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A computational framework that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins or genes, such as a set of disease-associated genes and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins and interactions that are potentially involved in the studied condition and their likely modes of action.

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ResponseNet classification

  • Animals
    • Homo sapiens
  • Fungi
    • Saccharomyces cerevisiae

ResponseNet specifications

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  • Esti Yeger-Lotem <>


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Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the Negev, Beer-Sheva, Israel

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