239 related articles for article (PubMed ID: 33876191)
1. Deep learning of gene relationships from single cell time-course expression data.
Yuan Y; Bar-Joseph Z
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33876191
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Deep embedded clustering with multiple objectives on scRNA-seq data.
Li X; Zhang S; Wong KC
Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33822877
[TBL] [Abstract][Full Text] [Related]
4. Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.
Flores M; Liu Z; Zhang T; Hasib MM; Chiu YC; Ye Z; Paniagua K; Jo S; Zhang J; Gao SJ; Jin YF; Chen Y; Huang Y
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34929734
[TBL] [Abstract][Full Text] [Related]
5. A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data.
Srinivasan S; Leshchyk A; Johnson NT; Korkin D
RNA; 2020 Oct; 26(10):1303-1319. PubMed ID: 32532794
[TBL] [Abstract][Full Text] [Related]
6. Inferring gene regulatory networks from single-cell transcriptomics based on graph embedding.
Gan Y; Yu J; Xu G; Yan C; Zou G
Bioinformatics; 2024 May; 40(5):. PubMed ID: 38810116
[TBL] [Abstract][Full Text] [Related]
7. dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.
Xu Y; Chen J; Lyu A; Cheung WK; Zhang L
Brief Bioinform; 2022 Nov; 23(6):. PubMed ID: 36168811
[TBL] [Abstract][Full Text] [Related]
8. GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.
Shrivastava H; Zhang X; Song L; Aluru S
J Comput Biol; 2022 Jan; 29(1):27-44. PubMed ID: 35050715
[TBL] [Abstract][Full Text] [Related]
9. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces.
Ding J; Regev A
Nat Commun; 2021 May; 12(1):2554. PubMed ID: 33953202
[TBL] [Abstract][Full Text] [Related]
10. scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses.
Wang J; Ma A; Chang Y; Gong J; Jiang Y; Qi R; Wang C; Fu H; Ma Q; Xu D
Nat Commun; 2021 Mar; 12(1):1882. PubMed ID: 33767197
[TBL] [Abstract][Full Text] [Related]
11. Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.
Brendel M; Su C; Bai Z; Zhang H; Elemento O; Wang F
Genomics Proteomics Bioinformatics; 2022 Oct; 20(5):814-835. PubMed ID: 36528240
[TBL] [Abstract][Full Text] [Related]
12. An active learning approach for clustering single-cell RNA-seq data.
Lin X; Liu H; Wei Z; Roy SB; Gao N
Lab Invest; 2022 Mar; 102(3):227-235. PubMed ID: 34244616
[TBL] [Abstract][Full Text] [Related]
13. Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self Organizing Maps.
Jansen C; Ramirez RN; El-Ali NC; Gomez-Cabrero D; Tegner J; Merkenschlager M; Conesa A; Mortazavi A
PLoS Comput Biol; 2019 Nov; 15(11):e1006555. PubMed ID: 31682608
[TBL] [Abstract][Full Text] [Related]
14. Network embedding-based representation learning for single cell RNA-seq data.
Li X; Chen W; Chen Y; Zhang X; Gu J; Zhang MQ
Nucleic Acids Res; 2017 Nov; 45(19):e166. PubMed ID: 28977434
[TBL] [Abstract][Full Text] [Related]
15. Pathway-Based Single-Cell RNA-Seq Classification, Clustering, and Construction of Gene-Gene Interactions Networks Using Random Forests.
Wang H; Sham P; Tong T; Pang H
IEEE J Biomed Health Inform; 2020 Jun; 24(6):1814-1822. PubMed ID: 31581101
[TBL] [Abstract][Full Text] [Related]
16. Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration.
Wang W; Tan H; Sun M; Han Y; Chen W; Qiu S; Zheng K; Wei G; Ni T
Nucleic Acids Res; 2021 May; 49(9):e54. PubMed ID: 33619563
[TBL] [Abstract][Full Text] [Related]
17. Enhancing biological signals and detection rates in single-cell RNA-seq experiments with cDNA library equalization.
Bacher R; Chu LF; Argus C; Bolin JM; Knight P; Thomson JA; Stewart R; Kendziorski C
Nucleic Acids Res; 2022 Jan; 50(2):e12. PubMed ID: 34850101
[TBL] [Abstract][Full Text] [Related]
18. scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.
Zheng X; Huang Y; Zou X
PLoS Comput Biol; 2020 Jul; 16(7):e1007471. PubMed ID: 32716923
[TBL] [Abstract][Full Text] [Related]
19. Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring.
Frost HR
Nucleic Acids Res; 2020 Sep; 48(16):e94. PubMed ID: 32633778
[TBL] [Abstract][Full Text] [Related]
20. CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.
Liu J; Yang M; Zhao W; Zhou X
Nucleic Acids Res; 2022 Jan; 50(2):704-716. PubMed ID: 34931240
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
