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 *

174 related articles for article (PubMed ID: 32053185)

  • 1. scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.
    Fei T; Yu T
    Bioinformatics; 2020 May; 36(10):3115-3123. PubMed ID: 32053185
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Mitigating the adverse impact of batch effects in sample pattern detection.
    Fei T; Zhang T; Shi W; Yu T
    Bioinformatics; 2018 Aug; 34(15):2634-2641. PubMed ID: 29506177
    [TBL] [Abstract][Full Text] [Related]  

  • 3. ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks.
    Wang Y; Liu T; Zhao H
    Bioinformatics; 2022 Aug; 38(16):3942-3949. PubMed ID: 35771600
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.
    Chen L; He Q; Zhai Y; Deng M
    Bioinformatics; 2021 May; 37(6):775-784. PubMed ID: 33098418
    [TBL] [Abstract][Full Text] [Related]  

  • 6. HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.
    Wang X; Wang J; Zhang H; Huang S; Yin Y
    Bioinformatics; 2022 Feb; 38(5):1295-1303. PubMed ID: 34864918
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning.
    Li X; Zhang S; Wong KC
    Bioinformatics; 2019 Aug; 35(16):2809-2817. PubMed ID: 30596898
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. SPsimSeq: semi-parametric simulation of bulk and single-cell RNA-sequencing data.
    Assefa AT; Vandesompele J; Thas O
    Bioinformatics; 2020 May; 36(10):3276-3278. PubMed ID: 32065619
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Detecting hidden batch factors through data-adaptive adjustment for biological effects.
    Yi H; Raman AT; Zhang H; Allen GI; Liu Z
    Bioinformatics; 2018 Apr; 34(7):1141-1147. PubMed ID: 29617963
    [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. CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity.
    Yan X; Zheng R; Wu F; Li M
    Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36821425
    [TBL] [Abstract][Full Text] [Related]  

  • 15. FlowGrid enables fast clustering of very large single-cell RNA-seq data.
    Fang X; Ho JWK
    Bioinformatics; 2021 Dec; 38(1):282-283. PubMed ID: 34289014
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells.
    Stassen SV; Siu DMD; Lee KCM; Ho JWK; So HKH; Tsia KK
    Bioinformatics; 2020 May; 36(9):2778-2786. PubMed ID: 31971583
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. flexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data.
    Szkop KJ; Moss DS; Nobeli I
    Bioinformatics; 2021 Jun; 37(10):1461-1464. PubMed ID: 33051680
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Spectral clustering based on learning similarity matrix.
    Park S; Zhao H
    Bioinformatics; 2018 Jun; 34(12):2069-2076. PubMed ID: 29432517
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.