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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

404 related items for PubMed ID: 30051742

  • 1. Inference of gene networks from gene expression time series using recurrent neural networks and sparse MAP estimation.
    Chen CK.
    J Bioinform Comput Biol; 2018 Aug; 16(4):1850009. PubMed ID: 30051742
    [Abstract] [Full Text] [Related]

  • 2. Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.
    Chen CK.
    Interdiscip Sci; 2018 Dec; 10(4):823-835. PubMed ID: 28748400
    [Abstract] [Full Text] [Related]

  • 3.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 4.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 5.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 6. Inference of Gene Regulatory Network Based on Local Bayesian Networks.
    Liu F, Zhang SW, Guo WF, Wei ZG, Chen L.
    PLoS Comput Biol; 2016 Aug; 12(8):e1005024. PubMed ID: 27479082
    [Abstract] [Full Text] [Related]

  • 7. Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks.
    Greenfield A, Hafemeister C, Bonneau R.
    Bioinformatics; 2013 Apr 15; 29(8):1060-7. PubMed ID: 23525069
    [Abstract] [Full Text] [Related]

  • 8. Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks.
    Chaitankar V, Ghosh P, Perkins EJ, Gong P, Zhang C.
    BMC Bioinformatics; 2010 Oct 07; 11 Suppl 6(Suppl 6):S19. PubMed ID: 20946602
    [Abstract] [Full Text] [Related]

  • 9.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 10. Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks.
    Monti M, Fiorentino J, Milanetti E, Gosti G, Tartaglia GG.
    Entropy (Basel); 2022 Jan 18; 24(2):. PubMed ID: 35205437
    [Abstract] [Full Text] [Related]

  • 11. Multi-objective Simulated Annealing Variants to Infer Gene Regulatory Network: A Comparative Study.
    Biswas S, Acharyya S.
    IEEE/ACM Trans Comput Biol Bioinform; 2021 Jan 18; 18(6):2612-2623. PubMed ID: 32386161
    [Abstract] [Full Text] [Related]

  • 12.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 13.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 14. Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization.
    Xu R, Wunsch Ii D, Frank R.
    IEEE/ACM Trans Comput Biol Bioinform; 2007 Jan 18; 4(4):681-92. PubMed ID: 17975278
    [Abstract] [Full Text] [Related]

  • 15.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 16. Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations.
    Li Y, Liu D, Li T, Zhu Y.
    BMC Bioinformatics; 2020 Jan 09; 21(1):12. PubMed ID: 31918656
    [Abstract] [Full Text] [Related]

  • 17. Inferring Gene Regulatory Networks From Single-Cell Transcriptomic Data Using Bidirectional RNN.
    Gan Y, Hu X, Zou G, Yan C, Xu G.
    Front Oncol; 2022 Jan 09; 12():899825. PubMed ID: 35692809
    [Abstract] [Full Text] [Related]

  • 18. A hybrid framework for reverse engineering of robust Gene Regulatory Networks.
    Jafari M, Ghavami B, Sattari V.
    Artif Intell Med; 2017 Jun 09; 79():15-27. PubMed ID: 28602483
    [Abstract] [Full Text] [Related]

  • 19. A Bi-Objective RNN Model to Reconstruct Gene Regulatory Network: A Modified Multi-Objective Simulated Annealing Approach.
    Biswas S, Acharyya S.
    IEEE/ACM Trans Comput Biol Bioinform; 2018 Jun 09; 15(6):2053-2059. PubMed ID: 29990170
    [Abstract] [Full Text] [Related]

  • 20. PEPN-GRN: A Petri net-based approach for the inference of gene regulatory networks from noisy gene expression data.
    Vatsa D, Agarwal S.
    PLoS One; 2021 Jun 09; 16(5):e0251666. PubMed ID: 33989333
    [Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 21.