BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

411 related articles for article (PubMed ID: 34424948)

  • 1. 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]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. 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]  

  • 8. scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.
    Dautle M; Zhang S; Chen Y
    Int J Mol Sci; 2023 Aug; 24(17):. PubMed ID: 37686146
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 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. GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference.
    Li S; Liu Y; Shen LC; Yan H; Song J; Yu DJ
    Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38261340
    [TBL] [Abstract][Full Text] [Related]  

  • 14. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network.
    Huang Z; Wang J; Lu X; Mohd Zain A; Yu G
    Brief Bioinform; 2023 Mar; 24(2):. PubMed ID: 36733262
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.
    Holland CH; Tanevski J; Perales-Patón J; Gleixner J; Kumar MP; Mereu E; Joughin BA; Stegle O; Lauffenburger DA; Heyn H; Szalai B; Saez-Rodriguez J
    Genome Biol; 2020 Feb; 21(1):36. PubMed ID: 32051003
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning.
    Lin Z; Ou-Yang L
    Brief Bioinform; 2023 Jan; 24(1):. PubMed ID: 36585783
    [TBL] [Abstract][Full Text] [Related]  

  • 17. scGMAI: a Gaussian mixture model for clustering single-cell RNA-Seq data based on deep autoencoder.
    Yu B; Chen C; Qi R; Zheng R; Skillman-Lawrence PJ; Wang X; Ma A; Gu H
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33300547
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference.
    Aubin-Frankowski PC; Vert JP
    Bioinformatics; 2020 Sep; 36(18):4774-4780. PubMed ID: 33026066
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Contrastive self-supervised clustering of scRNA-seq data.
    Ciortan M; Defrance M
    BMC Bioinformatics; 2021 May; 22(1):280. PubMed ID: 34044773
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 21.