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Prediction of Protein Complexes Based on Protein Interaction Data and Functional Annotation Data Using Kernel Methods

  • Shi-Hua Zhang
  • Xue-Mei Ning
  • Hong-Wei Liu
  • Xiang-Sun Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)

Abstract

Prediction of protein complexes is a crucial problem in computational biology. The increasing amount of available genomic data can enhance the identification of protein complexes. Here we describe an approach for predicting protein complexes based on integration of protein-protein interaction (PPI) data and protein functional annotation data. The basic idea is that proteins in protein complexes often interact with each other and protein complexes exhibit high functional consistency/even multiple functional consistency. We create a protein-protein relationship network (PPRN) via a kernel-based integration of these two genomic data. Then we apply the MCODE algorithm on PPRN to detect network clusters as numerically determined protein complexes. We present the results of the approach to yeast Sacchromyces cerevisiae. Comparison with well-known experimentally derived complexes and results of other methods verifies the effectiveness of our approach.

Keywords

Protein Complex Functional Annotation Kernel Method Protein Interaction Network Protein Interaction Data 
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

  • Shi-Hua Zhang
    • 1
  • Xue-Mei Ning
    • 1
  • Hong-Wei Liu
    • 2
  • Xiang-Sun Zhang
    • 1
  1. 1.Institute of Applied MathematicsAcademy of Mathematics and Systems Science Chinese Academy of SciencesBeijingChina
  2. 2.School of EconomicsRenmin University of ChinaBeijingChina

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