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 *

561 related articles for article (PubMed ID: 31392316)

  • 1. 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]  

  • 2. DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.
    Ye C; Speed TP; Salim A
    Bioinformatics; 2019 Dec; 35(24):5155-5162. PubMed ID: 31197307
    [TBL] [Abstract][Full Text] [Related]  

  • 3. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
    Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
    BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
    [TBL] [Abstract][Full Text] [Related]  

  • 4. scDoc: correcting drop-out events in single-cell RNA-seq data.
    Ran D; Zhang S; Lytal N; An L
    Bioinformatics; 2020 Aug; 36(15):4233-4239. PubMed ID: 32365169
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 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. Are dropout imputation methods for scRNA-seq effective for scATAC-seq data?
    Liu Y; Zhang J; Wang S; Zeng X; Zhang W
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34718405
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data.
    Zhang W; Wei Y; Zhang D; Xu EY
    Bioinformatics; 2020 May; 36(10):3124-3130. PubMed ID: 32053182
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning.
    Zhang XF; Ou-Yang L; Yang S; Zhao XM; Hu X; Yan H
    Bioinformatics; 2019 Nov; 35(22):4827-4829. PubMed ID: 31125056
    [TBL] [Abstract][Full Text] [Related]  

  • 14. scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.
    Su Y; Wang F; Zhang S; Liang Y; Wong KC; Li X
    Bioinformatics; 2022 Sep; 38(19):4537-4545. PubMed ID: 35984287
    [TBL] [Abstract][Full Text] [Related]  

  • 15. SwarnSeq: An improved statistical approach for differential expression analysis of single-cell RNA-seq data.
    Das S; Rai SN
    Genomics; 2021 May; 113(3):1308-1324. PubMed ID: 33662531
    [TBL] [Abstract][Full Text] [Related]  

  • 16. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data.
    Tang W; Bertaux F; Thomas P; Stefanelli C; Saint M; Marguerat S; Shahrezaei V
    Bioinformatics; 2020 Feb; 36(4):1174-1181. PubMed ID: 31584606
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.
    Müller S; Cho A; Liu SJ; Lim DA; Diaz A
    Bioinformatics; 2018 Sep; 34(18):3217-3219. PubMed ID: 29897414
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 29.