324 related articles for article (PubMed ID: 38317054)
1. CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data.
Wang X; Chai Z; Li S; Liu Y; Li C; Jiang Y; Liu Q
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38317054
[TBL] [Abstract][Full Text] [Related]
2. On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data.
Ng GYL; Tan SC; Ong CS
PLoS One; 2023; 18(10):e0292961. PubMed ID: 37856458
[TBL] [Abstract][Full Text] [Related]
3. Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance.
Fang C; Selega A; Campbell KR
Genome Biol; 2024 Jun; 25(1):159. PubMed ID: 38886757
[TBL] [Abstract][Full Text] [Related]
4. scMRA: a robust deep learning method to annotate scRNA-seq data with multiple reference datasets.
Yuan M; Chen L; Deng M
Bioinformatics; 2022 Jan; 38(3):738-745. PubMed ID: 34623390
[TBL] [Abstract][Full Text] [Related]
5. Dimensionality reduction and visualization of single-cell RNA-seq data with an improved deep variational autoencoder.
Jiang J; Xu J; Liu Y; Song B; Guo X; Zeng X; Zou Q
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37088976
[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. BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.
Zhan X; Yin Y; Zhang H
Bioinformatics; 2024 Mar; 40(3):. PubMed ID: 38439545
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data.
Li Z; Wang Y; Ganan-Gomez I; Colla S; Do KA
Bioinformatics; 2022 Oct; 38(21):4885-4892. PubMed ID: 36083008
[TBL] [Abstract][Full Text] [Related]
10. scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome.
Ren T; Huang S; Liu Q; Wang G
Comput Biol Med; 2023 Jan; 152():106409. PubMed ID: 36512878
[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. Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB; Kostka D
Bioinformatics; 2018 Jul; 34(13):i79-i88. PubMed ID: 29950006
[TBL] [Abstract][Full Text] [Related]
13. TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.
Liu Y; Wei G; Li C; Shen LC; Gasser RB; Song J; Chen D; Yu DJ
Brief Bioinform; 2023 May; 24(3):. PubMed ID: 37080771
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Identifying gene expression programs in single-cell RNA-seq data using linear correlation explanation.
Nussbaum YI; Hossain KSMT; Kaifi J; Warren WC; Shyu CR; Mitchem JB
J Biomed Inform; 2024 Jun; 154():104644. PubMed ID: 38631462
[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. 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]
18. Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.
Wang YM; Sun Y; Wang B; Wu Z; He XY; Zhao Y
Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 37991248
[TBL] [Abstract][Full Text] [Related]
19. scEMAIL: Universal and Source-free Annotation Method for scRNA-seq Data with Novel Cell-type Perception.
Wan H; Chen L; Deng M
Genomics Proteomics Bioinformatics; 2022 Oct; 20(5):939-958. PubMed ID: 36608843
[TBL] [Abstract][Full Text] [Related]
20. scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.
Chen Z; Miao Y; Tan Z; Hu Q; Wu Y; Li X; Guo W; Gu J
Bioinformatics; 2024 Feb; 40(2):. PubMed ID: 38243719
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]