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  1. Method: Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks
    • Prashanthi Ravichandran,
    • Princy Parsana,
    • Rebecca Keener,
    • Kasper D. Hansen,
    • and Alexis Battle
    Genome Res. September 2025 35: 2087-2103; Published in Advance July 17, 2025, doi:10.1101/gr.280808.125
    ...and AI Institute, Johns Hopkins University, Baltimore, Maryland 21218, USA Corresponding author: ajbattle@jhu.eduAbstractGene coexpression networks (GCNs) describe relationships among genes that maintain cellular identity and homeostasis. However, typical RNA-seq experiments often lack sufficient sample...
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  2. Research: Mapping multitissue regulatory variants reveals a liver-centric coexpression network associated with duck egg-laying performance
    • Yang Xi,
    • Jingjing Qi,
    • Zhao Yang,
    • Yutian Zeng,
    • Huicong Zhang,
    • Qiuyu Tao,
    • Mengru Xu,
    • Anqi Huang,
    • Shenqiang Hu,
    • Chunchun Han,
    • Lili Bai,
    • Jiwei Hu,
    • Jiwen Wang,
    • Liang Li,
    • Lingzhao Fang,
    • and Hehe Liu
    Genome Res. October 2025 35: 2211-2225; Published in Advance September 10, 2025, doi:10.1101/gr.280345.124
    ..., interacting physiological systems. An increasing number of studies indicate that, beyond the traditionally emphasized hypothalamic–pituitary–gonadal (HPG) axis, metabolic and immune tissues are integral components of the regulatory network controlling laying performance. For instance, the liver sustains high...
  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
    ...@eitech.edu.cnAbstractDeciphering the relationships between cis-regulatory elements (CREs) and target gene expression has been a long-standing unsolved problem in molecular biology, and the dynamics of CREs in different cell types make this problem more challenging. To address this challenge, we propose a scalable computational framework...
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  4. Method: Bayesian inference of sample-specific coexpression networks
    • Enakshi Saha,
    • Viola Fanfani,
    • Panagiotis Mandros,
    • Marouen Ben Guebila,
    • Jonas Fischer,
    • Katherine H. Shutta,
    • Dawn L. DeMeo,
    • Camila M. Lopes-Ramos,
    • and John Quackenbush
    Genome Res. September 2024 34: 1397-1410; Published in Advance August 12, 2024, doi:10.1101/gr.279117.124
    ...equally to this work. Corresponding author: johnq@hsph.harvard.eduAbstractGene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression...
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  5. 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
    ...data (Pratapa et al. 2020). Initial methods primarily rely on correlation or mutual information to infer regulatory interactions. For instance, weighted gene coexpression network analysis (WGCNA) (Langfelder and Horvath 2008) clusters coexpressed genes into modules to construct gene associations...
  6. 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
    ...the tumor microenvironment and identifying crucial therapeutic targets. In essence, the single-cell GRN generated using DigNet from data enables the discovery of significant cell-specific regulatory relationships and network nodes via downstream analyses. This heightened resolution and specificity in gene...
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  7. Method: Debiased personalized gene coexpression networks for population-scale scRNA-seq data
    • Shan Lu and
    • Sunduz Keles
    Genome Res. June 9, 2023 gr.277363.122; Published in Advance June 9, 2023, doi:10.1101/gr.277363.122
    ...the phenotypes of interest (Lareau et al., 2015). Instead of focusing on high centrality genes within a single coexpression network, detecting genes with differential centrality between phenotypic groups can elucidate regulatory alterations. Savino et al. (2020) offer insights into the potential regulatory...
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  8. 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
    ...–specific cis-regulatory DNA elements (CREs), we pinpoint 141 ADNC-associated genes. Using gene set enrichment analysis (GSEA) and network proximity analysis, we further identify nine candidate repurposable drugs that were associated with these ADNC-related genes. In summary, this cell type–specific multiomic...
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  9. 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
    .... Domain-adaptive neural networks improve cross-species prediction of transcription factor binding. Genome Res 32: 512–523. doi:10.1101/gr.275394.121 ↵de Boer CG, Taipale J. 2024. Hold out the : a roadmap to solving the cis-regulatory code. Nature 625: 41–50. doi:10.1038/s41586-023-06661-w ↵Dey KK, van de...
  10. Method: Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains
    • Hang Zhang,
    • Yi Zhang,
    • Kai Ming Ting,
    • Jie Zhang,
    • and Qiuran Zhao
    Genome Res. February 2025 35: 355-367; Published in Advance February 5, 2025, doi:10.1101/gr.278983.124
    ...clustering algorithm in ST, has similar issues because it is also based on density. Louvain is a community detection method that aims to find communities (i.e., clusters) in a network or graph. It maximizes modularity, which is a measure of the relative density of edges within each community compared...
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