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 *

268 related articles for article (PubMed ID: 31874597)

  • 1. DEEPSEN: a convolutional neural network based method for super-enhancer prediction.
    Bu H; Hao J; Gan Y; Zhou S; Guan J
    BMC Bioinformatics; 2019 Dec; 20(Suppl 15):598. PubMed ID: 31874597
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Advance in the research on super-enhancer.
    Sun CB; Zhang X
    Yi Chuan; 2016 Dec; 38(12):1056-1068. PubMed ID: 28034838
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Enhancer prediction with histone modification marks using a hybrid neural network model.
    Lim A; Lim S; Kim S
    Methods; 2019 Aug; 166():48-56. PubMed ID: 30905748
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Experimental Validation and Prediction of Super-Enhancers: Advances and Challenges.
    Kravchuk EV; Ashniev GA; Gladkova MG; Orlov AV; Vasileva AV; Boldyreva AV; Burenin AG; Skirda AM; Nikitin PI; Orlova NN
    Cells; 2023 Apr; 12(8):. PubMed ID: 37190100
    [TBL] [Abstract][Full Text] [Related]  

  • 5. SENet: A deep learning framework for discriminating super- and typical enhancers by sequence information.
    Luo H; Li Y; Liu H; Ding P; Yu Y; Luo L
    Comput Biol Chem; 2023 Aug; 105():107905. PubMed ID: 37348298
    [TBL] [Abstract][Full Text] [Related]  

  • 6. ES-ARCNN: Predicting enhancer strength by using data augmentation and residual convolutional neural network.
    Zhang TH; Flores M; Huang Y
    Anal Biochem; 2021 Apr; 618():114120. PubMed ID: 33535061
    [TBL] [Abstract][Full Text] [Related]  

  • 7. DeepSE: Detecting super-enhancers among typical enhancers using only sequence feature embeddings.
    Ji QY; Gong XJ; Li HM; Du PF
    Genomics; 2021 Nov; 113(6):4052-4060. PubMed ID: 34666191
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting enhancers with deep convolutional neural networks.
    Min X; Zeng W; Chen S; Chen N; Chen T; Jiang R
    BMC Bioinformatics; 2017 Dec; 18(Suppl 13):478. PubMed ID: 29219068
    [TBL] [Abstract][Full Text] [Related]  

  • 9. CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.
    Xuan P; Sheng N; Zhang T; Liu Y; Guo Y
    Int J Mol Sci; 2019 Aug; 20(17):. PubMed ID: 31480319
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Research progress of super enhancer in cancer].
    Wu ZQ; Mi ZY
    Yi Chuan; 2019 Jan; 41(1):41-51. PubMed ID: 30686784
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Super-enhancers in cancer.
    Thandapani P
    Pharmacol Ther; 2019 Jul; 199():129-138. PubMed ID: 30885876
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Super-enhancers in the control of cell identity and disease.
    Hnisz D; Abraham BJ; Lee TI; Lau A; Saint-André V; Sigova AA; Hoke HA; Young RA
    Cell; 2013 Nov; 155(4):934-47. PubMed ID: 24119843
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The molecular understanding of super-enhancer dysregulation in cancer.
    Yoshino S; Suzuki HI
    Nagoya J Med Sci; 2022 May; 84(2):216-229. PubMed ID: 35967935
    [TBL] [Abstract][Full Text] [Related]  

  • 14. PorcineAI-Enhancer: Prediction of Pig Enhancer Sequences Using Convolutional Neural Networks.
    Wang J; Zhang H; Chen N; Zeng T; Ai X; Wu K
    Animals (Basel); 2023 Sep; 13(18):. PubMed ID: 37760334
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Super enhancer lncRNAs: a novel hallmark in cancer.
    Song P; Han R; Yang F
    Cell Commun Signal; 2024 Apr; 22(1):207. PubMed ID: 38566153
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Oncogenic super-enhancers in cancer: mechanisms and therapeutic targets.
    Bacabac M; Xu W
    Cancer Metastasis Rev; 2023 Jun; 42(2):471-480. PubMed ID: 37059907
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.
    Sun C; Zhang N; Yu P; Wu X; Li Q; Li T; Li H; Xiao X; Shalmani A; Li L; Che D; Wang X; Zhang P; Chen Z; Liu T; Zhao J; Hua J; Liao M
    Mol Omics; 2020 Oct; 16(5):455-464. PubMed ID: 32568326
    [TBL] [Abstract][Full Text] [Related]  

  • 18. ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction.
    Mehmood F; Arshad S; Shoaib M
    Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38385876
    [TBL] [Abstract][Full Text] [Related]  

  • 19. DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.
    Chen S; Gan M; Lv H; Jiang R
    Genomics Proteomics Bioinformatics; 2021 Aug; 19(4):565-577. PubMed ID: 33581335
    [TBL] [Abstract][Full Text] [Related]  

  • 20. iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.
    Liao M; Zhao JP; Tian J; Zheng CH
    BMC Bioinformatics; 2022 Nov; 23(1):480. PubMed ID: 36376800
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 14.