BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

304 related articles for article (PubMed ID: 32108864)

  • 1. 2DImpute: imputation in single-cell RNA-seq data from correlations in two dimensions.
    Zhu K; Anastassiou D
    Bioinformatics; 2020 Jun; 36(11):3588-3589. PubMed ID: 32108864
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Imputing dropouts for single-cell RNA sequencing based on multi-objective optimization.
    Jin K; Li B; Yan H; Zhang XF
    Bioinformatics; 2022 Jun; 38(12):3222-3230. PubMed ID: 35485740
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data.
    Andreatta M; Carmona SJ
    Bioinformatics; 2021 May; 37(6):882-884. PubMed ID: 32845323
    [TBL] [Abstract][Full Text] [Related]  

  • 12. scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions.
    Liu J; Liu X; Ren X; Li G
    Bioinformatics; 2019 Nov; 35(21):4264-4271. PubMed ID: 30951147
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. DEsingle for detecting three types of differential expression in single-cell RNA-seq data.
    Miao Z; Deng K; Wang X; Zhang X
    Bioinformatics; 2018 Sep; 34(18):3223-3224. PubMed ID: 29688277
    [TBL] [Abstract][Full Text] [Related]  

  • 17. scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets.
    Andreatta M; Berenstein AJ; Carmona SJ
    Bioinformatics; 2022 Apr; 38(9):2642-2644. PubMed ID: 35258562
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. SPARSim single cell: a count data simulator for scRNA-seq data.
    Baruzzo G; Patuzzi I; Di Camillo B
    Bioinformatics; 2020 Mar; 36(5):1468-1475. PubMed ID: 31598633
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.