Searching journal content for articles similar to Schlitt et al. 13 (12): 2568.

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  1. 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
    ...of Mariculture Breeding, Xiamen University, Xiamen, Fujian 361000, China; 4Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision, Xiamen, Fujian 361005, China Corresponding author: wangying@xmu.edu.cnAbstractUnderstanding gene regulatory networks (GRNs) is crucial for deciphering cellular...
  2. 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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  3. 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
    ...s (Van de Peer et al. 2009; Kondrashov 2012; Wang et al. 2012). Although increased ploidy following WGD incurs fitness costs to an organism, it can confer advantages during times of environmental change and stress, including increased adaptability and robustness of gene regulatory networks (Ebadi et...
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  4. Method: Scalable cell-specific coexpression networks for granular regulatory pattern discovery with NeighbourNet
    • Yidi Deng,
    • Jiadong Mao,
    • Jarny Choi,
    • and Kim-Anh Le Cao
    Genome Res. March 5, 2026 gr.281171.125; Published in Advance March 5, 2026, doi:10.1101/gr.281171.125
    ...Gene networks provide a fundamental framework for understanding the molecular mechanisms 13 that govern gene expression. Advances in single-cell RNA sequencing (scRNA-seq) have enabled 14 network inference at cellular resolution; however, most existing approaches rely on predefined 15 clusters or cell...
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  5. Method: Analysis of coding gene expression from small RNA sequencing
    • Aygun Azadova,
    • Anthonia Ekperuoh,
    • Greg N. Brooke,
    • and Antonio Marco
    Genome Res. March 2026 36: 611-618; Published in Advance February 10, 2026, doi:10.1101/gr.281364.125
    ...of microRNA expression analyses is reflected by the existence of thousands of sRNA-seq studies in which matched total RNA-seq data are often unavailable. The lack of paired sequencing experiments limits the analysis of microRNA–gene regulatory networks. Here, we explore whether protein-coding gene...
  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
    ...Chuanyuan Wang and Zhi-Ping Liu Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China Corresponding author: zpliu@sdu.edu.cnAbstractA gene regulatory network (GRN) intricately encodes the interconnectedness of identities...
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  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
    ...differential gene expression for six major cell types intensified at severe ADNC. Integrating peak-to-gene linkages and motif enrichment analyses, we reconstruct transcription factor (TF)–target gene networks across six major brain cell types. By integrating -wide association study (GWAS) loci with cell type...
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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
    ...to profile the transcriptome at the cellular level, allowing for quantifying cell types and states, analyzing inter- and intrasample heterogeneity, discovering cell differentiation trajectories, and constructing gene regulatory networks (Zhao et al. 2022). Interpretation of such data can be challenging owing...
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  9. 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
    ...neural networks with transformers. Compared with current state-of-the-art models, ScPGE exhibits superior performance in predicting gene expression and yields higher accuracy in identifying active enhancer–gene interactions through attention mechanisms. By comprehensively analyzing ScPGE's predictions...
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  10. Research: Analyzing super-enhancer temporal dynamics reveals potential critical enhancers and their gene regulatory networks underlying skeletal muscle development
    • Song Zhang,
    • Chao Wang,
    • Shenghua Qin,
    • Choulin Chen,
    • Yongzhou Bao,
    • Yuanyuan Zhang,
    • Lingna Xu,
    • Qingyou Liu,
    • Yunxiang Zhao,
    • Kui Li,
    • Zhonglin Tang,
    • and Yuwen Liu
    Genome Res. December 2024 34: 2190-2202; Published in Advance October 21, 2024, doi:10.1101/gr.278344.123
    ...to postnatal stages, and pinpointing critical trait-affecting enhancers remains largely unaddressed. In the present study, we aim to fill this gap by identifying potential critical enhancers and their gene regulatory networks during PSM development.Our approach involves analyzing developmental trajectories...
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