BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

168 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. 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]  

  • 6. 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]  

  • 7. Boosting scRNA-seq data clustering by cluster-aware feature weighting.
    Li RY; Guan J; Zhou S
    BMC Bioinformatics; 2021 Jun; 22(Suppl 6):130. PubMed ID: 34078287
    [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.