Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

  • Mark D. M. Leiserson
  • Diana Tatar
  • Lenore J. Cowen
  • Benjamin J. Hescott
Conference paper

DOI: 10.1007/978-3-642-20036-6_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6577)
Cite this paper as:
Leiserson M.D.M., Tatar D., Cowen L.J., Hescott B.J. (2011) Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut. In: Bafna V., Sahinalp S.C. (eds) Research in Computational Molecular Biology. RECOMB 2011. Lecture Notes in Computer Science, vol 6577. Springer, Berlin, Heidelberg

Abstract

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mark D. M. Leiserson
    • 1
  • Diana Tatar
    • 1
  • Lenore J. Cowen
    • 1
  • Benjamin J. Hescott
    • 1
  1. 1.Department of Computer ScienceTufts UniversityMedford

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