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 *

163 related articles for article (PubMed ID: 36980868)

  • 1. Cell Type Annotation Model Selection: General-Purpose vs. Pattern-Aware Feature Gene Selection in Single-Cell RNA-Seq Data.
    Vasighizaker A; Trivedi Y; Rueda L
    Genes (Basel); 2023 Feb; 14(3):. PubMed ID: 36980868
    [TBL] [Abstract][Full Text] [Related]  

  • 2. On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.
    Ng GYL; Tan SC; Ong CS
    PLoS One; 2023; 18(10):e0292961. PubMed ID: 37856458
    [TBL] [Abstract][Full Text] [Related]  

  • 3. scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.
    Wang H; Zhao J; Zheng C; Su Y
    PLoS Comput Biol; 2022 Dec; 18(12):e1010772. PubMed ID: 36534702
    [TBL] [Abstract][Full Text] [Related]  

  • 4. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
    Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
    Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
    [TBL] [Abstract][Full Text] [Related]  

  • 5. scSwinFormer: A Transformer-Based Cell-Type Annotation Method for scRNA-Seq Data Using Smooth Gene Embedding and Global Features.
    Qin H; Shi X; Zhou H
    J Chem Inf Model; 2024 Aug; 64(16):6316-6323. PubMed ID: 39101690
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview.
    Slovin S; Carissimo A; Panariello F; Grimaldi A; Bouché V; Gambardella G; Cacchiarelli D
    Methods Mol Biol; 2021; 2284():343-365. PubMed ID: 33835452
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
    Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
    Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data.
    Chen Y; Zhang S
    Biomolecules; 2022 Oct; 12(10):. PubMed ID: 36291748
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Accurate feature selection improves single-cell RNA-seq cell clustering.
    Su K; Yu T; Wu H
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33611426
    [TBL] [Abstract][Full Text] [Related]  

  • 10. scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.
    Yuan M; Chen L; Deng M
    Bioinformatics; 2022 Jan; 38(3):738-745. PubMed ID: 34623390
    [TBL] [Abstract][Full Text] [Related]  

  • 11. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies.
    Baran Y; Doğan B
    Comput Biol Med; 2023 Mar; 155():106634. PubMed ID: 36774895
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network.
    Wang J; Xia J; Wang H; Su Y; Zheng CH
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36631401
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data.
    Xu J; Zhang A; Liu F; Chen L; Zhang X
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37200157
    [TBL] [Abstract][Full Text] [Related]  

  • 15. scNAME: neighborhood contrastive clustering with ancillary mask estimation for scRNA-seq data.
    Wan H; Chen L; Deng M
    Bioinformatics; 2022 Mar; 38(6):1575-1583. PubMed ID: 34999761
    [TBL] [Abstract][Full Text] [Related]  

  • 16. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
    Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
    BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis.
    Geddes TA; Kim T; Nan L; Burchfield JG; Yang JYH; Tao D; Yang P
    BMC Bioinformatics; 2019 Dec; 20(Suppl 19):660. PubMed ID: 31870278
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
    Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
    PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 9.