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 *

138 related articles for article (PubMed ID: 33003202)

  • 61. 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]  

  • 62. RESCUE: imputing dropout events in single-cell RNA-sequencing data.
    Tracy S; Yuan GC; Dries R
    BMC Bioinformatics; 2019 Jul; 20(1):388. PubMed ID: 31299886
    [TBL] [Abstract][Full Text] [Related]  

  • 63. Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data.
    Tjärnberg A; Mahmood O; Jackson CA; Saldi GA; Cho K; Christiaen LA; Bonneau RA
    PLoS Comput Biol; 2021 Jan; 17(1):e1008569. PubMed ID: 33411784
    [TBL] [Abstract][Full Text] [Related]  

  • 64. scHFC: a hybrid fuzzy clustering method for single-cell RNA-seq data optimized by natural computation.
    Wang J; Xia J; Tan D; Lin R; Su Y; Zheng CH
    Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35136924
    [TBL] [Abstract][Full Text] [Related]  

  • 65. SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency.
    Pu J; Wang B; Liu X; Chen L; Li SC
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715274
    [TBL] [Abstract][Full Text] [Related]  

  • 66. Using RNentropy to Detect Significant Variation in Gene Expression Across Multiple RNA-Seq or Single-Cell RNA-Seq Samples.
    Zambelli F; Pavesi G
    Methods Mol Biol; 2021; 2284():77-96. PubMed ID: 33835439
    [TBL] [Abstract][Full Text] [Related]  

  • 67. WASP: a versatile, web-accessible single cell RNA-Seq processing platform.
    Hoek A; Maibach K; Özmen E; Vazquez-Armendariz AI; Mengel JP; Hain T; Herold S; Goesmann A
    BMC Genomics; 2021 Mar; 22(1):195. PubMed ID: 33736596
    [TBL] [Abstract][Full Text] [Related]  

  • 68. Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data.
    Su Y; Lin R; Wang J; Tan D; Zheng C
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36715275
    [TBL] [Abstract][Full Text] [Related]  

  • 69. scHOIS: Determining Cell Heterogeneity Through Hierarchical Clustering Based on Optimal Imputation Strategy.
    Cheng X; Yan C; Jiang H; Qiu Y
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(2):1431-1444. PubMed ID: 37815942
    [TBL] [Abstract][Full Text] [Related]  

  • 70. Dimensionality Reduction of Single-Cell RNA Sequencing Data by Combining Entropy and Denoising AutoEncoder.
    Zhu X; Li J; Lin Y; Zhao L; Wang J; Peng X
    J Comput Biol; 2022 Oct; 29(10):1074-1084. PubMed ID: 35834604
    [No Abstract]   [Full Text] [Related]  

  • 71. 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]  

  • 72. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.
    Lin P; Troup M; Ho JW
    Genome Biol; 2017 Mar; 18(1):59. PubMed ID: 28351406
    [TBL] [Abstract][Full Text] [Related]  

  • 73. Machine learning and statistical methods for clustering single-cell RNA-sequencing data.
    Petegrosso R; Li Z; Kuang R
    Brief Bioinform; 2020 Jul; 21(4):1209-1223. PubMed ID: 31243426
    [TBL] [Abstract][Full Text] [Related]  

  • 74. SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.
    Qiu Y; Yan C; Zhao P; Zou Q
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37122068
    [TBL] [Abstract][Full Text] [Related]  

  • 75. A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis.
    Si T; Hopkins Z; Yanev J; Hou J; Gong H
    PLoS One; 2023; 18(11):e0292792. PubMed ID: 37948433
    [TBL] [Abstract][Full Text] [Related]  

  • 76. A Bayesian factorization method to recover single-cell RNA sequencing data.
    Wen ZH; Langsam JL; Zhang L; Shen W; Zhou X
    Cell Rep Methods; 2022 Jan; 2(1):100133. PubMed ID: 35474868
    [TBL] [Abstract][Full Text] [Related]  

  • 77. An Informative Approach to Single-Cell Sequencing Analysis.
    Kashima Y; Suzuki A; Suzuki Y
    Adv Exp Med Biol; 2019; 1129():81-96. PubMed ID: 30968362
    [TBL] [Abstract][Full Text] [Related]  

  • 78. 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]  

  • 79. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data.
    Talwar D; Mongia A; Sengupta D; Majumdar A
    Sci Rep; 2018 Nov; 8(1):16329. PubMed ID: 30397240
    [TBL] [Abstract][Full Text] [Related]  

  • 80. scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information.
    Qi J; Sheng Q; Zhou Y; Hua J; Xiao S; Jin S
    Cell Biosci; 2022 Sep; 12(1):142. PubMed ID: 36056412
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.