677 related articles for article (PubMed ID: 33430769)
1. JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.
Cui T; Wang T
BMC Genomics; 2021 Jan; 22(1):47. PubMed ID: 33430769
[TBL] [Abstract][Full Text] [Related]
2. A comprehensive assessment of hurdle and zero-inflated models for single cell RNA-sequencing analysis.
Cui T; Wang T
Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37507115
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. Consensus clustering of single-cell RNA-seq data by enhancing network affinity.
Cui Y; Zhang S; Liang Y; Wang X; Ferraro TN; Chen Y
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34160582
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. 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]
10. 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]
11. 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]
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. 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]
14. 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]
15. 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]
16. 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]
17. 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]
18. Deep Multi-Constraint Soft Clustering Analysis for Single-Cell RNA-Seq Data via Zero-Inflated Autoencoder Embedding.
He Y; Chen X; Tu NH; Luo J
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):2254-2265. PubMed ID: 37022218
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. CDSImpute: An ensemble similarity imputation method for single-cell RNA sequence dropouts.
Azim R; Wang S; Dipu SA
Comput Biol Med; 2022 Jul; 146():105658. PubMed ID: 35751187
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
