BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

448 related articles for article (PubMed ID: 35870203)

  • 1. CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.
    Ding Q; Yang W; Luo M; Xu C; Xu Z; Pang F; Cai Y; Anashkina AA; Su X; Chen N; Jiang Q
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35870203
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.
    Wu W; Liu Z; Ma X
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33535230
    [TBL] [Abstract][Full Text] [Related]  

  • 6. JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering.
    Lan W; Liu M; Chen J; Ye J; Zheng R; Zhu X; Peng W
    Methods; 2024 Feb; 222():1-9. PubMed ID: 38128706
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Network-Based Structural Learning Nonnegative Matrix Factorization Algorithm for Clustering of scRNA-Seq Data.
    Wu W; Ma X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):566-575. PubMed ID: 35316190
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Personalized Low-Rank Subspace Clustering Method Based on Locality and Similarity Constraints for scRNA-seq Data Analysis.
    Qiao TJ; Liu JX; Shang J; Yuan S; Zheng CH; Wang J
    IEEE J Biomed Health Inform; 2023 May; 27(5):2575-2584. PubMed ID: 37027680
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Visualization and Analysis of Single Cell RNA-Seq Data by Maximizing Correntropy Based Non-Negative Low Rank Representation.
    Jiao CN; Liu JX; Wang J; Shang J; Zheng CH
    IEEE J Biomed Health Inform; 2022 Apr; 26(4):1872-1882. PubMed ID: 34495855
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Learning deep features and topological structure of cells for clustering of scRNA-sequencing data.
    Wang H; Ma X
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35302164
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. 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]  

  • 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. scSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder.
    Zhao JP; Hou TS; Su Y; Zheng CH
    Methods; 2022 Dec; 208():66-74. PubMed ID: 36377123
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations.
    Mondal AK; Joshi I; Singh P; Ap P
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):1983-1994. PubMed ID: 37015582
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Single-cell data clustering based on sparse optimization and low-rank matrix factorization.
    Hu Y; Li B; Chen F; Qu K
    G3 (Bethesda); 2021 Jun; 11(6):. PubMed ID: 33787873
    [TBL] [Abstract][Full Text] [Related]  

  • 19. scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means.
    Wang X; Gao H; Qi R; Zheng R; Gao X; Yu B
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):2007-2015. PubMed ID: 37015596
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 23.