BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

186 related articles for article (PubMed ID: 38485768)

  • 1. Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.
    Zhang Y; Zhang P; Wu H
    Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38485768
    [TBL] [Abstract][Full Text] [Related]  

  • 2. iEnhancer-SKNN: a stacking ensemble learning-based method for enhancer identification and classification using sequence information.
    Wu H; Liu M; Zhang P; Zhang H
    Brief Funct Genomics; 2023 May; 22(3):302-311. PubMed ID: 36715222
    [TBL] [Abstract][Full Text] [Related]  

  • 3. BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.
    Yang B; Liu F; Ren C; Ouyang Z; Xie Z; Bo X; Shu W
    Bioinformatics; 2017 Jul; 33(13):1930-1936. PubMed ID: 28334114
    [TBL] [Abstract][Full Text] [Related]  

  • 4. HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.
    Liu Y; Wang Z; Yuan H; Zhu G; Zhang Y
    Brief Bioinform; 2023 Sep; 24(5):. PubMed ID: 37539835
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. SENIES: DNA Shape Enhanced Two-Layer Deep Learning Predictor for the Identification of Enhancers and Their Strength.
    Li Y; Kong F; Cui H; Wang F; Li C; Ma J
    IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):637-645. PubMed ID: 35015646
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Genome-wide identification and characterization of DNA enhancers with a stacked multivariate fusion framework.
    Wang Y; Hou Z; Yang Y; Wong KC; Li X
    PLoS Comput Biol; 2022 Dec; 18(12):e1010779. PubMed ID: 36520922
    [TBL] [Abstract][Full Text] [Related]  

  • 9. DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.
    Zhang T; Li L; Sun H; Wang G
    Bioinformatics; 2023 Jun; 39(6):. PubMed ID: 37294799
    [TBL] [Abstract][Full Text] [Related]  

  • 10. SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.
    Wang Y; Jaime-Lara RB; Roy A; Sun Y; Liu X; Joseph PV
    BMC Res Notes; 2021 Mar; 14(1):104. PubMed ID: 33741075
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Cross-species enhancer prediction using machine learning.
    MacPhillamy C; Alinejad-Rokny H; Pitchford WS; Low WY
    Genomics; 2022 Sep; 114(5):110454. PubMed ID: 36030022
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.
    Li Y; Shi W; Wasserman WW
    BMC Bioinformatics; 2018 May; 19(1):202. PubMed ID: 29855387
    [TBL] [Abstract][Full Text] [Related]  

  • 14. CENTRE: a gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction.
    Rapakoulia T; Lopez Ruiz De Vargas S; Omgba PA; Laupert V; Ulitsky I; Vingron M
    Bioinformatics; 2023 Nov; 39(11):. PubMed ID: 37982748
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.
    Basith S; Hasan MM; Lee G; Wei L; Manavalan B
    Brief Bioinform; 2021 Nov; 22(6):. PubMed ID: 34226917
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.
    Kim SG; Theera-Ampornpunt N; Fang CH; Harwani M; Grama A; Chaterji S
    BMC Syst Biol; 2016 Aug; 10 Suppl 2(Suppl 2):54. PubMed ID: 27490187
    [TBL] [Abstract][Full Text] [Related]  

  • 17. DEEP: a general computational framework for predicting enhancers.
    Kleftogiannis D; Kalnis P; Bajic VB
    Nucleic Acids Res; 2015 Jan; 43(1):e6. PubMed ID: 25378307
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Cross-species analysis of enhancer logic using deep learning.
    Minnoye L; Taskiran II; Mauduit D; Fazio M; Van Aerschot L; Hulselmans G; Christiaens V; Makhzami S; Seltenhammer M; Karras P; Primot A; Cadieu E; van Rooijen E; Marine JC; Egidy G; Ghanem GE; Zon L; Wouters J; Aerts S
    Genome Res; 2020 Dec; 30(12):1815-1834. PubMed ID: 32732264
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Predicting functional variants in enhancer and promoter elements using RegulomeDB.
    Dong S; Boyle AP
    Hum Mutat; 2019 Sep; 40(9):1292-1298. PubMed ID: 31228310
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.