737 related articles for article (PubMed ID: 36825817)
1. 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]
2. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data.
Shi Y; Wan J; Zhang X; Yin Y
Comput Biol Med; 2023 Sep; 164():107263. PubMed ID: 37531858
[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. 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]
5. 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]
6. 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]
7. 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]
8. Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.
Lee J; Kim S; Hyun D; Lee N; Kim Y; Park C
Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37233193
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.
Huang Z; Wang J; Lu X; Mohd Zain A; Yu G
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36733262
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. GNN-based embedding for clustering scRNA-seq data.
Ciortan M; Defrance M
Bioinformatics; 2022 Jan; 38(4):1037-1044. PubMed ID: 34850828
[TBL] [Abstract][Full Text] [Related]
14. 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]
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. scGCC: Graph Contrastive Clustering With Neighborhood Augmentations for scRNA-Seq Data Analysis.
Tian SW; Ni JC; Wang YT; Zheng CH; Ji CM
IEEE J Biomed Health Inform; 2023 Dec; 27(12):6133-6143. PubMed ID: 37751336
[TBL] [Abstract][Full Text] [Related]
17. Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data.
Chen S; Yan X; Zheng R; Li M
Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36567258
[TBL] [Abstract][Full Text] [Related]
18. AGImpute: imputation of scRNA-seq data based on a hybrid GAN with dropouts identification.
Zhu X; Meng S; Li G; Wang J; Peng X
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317025
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
