Transcriptograms: A Genome-Wide Gene Expression Analysis Method
In this chapter, we discuss the Transcriptogram method for statistically analyzing differential gene expression in a genome-wide profile. This technique suggests a method to hierarchically interrogate the data and, subsequently, narrow down to gene level.
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Fabricio Alves Barbosa da Silva Nicolas Carels Marcelo Trindade dos Santos Francisco José Pereira Lopes Editors
Networks in Systems Biology Applications for Disease Modeling
Computational Biology Volume 32
Advisory Editor Gordon Crippen, University of Michigan, Ann Arbor, MI, USA Editor-in-Chief Andreas Dress, CAS-MPG Partner Institute for Computational Biology, Shanghai, China Editorial Board Robert Giegerich, University of Bielefeld, Bielefeld, Germany Janet Kelso, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany Editor-in-Chief Michal Linial, Hebrew University of Jerusalem, Jerusalem, Israel Advisory Editor Joseph Felsenstein, University of Washington, Seattle, WA, USA Editor-in-Chief Olga Troyanskaya, Princeton University, Princeton, NJ, USA Advisory Editor Dan Gusfield, University of California, Davis, CA, USA Editorial Board Gene Myers, Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany Advisory Editor Sorin Istrail, Brown University, Providence, RI, USA Editorial Board Pavel Pevzner, University of California, San Diego, CA, USA
Editor-in-Chief Martin Vingron, Max Planck Institute for Molecular Genetics, Berlin, Germany Advisory Editors Thomas Lengauer, Max Planck Institute for Computer Science, Saarbrücken, Germany Marcella McClure, Montana State University, Bozeman, MT, USA Martin Nowak, Harvard University, Cambridge, MA, USA David Sankoff, University of Ottawa, Ottawa, ON, Canada Ron Shamir, Tel Aviv University, Tel Aviv, Israel Mike Steel, University of Canterbury, Christchurch, New Zealand Gary Stormo, Washington University in St. Louis, St. Louis, MO, USA Simon Tavaré, University of Cambridge, Cambridge, UK Tandy Warnow, University of Illinois at Urbana-Champaign, Urbana, IL, USA Lonnie Welch, Ohio University, Athens, OH, USA
Endorsed by the International Society for Computational Biology, the Computational Biology series publishes the very latest, high-quality research devoted to specific issues in computer-assisted analysis of biological data. The main emphasis is on current scientific developments and innovative techniques in computational biology (bioinformatics), bringing to light methods from mathematics, statistics and computer science that directly address biological problems currently under investigation. The series offers publications that present the state-of-the-art regarding the problems in question; show computational biology/bioinformatics methods at work; and finally discuss anticipated demands regarding developments in future methodology. Titles can range from focused monographs, to undergraduate and graduate textbooks, and professional text/reference works.
More information about this series at http://www.springer.com/series/5769
Fabricio Alves Barbosa da Silva Nicolas Carels Marcelo Trindade dos Santos Francisco José Pereira Lopes •
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Editors
Networks in Systems Biology Applications for Disease Modeling
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Editors Fabricio Alves Barbosa da Silva Scientific Computing Program (PROCC) Oswaldo Cruz Foundation Rio de Janeiro,
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