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 *

171 related articles for article (PubMed ID: 33488763)

  • 1. iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.
    Sun A; Xiao X; Xu Z
    Comput Math Methods Med; 2021; 2021():6636350. PubMed ID: 33488763
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.
    Kim J; Shujaat M; Tayara H
    Genomics; 2022 May; 114(3):110384. PubMed ID: 35533969
    [TBL] [Abstract][Full Text] [Related]  

  • 3. iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition.
    Xiao X; Xu ZC; Qiu WR; Wang P; Ge HT; Chou KC
    Genomics; 2019 Dec; 111(6):1785-1793. PubMed ID: 30529532
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.
    Umarov RK; Solovyev VV
    PLoS One; 2017; 12(2):e0171410. PubMed ID: 28158264
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.
    Zhu Y; Li F; Xiang D; Akutsu T; Song J; Jia C
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33227813
    [TBL] [Abstract][Full Text] [Related]  

  • 6. iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.
    Liu B; Yang F; Huang DS; Chou KC
    Bioinformatics; 2018 Jan; 34(1):33-40. PubMed ID: 28968797
    [TBL] [Abstract][Full Text] [Related]  

  • 7. iPromoter-Seqvec: identifying promoters using bidirectional long short-term memory and sequence-embedded features.
    Nguyen-Vo TH; Trinh QH; Nguyen L; Nguyen-Hoang PU; Rahardja S; Nguyen BP
    BMC Genomics; 2022 Oct; 23(Suppl 5):681. PubMed ID: 36192696
    [TBL] [Abstract][Full Text] [Related]  

  • 8. DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning.
    Wang C; Zhang J; Cheng L; Wu J; Xiao M; Xia J; Bin Y
    IEEE J Biomed Health Inform; 2022 Oct; 26(10):5258-5266. PubMed ID: 35867364
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Identification of TATA and TATA-less promoters in plant genomes by integrating diversity measure, GC-Skew and DNA geometric flexibility.
    Zuo YC; Li QZ
    Genomics; 2011 Feb; 97(2):112-20. PubMed ID: 21112384
    [TBL] [Abstract][Full Text] [Related]  

  • 10. iProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network.
    Shujaat M; Jin JS; Tayara H; Chong KT
    Front Microbiol; 2022; 13():1061122. PubMed ID: 36406389
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prevalence of the initiator over the TATA box in human and yeast genes and identification of DNA motifs enriched in human TATA-less core promoters.
    Yang C; Bolotin E; Jiang T; Sladek FM; Martinez E
    Gene; 2007 Mar; 389(1):52-65. PubMed ID: 17123746
    [TBL] [Abstract][Full Text] [Related]  

  • 12. pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters.
    Shujaat M; Wahab A; Tayara H; Chong KT
    Genes (Basel); 2020 Dec; 11(12):. PubMed ID: 33371507
    [TBL] [Abstract][Full Text] [Related]  

  • 13. iProm-Sigma54: A CNN Base Prediction Tool for
    Shujaat M; Kim H; Tayara H; Chong KT
    Cells; 2023 Mar; 12(6):. PubMed ID: 36980170
    [TBL] [Abstract][Full Text] [Related]  

  • 14. TC-motifs at the TATA-box expected position in plant genes: a novel class of motifs involved in the transcription regulation.
    Bernard V; Brunaud V; Lecharny A
    BMC Genomics; 2010 Mar; 11():166. PubMed ID: 20222994
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Heterogeneity of Arabidopsis core promoters revealed by high-density TSS analysis.
    Yamamoto YY; Yoshitsugu T; Sakurai T; Seki M; Shinozaki K; Obokata J
    Plant J; 2009 Oct; 60(2):350-62. PubMed ID: 19563441
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identification of plant promoter constituents by analysis of local distribution of short sequences.
    Yamamoto YY; Ichida H; Matsui M; Obokata J; Sakurai T; Satou M; Seki M; Shinozaki K; Abe T
    BMC Genomics; 2007 Mar; 8():67. PubMed ID: 17346352
    [TBL] [Abstract][Full Text] [Related]  

  • 17. TSSPlant: a new tool for prediction of plant Pol II promoters.
    Shahmuradov IA; Umarov RK; Solovyev VV
    Nucleic Acids Res; 2017 May; 45(8):e65. PubMed ID: 28082394
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Genome wide analysis of Arabidopsis core promoters.
    Molina C; Grotewold E
    BMC Genomics; 2005 Feb; 6():25. PubMed ID: 15733318
    [TBL] [Abstract][Full Text] [Related]  

  • 19. iPromoter-BnCNN: a novel branched CNN-based predictor for identifying and classifying sigma promoters.
    Amin R; Rahman CR; Ahmed S; Sifat MHR; Liton MNK; Rahman MM; Khan MZH; Shatabda S
    Bioinformatics; 2020 Dec; 36(19):4869-4875. PubMed ID: 32614400
    [TBL] [Abstract][Full Text] [Related]  

  • 20. iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory.
    Zhang ZM; Zhao JP; Wei PJ; Zheng CH
    Comput Methods Programs Biomed; 2022 Nov; 226():107087. PubMed ID: 36099675
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.