Searching journal content for articles similar to Zhang et al. 36 (3): 589.

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  1. Method: Early feature extraction drives model performance in high-resolution chromatin accessibility prediction
    • Aayush Grover,
    • Till Muser,
    • Liine Kasak,
    • Lin Zhang,
    • Ekaterina Krymova,
    • and Valentina Boeva
    Genome Res. March 2026 36: 619-629; Published in Advance January 12, 2026, doi:10.1101/gr.281042.125
    ...alternative for estimating changes in chromatin accessibility induced by the genetic variants in a cell type–specific manner.Numerous deep learning approaches have been proposed to predict chromatin accessibility from DNA sequence. Earlier methods (Zhou and Troyanskaya 2015; Kelley et al. 2016; Quang and Xie...
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  2. Method: Geometric deep learning framework for de novo genome assembly
    • Lovro Vrček,
    • Xavier Bresson,
    • Thomas Laurent,
    • Martin Schmitz,
    • Kenji Kawaguchi,
    • and Mile Šikić
    Genome Res. April 2025 35: 839-849; Published in Advance October 29, 2024, doi:10.1101/gr.279307.124
    ...haploid s.De novo assembly provides essential insights into organism's biology and evolution by reconstructing its from short DNA fragments (or reads), without the access to the original genomic sequence. The assembly process is based on intricate methods on strings and graphs which were first introduced...
  3. Method: A scalable computational framework for predicting gene expression from candidate cis-regulatory elements
    • Qinhu Zhang,
    • Siguo Wang,
    • Zhipeng Li,
    • Wenzheng Bao,
    • Wenjian Liu,
    • and De-Shuang Huang
    Genome Res. February 2026 36: 361-374; Published in Advance January 16, 2026, doi:10.1101/gr.281219.125
    ..., and then combines CNN with transformer to predict gene expression levels.ResultsOverview of ScPGEScPGE is a deep learning framework, composed of a feature-learning module, an interaction-learning module, and a prediction module, which predicts cell type–specific gene expression levels by integrating DNA sequences...
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  4. 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
    ...), whereas MAPLE utilizes an ensemble supervised learning strategy to infer gene activity scores from DNA methylation features (Uzun et al. 2021). However, these methods also have notable limitations: (1) Some existing methods are constrained by their unidirectional prediction capability, limiting...
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  5. Method: spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model
    • Tianyu Liu,
    • Tinglin Huang,
    • Wengong Jin,
    • Tinyi Chu,
    • Rex Ying,
    • and Hongyu Zhao
    Genome Res. April 2026 36: 754-768; Published in Advance February 3, 2026, doi:10.1101/gr.281001.125
    ...among genes. However, this method is limited to the imputation of protein-coding genes. To simultaneously impute and denoise gene expression profiles, we may consider genomic language models (gLMs) (Avsec et al. 2021; de Almeida and Pierrot 2022; Benegas et al. 2025), which model DNA sequence...
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  6. Method: Graph-based deep reinforcement learning for haplotype assembly with Ralphi
    • Enzo Battistella,
    • Anant Maheshwari,
    • Barış Ekim,
    • Bonnie Berger,
    • and Victoria Popic
    Genome Res. December 2025 35: 2617-2625; Published in Advance November 14, 2025, doi:10.1101/gr.280569.125
    ...-critic RL algorithm and the MFC reward.View larger version: In this window In a new window Figure 1. Overview of the Ralphi framework for haplotype assembly. As an example, a fragment graph is constructed from a set of six DNA fragments covering seven SNPs (the allele containing a sequencing error is shown...
  7. Method: A high-throughput screening method for selecting feature SNPs to evaluate breed diversity and infer ancestry
    • Meilin Zhang,
    • Heng Du,
    • Yu Zhang,
    • Yue Zhuo,
    • Zhen Liu,
    • Yahui Xue,
    • Lei Zhou,
    • Sixuan Zhou,
    • Wanying Li,
    • and Jian-Feng Liu
    Genome Res. August 2025 35: 1875-1886; Published in Advance July 15, 2025, doi:10.1101/gr.280176.124
    ...; Eusebi et al. 2020). Many studies compare and utilize different types of marker techniques, including mitochondrial DNA barcoding (mtDNA) (Guo et al. 2006), the Y-Chromosome technique (Bruford et al. 2003), minisatellite and microsatellite markers (Ćurković et al. 2016), and single...
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  8. Method: Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics
    • Zhen Li,
    • Xiaoyang Chen,
    • Xuegong Zhang,
    • Rui Jiang,
    • and Shengquan Chen
    Genome Res. October 2023 33: 1757-1773; Published in Advance October 30, 2023, doi:10.1101/gr.277891.123
    ...data, self-attention mechanism (Liu et al. 2021) to capture spatial patterns, and a ripple walk sampler (Bai et al. 2021) to enable scalable subgraph-based training and prediction. Featured as the first ST method which integrates reference data from various sources, PAST aims to extract latent...
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  9. 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
    ...; Shadrach et al. 2021) has revealed a response to DNA damage and cell injury as well as activation of compensatory regeneration.So far no comprehensive analysis has been conducted to dissect how vulnerable versus resilient neurons respond to ALS over time. We reasoned that an analysis of how differentially...
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  10. 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
    ...regions, influenced by the unique tertiary structures of these regions, thereby affecting the sequencing output. In contrast, linear regression analysis was not applicable to data obtained from the oligo(dT)-based protocol. This method uses a poly(T) primer to initiate cDNA synthesis, focusing almost...
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