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Journal of Applied Mathematics
Volume 2012, Article ID 897289, 23 pages
Research Article

Selecting Negative Samples for PPI Prediction Using Hierarchical Clustering Methodology

1Department of Computer Architecture and Computer Technology, University of Granada, 18017 Granada, Spain
2Department of Applied Mathematics, University of Granada, 18017 Granada, Spain

Received 12 September 2011; Accepted 17 November 2011

Academic Editor: Venky Krishnan

Copyright © 2012 J. M. Urquiza et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.