Searching journal content for articles similar to Tian et al. 33 (2): 232.

Displaying results 1-10 of 6327
For checked items
  1. Method: Simultaneous modeling of chromatin conformation changes from multiple single-cell interaction maps with ChromMovie
    • Krzysztof H. Banecki,
    • Haoxi Chai,
    • Yijun Ruan,
    • and Dariusz Plewczynski
    Genome Res. April 8, 2026 ; Published in Advance April 8, 2026, doi:10.1101/gr.280985.125
    ...within cellular genomics, we find significant advances in single-cell analysis. For instance, single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomics by offering advantages over traditional bulk analysis (Stegle et al. 2015; Bacher and Kendziorski 2016). Single-cell transcriptomics has...
    OPEN ACCESS ARTICLE
  2. 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
    ...context may help guide the model toward more accurate and interpretable translation outcomes. In addition, adopting continual learning strategies (Wang et al. 2024) would enable the model to adapt incrementally as new single-cell data sets become available, thereby enhancing its scalability...
    OPEN ACCESS ARTICLE
  3. Method: Recovering gene regulatory networks in single-cell multi-omics data with PRISM-GRN
    • Wenhao Zhang,
    • Lan Cao,
    • Xiaoxuan Gu,
    • Yongyu Long,
    • and Ying Wang
    Genome Res. January 2026 36: 142-158; Published in Advance October 9, 2025, doi:10.1101/gr.280757.125
    .... Its flexibility in integrating unpaired data without compromising performance makes it particularly valuable in scenarios in which paired data sets are difficult to obtain, enabling broader applications in genomics and single-cell research.PRISM-GRN achieves robust performance with limited prior...
  4. Method: Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover
    • An Wang,
    • Stephanie Hicks,
    • Donald Geman,
    • and Laurent Younes
    Genome Res. December 2025 35: 2744-2755; Published in Advance November 12, 2025, doi:10.1101/gr.280539.125
    ...for Imaging Science, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: awang87@jhu.eduAbstractThe selection of marker gene panels is critical for capturing the cellular and spatial heterogeneity in the expanding atlases of single-cell RNA sequencing (scRNA-seq) and spatial...
  5. 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
    ...problem (LeCun et al. 2015). Deep learning has been widely implemented in various single-cell data analyses, including data imputation (Arisdakessian et al. 2019), doublet identification (Bernstein et al. 2020), dimensionality reduction (Deng et al. 2019), batch effect corrections (Xu et al. 2022...
    OPEN ACCESS ARTICLE
  6. Method: Quantifying pathological progression from single-cell transcriptomic data with scPSS
    • Samin Rahman Khan,
    • M Saifur Rahman,
    • M. Sohel Rahman,
    • and Md Abul Hassan Samee
    Genome Res. February 2026 36: 375-386; Published in Advance January 14, 2026, doi:10.1101/gr.280411.125
    ...(negative pairs). ContrastiveVI is a contrastive deep generative model that adapts this principle to single-cell data by separating condition-specific variation from shared biological variation through two distinct sets of latent features: one shared across all cells and another specific to the condition...
  7. 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...
  8. Method: A unified analysis of atlas single-cell data
    • Hao Chen,
    • Nam D. Nguyen,
    • Matthew Ruffalo,
    • and Ziv Bar-Joseph
    Genome Res. May 2025 35: 1219-1233; Published in Advance February 18, 2025, doi:10.1101/gr.279631.124
    ..., Illinois 60607, USA; 3Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA Corresponding author: zivbj@cs.cmu.eduAbstractRecent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues...
    OPEN ACCESS ARTICLE
  9. Method: Polyomino reconstructs spatial transcriptomic profiles with single-cell resolution via a region-allocation method
    • Quanyou Cai,
    • Lihui Lin,
    • Xin Liu,
    • and Jiekai Chen
    Genome Res. November 2025 35: 2513-2526; Published in Advance October 22, 2025, doi:10.1101/gr.280532.125
    ...parameters.Mapping single-cell data to smFISH-based spatial dataFor seqFISH data (Lohoff et al. 2022), we performed spatial mapping of processed 10x Genomics v2 scRNA-seq data (Lohoff et al. 2022) consisting of 116,312 cells and 29,452 genes onto 19,416 spatial cells. Because seqFISH detected only 351 genes...
    OPEN ACCESS ARTICLE
  10. Method: Incorporating valuable prior knowledge to improve deep learning prediction of genetic perturbation responses
    • Xiuhao Fu,
    • Chao Yang,
    • Chunyan Cui,
    • Aoyun Geng,
    • Yidi Sun,
    • Chao Zha,
    • Feifei Cui,
    • Leyi Wei,
    • Quan Zou,
    • Xin Gao,
    • and Zilong Zhang
    Genome Res. March 26, 2026 gr.281523.125; Published in Advance March 26, 2026, doi:10.1101/gr.281523.125
    ...reveals significant linear relationships in low-dimensional 66 manifolds and captures variations induced by perturbation effects in high-dimensional 67 single-cell datasets(Lotfollahi et al. 2019). Roohani et al. developed GEARS, which 68 integrates deep learning with a gene relationship knowledge graph...
    ACCEPTED MANUSCRIPT
For checked items

Search Results

Next 10 » New Search

Modify Results

Citation format:
Results / page:
Results order:

This search

  • Alert me when new articles matching this search are published
  • Download all citations on this page to my citation manager

Preprint Server



Current Issue

  1. April 2026, 36 (4)
  1. Current Issue
  1. Alert me to new issues of Genome Research