146 related articles for article (PubMed ID: 35037023)
1. sc-REnF: An entropy guided robust feature selection for single-cell RNA-seq data.
Lall S; Ghosh A; Ray S; Bandyopadhyay S
Brief Bioinform; 2022 Mar; 23(2):. PubMed ID: 35037023
[TBL] [Abstract][Full Text] [Related]
2. Intrinsic entropy model for feature selection of scRNA-seq data.
Li L; Tang H; Xia R; Dai H; Liu R; Chen L
J Mol Cell Biol; 2022 Jun; 14(2):. PubMed ID: 35102420
[TBL] [Abstract][Full Text] [Related]
3. A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.
Lall S; Ray S; Bandyopadhyay S
PLoS Comput Biol; 2022 Mar; 18(3):e1009600. PubMed ID: 35271564
[TBL] [Abstract][Full Text] [Related]
4. A cofunctional grouping-based approach for non-redundant feature gene selection in unannotated single-cell RNA-seq analysis.
Deng T; Chen S; Zhang Y; Xu Y; Feng D; Wu H; Sun X
Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36754847
[TBL] [Abstract][Full Text] [Related]
5. Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations.
Mondal AK; Joshi I; Singh P; Ap P
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(3):1983-1994. PubMed ID: 37015582
[TBL] [Abstract][Full Text] [Related]
6. FEED: a feature selection method based on gene expression decomposition for single cell clustering.
Zhang C; Duan ZW; Xu YP; Liu J; Li HD
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37935617
[TBL] [Abstract][Full Text] [Related]
7. Dimension Reduction and Clustering Models for Single-Cell RNA Sequencing Data: A Comparative Study.
Feng C; Liu S; Zhang H; Guan R; Li D; Zhou F; Liang Y; Feng X
Int J Mol Sci; 2020 Mar; 21(6):. PubMed ID: 32235704
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.
Elyanow R; Dumitrascu B; Engelhardt BE; Raphael BJ
Genome Res; 2020 Feb; 30(2):195-204. PubMed ID: 31992614
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.
Sun Z; Wang T; Deng K; Wang XF; Lafyatis R; Ding Y; Hu M; Chen W
Bioinformatics; 2018 Jan; 34(1):139-146. PubMed ID: 29036318
[TBL] [Abstract][Full Text] [Related]
13. Accurate feature selection improves single-cell RNA-seq cell clustering.
Su K; Yu T; Wu H
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33611426
[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. Boosting scRNA-seq data clustering by cluster-aware feature weighting.
Li RY; Guan J; Zhou S
BMC Bioinformatics; 2021 Jun; 22(Suppl 6):130. PubMed ID: 34078287
[TBL] [Abstract][Full Text] [Related]
16. A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.
Zhang H; Lee CAA; Li Z; Garbe JR; Eide CR; Petegrosso R; Kuang R; Tolar J
PLoS Comput Biol; 2018 Apr; 14(4):e1006053. PubMed ID: 29630593
[TBL] [Abstract][Full Text] [Related]
17. CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.
Ding Q; Yang W; Luo M; Xu C; Xu Z; Pang F; Cai Y; Anashkina AA; Su X; Chen N; Jiang Q
Brief Bioinform; 2022 Sep; 23(5):. PubMed ID: 35870203
[TBL] [Abstract][Full Text] [Related]
18. RgCop-A regularized copula based method for gene selection in single-cell RNA-seq data.
Lall S; Ray S; Bandyopadhyay S
PLoS Comput Biol; 2021 Oct; 17(10):e1009464. PubMed ID: 34665808
[TBL] [Abstract][Full Text] [Related]
19. Highly Regional Genes: graph-based gene selection for single-cell RNA-seq data.
Wu Y; Hu Q; Wang S; Liu C; Shan Y; Guo W; Jiang R; Wang X; Gu J
J Genet Genomics; 2022 Sep; 49(9):891-899. PubMed ID: 35144027
[TBL] [Abstract][Full Text] [Related]
20. LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.
Lin L; Zhang Y; Qian W; Liu Y; Zhang Y; Lin F; Liu C; Lu G; Sun D; Guo X; Song Y; Song J; Yang C; Li J
Proc Natl Acad Sci U S A; 2022 Feb; 119(5):. PubMed ID: 35086932
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
