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 *

149 related articles for article (PubMed ID: 35476574)

  • 1. scTSSR2: Imputing Dropout Events for Single-Cell RNA Sequencing Using Fast Two-Side Self-Representation.
    Li B; Jin K; Ou-Yang L; Yan H; Zhang XF
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1445-1456. PubMed ID: 35476574
    [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. 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]  

  • 4. 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]  

  • 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. 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]  

  • 7. 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]  

  • 8. scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation.
    Jin K; Ou-Yang L; Zhao XM; Yan H; Zhang XF
    Bioinformatics; 2020 May; 36(10):3131-3138. PubMed ID: 32073600
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 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. 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]  

  • 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. 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]  

  • 14. Imputing single-cell RNA-seq data by considering cell heterogeneity and prior expression of dropouts.
    Zhang L; Zhang S
    J Mol Cell Biol; 2021 Apr; 13(1):29-40. PubMed ID: 33002136
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. 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]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. [Imputation method for dropout in single-cell transcriptome data].
    Jiang C; Hu L; Xu C; Ge Q; Zhao X
    Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2023 Aug; 40(4):778-783. PubMed ID: 37666769
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.