 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. 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]  

  • 9. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data.
    Xu J; Cai L; Liao B; Zhu W; Yang J
    Bioinformatics; 2020 May; 36(10):3139-3147. PubMed ID: 32073612
    [TBL] [Abstract][Full Text] [Related]  

  • 10. AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.
    Zhu X; Meng S; Li G; Wang J; Peng X
    Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317025
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Accurate and interpretable gene expression imputation on scRNA-seq data using IGSimpute.
    Xu K; Cheong C; Veldsman WP; Lyu A; Cheung WK; Zhang L
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37039664
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq.
    Raevskiy M; Yanvarev V; Jung S; Del Sol A; Medvedeva YA
    Int J Mol Sci; 2023 Mar; 24(7):. PubMed ID: 37047200
    [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. An efficient scRNA-seq dropout imputation method using graph attention network.
    Xu C; Cai L; Gao J
    BMC Bioinformatics; 2021 Dec; 22(1):582. PubMed ID: 34876032
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders.
    Bai L; Ji B; Wang S
    BMC Bioinformatics; 2024 Oct; 25(1):317. PubMed ID: 39354334
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. 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]  

  • 18. TsImpute: an accurate two-step imputation method for single-cell RNA-seq data.
    Zheng W; Min W; Wang S
    Bioinformatics; 2023 Dec; 39(12):. PubMed ID: 38039139
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DrImpute: imputing dropout events in single cell RNA sequencing data.
    Gong W; Kwak IY; Pota P; Koyano-Nakagawa N; Garry DJ
    BMC Bioinformatics; 2018 Jun; 19(1):220. PubMed ID: 29884114
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A posterior probability based Bayesian method for single-cell RNA-seq data imputation.
    Chen S; Zheng R; Tian L; Wu FX; Li M
    Methods; 2023 Aug; 216():21-38. PubMed ID: 37315825
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 60.