BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

531 related articles for article (PubMed ID: 36377123)

  • 1. 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]  

  • 2. scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder.
    Yu B; Chen C; Qi R; Zheng R; Skillman-Lawrence PJ; Wang X; Ma A; Gu H
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33300547
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 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. Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder.
    Jiang J; Xu J; Liu Y; Song B; Guo X; Zeng X; Zou Q
    Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37088976
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.
    An S; Shi J; Liu R; Chen Y; Wang J; Hu S; Xia X; Dong G; Bo X; He Z; Ying X
    Bioinformatics; 2024 Mar; 40(4):. PubMed ID: 38603616
    [TBL] [Abstract][Full Text] [Related]  

  • 13. scGAC: a graph attentional architecture for clustering single-cell RNA-seq data.
    Cheng Y; Ma X
    Bioinformatics; 2022 Apr; 38(8):2187-2193. PubMed ID: 35176138
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 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]  

  • 15. 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]  

  • 16. Attention-based deep clustering method for scRNA-seq cell type identification.
    Li S; Guo H; Zhang S; Li Y; Li M
    PLoS Comput Biol; 2023 Nov; 19(11):e1011641. PubMed ID: 37948464
    [TBL] [Abstract][Full Text] [Related]  

  • 17. scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.
    Zhang T; Ren J; Li L; Wu Z; Zhang Z; Dong G; Wang G
    Int J Mol Sci; 2024 May; 25(11):. PubMed ID: 38892162
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. DCRELM: dual correlation reduction network-based extreme learning machine for single-cell RNA-seq data clustering.
    Gao Q; Ai Q
    Sci Rep; 2024 Jun; 14(1):13541. PubMed ID: 38866896
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 27.