BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

241 related articles for article (PubMed ID: 34876032)

  • 1. 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]  

  • 2. scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error.
    Liu X; Wang H; Gao J
    Comput Struct Biotechnol J; 2024 Dec; 23():549-558. PubMed ID: 38274995
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 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. 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]  

  • 7. 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]  

  • 8. Single-cell RNA-seq data analysis based on directed graph neural network.
    Feng X; Zhang H; Lin H; Long H
    Methods; 2023 Mar; 211():48-60. PubMed ID: 36804214
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 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. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares.
    Gong L; Cui X; Liu Y; Lin C; Gao Z
    Comput Biol Med; 2024 Mar; 171():108225. PubMed ID: 38442556
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Single-cell RNA-seq data analysis using graph autoencoders and graph attention networks.
    Feng X; Fang F; Long H; Zeng R; Yao Y
    Front Genet; 2022; 13():1003711. PubMed ID: 36568390
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. 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]  

  • 17. Imputing single-cell RNA-seq data by combining graph convolution and autoencoder neural networks.
    Rao J; Zhou X; Lu Y; Zhao H; Yang Y
    iScience; 2021 May; 24(5):102393. PubMed ID: 33997678
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.
    Feng X; Xiu YH; Long HX; Wang ZT; Bilal A; Yang LM
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38171931
    [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 13.