Searching journal content for articles similar to Ribeiro-dos-Santos and Maurano 35 (11): 2539.

Displaying results 1-10 of 26
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  1. Method: Diffusion-based generation of gene regulatory networks from scRNA-seq data with DigNet
    • Chuanyuan Wang and
    • Zhi-Ping Liu
    Genome Res. February 2025 35: 340-354; Published in Advance December 18, 2024, doi:10.1101/gr.279551.124
    ...al. 2010; Aibar et al. 2017), dynamic Bayesian models (Liu et al. 2016), and deep learning-based algorithms (Shu et al. 2021). Although these existing methods have achieved some advancements in inferring GRNs from transcriptomic data, they predominantly concentrate on modeling the regulatory...
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  2. 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
    ...data (Eraslan et al. 2019). Since their emergence in the genomics field, machine learning techniques, and especially CNNs, have been applied to model a range of regulatory aspects, including cross-species enhancer predictions (Min et al. 2016; Quang and Xie 2016; Chen et al. 2018), TF binding sites...
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  3. Method: Graph-based self-supervised learning for repeat detection in metagenomic assembly
    • Ali Azizpour,
    • Advait Balaji,
    • Todd J. Treangen,
    • and Santiago Segarra
    Genome Res. September 2024 34: 1468-1476; Published in Advance July 19, 2024, doi:10.1101/gr.279136.124
    ...assemblies. Moreover, repeats introduce ambiguities for comparative genomics, hindering differentiation between identical or similar regions and complicating the understanding of gene functions, regulatory elements, and their role in genetic disorders (Treangen and Salzberg 2012). To overcome these obstacles...
  4. 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
    ...whether the scale of our library was an important factor in improving the accuracy of the model, we made learning curves from models trained on different sized subsets of the data. We saw a corresponding decrease in the predictive power of our models as the training size decreased (Supplemental Fig. S4C...
  5. Method: Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks
    • David R. Kelley,
    • Jasper Snoek,
    • and John L. Rinn
    Genome Res. July 2016 26: 990-999; Published in Advance May 3, 2016, doi:10.1101/gr.200535.115
    ...Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks David R. Kelley 1 , Jasper Snoek 2 and John L. Rinn 1 1Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge...
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  6. RECOMB 2022 Special/Methods: Learning probabilistic protein–DNA recognition codes from DNA-binding specificities using structural mappings
    • Joshua L. Wetzel,
    • Kaiqian Zhang,
    • and Mona Singh
    Genome Res. September 2022 32: 1776-1786; Published in Advance September 19, 2022, doi:10.1101/gr.276606.122
    ...; Kulakovskiy et al. 2018; Castro-Mondragon et al. 2022). These data have enabled large-scale construction of regulatory networks and have revealed the organization of regulatory circuits (Gerstein et al. 2012). Further, these data have been used to train machine learning models to uncover cooperative TF...
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  7. Research: SCRaMbLE generates designed combinatorial stochastic diversity in synthetic chromosomes
    • Yue Shen,
    • Giovanni Stracquadanio,
    • Yun Wang,
    • Kun Yang,
    • Leslie A. Mitchell,
    • Yaxin Xue,
    • Yizhi Cai,
    • Tai Chen,
    • Jessica S. Dymond,
    • Kang Kang,
    • Jianhui Gong,
    • Xiaofan Zeng,
    • Yongfen Zhang,
    • Yingrui Li,
    • Qiang Feng,
    • Xun Xu,
    • Jun Wang,
    • Jian Wang,
    • Huanming Yang,
    • Jef D. Boeke,
    • and Joel S. Bader
    Genome Res. January 2016 26: 36-49; Published in Advance November 13, 2015, doi:10.1101/gr.193433.115
    ...) and the -scale editing of Escherichia coli MG1655 to recode all UAG stop codons to UAA (Lajoie et al. 2013). Eukaryotic synthetic genomics has centered on Saccharomyces cerevisiae (yeast), simultaneously a powerful model organism and a producer of valuable products. Yeast synthesis provides access to biology...
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  8. Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
    • Andrew D. Bates,
    • Dawid Grzela,
    • Maciej Studzian,
    • Louise Brennan,
    • Moli Williams,
    • Conor Fawcett,
    • Beth Hammond,
    • Manreen Grewal,
    • Marcin Ratajewski,
    • Lukasz Pulaski,
    • and Urszula L. McClurg
    Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
    ...GRN (Chen et al. 2021a) offer improved specificity by integrating chromatin accessibility.Recent deep learning tools offer incremental improvements and may appeal to advanced users. However, for practical GOI analysis, especially in laboratories with limited IT resources, classical algorithms are often...
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  9. Method: Automated interpretable artificial intelligence genomic prediction with AIGP
    • Lei Wei,
    • Ziqin Jiang,
    • Baoliang Fan,
    • Yidan Yan,
    • Zhenqiang Xu,
    • Xiaoxiang Hu,
    • and Yuzhe Wang
    Genome Res. April 2026 36: 814-826; Published in Advance March 5, 2026, doi:10.1101/gr.281006.125
    ...miss optimal parameters or get trapped in local optima. Thus, SSA is a more precise, efficient choice for large-scale hyperparameter tuning of ML models. In addition, when comparing AIGP with deep learning models such as WheatGP and Cropformer (Wang et al. 2025a,b), these methods underperformed AIGP...
  10. Method: High-fidelity bidirectional translation between single-cell transcriptomes and DNA methylomes with scBOND
    • Kehan Lang,
    • Chenyang Jia,
    • Siyu Li,
    • Yi Guo,
    • Dingjun Hu,
    • and Shengquan Chen
    Genome Res. April 2026 36: 769-784; Published in Advance March 26, 2026, doi:10.1101/gr.281350.125
    ...neurons. Moreover, using RNA-only data, we reconstruct scDNAm profiles and identify cell-type- and stage-specific regulatory mechanisms in oligodendrocyte lineage. To further improve model generalization in paired data-scarce scenarios, we propose scBOND-Aug, a variant of scBOND equipped...
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