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Journal Abstract Search

626 related items for PubMed ID: 29069282

  • 1. Chromatin accessibility prediction via a hybrid deep convolutional neural network.
    Liu Q, Xia F, Yin Q, Jiang R.
    Bioinformatics; 2018 Mar 01; 34(5):732-738. PubMed ID: 29069282
    [Abstract] [Full Text] [Related]

  • 2. DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.
    Liu Q, Hua K, Zhang X, Wong WH, Jiang R.
    Genomics Proteomics Bioinformatics; 2022 Jun 01; 20(3):496-507. PubMed ID: 35293310
    [Abstract] [Full Text] [Related]

  • 3. 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 01; 19(4):565-577. PubMed ID: 33581335
    [Abstract] [Full Text] [Related]

  • 4. 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 15; 33(14):i92-i101. PubMed ID: 28881969
    [Abstract] [Full Text] [Related]

  • 5. 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 15; 36(2):496-503. PubMed ID: 31318408
    [Abstract] [Full Text] [Related]

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  • 8. DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.
    Quach B, Furey TS.
    Bioinformatics; 2017 Apr 01; 33(7):956-963. PubMed ID: 27993786
    [Abstract] [Full Text] [Related]

  • 9. ALTRE: workflow for defining ALTered Regulatory Elements using chromatin accessibility data.
    Baskin E, Farouni R, Mathé EA.
    Bioinformatics; 2017 Mar 01; 33(5):740-742. PubMed ID: 28011773
    [Abstract] [Full Text] [Related]

  • 10. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
    Kähärä J, Lähdesmäki H.
    Bioinformatics; 2015 Sep 01; 31(17):2852-9. PubMed ID: 25957350
    [Abstract] [Full Text] [Related]

  • 11. Integrating regulatory DNA sequence and gene expression to predict genome-wide chromatin accessibility across cellular contexts.
    Nair S, Kim DS, Perricone J, Kundaje A.
    Bioinformatics; 2019 Jul 15; 35(14):i108-i116. PubMed ID: 31510655
    [Abstract] [Full Text] [Related]

  • 12. Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.
    Xu C, Liu Q, Zhou J, Xie M, Feng J, Jiang T.
    Bioinformatics; 2020 Mar 01; 36(5):1397-1404. PubMed ID: 31693090
    [Abstract] [Full Text] [Related]

  • 13. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.
    Greenside P, Shimko T, Fordyce P, Kundaje A.
    Bioinformatics; 2018 Sep 01; 34(17):i629-i637. PubMed ID: 30423062
    [Abstract] [Full Text] [Related]

  • 14. Discover regulatory DNA elements using chromatin signatures and artificial neural network.
    Firpi HA, Ucar D, Tan K.
    Bioinformatics; 2010 Jul 01; 26(13):1579-86. PubMed ID: 20453004
    [Abstract] [Full Text] [Related]

  • 15. Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.
    Avsec Ž, Barekatain M, Cheng J, Gagneur J.
    Bioinformatics; 2018 Apr 15; 34(8):1261-1269. PubMed ID: 29155928
    [Abstract] [Full Text] [Related]

  • 16. Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.
    Onimaru K, Nishimura O, Kuraku S.
    PLoS One; 2020 Apr 15; 15(7):e0235748. PubMed ID: 32701977
    [Abstract] [Full Text] [Related]

  • 17. 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 01; 33(13):1930-1936. PubMed ID: 28334114
    [Abstract] [Full Text] [Related]

  • 18. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.
    Li Y, Shi W, Wasserman WW.
    BMC Bioinformatics; 2018 May 31; 19(1):202. PubMed ID: 29855387
    [Abstract] [Full Text] [Related]

  • 19. pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.
    Budach S, Marsico A.
    Bioinformatics; 2018 Sep 01; 34(17):3035-3037. PubMed ID: 29659719
    [Abstract] [Full Text] [Related]

  • 20. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.
    Pan X, Shen HB.
    BMC Bioinformatics; 2017 Feb 28; 18(1):136. PubMed ID: 28245811
    [Abstract] [Full Text] [Related]

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