Searching journal content for articles similar to Basler et al. 26 (7): 956.

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  1. Resource: Organ-level protein networks as a reference for the host effects of the microbiome
    • Robert H. Mills,
    • Jacob M. Wozniak,
    • Alison Vrbanac,
    • Anaamika Campeau,
    • Benoit Chassaing,
    • Andrew Gewirtz,
    • Rob Knight,
    • and David J. Gonzalez
    Genome Res. February 2020 30: 276-286; Published in Advance January 28, 2020, doi:10.1101/gr.256875.119
    ..., spleens, hearts, small intestines, and colons of conventionally raised and GF mice, identifying associations to colonization state in over 7000 proteins. Highly ranked associations were constructed into protein–protein interaction networks and visualized onto an interactive 3D mouse model for user...
  2. LETTER: Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae
    • Markus J. Herrgård,
    • Baek-Seok Lee,
    • Vasiliy Portnoy,
    • and Bernhard Ø. Palsson
    Genome Res. May 2006 16: 627-635; Published in Advance April 10, 2006, doi:10.1101/gr.4083206
    ...), metabolite level ( Villas-Boas et al. 2005 ), metabolic flux ( Sauer 2004 ), and high-throughput deletion strain phenotyping ( Giaever et al. 2002 ) data represent the states and outputs of these networks. Connecting large-scale component and interaction information to data on system states in order...
  3. Method: A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data
    • Norah Alghamdi,
    • Wennan Chang,
    • Pengtao Dang,
    • Xiaoyu Lu,
    • Changlin Wan,
    • Silpa Gampala,
    • Zhi Huang,
    • Jiashi Wang,
    • Qin Ma,
    • Yong Zang,
    • Melissa Fishel,
    • Sha Cao,
    • and Chi Zhang
    Genome Res. October 2021 31: 1867-1884; Published in Advance July 22, 2021, doi:10.1101/gr.271205.120
    ...as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network–based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear...
    OPEN ACCESS ARTICLE
  4. Methods: Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns
    • Christoph Kaleta,
    • Luís Filipe de Figueiredo,
    • and Stefan Schuster
    Genome Res. October 2009 19: 1872-1883; Published in Advance June 18, 2009, doi:10.1101/gr.090639.108
    ...Can the whole be less than the sum of its parts? Pathway analysis in genome-scale metabolic networks using elementary flux patterns Christoph Kaleta 1 , 2 , 3 , Luís Filipe de Figueiredo 1 and Stefan Schuster 1 1 Department of Bioinformatics...
  5. Perspective: Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression
    • Lesley T. MacNeil and
    • Albertha J.M. Walhout
    Genome Res. May 2011 21: 645-657; Published in Advance February 4, 2011, doi:10.1101/gr.097378.109
    ...-degree and out-degree and is referred to by that node’s ‘‘flux capacity’’ (Martinez et al. 2008). Finally, the ‘‘betweenness’’ of a node is the number of shortest paths that connect any pair of nodes in the network that pass through that node (Joy et al. 2005). Nodes with high betweenness are centrally located...
  6. Method: Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families
    • Prianka V. Patel,
    • Tara A. Gianoulis,
    • Robert D. Bjornson,
    • Kevin Y. Yip,
    • Donald M. Engelman,
    • and Mark B. Gerstein
    Genome Res. July 2010 20: 960-971; Published in Advance April 29, 2010, doi:10.1101/gr.102814.109
    ...Analysis of membrane proteins in metagenomics: Networks of correlated environmental features and protein families Prianka V. Patel 1 , 6 , Tara A. Gianoulis 2 , 6 , Robert D. Bjornson 3 , 4 , Kevin Y. Yip 1 , Donald M. Engelman 1...
  7. LETTER: Identification and analysis of evolutionarily cohesive functional modules in protein networks
    • Mónica Campillos,
    • Christian von Mering,
    • Lars Juhl Jensen,
    • and Peer Bork
    Genome Res. March 2006 16: 374-382; Published in Advance January 31, 2006, doi:10.1101/gr.4336406
    ...Identification and analysis of evolutionarily cohesive functional modules in protein networks Mónica Campillos , Christian von Mering , Lars Juhl Jensen , and Peer Bork 1 The European Molecular Biology Laboratory (EMBL), 69117 Heidelberg...
  8. LETTER: Extreme Pathway Lengths and Reaction Participation in Genome-Scale Metabolic Networks
    • Jason A. Papin,
    • Nathan D. Price,
    • and Bernhard Ø. Palsson
    Genome Res. December 1, 2002 12: 1889-1900; doi:10.1101/gr.327702
    ...steady-state flux distributions that the network can achieve by non-negative linear combinations of the extreme pathways; and (3) it enables the determination of time-invariant, topological properties of the network. The calculation of extreme pathways is computationally challenging and for large...
  9. Methods: Elucidation of Gene Interaction Networks Through Time-Lagged Correlation Analysis of Transcriptional Data
    • William A. Schmitt, Jr.,
    • R. Michael Raab,
    • and Gregory Stephanopoulos
    Genome Res. August 2004 14: 1654-1663; doi:10.1101/gr.2439804
    ...Elucidation of Gene Interaction Networks Through Time-Lagged Correlation Analysis of Transcriptional Data William A. Schmitt, Jr. , R. Michael Raab , and Gregory Stephanopoulos 1 Department of Chemical Engineering, Massachusetts Institute...
  10. Research: Regeneration alters open chromatin and cis-regulatory landscape of erythroid precursors
    • Yichao Zhou,
    • Venkatasai Rahul Dogiparthi,
    • Hannah L. Harris,
    • Suhita Ray,
    • Avik Choudhuri,
    • Song Yang,
    • Yi Zhou,
    • Leonard I. Zon,
    • M. Jordan Rowley,
    • and Kyle J. Hewitt
    Genome Res. July 2025 35: 1518-1529; Published in Advance June 2, 2025, doi:10.1101/gr.279949.124
    ...-activating factors driving stress erythropoiesis. To infer AP-1-dependent activities in the transcriptome after PHZ treatment, we performed single-cell regulatory network inference and clustering (SCENIC) analysis. “Regulon” scores of JUN, FOS, and FOSB increased in HSPCs and erythroid clusters 3 and 4, specifically...
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