Searching journal content for articles similar to Aravind and Iyer 12 (8): 1156.

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  1. 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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  2. Method: Predicting genotype-specific gene regulatory networks
    • Deborah Weighill,
    • Marouen Ben Guebila,
    • Kimberly Glass,
    • John Quackenbush,
    • and John Platig
    Genome Res. March 2022 32: 524-533; Published in Advance February 22, 2022, doi:10.1101/gr.275107.120
    ...individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known...
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  3. 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
    ...network Cell 1 Cell 100 Cell 300 Interpretation: how strongly gene 𝒒 predicts gene 𝒑 through PCs ෍ Weighted sum by cluster membership ෍ Learn soft clusters on cell or gene slices Figure 1. Caption next page. Figure 1. NeighbourNet (NNet) workflow for inferring cell-specific coexpression networks...
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  4. 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
    ...) for supervised link prediction in GRN inference, leveraging scRNA-seq data and existing regulatory information to predict latent TF–gene interactions. Similarly, Grace (Wang et al. 2024a) integrates structural causal models with graph neural networks to infer both GRNs and gene causality from scRNA-seq data...
  5. 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
    ...relationships between individual genes and their multiple partners. Although this approach can capture the local neighborhood influences, it face challenges in simultaneously modeling the interconnected and compatible regulatory relationships among a vast array of genes. Consequently, the derived networks...
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  6. 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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  7. 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
    ...development and are predictive of outcomes in some cancers (Oliveto et al. 2017). We calculated coexpression networks using paired mRNA and miRNA expression data from 101 breast cancer samples representing the five canonical molecular subtypes (obtained from the NCBI Gene Expression Omnibus [GEO; https...
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  8. Method: Spatial Cellular Networks from omics data with SpaCeNet
    • Stefan Schrod,
    • Niklas Lück,
    • Robert Lohmayer,
    • Stefan Solbrig,
    • Dennis Völkl,
    • Tina Wipfler,
    • Katherine H. Shutta,
    • Marouen Ben Guebila,
    • Andreas Schäfer,
    • Tim Beißbarth,
    • Helena U. Zacharias,
    • Peter J. Oefner,
    • John Quackenbush,
    • and Michael Altenbuchinger
    Genome Res. September 2024 34: 1371-1383; Published in Advance September 4, 2024, doi:10.1101/gr.279125.124
    ...network to predict cells' observed gene expression vector from respective cell type label and niche, with the latter resembling cell–cell communication in terms of statistical dependencies between cells. As such, NCEM identifies cell-type couplings. However, it does not identify gene pairs involved...
  9. Method: Modeling and predicting cancer clonal evolution with reinforcement learning
    • Stefan Ivanovic and
    • Mohammed El-Kebir
    Genome Res. July 2023 33: 1078-1088; Published in Advance June 21, 2023, doi:10.1101/gr.277672.123
    ...generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree...
  10. Method: Systematic identification of interchromosomal interaction networks supports the existence of specialized RNA factories
    • Borislav Hrisimirov Hristov,
    • William Stafford Noble,
    • and Alessandro Bertero
    Genome Res. October 2024 34: 1610-1623; Published in Advance September 25, 2024, doi:10.1101/gr.278327.123
    ...that jointly interact in trans. This method, trans-C, initiates probabilistic random walks with restarts from a set of seed loci to traverse an input Hi-C contact network, thereby identifying sets of trans-contacting loci. We validate trans-C in three increasingly complex models of established trans contacts...
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