136 related articles for article (PubMed ID: 32726427)
1. netAE: semi-supervised dimensionality reduction of single-cell RNA sequencing to facilitate cell labeling.
Dong Z; Alterovitz G
Bioinformatics; 2021 Apr; 37(1):43-49. PubMed ID: 32726427
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data.
Wei Z; Zhang S
Bioinformatics; 2021 Jul; 37(Suppl_1):i51-i58. PubMed ID: 34252936
[TBL] [Abstract][Full Text] [Related]
4. scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics.
Dong J; Zhang Y; Wang F
BMC Bioinformatics; 2022 May; 23(1):161. PubMed ID: 35513780
[TBL] [Abstract][Full Text] [Related]
5. Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation.
Chen L; He Q; Zhai Y; Deng M
Bioinformatics; 2021 May; 37(6):775-784. PubMed ID: 33098418
[TBL] [Abstract][Full Text] [Related]
6. Single-cell RNA-seq interpretations using evolutionary multiobjective ensemble pruning.
Li X; Zhang S; Wong KC
Bioinformatics; 2019 Aug; 35(16):2809-2817. PubMed ID: 30596898
[TBL] [Abstract][Full Text] [Related]
7. scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network.
Xu Z; Luo J; Xiong Z
Bioinformatics; 2022 Nov; 38(22):5042-5048. PubMed ID: 36193998
[TBL] [Abstract][Full Text] [Related]
8. SCHNEL: scalable clustering of high dimensional single-cell data.
Abdelaal T; de Raadt P; Lelieveldt BPF; Reinders MJT; Mahfouz A
Bioinformatics; 2020 Dec; 36(Suppl_2):i849-i856. PubMed ID: 33381821
[TBL] [Abstract][Full Text] [Related]
9. Evaluating single-cell cluster stability using the Jaccard similarity index.
Tang M; Kaymaz Y; Logeman BL; Eichhorn S; Liang ZS; Dulac C; Sackton TB
Bioinformatics; 2021 Aug; 37(15):2212-2214. PubMed ID: 33165513
[TBL] [Abstract][Full Text] [Related]
10. scPretrain: multi-task self-supervised learning for cell-type classification.
Zhang R; Luo Y; Ma J; Zhang M; Wang S
Bioinformatics; 2022 Mar; 38(6):1607-1614. PubMed ID: 34999749
[TBL] [Abstract][Full Text] [Related]
11. 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]
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. 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]
14. Phitest for analyzing the homogeneity of single-cell populations.
Li WV
Bioinformatics; 2022 Apr; 38(9):2639-2641. PubMed ID: 35238346
[TBL] [Abstract][Full Text] [Related]
15. Hubness reduction improves clustering and trajectory inference in single-cell transcriptomic data.
Amblard E; Bac J; Chervov A; Soumelis V; Zinovyev A
Bioinformatics; 2022 Jan; 38(4):1045-1051. PubMed ID: 34871374
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. scBatch: batch-effect correction of RNA-seq data through sample distance matrix adjustment.
Fei T; Yu T
Bioinformatics; 2020 May; 36(10):3115-3123. PubMed ID: 32053185
[TBL] [Abstract][Full Text] [Related]
18. scCAN: single-cell clustering using autoencoder and network fusion.
Tran B; Tran D; Nguyen H; Ro S; Nguyen T
Sci Rep; 2022 Jun; 12(1):10267. PubMed ID: 35715568
[TBL] [Abstract][Full Text] [Related]
19. ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data.
Smolander J; Junttila S; Venäläinen MS; Elo LL
Bioinformatics; 2021 May; 37(8):1107-1114. PubMed ID: 33151294
[TBL] [Abstract][Full Text] [Related]
20. Hopper: a mathematically optimal algorithm for sketching biological data.
DeMeo B; Berger B
Bioinformatics; 2020 Jul; 36(Suppl_1):i236-i241. PubMed ID: 32657375
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]