Using a Stochastic AdaBoost Algorithm to Discover Interactome Motif Pairs from Sequences

  • Huan Yu
  • Minping Qian
  • Minghua Deng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4115)


Protein interactome is an important research focus in the post-genomic era. The identification of interacting motif pairs is essential for exploring the mechanism of protein interactions. We describe a stochastic AdaBoost approach for discovering motif pairs from known interactions and pairs of proteins that are putatively not to interact. Our interacting motif pairs are validated by multiple-chain PDB structures and show more significant than those selected by traditional statistical method. Furthermore, in a cross-validated comparison, our model can be used to predict interactions between proteins with higher sensitivity (66.42%) and specificity (87.38%) comparing with the Naive Bayes model and the dominating model.


Motif Pair Traditional Statistical Method RCSB Protein Data Feature Vector Extraction Protein Interactome 
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

  • Huan Yu
    • 1
  • Minping Qian
    • 1
  • Minghua Deng
    • 1
  1. 1.LMAM, School of Mathematical Sciences and Center for Theoretical BiologyPeking UniversityBeijingP.R. China

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