BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

713 related articles for article (PubMed ID: 33098418)

  • 1. Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.
    Chen L; He Q; Zhai Y; Deng M
    Bioinformatics; 2021 May; 37(6):775-784. PubMed ID: 33098418
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation.
    Chen L; Zhai Y; He Q; Wang W; Deng M
    Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32674393
    [TBL] [Abstract][Full Text] [Related]  

  • 3. scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery.
    Zhai Y; Chen L; Deng M
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36869836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data.
    Wang Z; Wang H; Zhao J; Zheng C
    BMC Bioinformatics; 2023 May; 24(1):217. PubMed ID: 37237310
    [TBL] [Abstract][Full Text] [Related]  

  • 5. CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data.
    Wei Z; Zhang S
    Bioinformatics; 2021 Jul; 37(Suppl_1):i51-i58. PubMed ID: 34252936
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep enhanced constraint clustering based on contrastive learning for scRNA-seq data.
    Gan Y; Chen Y; Xu G; Guo W; Zou G
    Brief Bioinform; 2023 Jul; 24(4):. PubMed ID: 37313714
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. scCNC: a method based on capsule network for clustering scRNA-seq data.
    Wang HY; Zhao JP; Zheng CH; Su YS
    Bioinformatics; 2022 Aug; 38(15):3703-3709. PubMed ID: 35699473
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network.
    Xu Z; Luo J; Xiong Z
    Bioinformatics; 2022 Nov; 38(22):5042-5048. PubMed ID: 36193998
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Joint learning dimension reduction and clustering of single-cell RNA-sequencing data.
    Wu W; Ma X
    Bioinformatics; 2020 Jun; 36(12):3825-3832. PubMed ID: 32246821
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Contrastive self-supervised clustering of scRNA-seq data.
    Ciortan M; Defrance M
    BMC Bioinformatics; 2021 May; 22(1):280. PubMed ID: 34044773
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
    Srinivasan S; Leshchyk A; Johnson NT; Korkin D
    RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. scTPC: a novel semisupervised deep clustering model for scRNA-seq data.
    Qiu Y; Yang L; Jiang H; Zou Q
    Bioinformatics; 2024 May; 40(5):. PubMed ID: 38684178
    [TBL] [Abstract][Full Text] [Related]  

  • 19. SAFE-clustering: Single-cell Aggregated (from Ensemble) clustering for single-cell RNA-seq data.
    Yang Y; Huh R; Culpepper HW; Lin Y; Love MI; Li Y
    Bioinformatics; 2019 Apr; 35(8):1269-1277. PubMed ID: 30202935
    [TBL] [Abstract][Full Text] [Related]  

  • 20. scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data.
    Ranjan B; Schmidt F; Sun W; Park J; Honardoost MA; Tan J; Arul Rayan N; Prabhakar S
    BMC Bioinformatics; 2021 Apr; 22(1):186. PubMed ID: 33845760
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 36.