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Towards Detecting Protein Complexes from Protein Interaction Data

  • Pengjun Pei
  • Aidong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3992)

Abstract

High-throughput methods for detecting protein-protein interactions (PPI) have given researchers an initial global picture of protein interactions on a genomic scale. These interactions connect proteins into a large protein interaction network (PIN). However, both the size of the data sets and the noise in the data pose big challenges in effectively analyzing the data. In this paper, we investigate the problem of protein complex detection, i.e., finding biologically meaningful subsets of proteins, from the noisy protein interaction data. We identify the difficulties and propose a “seed-refine” approach, including a novel subgraph quality measure, an appropriate heuristics for finding good seeds and a novel subgraph refinement method. Our method considers the properties of protein complexes and the noisy interaction data. Experiments show the effectiveness of our method.

Keywords

Protein Interaction Network Protein Interaction Data Dense Subgraph Predict Protein Complex Detect Protein Complex 
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 2006

Authors and Affiliations

  • Pengjun Pei
    • 1
  • Aidong Zhang
    • 1
  1. 1.Department of Computer Science and EngineeringState University of New York at BuffaloBuffaloUSA

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