These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

351 related articles for article (PubMed ID: 30758825)

  • 1. Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data.
    Lamere AT; Li J
    Methods Mol Biol; 2019; 1935():141-153. PubMed ID: 30758825
    [TBL] [Abstract][Full Text] [Related]  

  • 2. LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering.
    Specht AT; Li J
    Bioinformatics; 2017 Mar; 33(5):764-766. PubMed ID: 27993778
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Pseudotime Reconstruction Using TSCAN.
    Ji Z; Ji H
    Methods Mol Biol; 2019; 1935():115-124. PubMed ID: 30758823
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. 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]  

  • 6. Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe.
    Qiu X; Rahimzamani A; Wang L; Ren B; Mao Q; Durham T; McFaline-Figueroa JL; Saunders L; Trapnell C; Kannan S
    Cell Syst; 2020 Mar; 10(3):265-274.e11. PubMed ID: 32135093
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data.
    Nguyen H; Tran D; Tran B; Pehlivan B; Nguyen T
    Brief Bioinform; 2021 May; 22(3):. PubMed ID: 34020546
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.
    Ballouz S; Verleyen W; Gillis J
    Bioinformatics; 2015 Jul; 31(13):2123-30. PubMed ID: 25717192
    [TBL] [Abstract][Full Text] [Related]  

  • 9. SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.
    Wang Y; Zhou F; Guan J
    Bioinformatics; 2024 Jul; 40(7):. PubMed ID: 38950180
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Exploiting single-cell expression to characterize co-expression replicability.
    Crow M; Paul A; Ballouz S; Huang ZJ; Gillis J
    Genome Biol; 2016 May; 17():101. PubMed ID: 27165153
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.
    Chan TE; Stumpf MPH; Babtie AC
    Cell Syst; 2017 Sep; 5(3):251-267.e3. PubMed ID: 28957658
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Network Inference from Single-Cell Transcriptomic Data.
    Todorov H; Cannoodt R; Saelens W; Saeys Y
    Methods Mol Biol; 2019; 1883():235-249. PubMed ID: 30547403
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Predicting gene regulatory networks of soybean nodulation from RNA-Seq transcriptome data.
    Zhu M; Dahmen JL; Stacey G; Cheng J
    BMC Bioinformatics; 2013 Sep; 14():278. PubMed ID: 24053776
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.
    Chen S; Mar JC
    BMC Bioinformatics; 2018 Jun; 19(1):232. PubMed ID: 29914350
    [TBL] [Abstract][Full Text] [Related]  

  • 16. PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.
    Song D; Li JJ
    Genome Biol; 2021 Apr; 22(1):124. PubMed ID: 33926517
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data.
    Sanchez-Castillo M; Blanco D; Tienda-Luna IM; Carrion MC; Huang Y
    Bioinformatics; 2018 Mar; 34(6):964-970. PubMed ID: 29028984
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Inference of Gene Coexpression Networks from Bulk-Based RNA-Sequencing Data.
    Lamere AT
    Methods Mol Biol; 2021; 2328():13-23. PubMed ID: 34251617
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Computational approaches for interpreting scRNA-seq data.
    Rostom R; Svensson V; Teichmann SA; Kar G
    FEBS Lett; 2017 Aug; 591(15):2213-2225. PubMed ID: 28524227
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 18.