Paul T Shannon

Summary

Affiliation: Institute for Systems Biology
Country: USA

Publications

  1. pmc Derivation of genetic interaction networks from quantitative phenotype data
    Becky L Drees
    Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA
    Genome Biol 6:R38. 2005
  2. pmc The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
    Richard Bonneau
    New York University, Biology Department, Center for Comparative Functional Genomics, New York, NY 10003, USA
    Genome Biol 7:R36. 2006
  3. pmc Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1
    Richard Bonneau
    Institute for Systems Biology, Seattle, WA 98103 8904, USA
    Genome Biol 5:R52. 2004
  4. pmc The Gaggle: an open-source software system for integrating bioinformatics software and data sources
    Paul T Shannon
    Institute for Systems Biology, Seattle, WA 98103, USA
    BMC Bioinformatics 7:176. 2006
  5. pmc The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications
    J Christopher Bare
    Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA
    BMC Bioinformatics 8:456. 2007
  6. pmc Integrated phosphoproteomics analysis of a signaling network governing nutrient response and peroxisome induction
    Ramsey A Saleem
    Institute for Systems Biology, Seattle, Washington 98103, USA
    Mol Cell Proteomics 9:2076-88. 2010

Collaborators

Detail Information

Publications6

  1. pmc Derivation of genetic interaction networks from quantitative phenotype data
    Becky L Drees
    Institute for Systems Biology, 1441 N, 34th Street, Seattle, WA 98103, USA
    Genome Biol 6:R38. 2005
    ..Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways...
  2. pmc The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo
    Richard Bonneau
    New York University, Biology Department, Center for Comparative Functional Genomics, New York, NY 10003, USA
    Genome Biol 7:R36. 2006
    ..Several specific regulatory predictions were experimentally tested and verified...
  3. pmc Comprehensive de novo structure prediction in a systems-biology context for the archaea Halobacterium sp. NRC-1
    Richard Bonneau
    Institute for Systems Biology, Seattle, WA 98103 8904, USA
    Genome Biol 5:R52. 2004
    ..The work reported here demonstrates that de novo structure prediction is now a viable option for providing general function information for many proteins of unknown function...
  4. pmc The Gaggle: an open-source software system for integrating bioinformatics software and data sources
    Paul T Shannon
    Institute for Systems Biology, Seattle, WA 98103, USA
    BMC Bioinformatics 7:176. 2006
    ..A solution to this problem should recognize that data types, formats and software in this high throughput age of biology are constantly changing...
  5. pmc The Firegoose: two-way integration of diverse data from different bioinformatics web resources with desktop applications
    J Christopher Bare
    Institute for Systems Biology, 1441 N 34th Street, Seattle, WA 98103, USA
    BMC Bioinformatics 8:456. 2007
    ..Opportunities for new ways of combining and re-using data are arising as a result of the increasing use of web protocols to transmit structured data...
  6. pmc Integrated phosphoproteomics analysis of a signaling network governing nutrient response and peroxisome induction
    Ramsey A Saleem
    Institute for Systems Biology, Seattle, Washington 98103, USA
    Mol Cell Proteomics 9:2076-88. 2010
    ..These properties are consistent with a scale-free topology, demonstrating that scale-free properties are conserved in condition-specific networks...