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Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

Author

Listed:
  • J. M. Urquiza
  • I. Rojas
  • H. Pomares
  • J. Herrera
  • J. P. Florido
  • O. Valenzuela

Abstract

Protein‐protein interactions (PPIs) play a crucial role in cellular processes. In the present work, a new approach is proposed to construct a PPI predictor training a support vector machine model through a mutual information filter‐wrapper parallel feature selection algorithm and an iterative and hierarchical clustering to select a relevance negative training set. By means of a selected suboptimum set of features, the constructed support vector machine model is able to classify PPIs with high accuracy in any positive and negative datasets.

Suggested Citation

  • J. M. Urquiza & I. Rojas & H. Pomares & J. Herrera & J. P. Florido & O. Valenzuela, 2012. "Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology," Journal of Applied Mathematics, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnljam:v:2012:y:2012:i:1:n:897289
    DOI: 10.1155/2012/897289
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    References listed on IDEAS

    as
    1. Peter Uetz & Loic Giot & Gerard Cagney & Traci A. Mansfield & Richard S. Judson & James R. Knight & Daniel Lockshon & Vaibhav Narayan & Maithreyan Srinivasan & Pascale Pochart & Alia Qureshi-Emili & Y, 2000. "A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae," Nature, Nature, vol. 403(6770), pages 623-627, February.
    2. Inyoung Kim & Yin Liu & Hongyu Zhao, 2007. "Bayesian Methods for Predicting Interacting Protein Pairs Using Domain Information," Biometrics, The International Biometric Society, vol. 63(3), pages 824-833, September.
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