Searching journal content for articles similar to Kelley et al. 26 (7): 990.

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  1. Method: Iterative improvement of deep learning models using synthetic regulatory genomics
    • André M. Ribeiro-dos-Santos and
    • Matthew T. Maurano
    Genome Res. November 2025 35: 2539-2549; Published in Advance October 22, 2025, doi:10.1101/gr.280540.125
    ..., synthetic regulatory genomics is not limited to derivatization of the reference sequence but can deliver a nearly unlimited set of sequences.Here, we explore the application of deep learning models to predict regulatory features at loci engineered through synthetic regulatory genomics. We evaluate...
  2. Research: Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis
    • Olga M. Sigalova,
    • Mattia Forneris,
    • Frosina Stojanovska,
    • Bingqing Zhao,
    • Rebecca R. Viales,
    • Adam Rabinowitz,
    • Fayrouz Hammal,
    • Benoît Ballester,
    • Judith B. Zaugg,
    • and Eileen E.M. Furlong
    Genome Res. May 2025 35: 1138-1153; Published in Advance April 15, 2025, doi:10.1101/gr.279652.124
    ...by 30%–50%. This fine-grained “mutagenesis” can reconstruct functionalized binding motifs for all factors. To prioritize causal variants, we trained a convolutional neural network (Basenji) to accurately predict binding from DNA sequence. The model can also predict measured allelic imbalance for strong...
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  3. Method: Sequential regulatory activity prediction across chromosomes with convolutional neural networks
    • David R. Kelley,
    • Yakir A. Reshef,
    • Maxwell Bileschi,
    • David Belanger,
    • Cory Y. McLean,
    • and Jasper Snoek
    Genome Res. May 2018 28: 739-750; Published in Advance March 27, 2018, doi:10.1101/gr.227819.117
    ...loci.ResultsBasenjiIn previous work, we introduced a deep convolutional neural network approach named Basset for modeling “peak”-based chromatin profiles, focusing particularly on DNase I hypersensitivity (Kelley et al. 2016). Given an input sequence of 500–1000 bp, the model makes a single binary...
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  4. Resource: Modeling transcriptional regulation of model species with deep learning
    • Evan M. Cofer,
    • João Raimundo,
    • Alicja Tadych,
    • Yuji Yamazaki,
    • Aaron K. Wong,
    • Chandra L. Theesfeld,
    • Michael S. Levine,
    • and Olga G. Troyanskaya
    Genome Res. June 2021 31: 1097-1105; Published in Advance April 22, 2021, doi:10.1101/gr.266171.120
    ...evolution. Nature 447: 714–719. doi:10.1038/nature05846 ↵Kelley DR. 2020. Cross-species regulatory sequence activity prediction. PLoS Comput Biol 16: e1008050. doi:10.1371/journal.pcbi.1008050 ↵Kelley DR, Snoek J, Rinn JL. 2016. Basset: learning the regulatory code of the accessible with deep convolutional...
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  5. Method: Cross-species analysis of enhancer logic using deep learning
    • Liesbeth Minnoye,
    • Ibrahim Ihsan Taskiran,
    • David Mauduit,
    • Maurizio Fazio,
    • Linde Van Aerschot,
    • Gert Hulselmans,
    • Valerie Christiaens,
    • Samira Makhzami,
    • Monika Seltenhammer,
    • Panagiotis Karras,
    • Aline Primot,
    • Edouard Cadieu,
    • Ellen van Rooijen,
    • Jean-Christophe Marine,
    • Giorgia Egidy,
    • Ghanem-Elias Ghanem,
    • Leonard Zon,
    • Jasper Wouters,
    • and Stein Aerts
    Genome Res. December 2020 30: 1815-1834; Published in Advance July 30, 2020, doi:10.1101/gr.260844.120
    ...in recent years with the advent of large training sets derived from -wide profiling. Three pivotal methods based on deep learning include DeepBind (Alipanahi et al. 2015), DeepSEA (Zhou and Troyanskaya 2015), and Basset (Kelley et al. 2016), the first convolutional neural networks (CNNs) applied to genomics...
