Searching journal content for articles similar to Nappi et al. 35 (12): 2682.

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  1. 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
    ...and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each...
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  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
    ...to annotate samples based on cell-type composition. By comparing aggregation strategies, we find that regressing confounders within studies and prioritizing larger studies optimizes network reconstruction. We apply these findings to infer three consensus networks (universal, cancer, noncancer) and 27 context...
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  3. 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
    ...), and STAGATE (Dong and Zhang 2022). These tools are widely used and highly recognized for the task of spatial domain detection. In detail, BayesSpace is a Bayesian statistical method designed to enhance the resolution of SRT data and perform clustering by incorporating spatial neighborhood information...
  4. Method: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
    • Cameron Park,
    • Shouvik Mani,
    • Nicolas Beltran-Velez,
    • Katie Maurer,
    • Teddy Huang,
    • Shuqiang Li,
    • Satyen Gohil,
    • Kenneth J. Livak,
    • David A. Knowles,
    • Catherine J. Wu,
    • and Elham Azizi
    Genome Res. September 2024 34: 1384-1396; Published in Advance September 5, 2024, doi:10.1101/gr.279126.124
    ...on databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method utilizes structured Gaussian process regression to unveil time...
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  5. Method: Full-resolution HLA and KIR gene annotations for human genome assemblies
    • Ying Zhou,
    • Li Song,
    • and Heng Li
    Genome Res. November 2024 34: 1931-1941; Published in Advance June 5, 2024, doi:10.1101/gr.278985.124
    ...a norm. We believe that Immuannot will provide high-fidelity HLA and KIR annotations to improve the completeness of IPD-IMGT/HLA and IPD-KIR and to enhance the research on these genes in general.MethodsIdentifying candidate genesImmuannot aligns known gene sequences in existing databases (which...
  6. Resource: Building better genome annotations across the tree of life
    • Adam H. Freedman and
    • Timothy B. Sackton
    Genome Res. May 2025 35: 1261-1276; Published in Advance April 15, 2025, doi:10.1101/gr.280377.124
    ...Building better annotations across the tree of life Adam H. Freedman and Timothy B. Sackton Informatics Group, Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts 02138, USA Corresponding author: adamfreedman@fas.harvard.eduAbstractRecent technological advances in long...
  7. Method: A Bayesian inference transcription factor activity model for the analysis of single-cell transcriptomes
    • Shang Gao,
    • Yang Dai,
    • and Jalees Rehman
    Genome Res. July 2021 31: 1296-1311; Published in Advance June 30, 2021, doi:10.1101/gr.265595.120
    ...cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. The Bayesian inference transcription factor activity model (BITFAM) integrates ChIP-seq transcription factor...
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  8. Method: Bayesian-based noninvasive prenatal diagnosis of single-gene disorders
    • Tom Rabinowitz,
    • Avital Polsky,
    • David Golan,
    • Artem Danilevsky,
    • Guy Shapira,
    • Chen Raff,
    • Lina Basel-Salmon,
    • Reut Tomashov Matar,
    • and Noam Shomron
    Genome Res. March 2019 29: 428-438; Published in Advance February 20, 2019, doi:10.1101/gr.235796.118
    ...) and for single-gene disorders (SGDs) of paternal origin. However, for SGDs of maternal origin, sensitivity poses a challenge that limits the testing to one genetic disorder at a time. Here, we present a Bayesian method for the NIPD of monogenic diseases that is independent of the mode of inheritance and parental...
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  9. Method: A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data
    • Yi Qiao,
    • Xiaomeng Huang,
    • Philip J. Moos,
    • Jonathan M. Ahmann,
    • Anthony D. Pomicter,
    • Michael W. Deininger,
    • John C. Byrd,
    • Jennifer A. Woyach,
    • Deborah M. Stephens,
    • and Gabor T. Marth
    Genome Res. January 2024 34: 94-105; Published in Advance January 9, 2024, doi:10.1101/gr.278234.123
    ...A Bayesian framework to study tumor subclone–specific expression by combining bulk DNA and single-cell RNA sequencing data Yi Qiao1,6, Xiaomeng Huang1,6, Philip J. Moos2, Jonathan M. Ahmann3, Anthony D. Pomicter3, Michael W. Deininger3,4, John C. Byrd5, Jennifer A. Woyach5, Deborah M. Stephens3...
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  10. Method: Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data
    • Jiebiao Wang,
    • Kathryn Roeder,
    • and Bernie Devlin
    Genome Res. October 2021 31: 1807-1818; Published in Advance April 9, 2021, doi:10.1101/gr.268722.120
    ...data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell type–specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses...
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