Searching journal content for articles similar to Cofer et al. 31 (6): 1097.

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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
    ...York University Grossman School of Medicine, New York, New York 10016, USA Corresponding author: maurano@nyu.eduAbstractDeep learning models can accurately reconstruct -wide epigenetic tracks from the reference sequence alone. But it is unclear what predictive power they have on sequence diverging from...
  2. Method: Modeling gene interactions in polygenic prediction via geometric deep learning
    • Han Li,
    • Jianyang Zeng,
    • Michael P. Snyder,
    • and Sai Zhang
    Genome Res. January 2025 35: 178-187; Published in Advance November 19, 2024, doi:10.1101/gr.279694.124
    ...-Net, an interpretable geometric deep learning–based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the -wide PRS at the single-gene resolution and then explicitly encapsulates gene–gene interactions...
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  3. Method: EnDeep4mC predicts DNA N4-methylcytosine sites using a dual-adaptive feature encoding framework in deep ensembles
    • Shuyu Zhang,
    • Quan Zou,
    • Mengting Niu,
    • Zhibin Lv,
    • Antony Stalin,
    • and Ximei Luo
    Genome Res. March 2026 36: 589-599; Published in Advance February 17, 2026, doi:10.1101/gr.280977.125
    ...species-specific modeling with ensemble deep learning architectures to systematically optimize feature encoding schemes. Evaluated across six species, EnDeep4mC demonstrates commendable prediction performance and significantly outperforms current state-of-the-art predictors. Cross-species validation...
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  4. 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
    ...genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several time points...
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  5. Method: Delineating yeast cleavage and polyadenylation signals using deep learning
    • Emily Kunce Stroup and
    • Zhe Ji
    Genome Res. July 2024 34: 1066-1080; Published in Advance June 24, 2024, doi:10.1101/gr.278606.123
    ...address this issue by developing deep learning models to deconvolute degenerate cis-regulatory elements and quantify their positional importance in mediating yeast poly(A) site formation, cleavage heterogeneity, and strength. In S. cerevisiae, cleavage heterogeneity is promoted by the depletion of U...
  6. Method: Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach
    • Fabien Wehbe,
    • Levi Adams,
    • Jordan Babadoudou,
    • Samantha Yuen,
    • Yoon-Seong Kim,
    • and Yoshiaki Tanaka
    Genome Res. January 2025 35: 135-146; Published in Advance December 2, 2024, doi:10.1101/gr.278812.123
    ...IDST), that infers the disease progression levels of individual cells in single-cell transcriptome profiles with weakly supervised deep learning. The weak supervision models utilize labeling functions that are automatically generated from a small subset of labeled data sets and give weak labels on large unclear data...
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  7. Method: Deep structural clustering reveals hidden systematic biases in RNA sequencing data
    • Qiang Su,
    • Yi Long,
    • Deming Gou,
    • Junmin Quan,
    • Xiaoming Zhou,
    • and Qizhou Lian
    Genome Res. November 2025 35: 2563-2577; Published in Advance September 19, 2025, doi:10.1101/gr.280713.125
    ...and 2D models in capturing the full complexity of higher-order structural influences, we propose a novel three-dimensional (3D) approach. This 3D analysis leverages a deep-learning variational autoencoder-Gaussian mixture model (VAE-GMM) to examine the high-dimensional structural similarities of k...
  8. Research: Transcriptional modulation unique to vulnerable motor neurons predicts ALS across species and SOD1 mutations
    • Irene Mei,
    • Susanne Nichterwitz,
    • Melanie Leboeuf,
    • Jik Nijssen,
    • Isadora Lenoel,
    • Dirk Repsilber,
    • Christian S. Lobsiger,
    • and Eva Hedlund
    Genome Res. September 2025 35: 1975-1991; Published in Advance July 17, 2025, doi:10.1101/gr.279501.124
    ....Machine learning and meta-analysis across SOD1 mutations and models identify strong disease predictorsNext, we set out to reveal which of the 129 upregulated DEGs identified in spinal SOD1G93A MNs at P112 (Fig. 2A) would be the strongest disease predictors. Toward this purpose, we used an independent single...
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  9. Perspective: Notable challenges posed by long-read sequencing for the study of transcriptional diversity and genome annotation
    • Carolina Monzó,
    • Adam Frankish,
    • and Ana Conesa
    Genome Res. April 2025 35: 583-592; Published in Advance March 3, 2025, doi:10.1101/gr.279865.124
    ...RNA-seq) can be compromised by factors such as RNA degradation, biases introduced during library preparation, sequencing errors, and inaccurate bioinformatic processing during mapping, transcript assembly, and quantification, which may lead to the incorrect identification of transcript models, i...
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  10. Research: Mutational scanning of CRX classifies clinical variants and reveals biochemical properties of the transcriptional effector domain
    • James L. Shepherdson,
    • David M. Granas,
    • Jie Li,
    • Zara Shariff,
    • Stephen P. Plassmeyer,
    • Alex S. Holehouse,
    • Michael A. White,
    • and Barak A. Cohen
    Genome Res. October 2024 34: 1540-1552; Published in Advance September 25, 2024, doi:10.1101/gr.279415.124
    ...in the intrinsically disordered transcriptional effector domain are particularly relevant in light of recent developments of machine learning-based computational variant effect predictors trained on protein sequence homology and structural data. The AlphaMissense, ESM1b, and EVE models predict variant pathogenicity...
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