Extracting Between-Pathway Models from E-MAP Interactions Using Expected Graph Compression

  • David R. Kelley
  • Carl Kingsford
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Genetic interactions (such as synthetic lethal interactions) have become quantifiable on a large-scale using the epistatic miniarray profile (E-MAP) method. An E-MAP allows the construction of a large, weighted network of both aggravating and alleviating genetic interactions between genes. By clustering genes into modules and establishing relationships between those modules, we can discover compensatory pathways. We introduce a general framework for applying greedy clustering heuristics to probabilistic graphs. We use this framework to apply a graph clustering method called graph summarization to an E-MAP that targets yeast chromosome biology. This results in a new method for clustering E-MAP data that we call Expected Graph Compression (EGC). We validate modules and compensatory pathways using enriched Gene Ontology annotations and a novel method based on correlated gene expression. EGC finds a number of modules that are not found by any previous methods to cluster E-MAP data. EGC also uncovers core submodules contained within several previously found modules, suggesting that EGC can reveal the finer structure of E-MAP networks.


Gene Ontology Genetic Interaction Probabilistic Graph Yeast Chromosome Correlate Gene Expression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David R. Kelley
    • 1
  • Carl Kingsford
    • 1
  1. 1.Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies and Department of Computer ScienceUniversity of MarylandCollege Park

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