Searching journal content for articles similar to Takenaka and Matsuda 13 (6b): 1558.

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  1. Method: Balancing gene ontology annotation specificity in protein function prediction based on the protein sequence large graph
    • Jiangyi Shao,
    • Shutao Chen,
    • Ziwen Wang,
    • Zixu Chen,
    • and Bin Liu
    Genome Res. April 15, 2026 gr.280816.125; Published in Advance April 15, 2026, doi:10.1101/gr.280816.125
    ...Balancing Gene Ontology annotation specificity in protein function prediction 1 based on the protein sequence large graph 2 Jiangyi Shao1,2, Shutao Chen1, Ziwen Wang 1, Zixu Chen1,2, Bin Liu1,2* 3 1 School of Computer Science and Technology, Beijing Institute of Technology, 4 Beijing 100081, China...
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  2. 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 robustness against conventional machine learning techniques, including Gaussian mixture models (GMM-only), principal component analysis-based GMMs, k-means clustering, and hierarchical clustering. These validations, using an extensive and diverse array of data sets, including synthetic RNA constructs...
  3. 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
    ...in reconstructing spatial transcriptomic profiles in comparison with deconvolution-based approaches such as cell2location (Kleshchevnikov et al. 2022) and RCTD (Cable et al. 2022). Based on the high-resolution seqFISH+ data set, we simulated spatial spot data by aggregating gene expression profiles from neighboring...
    OPEN ACCESS ARTICLE
  4. Method: Gene duplication is associated with gene diversification and potential neofunctionalization in lung cancer evolution
    • Paul Ashford,
    • Alexander M. Frankell,
    • Zofia Piszka,
    • Camilla S.M. Pang,
    • Mahnaz Abbasian,
    • Maise Al Bakir,
    • Mariam Jamal-Hanjani,
    • Nicholas McGranahan,
    • Charles Swanton,
    • and Christine A. Orengo
    Genome Res. March 2026 36: 561-577; Published in Advance February 19, 2026, doi:10.1101/gr.278663.123
    ...mutation cluster or hotspot (35% of cases; significance level of 5%). The majority of cases in which mutations were not clustered occurred in tumor-suppressor genes (TP53, PTEN, and VHL) (Supplemental Note 1; Supplemental Fig. 14), which acquire loss-of-function variants across a larger proportion of sites...
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  5. Method: Parallel analysis of replication timing, gene expression, and copy number with PARTAGE
    • Lakshana Sruthi Sadu Murari,
    • Quinn Dickinson,
    • Silvia Meyer-Nava,
    • and Juan Carlos Rivera-Mulia
    Genome Res. April 2026 36: 802-813; Published in Advance March 19, 2026, doi:10.1101/gr.281532.125
    ...joint profiling of copy number variation (CNV), RT, and gene expression from the same sample, providing a more accurate integrated view of the complex relationships between RT and gene regulation.Human cells duplicate their by the firing of thousands of origins that are activated in clusters following...
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  6. Method: STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data
    • Congcong Hu,
    • Nana Wei,
    • Jiyuan Yang,
    • Hua-Jun Wu,
    • and Xiaoqi Zheng
    Genome Res. June 2025 35: 1415-1428; Published in Advance May 12, 2025, doi:10.1101/gr.280031.124
    ...clustering framework for spatial transcriptomics data that aggregates outcomes from state-of-the-art tools using a variety of consensus strategies, including Onehot-based, average-based, hypergraph-based, and wNMF-based methods. Comprehensive assessments on simulated and real data from distinct experimental...
  7. Resource: Cell type–specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease
    • Yunxiao Ren,
    • Ming Hu,
    • Yang E. Li,
    • Andrew A. Pieper,
    • Jeffrey Cummings,
    • and Feixiong Cheng
    Genome Res. March 2026 36: 645-659; Published in Advance January 21, 2026, doi:10.1101/gr.280436.125
    ....To further investigate the temporal specificity of drug effects, we first categorized genes from the input set into three temporal expression stages based on differential expression profiles: low (low vs. non-AD), intermediate (inter vs. non-AD and inter vs. low AD), and high (high vs. non-AD, high vs. low...
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  8. Method: Robust and efficient annotation of cell states through gene signature scoring
    • Laure Ciernik,
    • Agnieszka Kraft,
    • Florian Barkmann,
    • Josephine Yates,
    • and Valentina Boeva
    Genome Res. March 2026 36: 630-644; Published in Advance February 18, 2026, doi:10.1101/gr.280926.125
    ...epithelial-to-mesenchymal transition. Overall, ANS provides a robust and reliable gene signature scoring framework, significantly improving the accuracy of score-based annotation of cell types and states in single-cell studies.High-throughput single-cell RNA sequencing (scRNA-seq) is a powerful technology...
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  9. Resource: Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease
    • Kangli Wang,
    • Weikun Xia,
    • Yingli Gu,
    • Songpeng Zu,
    • Qian Yang,
    • Maria Luisa Amaral,
    • Yaozhi Wang,
    • Allen Wang,
    • Xiang-Dong Fu,
    • William C. Mobley,
    • and Bing Ren
    Genome Res. April 2026 36: 849-864; Published in Advance March 4, 2026, doi:10.1101/gr.281113.125
    ...) of cells from the Allen Brain Cell (ABC) Atlas (Yao et al. 2023) scRNA-seq data and snRNA-seq data from this study, colored by data set. SN, substantia nigra. (C) UMAP embedding and clustering analysis of snRNA-seq data (n = 40,125 nuclei) from this study, with cells colored based on cell classes...
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  10. 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
    ...by the interplay of genes. Over the past decades, numerous computational methods have emerged to infer GRN from gene expression profiles. These include correlation-based networks (Chan et al. 2017; Specht and Li 2017), Gaussian graphical models (Kotiang and Eslami 2020), tree-based ensemble pipelines (Huynh-Thu et...
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