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  6. Method: Interpretation of allele-specific chromatin accessibility using cell state–aware deep learning
    • Zeynep Kalender Atak,
    • Ibrahim Ihsan Taskiran,
    • Jonas Demeulemeester,
    • Christopher Flerin,
    • David Mauduit,
    • Liesbeth Minnoye,
    • Gert Hulselmans,
    • Valerie Christiaens,
    • Ghanem-Elias Ghanem,
    • Jasper Wouters,
    • and Stein Aerts
    Genome Res. June 2021 31: 1082-1096; Published in Advance April 8, 2021, doi:10.1101/gr.260851.120
    ...obtained with the PWM approach using more advanced enhancer modeling. Machine-learning models can be trained on enhancers and take flanking sequence information into account. Examples of deep learning models are Basset (Kelley et al. 2016) and DeepSEA (Zhou and Troyanskaya 2015), which are available...
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  7. Research: Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences
    • Josh T. Cuperus,
    • Benjamin Groves,
    • Anna Kuchina,
    • Alexander B. Rosenberg,
    • Nebojsa Jojic,
    • Stanley Fields,
    • and Georg Seelig
    Genome Res. December 2017 27: 2015-2024; Published in Advance November 2, 2017, doi:10.1101/gr.224964.117
    ...Code file and on GitHub at https://github.com/Seeliglab/2017---Deep-learning-yeast-UTRs.Data accessHigh-throughput reads of selections from this study have been submitted to the Gene Expression Omnibus repository (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE104252. Individual...
  8. Resource: Impact of regulatory variation across human iPSCs and differentiated cells
    • Nicholas E. Banovich,
    • Yang I. Li,
    • Anil Raj,
    • Michelle C. Ward,
    • Peyton Greenside,
    • Diego Calderon,
    • Po Yuan Tung,
    • Jonathan E. Burnett,
    • Marsha Myrthil,
    • Samantha M. Thomas,
    • Courtney K. Burrows,
    • Irene Gallego Romero,
    • Bryan J. Pavlovic,
    • Anshul Kundaje,
    • Jonathan K. Pritchard,
    • and Yoav Gilad
    Genome Res. January 2018 28: 122-131; Published in Advance December 5, 2017, doi:10.1101/gr.224436.117
    ...variation in the population when learning the model parameters.To identify transcription factors that help predict the shared and cell-type–specific regulatory activity across loci, we computed DeepLIFT scores (Shrikumar et al. 2016) with respect to each filter in the first convolutional layer. Among 1320...
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  9. Method: Genome-wide prediction of DNA mutation effect on nucleosome positions for yeast synthetic genomics
    • Etienne Routhier,
    • Edgard Pierre,
    • Ghazaleh Khodabandelou,
    • and Julien Mozziconacci
    Genome Res. February 2021 31: 317-326; Published in Advance December 18, 2020, doi:10.1101/gr.264416.120
    .... 2011). We expect that these issues can be solved by using more sophisticated network architectures as well as increasing the computing power and that future developments in deep learning algorithms will become a game-changing technology for writing.MethodsData accession and preprocessingWe use...
  10. Method: Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay
    • Jennifer Hammelman,
    • Konstantin Krismer,
    • Budhaditya Banerjee,
    • David K. Gifford,
    • and Richard I. Sherwood
    Genome Res. October 2020 30: 1468-1480; Published in Advance September 24, 2020, doi:10.1101/gr.263228.120
    ..., we developed a deep learning model trained to predict DNase-accessible regions from underlying DNA sequence and cell type–specific DNase-seq training data. This method, which we call DeepAccess, trains an ensemble of 10 convolutional neural networks on DNase-seq data from ESCs and DE cells to predict...
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