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 *

492 related articles for article (PubMed ID: 35419595)

  • 1. Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.
    Jiang H; Huang Y; Li Q
    Brief Bioinform; 2022 May; 23(3):. PubMed ID: 35419595
    [TBL] [Abstract][Full Text] [Related]  

  • 2. ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.
    Jiang H; Huang Y; Li Q; Feng B
    BMC Bioinformatics; 2023 Nov; 24(1):417. PubMed ID: 37932672
    [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. Multi-View Clustering With Graph Learning for scRNA-Seq Data.
    Wu W; Zhang W; Hou W; Ma X
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(6):3535-3546. PubMed ID: 37486829
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. GE-Impute: graph embedding-based imputation for single-cell RNA-seq data.
    Wu X; Zhou Y
    Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35901457
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.
    Wang YM; Sun Y; Wang B; Wu Z; He XY; Zhao Y
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 37991248
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 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. ScCAEs: deep clustering of single-cell RNA-seq via convolutional autoencoder embedding and soft K-means.
    Hu H; Li Z; Li X; Yu M; Pan X
    Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34472585
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data.
    Xu J; Cui L; Zhuang J; Meng Y; Bing P; He B; Tian G; Kwok Pui C; Wu T; Wang B; Yang J
    Comput Biol Med; 2022 Jul; 146():105697. PubMed ID: 35697529
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks.
    Xu L; Li Z; Ren J; Liu S; Xu Y
    Comput Biol Med; 2024 Sep; 179():108921. PubMed ID: 39059210
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Visualization of Single Cell RNA-Seq Data Using t-SNE in R.
    Zhou B; Jin W
    Methods Mol Biol; 2020; 2117():159-167. PubMed ID: 31960377
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.
    Lei T; Chen R; Zhang S; Chen Y
    Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37769630
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data.
    Qi R; Wu J; Guo F; Xu L; Zou Q
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33003206
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 25.