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 *

284 related articles for article (PubMed ID: 34252966)

  • 1. EnHiC: learning fine-resolution Hi-C contact maps using a generative adversarial framework.
    Hu Y; Ma W
    Bioinformatics; 2021 Jul; 37(Suppl_1):i272-i279. PubMed ID: 34252966
    [TBL] [Abstract][Full Text] [Related]  

  • 2. HiCARN: resolution enhancement of Hi-C data using cascading residual networks.
    Hicks P; Oluwadare O
    Bioinformatics; 2022 Apr; 38(9):2414-2421. PubMed ID: 35274679
    [TBL] [Abstract][Full Text] [Related]  

  • 3. hicGAN infers super resolution Hi-C data with generative adversarial networks.
    Liu Q; Lv H; Jiang R
    Bioinformatics; 2019 Jul; 35(14):i99-i107. PubMed ID: 31510693
    [TBL] [Abstract][Full Text] [Related]  

  • 4. DFHiC: a dilated full convolution model to enhance the resolution of Hi-C data.
    Wang B; Liu K; Li Y; Wang J
    Bioinformatics; 2023 May; 39(5):. PubMed ID: 37084258
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Single-cell Hi-C data enhancement with deep residual and generative adversarial networks.
    Wang Y; Guo Z; Cheng J
    Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37498561
    [TBL] [Abstract][Full Text] [Related]  

  • 6. ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures.
    Ye T; Ma W
    Nucleic Acids Res; 2020 Dec; 48(21):e123. PubMed ID: 33074315
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.
    Hong H; Jiang S; Li H; Du G; Sun Y; Tao H; Quan C; Zhao C; Li R; Li W; Yin X; Huang Y; Li C; Chen H; Bo X
    PLoS Comput Biol; 2020 Feb; 16(2):e1007287. PubMed ID: 32084131
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.
    Fang T; Liu Y; Woicik A; Lu M; Jha A; Wang X; Li G; Hristov B; Liu Z; Xu H; Noble WS; Wang S
    Bioinformatics; 2024 Jun; 40(Suppl 1):i471-i480. PubMed ID: 38940142
    [TBL] [Abstract][Full Text] [Related]  

  • 9. HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.
    Liu T; Wang Z
    Bioinformatics; 2019 Nov; 35(21):4222-4228. PubMed ID: 31056636
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines.
    Khalil AIS; Muzaki SRBM; Chattopadhyay A; Sanyal A
    BMC Bioinformatics; 2020 Nov; 21(1):506. PubMed ID: 33160308
    [TBL] [Abstract][Full Text] [Related]  

  • 11. GenomeDISCO: a concordance score for chromosome conformation capture experiments using random walks on contact map graphs.
    Ursu O; Boley N; Taranova M; Wang YXR; Yardimci GG; Stafford Noble W; Kundaje A
    Bioinformatics; 2018 Aug; 34(16):2701-2707. PubMed ID: 29554289
    [TBL] [Abstract][Full Text] [Related]  

  • 12. HiCRep.py: fast comparison of Hi-C contact matrices in Python.
    Lin D; Sanders J; Noble WS
    Bioinformatics; 2021 Sep; 37(18):2996-2997. PubMed ID: 33576390
    [TBL] [Abstract][Full Text] [Related]  

  • 13. VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm for improving and generating Hi-C data.
    Highsmith M; Cheng J
    Sci Rep; 2021 Apr; 11(1):8880. PubMed ID: 33893353
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CscoreTool: fast Hi-C compartment analysis at high resolution.
    Zheng X; Zheng Y
    Bioinformatics; 2018 May; 34(9):1568-1570. PubMed ID: 29244056
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An integrative approach for fine-mapping chromatin interactions.
    Jaroszewicz A; Ernst J
    Bioinformatics; 2020 Mar; 36(6):1704-1711. PubMed ID: 31742318
    [TBL] [Abstract][Full Text] [Related]  

  • 16. C2c: Predicting Micro-C from Hi-C.
    Zhu H; Liu T; Wang Z
    Genes (Basel); 2024 May; 15(6):. PubMed ID: 38927609
    [TBL] [Abstract][Full Text] [Related]  

  • 17. multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments.
    Stansfield JC; Cresswell KG; Dozmorov MG
    Bioinformatics; 2019 Sep; 35(17):2916-2923. PubMed ID: 30668639
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Be-1DCNN: a neural network model for chromatin loop prediction based on bagging ensemble learning.
    Wu H; Zhou B; Zhou H; Zhang P; Wang M
    Brief Funct Genomics; 2023 Nov; 22(5):475-484. PubMed ID: 37133976
    [TBL] [Abstract][Full Text] [Related]  

  • 19. ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.
    Oluwadare O; Cheng J
    BMC Bioinformatics; 2017 Nov; 18(1):480. PubMed ID: 29137603
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Reference panel-guided super-resolution inference of Hi-C data.
    Zhang Y; Blanchette M
    Bioinformatics; 2023 Jun; 39(39 Suppl 1):i386-i393. PubMed ID: 37387127
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.