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Efficiently Enumerating All Connected Induced Subgraphs of a Large Molecular Network

  • Sean Maxwell
  • Mark R. Chance
  • Mehmet Koyutürk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8542)

Abstract

In systems biology, the solution space for a broad range of problems is composed of sets of functionally associated biomolecules. Since connectivity in molecular interaction networks is an indicator of functional association, such sets can be identified from connected induced subgraphs of molecular interaction networks. Applications typically quantify the relevance (e.g., modularity, conservation, disease association) of connected subnetworks using an objective function and use a search algorithm to identify sets of subnetworks that maximize this objective function. Efficient enumeration of connected subgraphs of a large graph is therefore useful for these applications, and many existing search algorithms can be used for this purpose. However, there is a lack of non-heuristic algorithms that minimize the total number of subgraphs evaluated during the search for subgraphs that maximize the objective function. Here, we propose and evaluate an algorithm that reduces the computations necessary to enumerate subgraphs that maximize an objective function given a monotonically decreasing bounding function.

Keywords

connected subgraph enumeration protein interaction networks branch-and-bound algorithms 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sean Maxwell
    • 1
  • Mark R. Chance
    • 1
  • Mehmet Koyutürk
    • 1
    • 2
  1. 1.Center for Proteomics and BioinformaticsCase Western Reserve UniversityClevelandUSA
  2. 2.Department of Electrical Engineering and Computer ScienceCase Western Reserve UniversityClevelandUSA

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