214 related articles for article (PubMed ID: 34962260)
1. SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.
Luo Q; Yu Y; Lan X
Brief Bioinform; 2022 Jan; 23(1):. PubMed ID: 34962260
[TBL] [Abstract][Full Text] [Related]
2. Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing data.
Chen G; Liu ZP
Bioinformatics; 2022 Sep; 38(19):4522-4529. PubMed ID: 35961023
[TBL] [Abstract][Full Text] [Related]
3. Predicting gene regulatory links from single-cell RNA-seq data using graph neural networks.
Mao G; Pang Z; Zuo K; Wang Q; Pei X; Chen X; Liu J
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37985457
[TBL] [Abstract][Full Text] [Related]
4. Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data.
McCalla SG; Fotuhi Siahpirani A; Li J; Pyne S; Stone M; Periyasamy V; Shin J; Roy S
G3 (Bethesda); 2023 Mar; 13(3):. PubMed ID: 36626328
[TBL] [Abstract][Full Text] [Related]
5. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.
Ye W; Ji G; Ye P; Long Y; Xiao X; Li S; Su Y; Wu X
BMC Genomics; 2019 May; 20(1):347. PubMed ID: 31068142
[TBL] [Abstract][Full Text] [Related]
6. Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data.
Mao G; Pang Z; Zuo K; Liu J
J Comput Biol; 2023 May; 30(5):619-631. PubMed ID: 36877552
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. scLINE: A multi-network integration framework based on network embedding for representation of single-cell RNA-seq data.
Li H; Xiao X; Wu X; Ye L; Ji G
J Biomed Inform; 2021 Oct; 122():103899. PubMed ID: 34481921
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.
Chen J; Cheong C; Lan L; Zhou X; Liu J; Lyu A; Cheung WK; Zhang L
Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34424948
[TBL] [Abstract][Full Text] [Related]
12. ScGSLC: An unsupervised graph similarity learning framework for single-cell RNA-seq data clustering.
Li J; Jiang W; Han H; Liu J; Liu B; Wang Y
Comput Biol Chem; 2021 Feb; 90():107415. PubMed ID: 33307360
[TBL] [Abstract][Full Text] [Related]
13. Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC.
Kumar N; Mishra B; Athar M; Mukhtar S
Methods Mol Biol; 2021; 2328():171-182. PubMed ID: 34251625
[TBL] [Abstract][Full Text] [Related]
14. scCompressSA: dual-channel self-attention based deep autoencoder model for single-cell clustering by compressing gene-gene interactions.
Zhang W; Yu R; Xu Z; Li J; Gao W; Jiang M; Dai Q
BMC Genomics; 2024 Apr; 25(1):423. PubMed ID: 38684946
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.
Lei T; Chen R; Zhang S; Chen Y
Brief Bioinform; 2023 Sep; 24(6):. PubMed ID: 37769630
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.
Wang CX; Zhang L; Wang B
Genome Biol; 2022 Apr; 23(1):102. PubMed ID: 35443717
[TBL] [Abstract][Full Text] [Related]
20. Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms.
Malekpour SA; Haghverdi L; Sadeghi M
Brief Bioinform; 2024 Mar; 25(3):. PubMed ID: 38653489
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
