BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

190 related articles for article (PubMed ID: 29764360)

  • 1. Prediction of enhancer-promoter interactions via natural language processing.
    Zeng W; Wu M; Jiang R
    BMC Genomics; 2018 May; 19(Suppl 2):84. PubMed ID: 29764360
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Predicting enhancer-promoter interactions by deep learning and matching heuristic.
    Min X; Ye C; Liu X; Zeng X
    Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33096548
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction.
    Min X; Lu F; Li C
    Curr Pharm Des; 2021; 27(15):1847-1855. PubMed ID: 33234095
    [TBL] [Abstract][Full Text] [Related]  

  • 4. EPI-Mind: Identifying Enhancer-Promoter Interactions Based on Transformer Mechanism.
    Ni Y; Fan L; Wang M; Zhang N; Zuo Y; Liao M
    Interdiscip Sci; 2022 Sep; 14(3):786-794. PubMed ID: 35633468
    [TBL] [Abstract][Full Text] [Related]  

  • 5. StackEPI: identification of cell line-specific enhancer-promoter interactions based on stacking ensemble learning.
    Fan Y; Peng B
    BMC Bioinformatics; 2022 Jul; 23(1):272. PubMed ID: 35820811
    [TBL] [Abstract][Full Text] [Related]  

  • 6. EPIHC: Improving Enhancer-Promoter Interaction Prediction by Using Hybrid Features and Communicative Learning.
    Liu S; Xu X; Yang Z; Zhao X; Liu S; Zhang W
    IEEE/ACM Trans Comput Biol Bioinform; 2022; 19(6):3435-3443. PubMed ID: 34473626
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.
    Jing F; Zhang SW; Zhang S
    BMC Bioinformatics; 2020 Nov; 21(1):507. PubMed ID: 33160328
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Recognition of long-range enhancer-promoter interactions by adding genomic signatures of segmented regulatory regions.
    Feng ZX; Li QZ
    Genomics; 2017 Oct; 109(5-6):341-352. PubMed ID: 28579514
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks.
    Xiao M; Zhuang Z; Pan W
    Genes (Basel); 2019 Dec; 11(1):. PubMed ID: 31905774
    [TBL] [Abstract][Full Text] [Related]  

  • 10. EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.
    Ahmed FS; Aly S; Liu X
    BMC Bioinformatics; 2024 Jun; 25(1):216. PubMed ID: 38890584
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Modeling aspects of the language of life through transfer-learning protein sequences.
    Heinzinger M; Elnaggar A; Wang Y; Dallago C; Nechaev D; Matthes F; Rost B
    BMC Bioinformatics; 2019 Dec; 20(1):723. PubMed ID: 31847804
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The impact of sequence length and number of sequences on promoter prediction performance.
    Carvalho SG; Guerra-Sá R; de C Merschmann LH
    BMC Bioinformatics; 2015; 16 Suppl 19(Suppl 19):S5. PubMed ID: 26695879
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.
    Le NQK; Ho QT; Nguyen TT; Ou YY
    Brief Bioinform; 2021 Sep; 22(5):. PubMed ID: 33539511
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Chromatin accessibility prediction via convolutional long short-term memory networks with k-mer embedding.
    Min X; Zeng W; Chen N; Chen T; Jiang R
    Bioinformatics; 2017 Jul; 33(14):i92-i101. PubMed ID: 28881969
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.
    Zeng W; Wang Y; Jiang R
    Bioinformatics; 2020 Jan; 36(2):496-503. PubMed ID: 31318408
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism.
    Hong Z; Zeng X; Wei L; Liu X
    Bioinformatics; 2020 Feb; 36(4):1037-1043. PubMed ID: 31588505
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A deep learning framework for enhancer prediction using word embedding and sequence generation.
    Geng Q; Yang R; Zhang L
    Biophys Chem; 2022 Jul; 286():106822. PubMed ID: 35605495
    [TBL] [Abstract][Full Text] [Related]  

  • 18. PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.
    Wang Y; Tai S; Zhang S; Sheng N; Xie X
    Genes (Basel); 2023 Jul; 14(7):. PubMed ID: 37510345
    [TBL] [Abstract][Full Text] [Related]  

  • 19. EPIP: a novel approach for condition-specific enhancer-promoter interaction prediction.
    Talukder A; Saadat S; Li X; Hu H
    Bioinformatics; 2019 Oct; 35(20):3877-3883. PubMed ID: 31410461
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Using Language Representation Learning Approach to Efficiently Identify Protein Complex Categories in Electron Transport Chain.
    Nguyen TT; Le NQ; Ho QT; Phan DV; Ou YY
    Mol Inform; 2020 Oct; 39(10):e2000033. PubMed ID: 32598045
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.