493 related articles for article (PubMed ID: 35869420)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data.
Lu F; Lin Y; Yuan C; Zhang XF; Ou-Yang L
IEEE/ACM Trans Comput Biol Bioinform; 2021; 18(6):2781-2787. PubMed ID: 34495837
[TBL] [Abstract][Full Text] [Related]
8. Imputation method for single-cell RNA-seq data using neural topic model.
Qi Y; Han S; Tang L; Liu L
Gigascience; 2022 Dec; 12():. PubMed ID: 38000911
[TBL] [Abstract][Full Text] [Related]
9. DrImpute: imputing dropout events in single cell RNA sequencing data.
Gong W; Kwak IY; Pota P; Koyano-Nakagawa N; Garry DJ
BMC Bioinformatics; 2018 Jun; 19(1):220. PubMed ID: 29884114
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. I-Impute: a self-consistent method to impute single cell RNA sequencing data.
Feng X; Chen L; Wang Z; Li SC
BMC Genomics; 2020 Nov; 21(Suppl 10):618. PubMed ID: 33208097
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. CMF-Impute: an accurate imputation tool for single-cell RNA-seq data.
Xu J; Cai L; Liao B; Zhu W; Yang J
Bioinformatics; 2020 May; 36(10):3139-3147. PubMed ID: 32073612
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares.
Gong L; Cui X; Liu Y; Lin C; Gao Z
Comput Biol Med; 2024 Mar; 171():108225. PubMed ID: 38442556
[TBL] [Abstract][Full Text] [Related]
17. ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion.
Pan X; Li Z; Qin S; Yu M; Hu H
BMC Genomics; 2021 Nov; 22(1):860. PubMed ID: 34844559
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. An efficient scRNA-seq dropout imputation method using graph attention network.
Xu C; Cai L; Gao J
BMC Bioinformatics; 2021 Dec; 22(1):582. PubMed ID: 34876032
[TBL] [Abstract][Full Text] [Related]
20. FRMC: a fast and robust method for the imputation of scRNA-seq data.
Wu H; Wang X; Chu M; Xiang R; Zhou K
RNA Biol; 2021 Oct; 18(sup1):172-181. PubMed ID: 34459719
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]